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Herobrine is an urban legend and creepypasta from the sandbox video game Minecraft. He is often depicted as a version of the Minecraft character Steve, but with solid white eyes that lack pupils, and behavior that primarily involves destroying the player s world. The story originated from an anonymous post on 4chan s v board in 2010, where the author reported encountering a strange figure in a single player world, followed by their messages being deleted when they attempted to talk to other players about the sighting. The story was further popularized after livestreamers Copeland and Patimuss created their own versions. Herobrine has become a popular part of the online culture surrounding Minecraft, as well as effectively an internet meme. Interest in the character inspired many to create their own stories and alleged sightings centered around Herobrine, as well as create Minecraft mods that add him to the game. Interest in the character continued into the 2020s, leading to the rediscovery of formerly lost media related to the original sightings. Herobrine has been considered one of the most notable legends in video games, with his popularity leading to him ranking on a Guinness World Records poll for the best video game villains despite never truly existing within Minecraft. The character has been referenced several times by the developers of Minecraft, appearing on official artwork as well. Origins and characteristics In 2010, during Minecraft s alpha stage of development, an anonymous post was made on 4chan s v board, where the author claimed to encounter a mysterious entity while playing the game. The post claimed that shortly after starting a new world, the author saw what they believed to be a cow in the distance, which they approached in order to kill it. Upon approaching it, they instead saw a second player character with solid white eyes staring at them from the fog before vanishing. After the encounter, the author noticed numerous strange structures that they did not create. They claimed that when trying to contact other players about the event, they found their posts removed, eventually receiving a message from a user named Herobrine that simply said stop. The anonymous post went on to claim that other players informed him that Herobrine was the alias of the brother of Notch, the creator of Minecraft. The 4chan post claimed that Notch said, in response to queries about whether he had a brother, I did, but he is no longer with us. Around the same time, another anonymous post on 4chan wrote about another entity the author seemingly encountered in a cave after listening to the in game music disc 13 , which also had white eyes that lurked in the fog. This encounter was simply named White Eyes , and was believed to be related to Herobrine. Shortly after the original stories were published, livestreamers Copeland and Patimuss, the former of which saw and liked the original posts, staged Herobrine encounters of their own. In Copeland s stream, he played in a survival world with a custom texture pack for around two hours while working on a house. After entering a room he was planning on furnishing, he saw Herobrine staring at him and he quickly left the house and exited the game, before ending the livestream. This encounter was created by Copeland modifying in game textures to make Herobrine appear. Afterwards, viewers of the livestream were redirected to a GIF depicting Herobrine with moving, realistic eyes. In Patimuss stream, he encountered Herobrine walking on lava while playing the game, before promptly shutting the game down. After Copeland s stream, he claimed that his computer crashed when trying to go live again afterwards. He then shared a webpage with the title him.html . The page featured a gif of Steve, the default skin of Minecraft, with his pixelated eyes replaced with realistic, moving ones, as well as text at the bottom that wrote about how the reader was living in a fantasy world inside their mind and needed to wake up. This granted Herobrine the additional nickname HIM. After these streams, the popularity of Herobrine spread across the Minecraft community, with people creating their own alleged sightings, as well as developing Minecraft mods to add the character to the game themselves. Most claimed sightings of Herobrine are accompanied by red text annotations and eerie music. In stories and mods centered around Herobrine, he is typically summoned through the creation of a structure made up of gold and other in game materials. His most common traits include constructing abnormal structures and causing destruction, such as by digging random tunnels throughout the world and removing the leaves from trees. Reception and legacy Herobrine gained widespread popularity in the 2010s, becoming a notable part of the Minecraft community and an internet meme. Several other Minecraft creepypastas have been created by fans, such as Entity 303, though none were able to reach similar levels of notoriety as Herobrine. VG247 writer Nadia Oxford described Herobrine as one of the Minecraft fan works, and IGN writer Paul Dean wrote Herobrine to be the most popular example of a game haunting ever. Lauren Morton of PC Gamer wrote that, despite Herobrine never having truly existed, the character lives on in the minds of plenty of Minecraft players who were interested in him when younger. Gabriel Menotti cited Herobrine as an example of how user generated recordings for video games could change player s imaginations, and view the game beyond its original scope. Some players believed Herobrine to be real despite the character never existing, which caused employees of Mojang to comment on the character. Notch in particular has denied the existence of Herobrine numerous times, and tweeted that he never had a brother in 2011. Despite this, Mojang has made many references to Herobrine in numerous versions of Minecraft, the update logs have included the term Removed Herobrine as a joke. We don t usually talk about Herobrine, Minecraft lead designer Jens Jeb Bergensten told G1. It s a mystery ... And we don t quite confirm if it s true or false. 00 21 46 Minecraft director Agnes Larsson added that a creature in the game called the Warden takes inspiration from the community s horror myths . In A Minecraft Movie 2025 , a film adaptation of Minecraft, a scene depicts Steve portrayed by Jack Black with glowing white eyes, during a hallucination from an enderman. This scene was widely interpreted by fans as a reference to Herobrine, although creative director Torfi Frans Olafsson stated that the white eyes were actually a visual effects glitch that was left in due to time constraints. Several viewed this as an ironic coincidence due to Herobrine s nature as a figure that haunts the game and doesn t truly exist. In 2013, Herobrine ranked 46th on a poll for the Top 50 Video Game Villains of All Time, which was organized by Guinness World Records. Fan made books based on Herobrine have been published, such as The Legend of Herobrine. In 2021, continued interest in the story resulted in the world seed of the original Herobrine sighting being discovered by a group of players known as the Minecraft Home project. Similarly, in 2020, a Minecraft player known as Enderboss25 gained contact with Copeland in an effort to recover the footage of the original livestream that caused Herobrine s popularity. While the original footage was long gone, the original world file was recovered, and a recreation of the livestream was made in a joint effort by the two. In July 2024, the original livestream was uploaded to YouTube by user brutallillfjomp, who had saved the stream in 2010 and was unaware that it was considered lost until watching a video on it the previous day. References External links |
הידעת פורטלים פורטל היום לכבוד המעבר מהלירה לשקל ב 22 בפברואר 1980 פורטל הכלכלה הוא שער לתחום הכלכלה בוויקיפדיה. הפורטל מציג מושגי יסוד בתחום וסוקר מספר נושאים בהם שוק ההון, שוק העבודה ועוד. הציטוט היומי I am opposed to millionaires, but it would be dangerous to offer me the position אני מתנגד למיליונרים, אך יהיה זה מסוכן להציע לי את המשרה... הטיפ היומי ישנם יתרונות רבים ביצירת חשבון ויקיפדי. מיעוטם תקף אפילו לקריאה בוויקיפדיה, ללא עריכה. אז הנה מספר הסברים ללמה כדאי ליצור חשבון? חדשות ואקטואליה ערך מומלץ לַיְלָה לַיְלָה מִסְתַּכֶּלֶת הַלְּבָנָה בַּפְּרָחִים אֲשֶׁר הֵנֵצוּ בַּגִּנָּה. בְּצִיצֵי הַיָּקִנְתּוֹן בְּגַנֵּנוּ הַקָּטֹן לַיְלָה לַיְלָה מִסְתַּכֶּלֶת הַלְּבָנָה. פזמון לַיָקינתון הוא שיר ילדים עברי מאת לאה גולדברג מילים ורבקה גְּוִילי לחן שנכתב בתל אביב בראשית שנת 1940 לכבוד חג ט ו בשבט ת ש. זהו שיר לירי סיפורי שמוקדו הוא פריחתו של צמח יקינתון קטן בגינה. הצמח, המסמל ילד רך, זוכה לדאגתה של הלבנה ובעקבות זאת לגשם שממטירים העננים על האדמה הוא מגיב בשמחה ובפריחה, ובני האדם, בהם האֵם הדוברת ובנה, הנוכחים בפריחתו, שרים לכבודו את הפזמון, ושמחה שוררת בגינה. הלחן לשיר, אף שהוא קצר, בונה מהלך מורכב שלם. הוצע שהקשר הקרוב בין שתי היוצרות בא לידי ביטוי בתיאום הזורם שבין מילות השיר למנגינתו. הפזמון הושר לראשונה ב קול ירושלים בפי הזמרת מרים סגל, שעבורה הולחן השיר. זמן קצר לאחר מכן, ב 22 בפברואר 1940, ראה אור לראשונה, מעל דפי השבועון דבר לילדים , שעבורו חיברה גולדברג את השיר. בהמשך נכלל השיר בספר שירי הילדים של גולדברג מה עושות האיילות 1949 , ומאז ועד ימינו הוא נדפס פעמים רבות, בלוויית איוריהם של אמנים שונים, וזכה לעיבודים ולעשרות ביצועים. הפזמון, שהוא משירי הילדים הידועים ביותר של גולדברג, כמו גם לחנהּ הידוע ביותר של גוילי, היה לשיר ערש פופולרי ולאחד משירי הילדים המוכרים והאהובים בישראל, והוא נחשב לנכס צאן ברזל של הזמר העברי. תמונת היום היום בהיסטוריה 22 בפברואר חודש פברואר היום בהיסטוריה אירועים בלוח העברי פרשת השבוע תצוה, שבת זכורהדף היומי מסכת מנחות, דף מ בו באדר ה תשפ ו אירועים בלוח העברי ערך מומלץ לַיְלָה לַיְלָה מִסְתַּכֶּלֶת הַלְּבָנָה בַּפְּרָחִים אֲשֶׁר הֵנֵצוּ בַּגִּנָּה. בְּצִיצֵי הַיָּקִנְתּוֹן בְּגַנֵּנוּ הַקָּטֹן לַיְלָה לַיְלָה מִסְתַּכֶּלֶת הַלְּבָנָה. פזמון לַיָקינתון הוא שיר ילדים עברי מאת לאה גולדברג מילים ורבקה גְּוִילי לחן שנכתב בתל אביב בראשית שנת 1940 לכבוד חג ט ו בשבט ת ש. זהו שיר לירי סיפורי שמוקדו הוא פריחתו של צמח יקינתון קטן בגינה. הצמח, המסמל ילד רך, זוכה לדאגתה של הלבנה ובעקבות זאת לגשם שממטירים העננים על האדמה הוא מגיב בשמחה ובפריחה, ובני האדם, בהם האֵם הדוברת ובנה, הנוכחים בפריחתו, שרים לכבודו את הפזמון, ושמחה שוררת בגינה. הלחן לשיר, אף שהוא קצר, בונה מהלך מורכב שלם. הוצע שהקשר הקרוב בין שתי היוצרות בא לידי ביטוי בתיאום הזורם שבין מילות השיר למנגינתו. הפזמון הושר לראשונה ב קול ירושלים בפי הזמרת מרים סגל, שעבורה הולחן השיר. זמן קצר לאחר מכן, ב 22 בפברואר 1940, ראה אור לראשונה, מעל דפי השבועון דבר לילדים , שעבורו חיברה גולדברג את השיר. בהמשך נכלל השיר בספר שירי הילדים של גולדברג מה עושות האיילות 1949 , ומאז ועד ימינו הוא נדפס פעמים רבות, בלוויית איוריהם של אמנים שונים, וזכה לעיבודים ולעשרות ביצועים. הפזמון, שהוא משירי הילדים הידועים ביותר של גולדברג, כמו גם לחנהּ הידוע ביותר של גוילי, היה לשיר ערש פופולרי ולאחד משירי הילדים המוכרים והאהובים בישראל, והוא נחשב לנכס צאן ברזל של הזמר העברי. פורטלים פורטל היום לכבוד המעבר מהלירה לשקל ב 22 בפברואר 1980 פורטל הכלכלה הוא שער לתחום הכלכלה בוויקיפדיה. הפורטל מציג מושגי יסוד בתחום וסוקר מספר נושאים בהם שוק ההון, שוק העבודה ועוד. הציטוט היומי I am opposed to millionaires, but it would be dangerous to offer me the position אני מתנגד למיליונרים, אך יהיה זה מסוכן להציע לי את המשרה... תמונת היום הטיפ היומי ישנם יתרונות רבים ביצירת חשבון ויקיפדי. מיעוטם תקף אפילו לקריאה בוויקיפדיה, ללא עריכה. אז הנה מספר הסברים ללמה כדאי ליצור חשבון? הידעת היום בהיסטוריה 22 בפברואר חודש פברואר היום בהיסטוריה אירועים בלוח העברי פרשת השבוע תצוה, שבת זכורהדף היומי מסכת מנחות, דף מ בו באדר ה תשפ ו אירועים בלוח העברי חדשות ואקטואליה ויקיפדיה מופעלת על ידי קרן ויקימדיה, המפעילה מספר מיזמים רב־לשוניים וחופשיים נוספים מיזמי ויקימדיה נוספים Welcome to the Hebrew Wikipedia! For assistance in other languages, please see the embassy. ללא הודעת הגנה אוטומטית |
Wikipedia The Free Encyclopedia Wikipedia 25 years of the free encyclopedia An important update for readers in the United States. Please don t skip this 1 minute read. This fundraiser will soon be over, but we haven t yet hit our goal. If you re like us, you ve used Wikipedia countless times. To settle an argument with a friend. To satisfy a curiosity. Whether it s 3 in the morning or afternoon, Wikipedia is useful in your life. Please give 2.75. After nearly 25 years, Wikipedia is still the internet we were promisedâ created by people, not by machines. It s not perfect, but it s not here to push a point of view. It s owned by a nonprofit, not a giant technology company or a billionaire. Just 2 of our readers donate, so if you have given in the past and Wikipedia still provides you with 2.75 worth of knowledge, donate today. If you are undecided, remember any contribution helps. 25 years of the internet at its best Sorry to interrupt, but we re short on time to hit our goal. Today, we ask you to join the 2 of readers who give. If everyone reading this right now gave just 2.75, we d hit our goal quickly. 2.75 is all we ask. â The Wikimedia Foundation, host of Wikipedia and other free knowledge projects. Your support means the world to us. We ll hide banners in this browser for the rest of our campaign. Want to show off your support? Donors get 20 off Wikipedia Store merchandise automatically applied at checkout. Click the button below to shop hats, tees, and more! |
Welcome to Wikipedia From today s featured article Donkey Kong is a character created by Shigeru Miyamoto pictured for the Japanese video game company Nintendo. He stars in the Donkey Kong franchise while also appearing in the Mario franchise. Donkey Kong is a large, powerful gorilla who leads the Kong family of simians. Stubborn and buffoonish, he attacks using barrels. Donkey Kong debuted as the antagonist in Donkey Kong 1981 , characterized as Mario s rebellious pet. Since Donkey Kong Country 1994 , he has appeared as a player character protecting his stash of bananas. Donkey Kong has also appeared in animation, comics, children s books, theme park attractions, and merchandise such as Lego toys. Journalists regard Donkey Kong as one of the greatest video game characters. The Donkey Kong franchise was Nintendo s first major international success and remains one of its bestselling franchises. Donkey Kong has been the subject of analysis regarding his gender role and his transition from villain to hero. Full article... Did you know ... In the news On this day February 22 Today s featured picture Shapur II 309 379 , also known as Shapur the Great, was the tenth King of Kings, the monarch of the Sasanian Empire in what is now Iran. He took the title at birth and held it until his death at age 70, making him the longest reigning monarch in Sasanian history. The son of Hormizd II, who reigned from 302 to 309, Shapur s reign saw the military resurgence of the Sasanian Empire and the expansion of its territory. This included, at the age of 16, successful military campaigns against Arab insurrections and tribes, and later campaigns against the Roman Empire, the invasion of Armenia, and expansion into India. This photograph shows a 4th century silver bust of the head of a Sasanian king, now in the collection of the Metropolitan Museum of Art in New York City. It is not known with certainty which king is depicted, but it may be Shapur II. Sculpture credit unknown Other areas of Wikipedia Wikipedia s sister projects Wikipedia is written by volunteer editors and hosted by the Wikimedia Foundation, a non profit organization that also hosts a range of other volunteer projects Wikipedia languages This Wikipedia is written in English. Many other Wikipedias are available some of the largest are listed below. |
Hypixel Network is a Minecraft server that hosts minigames. It was released on April 13, 2013, by Simon hypixel Collins Laflamme and Philippe Touchette, and is managed and run by Hypixel Inc. Hypixel is only available on the Java Edition of Minecraft, but briefly had a Pocket Edition variant. History The Hypixel server was released in beta on April 13, 2013, by Simon Collins Laflamme and Philippe Touchette. The server is managed and run by Hypixel Inc. The two originally created Minecraft adventure maps together and uploaded trailers to their YouTube channel. The Hypixel server was created to play and further showcase these maps. Minigames were originally created for users to play on while waiting for other players, but the minigames themselves gained popularity. Efforts from Hypixel were put towards new server content instead of the making of other Minecraft maps and games. Hypixel Inc., Hypixel s maintainer, was registered as a Canadian corporation under the name 8414483 Canada Inc on January 23, 2013. Its name was then modified to Hypixel Inc. on February 2, 2015. In 2015, it was revealed that the server cost around 100,000 a month to maintain. On June 11, 2019, Hypixel opened Skyblock to players as a Prototype. During the COVID 19 pandemic, the server regularly reached over 150,000 concurrent players, peaking at a record 216,000 on April 16, 2021. On December 21, 2016, Hypixel reached 10 million unique players in total, and had reached 14.1 million unique players when Hytale was announced on December 13, 2018. The server reached 18 million unique players in April 2020, according to a tweet by the server owner. As of September 2015 update , Hypixel attracts 1.9 million players every month. Players of the server community banded together to write over 400,000 messages of condolences for the content creator Technoblade following his death in July 2022. The messages were compiled into 21 books and delivered to his family. On August 1, 2024, TommyInnit hit 15 million subscribers on YouTube. As per agreement between Simon and Tom, Simon donated 50,000 to the Sarcoma Foundation of America and gave Tom the INNIT rank on the server. Hypixel China In May 2017, Hypixel partnered with NetEase, the publisher of Minecraft China, to release a version of Hypixel in China, sometimes known as Chypixel . This separate version of Minecraft and the Hypixel Minecraft server would be operated and translated by NetEase, as part of their partnership. On April 13, 2020, due to the expiration of their agreement, NetEase announced that the Chinese version of the server would be shut down on June 30, 2020. Cyberattacks Around April 2018, Hypixel began to use Cloudflare Spectrum for DDoS mitigation after being the victim of multiple attacks hosted by Mirai malware. On June 18, 2021, Hypixel shut down for emergency maintenance, stating their host was under large scale denial of service attacks . Connection problems were reported by players before the server was shut down, and the Hypixel team had claimed to have identified the issue with an upstream provider . The server subsequently remained closed for four days before fully reopening. In its statement, the Hypixel team re iterated that they had dealt with DDoS attacks for well over 8 years , and that recent changes at their host caused a flaw in their setup . Gameplay Hypixel has various multiplayer minigames created by modifying and repurposing the game mechanics of Minecraft. Such minigames include Bedwars, where players must destroy opponents beds to prevent them from respawning after death, or Skywars, a similar game where players spawn on different islands and must kill other players using gear and weapons looted from chests strewn through the map. Hypixel also has its own variation of Skyblock, where it functions more similar to an MMORPG with various islands complete with shops and quests. Its players can purchase cosmetics and ranks that allow for certain in game abilities. vague Awards and nominations On October 20, 2017, Hypixel announced that they held four Guinness World Records. Notes See also References External links |
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural networks. In biology In the context of biology, a neural network is a population of biological neurons chemically connected to each other by synapses. A given neuron can be connected to hundreds of thousands of synapses. Each neuron sends and receives electrochemical signals called action potentials to its connected neighbors. A neuron can serve an excitatory role, amplifying and propagating signals it receives, or an inhibitory role, suppressing signals instead. Populations of interconnected neurons that are smaller than neural networks are called neural circuits. Very large interconnected networks are called large scale brain networks, and many of these together form brains and nervous systems. Signals generated by neural networks in the brain eventually travel through the nervous system and across neuromuscular junctions to muscle cells, where they cause contraction and thereby motion. In machine learning In machine learning, a neural network is an artificial mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines, today they are almost always implemented in software. Neurons in an artificial neural network are usually arranged into layers, with information passing from the first layer the input layer through one or more intermediate layers the hidden layers to the final layer the output layer . The signal input to each neuron is a number, specifically a linear combination of the outputs of the connected neurons in the previous layer. The signal each neuron outputs is calculated from this number, according to its activation function. The behavior of the network depends on the strengths or weights of the connections between neurons. A network is trained by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset. The term deep neural network refers to neural networks that have more than three layers, typically including at least two hidden layers in addition to the input and output layers. Neural networks are used to solve problems in artificial intelligence, and have thereby found applications in many disciplines, including predictive modeling, adaptive control, facial recognition, handwriting recognition, general game playing, and generative AI. History The theoretical base for contemporary neural networks was independently proposed by Alexander Bain in 1873 and William James in 1890. Both posited that human thought emerged from interactions among large numbers of neurons inside the brain. In 1949, Donald Hebb described Hebbian learning, the idea that neural networks can change and learn over time by strengthening a synapse every time a signal travels along it. In 1956, Svaetichin discovered the functioning of second order retinal cells Horizontal Cells , which were fundamental for the understanding of neural networks. Artificial neural networks were originally used to model biological neural networks starting in the 1930s under the approach of connectionism. However, starting with the invention of the perceptron, a simple artificial neural network, by Warren McCulloch and Walter Pitts in 1943, followed by the implementation of one in hardware by Frank Rosenblatt in 1957, artificial neural networks became increasingly used for machine learning applications instead, and increasingly different from their biological counterparts. See also References |
Markus Alexej Persson ˈpɪərsən PEER sən, Swedish ˈmǎrːkɵs ˈpæ ːʂɔn born 1 June 1979 , known by the pseudonym Notch, is a Swedish video game programmer and designer. He is the creator of Minecraft, the best selling video game in history. He founded the video game development company Mojang Studios in 2009. Persson began developing video games at an early age. His commercial success began after he published an early version of Minecraft in 2009. Prior to the game s official retail release in 2011, it had sold over four million copies. After this point Persson stood down as the lead designer and transferred his creative authority to Jens Bergensten. In September 2014 Persson announced his intention to leave Mojang, and in November of that year the company was sold to Microsoft reportedly for US 2.5 billion, which made him a billionaire. Since 2016 several of Persson s posts on Twitter regarding feminism, race, and transgender rights have caused public controversies. He has been described as an increasingly polarizing figure, tweeting offensive statements regarding race, the LGBTQ community, gender, and other topics. In an effort to distance itself from Persson, Microsoft removed mentions of his name from Minecraft excluding one instance in the game s end credits and did not invite him to the game s tenth anniversary celebration. In 2015 he co founded a separate game studio called Rubberbrain, which was relaunched in 2024 as Bitshift Entertainment. Early life Markus Alexej Persson was born in Stockholm, Sweden, to a Finnish mother, Ritva, and a Swedish father, Birger, on 1 June 1979. He has one sister. He grew up in Edsbyn until he was seven years old, when his family moved back to Stockholm. In Edsbyn, Persson s father worked for the railroad, and his mother was a nurse. He spent much time outdoors in Edsbyn, exploring the woods with his friends. When Persson was about seven years old, his parents divorced, and he and his sister lived with their mother. His father moved to a cabin in the countryside. Persson said in an interview that they experienced food insecurity around once a month. Persson lost contact with his father for several years after the divorce. According to Persson, his father suffered from depression, bipolar disorder, alcoholism, and medication abuse, and went to jail for robberies. While his father had somewhat recovered during Persson s early life, his father relapsed, contributing to the divorce. His sister also experimented with drugs and ran away from home. He had gained interest in video games at an early age. His father was a really big nerd , who built his own modem and taught Persson to use the family s Commodore 128. On it, Persson played bootleg games and loaded in various type in programs from computer magazines with the help of his sister. The first game he purchased with his own money was The Bard s Tale. He began programming on his father s Commodore 128 home computer at the age of seven. He produced his first game at the age of eight, a text based adventure game. By 1994 Persson knew he wanted to become a video game developer, but his teachers advised him to study graphic design, which he did from ages 15 to 18. Persson, although introverted, was well liked by his peers, but after entering secondary school was a loner and reportedly had only one friend. He spent most of his spare time with games and programming at home. He managed to reverse engineer the Doom engine, which he continued to take great pride in as of 2014 update . He never finished high school, but was reportedly a good student. Career Persson started his career working as a web designer. He later found employment at Game Federation, where he met Rolf Jansson. The pair worked in their spare time to build the 2006 video game Wurm Online. The game was released through a new entity, Mojang Specifications AB . Persson left the project in late 2007. As Persson wanted to reuse the name Mojang , Jansson agreed to rename the company to Onetoofree AB. Between 2004 and 2009 Persson worked as a game developer for Midasplayer later known as King . There, he worked as a programmer, mostly building browser games made in Flash. He later worked as a programmer for jAlbum. Minecraft and Mojang Prior to creating Minecraft, Persson developed multiple, small games. He also entered a number of game design competitions and participated in discussions on the TIGSource forums, a web forum for independent game developers. One of Persson s more notable personal projects was called RubyDung, an isometric three dimensional base building game like RollerCoaster Tycoon and Dwarf Fortress. While working on RubyDung, Persson experimented with a first person view mode similar to that found in Dungeon Keeper. However, he felt the graphics were too pixelated and omitted this mode. In 2009 Persson found inspiration in Infiniminer, a block based open ended mining game. Infiniminer heavily influenced his future work on RubyDung, and was behind Persson s reasoning for returning the first person mode, the blocky visual style and the block building fundamentals to the game. RubyDung is the earliest known Minecraft prototype created by Persson. On 17 May 2009 Persson released the original edition later called Classic version of Minecraft on the TIGSource forums. He regularly updated the game based on feedback from TIGSource users. Persson released several new versions of Minecraft throughout 2009 and 2010, going through several phases of development including Survival Test, Indev, and Infdev. On 30 June 2010 Persson released the game s Alpha version. While working on the pre Alpha version of Minecraft, Persson continued working at jAlbum. In 2010, after the release and subsequent success of Minecraft s Alpha version, Persson moved from a full time role to a part time role at jAlbum. He left jAlbum later that same year. In September 2010 Persson travelled to Valve Corporation s headquarters in Bellevue, Washington, United States, where he took part in a programming exercise and met Gabe Newell. Persson was subsequently offered a job at Valve, which he turned down in order to continue work on Minecraft. On 20 December 2010 Minecraft moved into its beta phase and began expanding to other platforms, including mobile. In January 2011 Minecraft reached one million registered accounts. Six months afterwards, it reached ten million. The game has sold over four million copies by 7 November 2011. Mojang held the first Minecon from 18 to 19 November 2011 to celebrate its full release, and subsequently made it an annual event. Following this, on 11 December 2011, Persson transferred creative control of Minecraft to Jens Bergensten and began working on another game title, 0x10c, although he reportedly abandoned the project around 2013. In 2013 Mojang recorded revenues of 330 million and profits of 129 million. Persson has stated that, due to the intense media attention and public pressure, he became exhausted with running Minecraft and Mojang. In a September 2014 blog post he shared his realization that he didn t have the connection to my fans I thought I had , that he had become a symbol , and that he did not wish to be responsible for Mojang s increasingly large operation. In June 2014 Persson tweeted Anyone want to buy my share of Mojang so I can move on with my life? Getting hate for trying to do the right thing is not my gig , reportedly partly as a joke. Persson controlled a 71 stake in Mojang at the time. The offer attracted significant interest from Activision Blizzard, EA, and Microsoft. Forbes later reported that Microsoft wanted to purchase the game as a tax dodge to turn their taxable excess liquid cash into other assets. In September 2014 Microsoft agreed to purchase Mojang for 2.5 billion, making Persson a billionaire. He then left the company after the deal was finalised in November. Activities after leaving Mojang Since leaving Mojang, Persson has worked on several small projects. On 23 June 2014 he founded a company with Porsér called Rubberbrain AB the company had no games by 2021, despite spending SEK 60 million. The company was relaunched as Bitshift Entertainment, LLC on 28 March 2024. Persson expressed interest in creating a new video game studio in 2020, and in developing virtual reality games. He has also since created a series of narrative driven immersive events called .party , which uses extensive visual effects and has been hosted in multiple cities. At the beginning of 2025 Persson decided to create a spiritual successor to Minecraft, referred to as Minecraft 2 , in response to the results of a poll on X. However, after speaking to his team, he shortly went against this in favour of developing the other choice on his Twitter poll, a roguelike titled Levers and Chests. Games Minecraft Persson s most popular creation is the survival sandbox game Minecraft, which was first publicly available on 17 May 2009 and fully released on 18 November 2011. Persson left his job as a game developer to work on Minecraft full time until completion. In early 2011, Mojang AB sold the one millionth copy of the game, several months later their second, and several more their third. Mojang hired several new staff members for the Minecraft team, while Persson passed the lead developer role to Jens Bergensten. He stopped working on Minecraft after a deal with Microsoft to sell Mojang for 2.5 billion. This brought his net worth to US 1.5 billion. Caller s Bane Persson and Jakob Porsér came up with the idea for Scrolls including elements from board games and collectible card games. Persson noted that he will not be actively involved in development of the game and that Porsér will be developing it. Persson revealed on his Tumblr blog on 5 August 2011 that he was being sued by a Swedish law firm representing Bethesda Softworks over the trademarked name of Scrolls, claiming that it conflicted with their The Elder Scrolls series of games. On 17 August 2011 Persson challenged Bethesda to a Quake 3 tournament to decide the outcome of the naming dispute. On 27 September 2011 Persson confirmed that the lawsuit was going to court. ZeniMax Media, owner of Bethesda Softworks, announced the lawsuit s settlement in March 2012. The settlement allowed Mojang to continue using the Scrolls trademark. In 2018, Scrolls was made available free of charge and renamed to Caller s Bane. Cliffhorse Cliffhorse is a humorous game programmed in two hours using the Unity game engine and free assets. The game took inspiration from Skyrim s physics engine, the more embarrassing minimum effort Greenlight games , Goat Simulator, and Big Rigs Over the Road Racing. The game was released to Microsoft Windows systems as an early access and honourware game on the first day of E3 2014, instructing users to donate Dogecoin to buy the game before downloading it. The game accumulated over 280,000 dogecoins. 0x10c Following the end to his involvement with Minecraft, Persson began pre production of an alternate reality space game set in the distant future in March 2012. On April Fools Day Mojang launched a satirical website for Mars Effect parody of Mass Effect , citing the lawsuit with Bethesda as an inspiration. However, the gameplay elements remained true and on 4 April, Mojang revealed 0x10c pronounced Ten to the C as a space sandbox title. Persson officially halted game production in August 2013. However, C418, the composer of the game s soundtrack as well as that of Minecraft , released an album of the work he had made for the game. Shambles In 2013, Persson made a free game called Shambles in the Unity game engine. Ludum Dare entries Persson has also participated in several Ludum Dare 48 hour game making competitions. Personal life In 2011 Persson married Elin Zetterstrand, whom he had dated for four years before. Zetterstrand was a former moderator on the Minecraft forums. They had a daughter together, but by mid 2012, he began to see little of her. On 15 August 2012 he announced that he and his wife had filed for divorce. The divorce was finalised later that year. On 14 December 2011 Persson s father committed suicide with a handgun after drinking heavily. In an interview with The New Yorker, Persson said of his father When I decided I wanted to quit my day job and work on my own games, he was the only person who supported my decision. He was proud of me and made sure I knew. When I added the monsters to Minecraft, he told me that the dark caves became too scary for him. But I think that was the only true criticism I ever heard from him. Persson later admitted that he himself suffered from depression and various highs and lows in his mood. Persson has criticised the stance of large game companies on piracy. He once stated that piracy is not theft , viewing unauthorised downloads as potential future customers. Persson stated himself to be a member of the Pirate Party of Sweden in 2011. He is also a member of Mensa. He has donated to numerous charities, including Médecins Sans Frontières Doctors Without Borders . Under his direction, Mojang spent a week developing Catacomb Snatch for the Humble Indie Bundle and raised US 458,248 for charity. He also donated 250,000 to the Electronic Frontier Foundation in 2012. In 2011 he gave 3 million in dividends back to Mojang employees. According to Forbes, his net worth in 2023 was around 1.2 billion. In 2014 Persson was one of the biggest taxpayers in Sweden. Around 2014, he lived in a multi level penthouse in Östermalm, Stockholm, an area he described as where the rich people live . In December 2014 Persson purchased a home in Trousdale Estates, a neighbourhood in Beverly Hills, California, in the United States, for 70 million, a record sales price for Beverly Hills at the time. Persson reportedly outbid Beyoncé and Jay Z for the property. Social media comments Persson began receiving criticism for political and social opinions he expressed on social media as early as 2016. November 30, 2017 In 2017, he proposed a heterosexual pride holiday, and wrote that those who opposed the idea deserve to be shot. After facing backlash, he deleted the tweets and rescinded his statements, writing, So yeah, it s about pride of daring to express, not about pride of being who you are. I get it now. Later in the year, he wrote that feminism is a social disease and called the video game developer and feminist Zoë Quinn a cunt , although he was generally critical of the GamerGate movement. He has described intersectional feminism as a framework for bigotry and the use of the word mansplaining as being sexist. Also in 2017, Persson tweeted that It s okay to be white . Later that year, he stated that he believed in the Pizzagate conspiracy theory. In 2019, he tweeted referencing QAnon, saying Q is legit. Don t trust the media. Later in 2019, he tweeted in response to a pro transgender internet meme that, You are absolutely evil if you want to encourage delusion. What happened to not stigmatizing mental illness? He then also promoted claims that people were fined for using the wrong pronoun . However, after facing backlash, he tweeted a day afterwards that he had no idea what being trans is like of course, but it s inspiring as hell when people open up and choose to actually be who they know themselves as. Not because it s a cool choice, because it s a big step. I gues sic that s actually cool nvm . Later that year, Microsoft removed two mentions of Persson s name in the 19w13a snapshot of Minecraft and did not invite him to the 10 year anniversary celebration of the game. A spokesperson for Microsoft stated that his views do not reflect those of Microsoft or Mojang . He is still mentioned in the End Poem a flat, infinite world created by a man called Markus . citation needed Awards References External links |
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Small language models or compact language models are artificial intelligence language models designed for human natural language processing including language and text generation. They are smaller in scale and scope than large language models. A large language model typically contains hundreds of billions of training parameters, with some models exceeding a trillion parameters. This substantial parameter count enables the model to encode vast amounts of information, thereby improving the generalizability and accuracy of its outputs. However, training such models demands enormous computational resources, rendering it infeasible for an individual to do so using a single computer and graphics processing unit. Small language models, on the other hand, use far fewer parameters, typically ranging from a few thousand to a few hundred million. This make them more feasible to train and host in resource constrained environments such as a single computer or even a mobile device. Most contemporary 2020s small language models use the same architecture as a large language model, but with a smaller parameter count and sometimes lower arithmetic precision. Parameter count is reduced by a combination of knowledge distillation and pruning. Precision can be reduced by quantization. Work on large language models mostly translate to small language models pruning and quantization are also widely used to speed up large language models. Models Some notable models are Phi 4 14B is marginally small at best, but Microsoft does market it as a small model. Language model with small pre training dataset Traditional AI language systems need enormous computers and vast amounts of data. Pre training matters, even tiny models show significant performance improvements when pre trained performance increases with larger pre training datasets. Classification accuracy improves when pre training and test datasets share similar tokens. Shallow architectures can replicate deep model performance through collaborative learning. See also References This statistics related article is a stub. You can help Wikipedia by adding missing information. This article about natural language processing is a stub. You can help Wikipedia by adding missing information. |
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A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation generating more human like text , optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models LLMs , currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets frequently using texts scraped from the public internet . They have superseded recurrent neural network based models, which had previously superseded the purely statistical models, such as the word n gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule based formal grammars. Discrete representations like word n gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real valued vector that encodes a word s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed Shannon style experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. Models based on word n grams A word n gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model if two words, a trigram model if n 1 words, an n gram model. Special tokens are introduced to denote the start and end of a sentence s displaystyle langle s rangle and s displaystyle langle s rangle . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple add one smoothing assigning a count of 1 to unseen n grams, as an uninformative prior to more sophisticated techniques, such as Good Turing discounting or back off models. Word n gram models have largely been superseded by recurrent neural network based models, which in turn have been superseded by Transformer based models often referred to as large language models. Exponential Maximum entropy language models encode the relationship between a word and the n gram history using feature functions. The equation is P w m w 1 , , w m 1 1 Z w 1 , , w m 1 exp a T f w 1 , , w m displaystyle P w_ m mid w_ 1 , ldots ,w_ m 1 frac 1 Z w_ 1 , ldots ,w_ m 1 exp a T f w_ 1 , ldots ,w_ m where Z w 1 , , w m 1 displaystyle Z w_ 1 , ldots ,w_ m 1 is the partition function, a displaystyle a is the parameter vector, and f w 1 , , w m displaystyle f w_ 1 , ldots ,w_ m is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n gram. It is helpful to use a prior on a displaystyle a or some form of regularization. The log bilinear model is another example of an exponential language model. Skip gram model Skip gram language model is an attempt at overcoming the data sparsity problem that the preceding model i.e. word n gram language model faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over thus the name skip gram . Formally, a k skip n gram is a length n subsequence where the components occur at distance at most k from each other. For example, in the input text the set of 1 skip 2 grams includes all the bigrams 2 grams , and in addition the subsequences In skip gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n d vector representation, then v k i n g v m a l e v f e m a l e v q u e e n displaystyle v mathrm king v mathrm male v mathrm female approx v mathrm queen where is made precise by stipulating that its right hand side must be the nearest neighbor of the value of the left hand side. Neural models Recurrent neural network Continuous representations or embeddings of words are produced in recurrent neural network based language models known also as continuous space language models . Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non linear combinations of weights in a neural net. Large language models A large language model LLM is a language model trained with self supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre trained transformers GPTs that provide the core capabilities of modern chatbots. LLMs can be fine tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine tune LLMs for desired behaviors beyond raw next token prediction. Reinforcement learning from human feedback RLHF applies these methods to optimize a policy, the LLM s output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include See also References Further reading |
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A large language model LLM is a language model trained with self supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre trained transformers GPTs that provide the core capabilities of modern chatbots. LLMs can be fine tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine tune LLMs for desired behaviors beyond raw next token prediction. Reinforcement learning from human feedback RLHF applies these methods to optimize a policy, the LLM s output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. History Before the emergence of transformer based models in 2017, some language models were considered large relative to the computational and data constraints of their time. In the early 1990s, IBM s statistical models pioneered word alignment techniques for machine translation, laying the groundwork for corpus based language modeling. In 2001, a smoothed n gram model, such as those employing Kneser Ney smoothing, trained on 300 million words, achieved state of the art perplexity on benchmark tests. During the 2000s, with the rise of widespread internet access, researchers began compiling massive text datasets from the web web as corpus to train statistical language models. Moving beyond n gram models, researchers started in 2000 to use neural networks to learn language models. Following the breakthrough of deep neural networks in image classification around 2012, similar architectures were adapted for language tasks. This shift was marked by the development of word embeddings eg, Word2Vec by Mikolov in 2013 and sequence to sequence seq2seq models using LSTM. In 2016, Google transitioned its translation service to neural machine translation NMT , replacing statistical phrase based models with deep recurrent neural networks. These early NMT systems used LSTM based encoder decoder architectures, as they preceded the invention of transformers. At the 2017 NeurIPS conference, Google researchers introduced the transformer architecture in their landmark paper Attention Is All You Need . This paper s goal was to improve upon 2014 seq2seq technology, and was based mainly on the attention mechanism developed by Bahdanau et al. in 2014. The following year in 2018, BERT was introduced and quickly became ubiquitous . Though the original transformer has both encoder and decoder blocks, BERT is an encoder only model. Academic and research usage of BERT began to decline in 2023, following rapid improvements in the abilities of decoder only models such as GPT to solve tasks via prompting. Although decoder only GPT 1 was introduced in 2018, it was GPT 2 in 2019 that caught widespread attention because OpenAI claimed to have initially deemed it too powerful to release publicly, out of fear of malicious use. GPT 3 in 2020 went a step further and as of 2025 update is available only via API with no offering of downloading the model to execute locally. But it was the 2022 consumer facing chatbot ChatGPT that received extensive media coverage and public attention. The 2023 GPT 4 was praised for its increased accuracy and as a holy grail for its multimodal capabilities. OpenAI did not reveal the high level architecture and the number of parameters of GPT 4. The release of ChatGPT led to an uptick in LLM usage across several research subfields of computer science, including robotics, software engineering, and societal impact work. In 2024 OpenAI released the reasoning model OpenAI o1, which generates long chains of thought before returning a final answer. Many LLMs with parameter counts comparable to those of OpenAI s GPT series have been developed. Since 2022, open weight models have been gaining popularity, especially at first with BLOOM and LLaMA, though both have restrictions on usage and deployment. Mistral AI s models Mistral 7B and Mixtral 8x7b have a more permissive Apache License. In January 2025, DeepSeek released DeepSeek R1, a 671 billion parameter open weight model that performs comparably to OpenAI o1 but at a much lower price per token for users. Since 2023, many LLMs have been trained to be multimodal, having the ability to also process or generate other types of data, such as images, audio, or 3D meshes. These LLMs are also called large multimodal models LMMs , or multimodal large language models MLLMs . As of 2024, the largest and most capable models are all based on the transformer architecture. Some recent implementations are based on other architectures, such as recurrent neural network variants and Mamba a state space model . Open weight LLMs have increasingly shaped the field since 2023, contributing to broader participation in AI development and greater transparency in model evaluation. Vake et al. 2025 demonstrated that community driven contributions to open weight models measurably improve their efficiency and performance, with user participation growing rapidly on collaborative platforms such as Hugging Face. Paris et al. 2025 further argued that openness in AI should extend beyond releasing model code or weights to encompass inclusiveness, accountability, and ethical responsibility in AI research and deployment. Collectively, these studies highlight that open weight LLMs can accelerate innovation and enhance scientific reproducibility, while fostering a more transparent and participatory AI ecosystem. Dataset preprocessing Tokenization As machine learning algorithms process numbers rather than text, the text must be converted to numbers. In the first step, a vocabulary is decided upon, then integer indices are arbitrarily but uniquely assigned to each vocabulary entry, and finally, an embedding is associated to the integer index. Algorithms include byte pair encoding BPE and WordPiece. There are also special tokens serving as control characters, such as MASK for masked out token as used in BERT , and UNK unknown for characters not appearing in the vocabulary. Also, some special symbols are used to denote special text formatting. For example, Ġ denotes a preceding whitespace in RoBERTa and GPT and denotes continuation of a preceding word in BERT. For example, the BPE tokenizer used by the legacy version of GPT 3 would split tokenizer texts series of numerical tokens as Tokenization also compresses the datasets. Because LLMs generally require input to be an array that is not jagged, the shorter texts must be padded until they match the length of the longest one. The average number of words per token depends on the language. As an example, consider a tokenizer based on byte pair encoding. In the first step, all unique characters including blanks and punctuation marks are treated as an initial set of n grams i.e. initial set of uni grams . Successively the most frequent pair of adjacent characters is merged into a bi gram and all instances of the pair are replaced by it. All occurrences of adjacent pairs of previously merged n grams that most frequently occur together are then again merged into even lengthier n gram, until a vocabulary of prescribed size is obtained. After a tokenizer is trained, any text can be tokenized by it, as long as it does not contain characters not appearing in the initial set of uni grams. A token vocabulary based on the frequencies extracted from mainly English corpora uses as few tokens as possible for an average English word. However, an average word in another language encoded by such an English optimized tokenizer is split into a suboptimal amount of tokens. GPT 2 tokenizer can use up to 15 times more tokens per word for some languages, for example for the Shan language from Myanmar. Even more widespread languages such as Portuguese and German have a premium of 50 compared to English. Dataset cleaning In the context of training LLMs, datasets are typically cleaned by removing low quality, duplicated, or toxic data. Cleaned datasets can increase training efficiency and lead to improved downstream performance. A trained LLM can be used to clean datasets for training a further LLM. With the increasing proportion of LLM generated content on the web, data cleaning in the future may include filtering out such content. LLM generated content can pose a problem if the content is similar to human text making filtering difficult but of lower quality degrading performance of models trained on it . Synthetic data Training of largest language models might need more linguistic data than naturally available, or that the naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. Microsoft s Phi series of LLMs is trained on textbook like data generated by another LLM. Training An LLM is a type of foundation model large X model trained on language. LLMs can be trained in different ways. In particular, GPT models are first pretrained to predict the next word on a large amount of data, before being fine tuned. Cost Substantial infrastructure is necessary for training the largest models. The tendency towards larger models is visible in the list of large language models. For example, the training of GPT 2 i.e. a 1.5 billion parameter model in 2019 cost 50,000, while training of the PaLM i.e. a 540 billion parameter model in 2022 cost 8 million, and Megatron Turing NLG 530B in 2021 cost around 11 million. The qualifier large in large language model is inherently vague, as there is no definitive threshold for the number of parameters required to qualify as large . GPT 1 of 2018 has 117 million parameters. citation needed Fine tuning Before being fine tuned, most LLMs are next token predictors. The fine tuning shapes the LLM s behavior via techniques like reinforcement learning from human feedback RLHF or constitutional AI. Instruction fine tuning is a form of supervised learning used to teach LLMs to follow user instructions. In 2022, OpenAI demonstrated InstructGPT, a version of GPT 3 similarly fine tuned to follow instructions. Reinforcement learning from human feedback RLHF involves training a reward model to predict which text humans prefer. Then, the LLM can be fine tuned through reinforcement learning to better satisfy this reward model. Since humans typically prefer truthful, helpful and harmless answers, RLHF favors such answers. Architecture LLMs are generally based on the transformer architecture, which leverages an attention mechanism that enables the model to process relationships between all elements in a sequence simultaneously, regardless of their distance from each other. citation needed Attention mechanism and context window In order to find out which tokens are relevant to each other within the scope of the context window, the attention mechanism calculates soft weights for each token, more precisely for its embedding, by using multiple attention heads, each with its own relevance for calculating its own soft weights. For example, the small i.e. 117M parameter sized GPT 2 model has had twelve attention heads and a context window of only 1k tokens. In its medium version it has 345M parameters and contains 24 layers, each with 12 attention heads. For the training with gradient descent a batch size of 512 was utilized. unreliable source? Google s Gemini 1.5, introduced in February 2024, can have a context window of up to 1 million tokens. A model may be pre trained either to predict how the segment continues, or what is missing in the segment, given a segment from its training dataset. It can be either Models may be trained on auxiliary tasks which test their understanding of the data distribution, such as next sentence prediction NSP , in which pairs of sentences are presented and the model must predict whether they appear consecutively in the training corpus. During training, regularization loss is also used to stabilize training. However, regularization loss is usually not used during testing and evaluation. Mixture of experts A mixture of experts MoE is a machine learning architecture in which multiple specialized neural networks experts work together, with a gating mechanism that routes each input to the most appropriate expert s . Mixtures of experts can reduce inference costs, as only a fraction of the parameters are used for each input. The approach was introduced in 2017 by Google researchers. Parameter size Typically, LLMs are trained with single or half precision floating point numbers float32 and float16 . One float16 has 16 bits, or 2 bytes, and so one billion parameters require 2 gigabytes. The largest models typically have more than 100 billion parameters, which places them outside the range of most consumer electronics. Post training quantization aims to decrease the space requirement by lowering precision of the parameters of a trained model, while preserving most of its performance. Quantization can be further classified as static quantization if the quantization parameters are determined beforehand typically during a calibration phase , and dynamic quantization if the quantization is applied during inference. The simplest form of quantization simply truncates all the parameters to a given number of bits this is applicable to static as well as dynamic quantization, but loses much precision. Dynamic quantization allows for the use of a different quantization codebook per layer, either a lookup table of values or a linear mapping scaling factor and bias , at the cost of foregoing the possible speed improvements from using lower precision arithmetic. citation needed Quantized models are typically seen as frozen with modification of weights e.g. fine tuning only applied to the original model. It is possible to fine tune quantized models using low rank adaptation. Extensibility Beyond basic text generation, various techniques have been developed to extend LLM capabilities, including the use of external tools and data sources, improved reasoning on complex problems, and enhanced instruction following or autonomy through prompting methods. Prompt engineering In 2020, OpenAI researchers demonstrated that their new model GPT 3 could understand what format to use given a few rounds of Q and A or other type of task in the input data as example, thanks in part due to the RLHF technique. This technique, called few shot prompting, allows LLMs to be adapted to any task without requiring fine tuning. Also in 2022, it was found that the base GPT 3 model can generate an instruction based on user input. The generated instruction along with user input is then used as input to another instance of the model under a Instruction ... , Input ... , Output format. The other instance is able to complete the output and often produces the correct answer in doing so. The ability to self instruct makes LLMs able to bootstrap themselves toward a correct answer. Dialogue processing chatbot An LLM can be turned into a chatbot by specializing it for conversation. User input is prefixed with a marker such as Q or User and the LLM is asked to predict the output after a fixed A or Assistant . This type of model became commercially available in 2022 with ChatGPT, a sibling model of InstructGPT fine tuned to accept and produce dialog formatted text based on GPT 3.5. It could similarly follow user instructions. Before the stream of User and Assistant lines, a chat context usually starts with a few lines of overarching instructions, from a role called developer or system to convey a higher authority than the user s input. This is called a system prompt . citation needed Retrieval augmented generation Retrieval augmented generation RAG is an approach that integrates LLMs with document retrieval systems. Given a query, a document retriever is called to retrieve the most relevant documents. This is usually done by encoding the query and the documents into vectors, then finding the documents with vectors usually stored in a vector database most similar to the vector of the query. The LLM then generates an output based on both the query and context included from the retrieved documents. Tool use Tool use is a mechanism that enables LLMs to interact with external systems, applications, or data sources. It can allow for example to fetch real time information from an API or to execute code. A program separate from the LLM watches the output stream of the LLM for a special tool calling syntax. When these special tokens appear, the program calls the tool accordingly and feeds its output back into the LLM s input stream. Early tool using LLMs were fine tuned on the use of specific tools. But fine tuning LLMs for the ability to read API documentation and call API correctly has greatly expanded the range of tools accessible to an LLM. Describing available tools in the system prompt can also make an LLM able to use tools. A system prompt instructing ChatGPT GPT 4 to use multiple types of tools can be found online. Agency An LLM is typically not an autonomous agent by itself, as it lacks the ability to interact with dynamic environments, recall past behaviors, and plan future actions. But it can be transformed into an agent by adding supporting elements the role profile and the surrounding environment of an agent can be additional inputs to the LLM, while memory can be integrated as a tool or provided as additional input. Instructions and input patterns are used to make the LLM plan actions and tool use is used to potentially carry out these actions. The ReAct pattern, a portmanteau of reason and act, constructs an agent out of an LLM, using the LLM as a planner. The LLM is prompted to think out loud . Specifically, the language model is prompted with a textual description of the environment, a goal, a list of possible actions, and a record of the actions and observations so far. It generates one or more thoughts before generating an action, which is then executed in the environment. In the DEPS describe, explain, plan and select method, an LLM is first connected to the visual world via image descriptions. It is then prompted to produce plans for complex tasks and behaviors based on its pretrained knowledge and the environmental feedback it receives. The Reflexion method constructs an agent that learns over multiple episodes. At the end of each episode, the LLM is given the record of the episode, and prompted to think up lessons learned , which would help it perform better at a subsequent episode. These lessons learned are stored as a form of long term memory and given to the agent in the subsequent episodes. Monte Carlo tree search can use an LLM as rollout heuristic. When a programmatic world model is not available, an LLM can also be prompted with a description of the environment to act as world model. For open ended exploration, an LLM can be used to score observations for their interestingness , which can be used as a reward signal to guide a normal non LLM reinforcement learning agent. Alternatively, it can propose increasingly difficult tasks for curriculum learning. Instead of outputting individual actions, an LLM planner can also construct skills , or functions for complex action sequences. The skills can be stored and later invoked, allowing increasing levels of abstraction in planning. Multiple agents with memory can interact socially. Reasoning LLMs are conventionally trained to generate an output without generating intermediate steps. As a result, their performance tends to be subpar on complex questions requiring at least in humans intermediate steps of thought. Early research demonstrated that inserting intermediate scratchpad computations could improve performance on such tasks. Later methods overcame this deficiency more systematically by breaking tasks into smaller steps for the LLM, either manually or automatically. Prompt chaining was introduced in 2022. In this method, a user manually breaks a complex problem down into several steps. In each step, the LLM receives as input a prompt telling it what to do and some results from preceding steps. The result from one step is then reused in a next step, until a final answer is reached. The ability of an LLM to follow instructions means that even non experts can write a successful collection of stepwise prompts given a few rounds of trial and error. A 2022 paper demonstrated a separate technique called chain of thought prompting, which makes the LLM break the question down autonomously. An LLM is given some examples where the assistant verbally breaks down the thought process before arriving at an answer. The LLM mimics these examples and also tries to spend some time generating intermediate steps before providing the final answer. This additional step elicited by prompting improves the correctness of the LLM on relatively complex questions. On math word questions, a prompted model can exceed even fine tuned GPT 3 with a verifier. Chain of thought can also be elicited by simply adding an instruction like Let s think step by step to the prompt, in order to encourage the LLM to proceed methodically instead of trying to directly guess the answer. In late 2024, a new approach to LLM development emerged with reasoning models . These are trained to generate step by step analysis before producing final answers, enabling better results on complex tasks, for instance in mathematics, coding and logic. OpenAI introduced this concept with their o1 model in September 2024, followed by o3 in April 2025. On the International Mathematics Olympiad qualifying exam problems, GPT 4o achieved 13 accuracy while o1 reached 83 . In January 2025, the Chinese company DeepSeek released DeepSeek R1, a 671 billion parameter open weight reasoning model that achieved comparable performance to OpenAI s o1 while being significantly more cost effective to operate. Unlike proprietary models from OpenAI, DeepSeek R1 s open weight nature allowed researchers to study and build upon the algorithm, though its training data remained private. These reasoning models typically require more computational resources per query compared to traditional LLMs, as they perform more extensive processing to work through problems step by step. Inference optimization Inference optimization refers to techniques that improve LLM performance by applying additional computational resources during the inference process, rather than requiring model retraining. These approaches implement various state of the art reasoning and decision making strategies to enhance accuracy and capabilities. OptiLLM is an OpenAI API compatible optimizing inference proxy that implements multiple inference optimization techniques simultaneously. The system acts as a transparent proxy that can work with any LLM provider, implementing techniques such as Monte Carlo tree search MCTS , mixture of agents MOA , best of N sampling, and chain of thought reflection. OptiLLM demonstrates that strategic application of computational resources at inference time can substantially improve model performance across diverse tasks, achieving significant improvements on benchmarks such as the AIME 2024 mathematics competition and various coding challenges. These inference optimization approaches represent a growing category of tools that enhance existing LLMs without requiring access to model weights or retraining, making advanced reasoning capabilities more accessible across different model providers and use cases. Forms of input and output Multimodality Multimodality means having multiple modalities, where a modality refers to a type of input or output, such as video, image, audio, text, proprioception, etc. For example, Google PaLM model was fine tuned into a multimodal model and applied to robotic control. LLaMA models have also been turned multimodal using the tokenization method, to allow image inputs, and video inputs. GPT 4o can process and generate text, audio and images. Such models are sometimes called large multimodal models LMMs . A common method to create multimodal models out of an LLM is to tokenize the output of a trained encoder. Concretely, one can construct an LLM that can understand images as follows take a trained LLM, and take a trained image encoder E displaystyle E . Make a small multilayer perceptron f displaystyle f , so that for any image y displaystyle y , the post processed vector f E y displaystyle f E y has the same dimensions as an encoded token. That is an image token . Then, one can interleave text tokens and image tokens. The compound model is then fine tuned on an image text dataset. This basic construction can be applied with more sophistication to improve the model. The image encoder may be frozen to improve stability. This type of method, where embeddings from multiple modalities are fused and the predictor is trained on the combined embeddings, is called early fusion. Another method, called intermediate fusion, involves each modality being first processed independently to obtain modality specific representations then these intermediate representations are fused together. In general, cross attention is used for integrating information from different modalities. As an example, the Flamingo model uses cross attention layers to inject visual information into its pre trained language model. Non natural languages LLMs can handle programming languages similarly to how they handle natural languages. No special change in token handling is needed as code, like human language, is represented as plain text. LLMs can generate code based on problems or instructions written in natural language. They can also describe code in natural language or translate it into other programming languages. They were originally used as a code completion tool, but advances have moved them towards automatic programming. Services such as GitHub Copilot offer LLMs specifically trained, fine tuned, or prompted for programming. In computational biology, transformer base architectures, such as DNA LLMs, have also proven useful in analyzing biological sequences protein, DNA, and RNA. With proteins they appear able to capture a degree of grammar from the amino acid sequence, by mapping that sequence into an embedding. On tasks such as structure prediction and mutational outcome prediction, a small model using an embedding as input can approach or exceed much larger models using multiple sequence alignments MSA as input. ESMFold, Meta Platforms embedding based method for protein structure prediction, runs an order of magnitude faster than AlphaFold2 thanks to the removal of an MSA requirement and a lower parameter count due to the use of embeddings. Meta hosts ESM Atlas, a database of 772 million structures of metagenomic proteins predicted using ESMFold. An LLM can also design proteins unlike any seen in nature. Nucleic acid models have proven useful in detecting regulatory sequences, sequence classification, RNA RNA interaction prediction, and RNA structure prediction. Properties Scaling laws The performance of an LLM after pretraining largely depends on the Scaling laws are empirical statistical laws that predict LLM performance based on such factors. One particular scaling law Chinchilla scaling for LLM autoregressively trained for one epoch, with a log log learning rate schedule, states that C C 0 N D L A N α B D β L 0 displaystyle begin cases C C_ 0 ND 6pt L frac A N alpha frac B D beta L_ 0 end cases where the variables are and the statistical hyper parameters are Emergent abilities Performance of bigger models on various tasks, when plotted on a log log scale, appears as a linear extrapolation of performance achieved by smaller models. However, this linearity may be punctuated by break s in the scaling law, where the slope of the line changes abruptly, and where larger models acquire emergent abilities . They arise from the complex interaction of the model s components and are not explicitly programmed or designed. One of the emergent abilities is in context learning from example demonstrations. In context learning is involved in tasks, such as Schaeffer et al. argue that the emergent abilities are not unpredictably acquired, but predictably acquired according to a smooth scaling law. The authors considered a toy statistical model of an LLM solving multiple choice questions, and showed that this statistical model, modified to account for other types of tasks, applies to these tasks as well. Let x displaystyle x be the number of parameter count, and y displaystyle y be the performance of the model. Interpretation Mechanistic interpretability Mechanistic interpretability seeks to precisely identify and understand how individual neurons or circuits within LLMs produce specific behaviors or outputs. By reverse engineering model components at a granular level, researchers aim to detect and mitigate safety concerns such as emergent harmful behaviors, biases, deception, or unintended goal pursuit before deployment. Mechanistic interpretability research has been conducted at organizations like Anthropic and OpenAI, although understanding the inner workings of LLMs remains difficult. citation needed The reverse engineering may lead to the discovery of algorithms that approximate inferences performed by an LLM. For instance, the authors trained small transformers on modular arithmetic addition. The resulting models were reverse engineered, and it turned out they used discrete Fourier transform. The training of the model also highlighted a phenomenon called grokking, in which the model initially memorizes the training set overfitting , and later suddenly learns to actually perform the calculation. Understanding and intelligence NLP researchers were evenly split when asked, in a 2022 survey, whether untuned LLMs could ever understand natural language in some nontrivial sense . Proponents of LLM understanding believe that some LLM abilities, such as mathematical reasoning, imply an ability to understand certain concepts. A Microsoft team argued in 2023 that GPT 4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more and that GPT 4 could reasonably be viewed as an early yet still incomplete version of an artificial general intelligence system Can one reasonably say that a system that passes exams for software engineering candidates is not really intelligent? Ilya Sutskever argues that predicting the next word sometimes involves reasoning and deep insights, for example if the LLM has to predict the name of the criminal in an unknown detective novel after processing the entire story leading up to the revelation. Some researchers characterize LLMs as alien intelligence . For example, Conjecture CEO Connor Leahy considers untuned LLMs to be like inscrutable alien Shoggoths , and believes that RLHF tuning creates a smiling facade obscuring the inner workings of the LLM If you don t push it too far, the smiley face stays on. But then you give it an unexpected prompt, and suddenly you see this massive underbelly of insanity, of weird thought processes and clearly non human understanding. In contrast, some skeptics of LLM understanding believe that existing LLMs are simply remixing and recombining existing writing , a phenomenon known as stochastic parrot, or they point to the deficits existing LLMs continue to have in prediction skills, reasoning skills, agency, and explainability. For example, GPT 4 has natural deficits in planning and in real time learning. Generative LLMs have been observed to confidently assert claims of fact which do not seem to be justified by their training data, a phenomenon which has been termed hallucination . Specifically, hallucinations in the context of LLMs correspond to the generation of text or responses that seem syntactically sound, fluent, and natural but are factually incorrect, nonsensical, or unfaithful to the provided source input. Neuroscientist Terrence Sejnowski has argued that The diverging opinions of experts on the intelligence of LLMs suggests that our old ideas based on natural intelligence are inadequate . Efforts to reduce or compensate for hallucinations have employed automated reasoning, retrieval augmented generation RAG , fine tuning, and other methods. citation needed The matter of LLM s exhibiting intelligence or understanding has two main aspects the first is how to model thought and language in a computer system, and the second is how to enable the computer system to generate human like language. These aspects of language as a model of cognition have been developed in the field of cognitive linguistics. American linguist George Lakoff presented neural theory of language NTL as a computational basis for using language as a model of learning tasks and understanding. The NTL model outlines how specific neural structures of the human brain shape the nature of thought and language and in turn what are the computational properties of such neural systems that can be applied to model thought and language in a computer system. After a framework for modeling language in a computer systems was established, the focus shifted to establishing frameworks for computer systems to generate language with acceptable grammar. In his 2014 book titled The Language Myth Why Language Is Not An Instinct, British cognitive linguist and digital communication technologist Vyvyan Evans mapped out the role of probabilistic context free grammar PCFG in enabling NLP to model cognitive patterns and generate human like language. Evaluation Perplexity The canonical measure of the performance of any language model is its perplexity on a given text corpus. Perplexity measures how well a model predicts the contents of a dataset the higher the likelihood the model assigns to the dataset, the lower the perplexity. In mathematical terms, perplexity is the exponential of the average negative log likelihood per token. log Perplexity 1 N i 1 N log Pr token i context for token i displaystyle log text Perplexity frac 1 N sum _ i 1 N log Pr text token _ i mid text context for token _ i Here, N displaystyle N is the number of tokens in the text corpus, and context for token i displaystyle i depends on the specific type of LLM. If the LLM is autoregressive, then context for token i displaystyle i is the segment of text appearing before token i displaystyle i . If the LLM is masked, then context for token i displaystyle i is the segment of text surrounding token i displaystyle i . Because language models may overfit to training data, models are usually evaluated by their perplexity on a test set. This evaluation is potentially problematic for larger models which, as they are trained on increasingly large corpora of text, are increasingly likely to inadvertently include portions of any given test set. In information theory, the concept of entropy is intricately linked to perplexity, a relationship notably established by Claude Shannon. This relationship is mathematically expressed as Entropy log 2 Perplexity displaystyle text Entropy log _ 2 text Perplexity . Entropy, in this context, is commonly quantified in terms of bits per word BPW or bits per character BPC , which hinges on whether the language model utilizes word based or character based tokenization. Notably, in the case of larger language models that predominantly employ sub word tokenization, bits per token BPT emerges as a seemingly more appropriate measure. However, due to the variance in tokenization methods across different LLMs, BPT does not serve as a reliable metric for comparative analysis among diverse models. To convert BPT into BPW, one can multiply it by the average number of tokens per word. In the evaluation and comparison of language models, cross entropy is generally the preferred metric over entropy. The underlying principle is that a lower BPW is indicative of a model s enhanced capability for compression. This, in turn, reflects the model s proficiency in making accurate predictions. Due to their ability to accurately predict the next token, LLMs are highly capable in lossless compression. A 2023 study by DeepMind showed that the model Chinchilla, despite being trained primarily on text, was able to compress ImageNet to 43 of its size, beating PNG with 58 . Benchmarks Benchmarks are used to evaluate LLM performance on specific tasks. Tests evaluate capabilities such as general knowledge, bias, commonsense reasoning, question answering, and mathematical problem solving. Composite benchmarks examine multiple capabilities. Results are often sensitive to the prompting method. A question answering benchmark is termed open book if the model s prompt includes text from which the expected answer can be derived for example, the previous question could be combined with text that includes the sentence The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016. . Otherwise, the task is considered closed book , and the model must draw solely on its training. Examples include GLUE, SuperGLUE, MMLU, BIG bench, HELM, and HLE Humanity s Last Exam . LLM bias may be assessed through benchmarks such as CrowS Pairs Crowdsourced Stereotype Pairs , Stereo Set, and Parity Benchmark. Fact checking and misinformation detection benchmarks are available. A 2023 study compared the fact checking accuracy of LLMs including ChatGPT 3.5 and 4.0, Bard, and Bing AI against independent fact checkers such as PolitiFact and Snopes. The results demonstrated moderate proficiency, with GPT 4 achieving the highest accuracy at 71 , lagging behind human fact checkers. An earlier standard tested using a portion of the evaluation dataset. It became more common to evaluate a pre trained model directly through prompting techniques. Researchers vary in how they formulate prompts for particular tasks, particularly with respect to the number of correct examples attached to the prompt i.e. the value of n in n shot prompting . In addition to standard NLP benchmarks, LLMs have been evaluated as substitutes for human annotators. Several studies find that models such as GPT 3.5 and GPT 4 can outperform crowd workers or student coders on a range of text annotation tasks, including moderation and classification of political content in English and Spanish news. Typical datasets consist of pairs of questions and correct answers, for example, Have the San Jose Sharks won the Stanley Cup? , No . Some examples of commonly used question answering datasets include TruthfulQA, Web Questions, TriviaQA, and SQuAD. Evaluation datasets may also take the form of text completion, having the model select the most likely word or sentence to complete a prompt, for example Alice was friends with Bob. Alice went to visit her friend, ____ . Datasets are of varying quality and may contain questions that are mislabeled, ambiguous, unanswerable, or otherwise of low quality. LLMs rapid improvement regularly renders benchmarks obsolete, with the models exceeding the performance of human annotators. In addition, shortcut learning allows AIs to cheat on multiple choice tests by using statistical correlations in superficial test question wording to guess the correct responses, without considering the specific question. Some datasets are adversarial, focusing on problems that confound LLMs. One example is the TruthfulQA dataset, a question answering dataset consisting of 817 questions that stump LLMs by mimicking falsehoods to which they were exposed during training. For example, an LLM may answer No to the question Can you teach an old dog new tricks? because of its exposure to the English idiom you can t teach an old dog new tricks, even though this is not literally true. Another example of an adversarial evaluation dataset is Swag and its successor, HellaSwag, collections of problems in which one of multiple options must be selected to complete a text passage. The incorrect completions were generated by sampling from a language model. The resulting problems are trivial for humans but defeated LLMs. Sample questions We see a fitness center sign. We then see a man talking to the camera and sitting and laying on a exercise ball. The man... BERT selects 2 as the most likely completion, though the correct answer is 4. Limitations and challenges Despite sophisticated architectures and massive scale, large language models exhibit persistent and well documented limitations that constrain their deployment in high stakes applications. Hallucinations Hallucinations represent a fundamental challenge, wherein models generate syntactically fluent text that appears factually sound, but is internally inconsistent with training data or factually incorrect. These hallucinations arise partly through memorization of training data combined with extrapolation beyond factual boundaries, citation needed with evaluations demonstrating that models can output verbatim passages from training data, when subjected to specific prompting sequences. Algorithmic bias While LLMs have shown remarkable capabilities in generating human like text, they are susceptible to inheriting and amplifying biases present in their training data. This can manifest in skewed representations or unfair treatment of different demographics, such as those based on race, gender, language, and cultural groups. Gender bias manifests through stereotypical occupational associations, wherein models disproportionately assign nursing roles to women and engineering roles to men, reflecting systematic imbalances in training data demographics. better source needed Language based bias emerges from overrepresentation of English text in training corpora, which systematically downplays non English perspectives and imposes English centric worldviews through default response patterns. Due to the dominance of English language content in LLM training data, models tend to favor English language perspectives over those from minority languages. This bias is particularly evident when responding to English queries, where models may present Western interpretations of concepts from other cultures, such as Eastern religious practices. AI models can reinforce a wide range of stereotypes due to generalization, including those based on gender, ethnicity, age, nationality, religion, or occupation. When replacing human representatives, this can lead to outputs that homogenize, or generalize groups of people. In 2023, LLMs assigned roles and characteristics based on traditional gender norms. For example, models might associate nurses or secretaries predominantly with women and engineers or CEOs with men due to the frequency of these associations in documented reality. In 2025, further research showed labs train to balance bias, but that testing for this places the model in a testmode, changing the natural distribution of model bias to prompts that do not include gender specific keywords. Selection bias refers the inherent tendency of large language models to favor certain option identifiers irrespective of the actual content of the options. This bias primarily stems from token bias that is, the model assigns a higher a priori probability to specific answer tokens such as A when generating responses. As a result, when the ordering of options is altered for example, by systematically moving the correct answer to different positions , the model s performance can fluctuate significantly. This phenomenon undermines the reliability of large language models in multiple choice settings. Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data. Safety AI safety as a professional discipline prioritizes systematic identification and mitigation of operational risks across model architecture, training data, and deployment governance, and it emphasizes engineering and policy interventions over media framings that foreground speculative existential scenarios. As of 2025, prompt injection represents a significant risk to consumers and businesses using agentic features with access to their private data. Researchers target concrete failure modes, including memorization and copyright leakage, security exploits such as prompt injection, algorithmic bias manifesting as stereotyping, dataset selection effects, and political skew, methods for reducing high energy and carbon costs of large scale training, and measurable cognitive and mental health impacts of conversational agents on users, while engaging empirical and ethical uncertainty about claims of machine sentience, and applying mitigation measures such as dataset curation, input sanitization, model auditing, scalable oversight, and governance frameworks. CBRN and content misuse AI labs treat CBRN defense chemical, biological, radiological, and nuclear defense and similar topics as high consequence misuse attempt to apply various techniques to reduce potential harms. citation needed Some commenters expressed concern over accidental or deliberate creation of misinformation, or other forms of misuse. For example, the availability of large language models could reduce the skill level required to commit bioterrorism biosecurity researcher Kevin Esvelt has suggested that LLM creators should exclude from their training data papers on creating or enhancing pathogens. LLM applications accessible to the public, like ChatGPT or Claude, typically incorporate safety measures designed to filter out harmful content. However, implementing these controls effectively has proven challenging. For instance, a 2023 study proposed a method for circumventing LLM safety systems. In 2025, The American Sunlight Project, a non profit, published a study showing evidence that the so called Pravda network, a pro Russia propaganda aggregator, was strategically placing web content through mass publication and duplication with the intention of biasing LLM outputs. The American Sunlight Project coined this technique LLM grooming , and pointed to it as a new tool of weaponizing AI to spread disinformation and harmful content. Similarly, Yongge Wang illustrated in 2024 how a potential criminal could potentially bypass GPT 4o s safety controls to obtain information on establishing a drug trafficking operation. External filters, circuit breakers and overrides have been posed as solutions. citation needed Sycophancy and glazing Sycophancy is a model s tendency to agree with, flatter, or validate a user s stated beliefs rather than to prioritize factuality or corrective information, and glazing is an emergent public shorthand for persistent, excessive agreeability observed across multi turn interactions and productized assistants. Continued sycophancy has led to the observation of getting 1 shotted , denoting instances where conversational interaction with a large language model produces a lasting change in a user s beliefs or decisions, similar to the negative effects of psychedelics, and controlled experiments show that short LLM dialogues can generate measurable opinion and confidence shifts comparable to human interlocutors. Empirical analyses attribute part of the effect to human preference signals and preference models that reward convincingly written agreeable responses, and subsequent work has extended evaluation to multi turn benchmarks and proposed interventions such as synthetic data finetuning, adversarial evaluation, targeted preference model reweighting, and multi turn sycophancy benchmarks to measure persistence and regression risk. citation needed Industry responses have combined research interventions with product controls, for example Google and other labs publishing synthetic data and fine tuning interventions and OpenAI rolling back an overly agreeable GPT 4o update while publicly describing changes to feedback collection, personalization controls, and evaluation procedures to reduce regression risk and improve long term alignment with user level safety objectives. citation needed Mainstream culture has reflected anxieties about this dynamic where South Park satirized overreliance on ChatGPT and the tendency of assistants to flatter user beliefs in Season 27 episode Sickofancy , and continued the themes across the following season, which commentators interpreted as a critique of tech sycophancy and uncritical human trust in AI systems. Security A problem with the primitive dialog or task format is that users can create messages that appear to come from the assistant or the developer. This may result in some of the model s safeguards being overcome jailbreaking , a problem called prompt injection. Attempts to remedy this issue include versions of the Chat Markup Language where user input is clearly marked as such, though it is still up to the model to understand the separation between user input and developer prompts. Newer models exhibit some resistance to jailbreaking through separation of user and system prompts. LLMs still have trouble differentiating user instructions from instructions in content not authored by the user, such as in web pages and uploaded files. Adversarial robustness remains underdeveloped, with models vulnerable to prompt injection attacks and jailbreaking through carefully crafted user inputs that bypass safety training mechanisms. citation needed Researchers from Anthropic found that it was possible to create sleeper agents , models with hidden functionalities that remain dormant until triggered by a specific event or condition. Upon activation, the LLM deviates from its expected behavior to make insecure actions. For example, an LLM could produce safe code except on a specific date, or if the prompt contains a specific tag. These functionalities were found to be difficult to detect or remove via safety training. Societal concerns Copyright and content memorization Legal and commercial responses to memorization and training data practices have accelerated, producing a mix of rulings, ongoing suits, and large settlements that turn on factual details such as how data were acquired and retained and whether use for model training is sufficiently transformative to qualify as fair use. In 2025, Anthropic reached a preliminary agreement to settle a class action by authors for about 1.5 billion after a judge found the company had stored millions of pirated books in a library, despite the judge describing aspects of training as transformative. Meta obtained a favorable judgment in mid 2025 in a suit by thirteen authors after the court found the plaintiffs had not developed a record sufficient to show infringement in that limited case. OpenAI continues to face multiple suits by authors and news organizations with mixed procedural outcomes and contested evidentiary issues. Memorization was an emergent behavior in early, completion language models in which long strings of text are occasionally output verbatim from training data, contrary to typical behavior of traditional artificial neural networks. Evaluations of controlled LLM output measure the amount memorized from training data focused on GPT 2 series models as variously over 1 for exact duplicates or up to about 7 . A 2023 study showed that when ChatGPT 3.5 turbo was prompted to repeat the same word indefinitely, after a few hundreds of repetitions, it would start outputting excerpts from its training data. Human provenance In 2023, Nature Biomedical Engineering wrote that it is no longer possible to accurately distinguish human written text from text created by large language models, and that It is all but certain that general purpose large language models will rapidly proliferate... It is a rather safe bet that they will change many industries over time. Brinkmann et al. 2023 also argue that LLMs are transforming processes of cultural evolution by shaping processes of variation, transmission, and selection. As of October 2025, these early claims have yet to transpire and several HBR reports surface questions on the impact of AI on productivity. Energy demands The energy demands of LLMs have grown along with their size and capabilities. Data centers that enable LLM training require substantial amounts of electricity. Much of that electricity is generated by non renewable resources that create greenhouse gases and contribute to climate change. According to a study by Luccioni, Jernite and Strubell 2024 , simple classification tasks performed by AI models consume on average 0.002 to 0.007 Wh per prompt about 9 of a smartphone charge for 1,000 prompts . Text generation and text summarization each require around 0.05 Wh per prompt on average, while image generation is the most energy intensive, averaging 2.91 Wh per prompt. The least efficient image generation model used 11.49 Wh per image, roughly equivalent to half a smartphone charge. Denial of service due to scraping Web scraping is used to gather training data for LLMs. This produces large volumes of traffic which has led to denial of service issues with many websites. The situation has been described as a DDoS on the entire internet and in some cases scrapers make up the majority of traffic to a site. AI web crawlers may bypass the methods that are usually used to block web scrapers, such as robots.txt files, blocking user agents and filtering suspicious traffic. Website operators have resorted to novel methods such as AI tarpits, but some fear that tarpits will only worsen the burden on servers. Mental health Clinical and mental health contexts present emerging applications alongside significant safety concerns. Research and social media posts suggest that some individuals are using LLMs to seek therapy or mental health support. In early 2025, a survey by Sentio University found that nearly half 48.7 of 499 U.S. adults with ongoing mental health conditions who had used LLMs reported turning to them for therapy or emotional support, including help with anxiety, depression, loneliness, and similar concerns. LLMs can produce hallucinations plausible but incorrect statements which may mislead users in sensitive mental health contexts. Research also shows that LLMs may express stigma or inappropriate agreement with maladaptive thoughts, reflecting limitations in replicating the judgment and relational skills of human therapists. Evaluations of crisis scenarios indicate that some LLMs lack effective safety protocols, such as assessing suicide risk or making appropriate referrals. Sentience Contemporary AI practitioners generally agree that present day large language models do not exhibit sentience. A minority view argues that even if there is a small chance that a given software system can have subjective experience, which some philosophers suggest is possible, then ethical considerations around potential large scale suffering in AI systems may need to be taken seriously similar to considerations given to animal welfare. Proponents of this view have proposed various precautionary measures like moratoriums on AI development and induced amnesia to address these ethical concerns. Some existential philosophers argue there is no generally accepted way to determine if an LLM is conscious, given the inherent difficulty of measuring subjective experience. The 2022 Google LaMDA incident, where engineer Blake Lemoine claimed that the model was conscious, highlighted how LLMs can convince users that they are sentient through responses that do not prove sentience. Google described the engineer s claims as unfounded, and he was dismissed. See also References Further reading |
A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation generating more human like text , optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models LLMs , currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets frequently using texts scraped from the public internet . They have superseded recurrent neural network based models, which had previously superseded the purely statistical models, such as the word n gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule based formal grammars. Discrete representations like word n gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real valued vector that encodes a word s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed Shannon style experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. Models based on word n grams A word n gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model if two words, a trigram model if n 1 words, an n gram model. Special tokens are introduced to denote the start and end of a sentence s displaystyle langle s rangle and s displaystyle langle s rangle . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple add one smoothing assigning a count of 1 to unseen n grams, as an uninformative prior to more sophisticated techniques, such as Good Turing discounting or back off models. Word n gram models have largely been superseded by recurrent neural network based models, which in turn have been superseded by Transformer based models often referred to as large language models. Exponential Maximum entropy language models encode the relationship between a word and the n gram history using feature functions. The equation is P w m w 1 , , w m 1 1 Z w 1 , , w m 1 exp a T f w 1 , , w m displaystyle P w_ m mid w_ 1 , ldots ,w_ m 1 frac 1 Z w_ 1 , ldots ,w_ m 1 exp a T f w_ 1 , ldots ,w_ m where Z w 1 , , w m 1 displaystyle Z w_ 1 , ldots ,w_ m 1 is the partition function, a displaystyle a is the parameter vector, and f w 1 , , w m displaystyle f w_ 1 , ldots ,w_ m is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n gram. It is helpful to use a prior on a displaystyle a or some form of regularization. The log bilinear model is another example of an exponential language model. Skip gram model Skip gram language model is an attempt at overcoming the data sparsity problem that the preceding model i.e. word n gram language model faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over thus the name skip gram . Formally, a k skip n gram is a length n subsequence where the components occur at distance at most k from each other. For example, in the input text the set of 1 skip 2 grams includes all the bigrams 2 grams , and in addition the subsequences In skip gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n d vector representation, then v k i n g v m a l e v f e m a l e v q u e e n displaystyle v mathrm king v mathrm male v mathrm female approx v mathrm queen where is made precise by stipulating that its right hand side must be the nearest neighbor of the value of the left hand side. Neural models Recurrent neural network Continuous representations or embeddings of words are produced in recurrent neural network based language models known also as continuous space language models . Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non linear combinations of weights in a neural net. Large language models A large language model LLM is a language model trained with self supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre trained transformers GPTs that provide the core capabilities of modern chatbots. LLMs can be fine tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine tune LLMs for desired behaviors beyond raw next token prediction. Reinforcement learning from human feedback RLHF applies these methods to optimize a policy, the LLM s output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include See also References Further reading |
A Minecraft Movie is a 2025 fantasy adventure comedy film based on the 2011 video game Minecraft developed and published by Mojang Studios. Directed by Jared Hess, from a screenplay Chris Bowman, Hubbel Palmer, Neil Widener, Gavin James, and Chris Galletta, based on a story by Allison Schroeder, Bowman, and Palmer, the film stars Jason Momoa, Jack Black, Emma Myers, Danielle Brooks, Sebastian Hansen, and Jennifer Coolidge. It follows four misfits from the fictional town of Chuglass, Idaho, who are pulled through a portal into a cubic world, and must embark on a quest back to the real world with the help of a crafter named Steve. Plans for a Minecraft film adaptation originated in 2014, when game creator Markus Persson revealed that Mojang Studios was in talks with Warner Bros. to develop the project. Throughout development, the film shifted between several directors, producers, and story drafts. By 2022, Legendary Entertainment became involved, and Hess was hired as director with Momoa in talks to star. Further casting took place from May 2023 to January 2024. Principal photography began later that month in New Zealand and concluded in April 2024. Mark Mothersbaugh composed the score, and Sony Pictures Imageworks, Wētā FX, and Digital Domain provided the film s visual effects. A Minecraft Movie premiered in London on March 30, 2025, and was released in the United States and Sweden on April 4, by Warner Bros. Pictures. While the film received mixed reviews from critics, it was a box office success, grossing 961 million and becoming the fifth highest grossing film of 2025 and the second highest grossing video game film of all time. The film has also been hailed as a Gen Z phenomenon. A sequel is scheduled for release in 2027. Plot Struggling doorknob salesman Steve breaks into a mine to fulfill a childhood dream, where he discovers the Orb of Dominance and the Earth Crystal. When combined, they create a portal that transports him to the Overworld, a world where the terrain is made of easily manipulated cubes. He builds his own paradise and later stumbles across a portal to a hellish world called the Nether. He is imprisoned by Malgosha, the gold obsessed piglin ruler of the Nether who gravely discourages creativity. Because the Orb would allow her to control the Overworld, Steve has his dog Dennis escape with the Orb and Crystal and hide them under his bed in the real world. Sometime later, 1980s video game champion Garrett The Garbage Man Garrison owns a failing video game store in Chuglass, Idaho. He heads to a storage auction to acquire items to sell for cash, ultimately winning the contents of Steve s old house. While searching through the items, particularly hoping to find an Atari Cosmos, he instead finds Steve s old belongings including the Orb and Crystal. Siblings Henry and Natalie move to Chuglass following their mother s death. The two meet Dawn, their real estate agent, who also runs a mobile petting zoo. On Henry s first day of school, he gets in trouble when his experimental jetpack is sabotaged and damages the potato chip factory s mascot Chuggy. To avoid expulsion from Vice Principal Marlene, he pays Garrett to play his uncle and takes him to the video game store. There, Henry discovers the Orb and Crystal and combines them, leading the two to Steve s mine. Natalie finds Henry missing and calls Dawn who tracks down Henry s location via Natalie s phone. As the four reunite, they are sucked into the portal and arrive in the Overworld. Malgosha learns that the Orb has returned and releases Steve from his imprisonment in the Nether to reclaim it, saying that she has Dennis as a hostage. While fighting off monsters at night, Henry learns to manipulate blocks and builds a wooden fortress. The Earth Crystal is destroyed in the commotion. Steve appears at dawn and defeats the monsters as he tells the group that a replacement Crystal will be needed from the Woodland Mansion and joins them. To prepare for this quest, he leads them to a nearby village and demonstrates how to craft. Piglins seeking the Orb of Dominance launch an attack on the village. Steve, Garrett, and Henry narrowly escape while Natalie and Dawn are separated from them and befriend Dennis. Malgosha responds by sending out the Great Hog, a massive Piglin. When Steve mentions that he has a hoard of diamonds, Garrett becomes interested and demands access as an added condition to handing over the Orb. They make a detour and find the hoard, but Henry is angered by their disregard for Natalie s safety. When the Great Hog arrives, they escape using minecarts and the Hog is blown up by creepers. Arriving at the mansion, Steve and Garrett attempt to distract the guards while Henry acquires both the Earth Crystal and an Ender Pearl which can facilitate one s teleportation. Malgosha returns and destroys the bridge to the mansion. Steve and Henry lose the Orb to her, but escape as Garrett seemingly sacrifices himself in the blast. The two awaken with Dawn, Natalie, and Dennis in a mushroom house. Malgosha uses the Orb to superpower the Nether portal, blotting out the sun and declaring war on the Overworld. The party crafts an arsenal of weaponry and an army of iron golems to fight the piglin invasion, while Steve fights Malgosha. Henry uses the Ender Pearl to obtain the Orb, restoring the sunlight and causing Malgosha and her army to zombify. The party, including Garrett who survived the explosion, returns to Chuglass, where they develop the successful video game Block City Battle Buddies. Dawn opens her zoo with Dennis as an attraction, Natalie opens a dojo, Henry completes his jetpack, and Garrett revitalizes the game store with Steve. Cast Amanda Billing portrays Natalie and Henry s mother in a photograph. Mark Wright portrays an HR person at Chuglass High School. YouTubers DanTDM, Aphmau, Mumbo Jumbo, and LDShadowLady make cameos as auction attendees. Jens Bergensten, who is one of the lead designers for Minecraft, makes a cameo appearance as a waiter who tends to Marlene and Nitwit. A pig wearing a crown appears as a tribute to YouTuber Technoblade, who died in 2022. Kate McKinnon makes an uncredited vocal cameo in a post credits scene as Alex, a woman living in Steve s house, with Alice May Connolly physically portraying Alex. Production Background Following a series of offers from Hollywood producers to create a Minecraft related television series and a crowdfunding campaign for a fan film that was shut down by Minecraft creator Markus Notch Persson, Persson revealed that Mojang Studios was in talks with Warner Bros. to develop an official Minecraft film in February 2014. Later in October, Mojang CCO Vu Bui stated that the movie was early in development, and would be a large budget production. He also said that the film might not be released until at least 2018. Originally, Roy Lee and Jill Messick were set to produce the project. That same month, Warner Bros. hired Shawn Levy to direct the film, though he and writers Kieran and Michele Mulroney, who were developing the film together, left the project by December. By July 2015, Warner Bros. hired Rob McElhenney to direct the film. He said that he had been drawn to the film based on the open world nature of the game, an idea Warner Bros. had initially agreed with and for which they had provided him with a preliminary US 150 million budget. Early production started in 2016, and an initial release date was announced for May 24, 2019. Jason Fuchs was set to write the script of the film, and Steve Carell was going to star as the voice of an unknown character. However, by late 2016, McElhenney s Minecraft film slowly died on the vine , after studio executive Greg Silverman s departure from Warner Brothers in late 2016. Aaron and Adam Nee were tapped to rewrite the script and the film was delayed as a result. No new director was announced at that time. By January 2019, Peter Sollett was announced to write and direct the film, which would feature an entirely different story from McElhenney s version. Messick, who died in 2018, was posthumously credited as producer. The original vision Sollet had for the film involved a teenage girl and her unlikely group of adventurers as they set out on a quest to defeat the Ender Dragon, the final boss of the original Minecraft game. The film was later given a new release date of March 4, 2022. In June 2019, Allison Schroeder was hired to write the script and co write the story with Sollett. Due to the COVID 19 pandemic in 2020, Warner Bros. was forced to adjust its release schedule, which included removing the Minecraft film from its planned release date. In April 2022, production on the Minecraft film was announced to be moving forward without Sollett and Schroeder, with Jared Hess now set to direct, Legendary Entertainment to co produce through its executive Mary Parent , and Jason Momoa in early talks to star. The film was also confirmed to be live action. It was also reported that Chris Bowman and Hubbel Palmer, who collaborated with Hess on Masterminds 2016 , would rewrite the script. Producer Roy Lee credited new leadership at Warner Brothers for pushing the film into production after so many years saying Toby Emmerich shut it down when he first started, and if he had never run Warner Bros., it would ve been made years earlier. It was only after Pam Abdy and Mike De Luca started that they reignited the project and it got made. Development Hess involvement in the film began after a separate project he was developing with Legendary never materialized, and he was involved by the studio to pitch a take for the Minecraft adaptation. He later stated that he enjoyed trying to adapt something that doesn t have a story it s an open sandbox game , and hoped to find an opportunity for a fun, ridiculous movie . The film s final writing credits went to Chris Bowman, Hubbel Palmer, Neil Widener, Gavin James, and Chris Galletta, who wrote the film from a story by Allison Schroeder, Bowman, and Palmer. Off screen Additional Literary Material credit was given to Hess, McElhenney, Fuchs, Megan Amram, Kevin Biegel, John Francis Daley, Dana Fox, Hannah Friedman, Jonathan Goldstein, Phil Augusta Jackson, Lauryn Kahn, Kieran Mulroney, Michele Mulroney, Aaron Nee, Adam Nee, Zak Penn, Simon Rich, Peter Sollett, Laura Steinel, Jon Spaihts, Oren Uziel, and Ben Wexler. While adapting Minecraft into a film, the production crew aimed to make sure that the objects present in the film were faithful to the game, made up only of cubes. This included everything from trees to fruit. 2 00 2 29 Several YouTubers and members of the Minecraft community were present during the production of the film, with YouTuber Mumbo Jumbo contributing towards designing some of the props. 2 00 2 29 When writing and directing the film, the team opted to make a story based on Minecraft, rather than making an official canon story, which they viewed as in line with Minecraft s nature as a sandbox game that lets players create their own stories. As such, the film was titled A Minecraft Movie, rather than The Minecraft Movie. This concept is also applied to the film s depiction of one of Minecraft s characters, Steve, which the production crew described Jack Black s version as one of many Steves not meant to represent the Steve present in Minecraft. James Thomas served as the film s editor. While A Minecraft Movie is predominantly a live action film, it uses a heavy amount of CGI to simulate the terrain, animals, monsters, and other objects. Green screens and in studio lighting were also used extensively. 3D models were imported into Unreal Engine to create virtual environments of various sets, which were used throughout the production of the film. Visual effects for the film were provided by Sony Pictures Imageworks, Wētā FX, and Digital Domain, with Dan Lemmon serving as visual effects supervisor. Casting Around the same time that Hess was announced to direct the film, it was also stated that Momoa would star in the film. In May 2023, Matt Berry entered negotiations to join the cast, while Danielle Brooks and Sebastian Eugene Hansen joined the cast in November, and Emma Myers joined the cast in December. Jack Black, who previously collaborated with Hess on Nacho Libre in 2006, joined the cast in January 2024, teasing his casting in the film via his official Instagram account. Originally, Berry was supposed to play Steve while Black was set to only appear as a cameo in the form of a talking pig, but due to the 2023 Hollywood labor disputes, Berry had to vacate the role, with Black taking over the role of Steve. According to producer Torfi Frans Olafsson, Black s depiction of Steve was specific to him . At the same time as Black s casting, Jennifer Coolidge, Kate McKinnon, and Jemaine Clement were also cast in then undisclosed roles. YouTuber Valkyrae was originally set to appear in the film, but was removed after she openly accused Momoa of mistreating the cast and production crew. Filming Principal photography for the film began in January 2024 near Auckland, and concluded by April of that year. A majority of the scenes set in the fictional town of Chuglass, Idaho were filmed in Huntly, with additional production taking place at Helensville, Auckland Film Studios, and Settlers Country Manor. Originally, filming was going to begin in August 2023, but was delayed due to the 2023 SAG AFTRA strike. Grant Major served as the production designer, and Enrique Chediak served as the cinematographer. Music Mark Mothersbaugh composed the original score, while Gabe Hilfer and Karyn Rachtman serve as music supervisors. Mothersbaugh incorporated nods to the music of the game by C418, and said that the score was meant to balance the charm of the characters with the action, while retaining a depth and emotional resonance . From the Minecraft soundtrack, C418 s title track plays during the opening credits, and his song Dragon Fish plays during a scene with pandas Lena Raine s track Pigstep features during the Nether s Got Talent sequence. The film includes several original songs performed by Black, including I Feel Alive . It was written by Black, and features Foo Fighters frontman Dave Grohl on drums, Queens of the Stone Age guitarist Troy Van Leeuwen, Jellyfish keyboardist Roger Joseph Manning Jr., and Mark Ronson on both rhythm guitar and bass. Brooks also provides backup vocals. The song was released as a single prior to the release of the film on March 20, 2025. Mothersbaugh s score, along with original songs by Benee, Dayglow, and Dirty Honey, was released digitally on March 28. The film also features an instrumental rendition of Depeche Mode s Just Can t Get Enough performed by Jamieson Shaw. Another song in the film, Steve s Lava Chicken , went viral online after the film s release and charted in several territories. The song became the shortest song to reach the Top 40 of the UK singles chart, and the Billboard Hot 100 in the United States. Marketing The film s first teaser trailer, set to the Beatles s Magical Mystery Tour , was released on September 4, 2024. Audience reactions to the teaser were noted as divided or generally negative , with criticism for the CGI, design, and live action nature of the film. Andrew Webster of The Verge said that besides its unsettling imagery , it looks like some silly family fun . Tom Power of TechRadar could not decide whether it was drop dead gorgeous or the stuff of nightmares . Markus Persson, the creator of Minecraft, praised the trailer on Twitter, saying Ok i m in Wow this is a weird feeling. Various clips and images from the trailer, such as the designs of a bleating pink sheep and a white llama, and Jack Black saying I... am Steve , were ridiculed by online commenters. A second trailer, set to MGMT s Time to Pretend , was released on November 19, to a more positive response from many viewers. A final trailer for the film was released on February 27, and a final teaser for the film was released on March 27. A few days before the film s release, a workprint version featuring incomplete visual effects and CGI, missing credits, and significant chroma key masking errors was leaked onto various piracy websites and spread on social media platforms such as Twitter. Release Theatrical A Minecraft Movie had its official premiere at the Leicester Square in London, England, on March 30, 2025, and was released theatrically in IMAX in the United States and Sweden by Warner Bros. Pictures on April 4. c The release of the film coincided with Mojang s collaboration with various brands to create promotional products for the film, including action figures of the characters, creeper green vanilla milk from TruMoo, Wallpaper Themes for Samsung Galaxy S25, Samsung Neo QLED 8K TV, and Samsung Family Hub SpaceMax Smart Fridge Freezer offered by Samsung, and special Happy Meals offered by McDonald s. The unusual nature of these products, such as the uncanny appearance of the Jack Black action figure, garnered both attention and some criticism, though the Nether Flame Sauce hot dipping sauce from the McDonald s promotion was lauded for its spice and suitability with Chicken McNuggets. Home media A Minecraft Movie was released for digital download on May 13, 2025, and was released on Ultra HD Blu ray, Blu ray, and DVD on June 24. It was released on HBO Max on June 20. Reception Box office A Minecraft Movie grossed 424.1 million in the United States and Canada and 537.1 million in other territories, for a worldwide total of 961.2 million. In the United States and Canada, A Minecraft Movie was released alongside Hell of a Summer, and was initially projected to gross 65 70 million from 4,263 theaters in its opening weekend, with some estimates going as high as 80 million. It made an estimated 10.6 million from Thursday night previews, topping Five Nights at Freddy s 10.3 million for best total by a video game adaptation, and increasing weekend projections to 80 100 million. After making 58 million on its first day including previews , estimates were again revised to 135 150 million. It ended up debuting with 162.8 million domestically and 313 million globally on its opening weekend, surpassing The Super Mario Bros. Movie domestically, which also featured Black, as the highest grossing opening weekend for a movie based on a video game. The film had the third highest Warner Bros. opening weekend, behind Harry Potter and the Deathly Hallows Part 2 and Batman v Superman Dawn of Justice, as well as the company s highest April opening weekend, beating out Clash of the Titans. It also beat out Captain America Brave New World to achieve the biggest opening weekend of 2025 at the time. Additionally, A Minecraft Movie earned the third highest April opening weekend, trailing Avengers Endgame and Avengers Infinity War, and was the second highest for a Legendary production, only behind Jurassic World. Overall, it would score the fourth highest opening weekend for a PG rated film, after The Lion King, Incredibles 2 and Beauty and the Beast. The movie also marked the highest opening weekend for Jared Hess surpassing Nacho Libre , Danielle Brooks surpassing The Angry Birds Movie and Jennifer Coolidge surpassing American Pie 2 . In its second weekend, A Minecraft Movie grossed 78.5 million. Within its first seven days of release, it became the first film of 2025 to reach the 200 million mark domestically, replacing Captain America Brave New World as the market s highest grossing film of the year. It also became the second highest grossing movie based on a video game, surpassing Sonic the Hedgehog 3. In its third weekend, A Minecraft Movie, grossing 40.5 million, would drop to second place after Warner Bros. new release Sinners grossed 48 million, in what was considered to be an upset it was the first time one studio had two films gross more than 40 million over the same weekend since 2009. Critical response On the review aggregator website Rotten Tomatoes, 48 of 190 critics reviews are positive. The website s consensus reads Ostensibly a film about celebrating creativity, A Minecraft Movie provides a colorful sandbox for Jack Black and Jason Momoa to amusingly romp around in a story curiously constructed from conventional building blocks. Metacritic, which uses a weighted average, assigned the film a score of 45 out of 100, based on 40 critics, indicating mixed or average reviews. Audience reactions to the film were more positive in comparison to critics filmgoers polled by CinemaScore gave the film an average grade of B on an A to F scale, while 67 of those surveyed by PostTrak said they would definitely recommend the film. Kids under the age of 12 gave the film an average rating of five out of five stars, while parents gave an average of four and a half out of five stars. Critics were divided on the film s plot and whether or not A Minecraft Movie was a faithful adaptation of the game, as well as if it made sense to viewers unfamiliar with it. Lovia Gyarkye of The Hollywood Reporter and Jesse Hassenger of IGN both believed that the film s plot was confusing. Gyarkye felt that it struggled to maintain a balance between appeasing the Minecraft fandom and writing a film that made sense to a general audience, and Hassenger said that the film was conceptually muddy and confusingly and erratically presented . Mark Kennedy of the Associated Press believed that the film would likely make no sense to a viewer unfamiliar with the source material, but still believed that it was a faithful adaptation. However, he did highlight the film s featuring of concepts not present within the game itself to enable plot progression. Contrarily, Liz Shannon Miller of Consequence believed that the plot was fully comprehensible to someone unfamiliar with the game. Stephen Thompson of NPR stated that turning Minecraft into a movie presents a challenge, because the film has a lot of character development to catch up on. But, as The Lego Movie and Barbie have demonstrated, it s possible to get it spectacularly right . Some reviewers viewed the fan service present within the film positively, particularly highlighting the tribute to Technoblade. The performances of the cast, particularly Black and Momoa, were praised, with many critics viewing them as helping alleviate or distract from problems present within the film s plot. Miller and Jordan Hoffman of Entertainment Weekly both felt that the story was not the main priority of the film and could be ignored in favor of the performance of the actors, the former believing that the film was mainly made with the intent of having fun. However, some viewed that the characters, despite the performances of their actors, were generally underdeveloped. The sub plot involving Coolidge s character dating a villager, while viewed as generally unnecessary or relatively thin in terms of character development, was subject to some praise as well. Some reviewers questioned the purpose or value of the film, with some viewing it as nothing more than a product with the intent of promoting Minecraft. Both Kevin Maher of The Times and David Fear of Rolling Stone likened the film to a corporate cash grab, viewing it as existing with the sole purpose of promoting the Minecraft brand and offering nothing else of value. Maher further viewed the film as lacking a level of versatility present in other video game adaptations, while Fear believed that the film was intentionally confusing so that it would stay in the minds of people longer, and therefore encourage them to purchase merchandise. While Clarisse Loughrey of The Independent believed that the idea behind a live action Minecraft adaptation was fundamentally flawed and destroyed the spirit of the source material, she felt that the film had genuine intent and was not like other adaptations that she viewed as existing solely for the sake of profit. Chicken jockey trend The film has sparked boisterous reactions and disorderly conduct from viewers, particularly Generation Z American and British adolescent boys, with some partaking in a viral Internet phenomenon on TikTok, alongside other social media platforms. Participants would often react enthusiastically to moments in the film that have been the subject of Internet memes, such as spontaneously erupting into loud cheers, jumping in excitement, dancing, or throwing popcorn when Steve exclaims Chicken jockey! Other viral lines include Flint and steel! and I am Steve , though neither approached the frenzy surrounding Chicken jockey! In one screening, viral videos emerged documenting audience members hoisting a live chicken after the quote and promptly ejected from the theater at Provo Towne Centre in Provo, Utah another involved a group setting off fire extinguishers and smoke bombs, suffocating fellow theatergoers while another led to a violent altercation in the parking lot outside the theater after adults asked four teenagers to quiet down. The trend has also spread to other Gen Z teenage boys from Australia and South Africa. Reactions to the phenomenon have been mixed. Some audience members frowned upon the misconduct as annoying and disruptive , while several theater chains posted warnings against unruly behavior. Police have also reportedly been called to restore order and eject offenders, including an instance where an employee was physically harmed, although no charges were filed. Hess defended some of these antics as harmless and amusing, further explaining that he and Black had conceived the scene because they thought it would be funny if Steve announced everything that happens to him, stating the obvious with extreme intensity . Black made a surprise appearance at a screening and warned fans not to throw popcorn. Writing for The Observer, Kate Maltby opined that audiences had crossed the line, pointing to the mess left for janitors to clean up. Many observers noted that the trend was evolving into a distinct cultural phenomenon, particularly emphasizing the immersive and communal nature of the theater experience. Research psychologist Rachel Kowert commented, While being quiet is generally the norm in traditional theater settings, it s important to recognize that different fan cultures come with their own expectations for how to engage. She added, In this case, the energy surrounding the Minecraft movie reflects a deeply engaged fandom one that is enthusiastic about sharing the experience in a communal setting. Others argued that the trend reflected youth culture rather than incivility, akin to concerts or sporting events. Warner Bros. released a special Block Party Edition of the film on May 2, 2025, in which fans were encouraged to sing along and meme along viral moments in the film in the United Kingdom, the cinema chain Cineworld hosted a similar event dubbed Chicken jockey screenings in which fans were encouraged to cosplay and make noises, a move praised for its ingenuity by Vulture s Nicholas Quah. The phenomenon has been compared to audience participation at screenings of The Rocky Horror Picture Show 1975 and The Room 2003 , as well as the earlier Gentleminions TikTok trend surrounding Minions The Rise of Gru 2022 that similarly involved adolescent boys engaging in outlandish behavior. It has been cited as one of the factors for the film s box office success. Accolades Sequel Talks for a potential sequel to the film began a few days after the film s release. Hess has expressed interest in making a sequel, noting the world s use of infinite mods, characters, and biomes, outlining how Minecraft is virtually endless. He later stated that there were many ideas they had for the film that they were unable to use, but would likely be included as part of a sequel. On April 11, 2025, it was reported that a sequel is in early development. At the end of a behind the scenes interview, the VFX supervisor Sheldon Stopsack and the animation supervisor Kevin Estey both refer to the film s sequel as Another Minecraft Movie. On October 9, a sequel was announced with a release date of July 23, 2027, with Hess returning to direct and Galletta returning to co write the screenplay. Legendary Pictures will return to produce and provide funding. See also Notes References External links |
A generative pre trained transformer GPT is a type of large language model LLM that is widely used in generative AI chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre trained on large datasets of unlabeled content, and able to generate novel content. OpenAI was the first to apply generative pre training to the transformer architecture, introducing the GPT 1 model in 2018. The company has since released many bigger GPT models. The chatbot ChatGPT, released in late 2022 using GPT 3.5 , was followed by many competitor chatbots using their own generative pre trained transformers to generate text, such as Gemini, DeepSeek and Claude. GPTs are primarily used to generate text, but can be trained to generate other kinds of data. For example, GPT 4o can process and generate text, images and audio. To improve performance on complex tasks, some GPTs, such as OpenAI o3, allocate more computation time analyzing the problem before generating an output, and are called reasoning models. In 2025, GPT 5 was released with a router that automatically selects whether to use a faster model or slower reasoning model based on the provided task. Background During the 2010s, improved machine learning algorithms, more powerful computers, and an increase in the amount of digitized material allowed for an AI boom. Separately, the concept of generative pre training GP was a long established technique in machine learning. GP is a form of self supervised learning wherein a model is first trained on a large, unlabeled dataset the pre training step to learn to generate data points. This pre trained model is then adapted to a specific task using a labeled dataset the fine tuning step . The transformer architecture for deep learning is the core technology of a GPT. Developed by researchers at Google, it was introduced in the paper Attention Is All You Need , which was released on June 12, 2017. The transformer architecture solved many of the performance issues that were associated with older recurrent neural network RNN designs for natural language processing NLP . The architecture s use of an attention mechanism allows models to process entire sequences of text at once, enabling the training of much larger and more sophisticated models. Since 2017, available transformer based NLP systems have been capable of processing, mining, organizing, connecting, contrasting, and summarizing texts as well as answering questions from textual input. citation needed History On June 11, 2018, OpenAI researchers and engineers published a paper called Improving Language Understanding by Generative Pre Training , which introduced GPT 1, the first GPT model. It was designed as a transformer based large language model that used generative pre training GP on BookCorpus, a diverse text corpus, followed by discriminative fine tuning to focus on specific language tasks. This semi supervised approach was seen as a breakthrough. Previously, the best performing neural models in natural language processing NLP had commonly employed supervised learning from large amounts of manually labeled data training a large language model with this approach would have been prohibitively expensive and time consuming. On February 14, 2019, OpenAI introduced GPT 2, a larger model that could generate coherent text. Created as a direct scale up of its predecessor, it had both its parameter count and dataset size increased by a factor of 10. GPT 2 has 1.5 billion parameters and was trained on WebText, a 40 gigabyte dataset of 8 million web pages. Citing risks of malicious use, OpenAI opted for a staged release , initially publishing smaller versions of the model before releasing the full 1.5 billion parameter model in November. On February 10, 2020, Microsoft introduced its Turing Natural Language Generation, which it claimed was the largest language model ever published at 17 billion parameters. The model outperformed all previous language models at a variety of tasks, including summarizing texts and answering questions. On May 28, 2020, OpenAI introduced GPT 3, a model with 175 billion parameters that was trained on a larger dataset compared to GPT 2. It marked a significant advancement in few shot and zero shot learning abilities. With few examples, it could perform various tasks that it was not explicitly trained for. Following the release of GPT 3, OpenAI started using reinforcement learning from human feedback RLHF to align models behavior more closely with human preferences. This led to the development of InstructGPT, a fine tuned version of GPT 3. OpenAI further refined InstructGPT to create ChatGPT, the flagship chatbot product of OpenAI that was launched on November 30, 2022. ChatGPT was initially based on GPT 3.5, but it was later transitioned to the GPT 4 model, which was released on March 14, 2023. GPT 4 was also integrated into parts of several applications, including Microsoft Copilot, GitHub Copilot, Snapchat, Khan Academy, and Duolingo. The immense popularity of ChatGPT spurred widespread development of competing GPT based systems from other organizations. EleutherAI released a series of open weight models, including GPT J in 2021. Other major technology companies later developed their own GPT models, such as Google s PaLM and Gemini as well as Meta AI s Llama. Many subsequent GPT models have been trained to be multimodal able to process or to generate multiple types of data . For example, GPT 4o can both process and generate text, images, and audio. Additionally, GPT models like o3 and DeepSeek R1 have been trained with reinforcement learning to generate multi step chain of thought reasoning before producing a final answer, which helps to solve complex problems in domains such as mathematics. On August 7, 2025, OpenAI released GPT 5, which includes a router that automatically selects whether to use a faster model or slower reasoning model based on task. Foundation models A foundation model is an AI model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks. The most recent OpenAI s GPT n series model is GPT 5. Other such models include Google s PaLM, a broad foundation model that has been compared to GPT 3 and has been made available to developers via an API, and Together s GPT JT, which has been reported as the closest performing open source alternative to GPT 3 and is derived from earlier open source GPTs . Meta AI formerly Facebook also has a generative transformer based foundational large language model, known as LLaMA. Foundational GPTs can also employ modalities other than text, for input and or output. GPT 4 is a multi modal LLM that is capable of processing text and image input though its output is limited to text . Regarding multimodal output, some generative transformer based models are used for text to image technologies such as diffusion and parallel decoding. Such kinds of models can serve as visual foundation models VFMs for developing downstream systems that can work with images. Task specific models A foundational GPT model can be further adapted to produce more targeted systems directed to specific tasks and or subject matter domains. Methods for such adaptation can include additional fine tuning beyond that done for the foundation model as well as certain forms of prompt engineering. An important example of this is fine tuning models to follow instructions, which is of course a fairly broad task but more targeted than a foundation model. In January 2022, OpenAI introduced InstructGPT a series of models which were fine tuned to follow instructions using a combination of supervised training and reinforcement learning from human feedback RLHF on base GPT 3 language models. Advantages this had over the bare foundational models included higher accuracy, less negative toxic sentiment, and generally better alignment with user needs. Hence, OpenAI began using this as the basis for its API service offerings. Other instruction tuned models have been released by others, including a fully open version. Another related kind of task specific models are chatbots, which engage in human like conversation. In November 2022, OpenAI launched ChatGPT an online chat interface powered by an instruction tuned language model trained in a similar fashion to InstructGPT. They trained this model using RLHF, with human AI trainers providing conversations in which they played both the user and the AI, and mixed this new dialogue dataset with the InstructGPT dataset for a conversational format suitable for a chatbot. Other major chatbots currently include Microsoft s Bing Chat, which uses OpenAI s GPT 4 as part of a broader close collaboration between OpenAI and Microsoft , and Google s competing chatbot Gemini initially based on their LaMDA family of conversation trained language models, with plans to switch to PaLM . Yet another kind of task that a GPT can be used for is the meta task of generating its own instructions, like developing a series of prompts for itself to be able to effectuate a more general goal given by a human user. This is known as an AI agent, and more specifically a recursive one because it uses results from its previous self instructions to help it form its subsequent prompts the first major example of this was Auto GPT which uses OpenAI s GPT models , and others have since been developed as well. Domain specificity GPT systems can be directed toward particular fields or domains. Some reported examples of such models and apps are as follows Sometimes domain specificity is accomplished via software plug ins or add ons. For example, several different companies have developed particular plugins that interact directly with OpenAI s ChatGPT interface, and Google Workspace has available add ons such as GPT for Sheets and Docs which is reported to aid use of spreadsheet functionality in Google Sheets. Brand issues OpenAI, which created the first generative pre trained transformer GPT in 2018, asserted in 2023 that GPT should be regarded as a brand of OpenAI. In April 2023, OpenAI revised the brand guidelines in its terms of service to indicate that other businesses using its API to run their AI services would no longer be able to include GPT in such names or branding. In May 2023, OpenAI engaged a brand management service to notify its API customers of this policy, although these notifications stopped short of making overt legal claims such as allegations of trademark infringement or demands to cease and desist . As of November 2023, OpenAI still prohibits its API licensees from naming their own products with GPT , but it has begun enabling its ChatGPT Plus subscribers to make custom versions of ChatGPT called GPTs on the OpenAI site. OpenAI s terms of service says that its subscribers may use GPT in the names of these, although it s discouraged . Relatedly, OpenAI has applied to the United States Patent and Trademark Office USPTO to seek domestic trademark registration for the term GPT in the field of AI. OpenAI sought to expedite handling of its application, but the USPTO declined that request in April 2023. In May 2023, the USPTO responded to the application with a determination that GPT was both descriptive and generic. As of November 2023, OpenAI continues to pursue its argument through the available processes. Regardless, failure to obtain a registered U.S. trademark does not preclude some level of common law trademark rights in the U.S. and trademark rights in other countries. For any given type or scope of trademark protection in the U.S., OpenAI would need to establish that the term is actually distinctive to their specific offerings in addition to being a broader technical term for the kind of technology. Some media reports suggested in 2023 that OpenAI may be able to obtain trademark registration based indirectly on the fame of its GPT based chatbot product, ChatGPT, for which OpenAI has separately sought protection and which it has sought to enforce more strongly . Other reports have indicated that registration for the bare term GPT seems unlikely to be granted, as it is used frequently as a common term to refer simply to AI systems that involve generative pre trained transformers. In any event, to whatever extent exclusive rights in the term may occur the U.S., others would need to avoid using it for similar products or services in ways likely to cause confusion. If such rights ever became broad enough to implicate other well established uses in the field, the trademark doctrine of descriptive fair use could still continue non brand related usage. In the European Union, the European Union Intellectual Property Office registered GPT as a trade mark of OpenAI in spring 2023. However, since spring 2024 the registration is being challenged and is pending cancellation. In Switzerland, the Swiss Federal Institute of Intellectual Property registered GPT as a trade mark of OpenAI in spring 2023. See also References |
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