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{\displaystyle 1\leq k\leq 2m.} We see that σ ( x ) ∈ R {\displaystyle \sigma (x)\in R} iff for some h {\displaystyle h} in H {\displaystyle H} satisfies, | 1 m | { 1 ≤ i ≤ m : h ( x σ i ) = 1 } | − 1 m | { m + 1 ≤ i ≤ 2 m : h ( x σ i ) = 1 } | | ≥ ε 2 {\displaystyle |{\frac {1}{m}}|\{1\leq i\leq m:h(x_{\sigma _{i}})=1... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 301 | 575 | null |
Hence if we define w i j = 1 {\displaystyle w_{i}^{j}=1} if h j ( x i ) = 1 {\displaystyle h_{j}(x_{i})=1} and w i j = 0 {\displaystyle w_{i}^{j}=0} otherwise. For 1 ≤ i ≤ m {\displaystyle 1\leq i\leq m} and 1 ≤ j ≤ t {\displaystyle 1\leq j\leq t} , we have that σ ( x ) ∈ R {\displaystyle \sigma (x)\in R} iff for some ... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 286 | 583 | null |
For 1 ≤ i ≤ m {\displaystyle 1\leq i\leq m} and 1 ≤ j ≤ t {\displaystyle 1\leq j\leq t} , we have that σ ( x ) ∈ R {\displaystyle \sigma (x)\in R} iff for some j {\displaystyle j} in 1 , … , t {\displaystyle {1,\ldots ,t}} satisfies | 1 m ( ∑ i w σ ( i ) j − ∑ i w σ ( m + i ) j ) | ≥ ε 2 {\displaystyle |{\frac {1}{m}}\... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 411 | 804 | null |
By union bound we get Pr [ σ ( x ) ∈ R ] ≤ t ⋅ max ( Pr [ | 1 m ( ∑ i w σ i j − ∑ i w σ m + i j ) | ≥ ε 2 ] ) {\displaystyle \Pr[\sigma (x)\in R]\leq t\cdot \max \left(\Pr[|{\frac {1}{m}}\left(\sum _{i}w_{\sigma _{i}}^{j}-\sum _{i}w_{\sigma _{m+i}}^{j}\right)|\geq {\frac {\varepsilon }{2}}]\right)} ≤ Π H ( 2 m ) ⋅ max ... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 449 | 883 | null |
{\displaystyle \leq \Pi _{H}(2m)\cdot \max \left(\Pr \left[\left|{\frac {1}{m}}\left(\sum _{i}w_{\sigma _{i}}^{j}-\sum _{i}w_{\sigma _{m+i}}^{j}\right)\right|\geq {\frac {\varepsilon }{2}}\right]\right).} Since, the distribution over the permutations σ {\displaystyle \sigma } is uniform for each i {\displaystyle i} , s... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 517 | 1,041 | null |
Thus, Pr [ | 1 m ( ∑ i ( w σ i j − w σ m + i j ) ) | ≥ ε 2 ] = Pr [ | 1 m ( ∑ i | w i j − w m + i j | β i ) | ≥ ε 2 ] , {\displaystyle \Pr \left[\left|{\frac {1}{m}}\left(\sum _{i}\left(w_{\sigma _{i}}^{j}-w_{\sigma _{m+i}}^{j}\right)\right)\right|\geq {\frac {\varepsilon }{2}}\right]=\Pr \left[\left|{\frac {1}{m}}\lef... | Wikipedia - Uniform convergence in probability - Proof of uniform convergence theorem > Reduction to a finite class | 337 | 750 | null |
Article: Training, validation, and test data sets. In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input ... | Wikipedia - Training, validation, and test data sets - Summary | 322 | 1,649 | null |
Successively, the fitted model is used to predict the responses for the observations in a second data set called the validation data set. The validation data set provides an unbiased evaluation of a model fit on the training data set while tuning the model's hyperparameters (e.g. the number of hidden units—layers and l... | Wikipedia - Training, validation, and test data sets - Summary | 315 | 1,470 | null |
Section: Training data set. A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combina... | Wikipedia - Training, validation, and test data sets - Training data set | 224 | 1,098 | null |
Section: Validation data set. A validation data set is a data set of examples used to tune the hyperparameters (i.e. the architecture) of a model. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each l... | Wikipedia - Training, validation, and test data sets - Validation data set | 341 | 1,686 | null |
Various networks are trained by minimization of an appropriate error function defined with respect to a training data set. The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set i... | Wikipedia - Training, validation, and test data sets - Validation data set | 154 | 847 | null |
Section: Test data set. A test data set is a data set that is independent of the training data set, but that follows the same probability distribution as the training data set. If a model fit to the training data set also fits the test data set well, minimal overfitting has taken place (see figure below). A better fitt... | Wikipedia - Training, validation, and test data sets - Test data set | 283 | 1,369 | null |
Section: Confusion in terminology. Testing is trying something to find out about it ("To put to the proof; to prove the truth, genuineness, or quality of by experiment" according to the Collaborative International Dictionary of English) and to validate is to prove that something is valid ("To confirm; to render valid" ... | Wikipedia - Training, validation, and test data sets - Confusion in terminology | 229 | 1,151 | null |
Section: Causes of error. Omissions in the training of algorithms are a major cause of erroneous outputs. Types of such omissions include: Particular circumstances or variations were not included. Obsolete data Ambiguous input information Inability to change to new environments Inability to request help from a human or... | Wikipedia - Training, validation, and test data sets - Causes of error | 166 | 919 | null |
Article: Vector database. A vector database, vector store or vector search engine is a database that uses the vector space model to store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, so that one can search ... | Wikipedia - Vector database - Summary | 343 | 1,802 | null |
Article: Version space learning. Version space learning is a logical approach to machine learning, specifically binary classification. Version space learning algorithms search a predefined space of hypotheses, viewed as a set of logical sentences. Formally, the hypothesis space is a disjunction H 1 ∨ H 2 ∨ . . . ∨ H n ... | Wikipedia - Version space learning - Summary | 190 | 800 | null |
Section: The version space algorithm. In settings where there is a generality-ordering on hypotheses, it is possible to represent the version space by two sets of hypotheses: (1) the most specific consistent hypotheses, and (2) the most general consistent hypotheses, where "consistent" indicates agreement with observed... | Wikipedia - Version space learning - The version space algorithm | 323 | 1,474 | null |
These maximal hypotheses essentially constitute a (optimistic) claim that the true concept is defined just by the negative data already observed: Thus, if a novel (never-before-seen) data point is observed, it should be assumed to be positive. (I.e., if data has not previously been ruled out, then it's ruled in.) Thus,... | Wikipedia - Version space learning - The version space algorithm | 174 | 839 | null |
Section: Historical background. The notion of version spaces was introduced by Mitchell in the early 1980s as a framework for understanding the basic problem of supervised learning within the context of solution search. Although the basic "candidate elimination" search method that accompanies the version space framewor... | Wikipedia - Version space learning - Historical background | 183 | 948 | null |
Article: Weight initialization. In deep learning, weight initialization or parameter initialization describes the initial step in creating a neural network. A neural network contains trainable parameters that are modified during training: weight initialization is the pre-training step of assigning initial values to the... | Wikipedia - Weight initialization - Summary | 194 | 1,036 | null |
Section: Constant initialization. We discuss the main methods of initialization in the context of a multilayer perceptron (MLP). Specific strategies for initializing other network architectures are discussed in later sections. For an MLP, there are only two kinds of trainable parameters, called weights and biases. Each... | Wikipedia - Weight initialization - Constant initialization | 331 | 1,082 | null |
The simplest form is zero initialization: W ( l ) = 0 , b ( l ) = 0 {\displaystyle W^{(l)}=0,b^{(l)}=0} Zero initialization is usually used for initializing biases, but it is not used for initializing weights, as it leads to symmetry in the network, causing all neurons to learn the same features. In this page, we assum... | Wikipedia - Weight initialization - Constant initialization | 288 | 1,185 | null |
Section: Random initialization > LeCun initialization. LeCun initialization, popularized in (LeCun et al., 1998), is designed to preserve the variance of neural activations during the forward pass. It samples each entry in W ( l ) {\displaystyle W^{(l)}} independently from a distribution with mean 0 and variance 1 / n ... | Wikipedia - Weight initialization - Random initialization > LeCun initialization | 157 | 527 | null |
Section: Random initialization > Glorot initialization. Glorot initialization (or Xavier initialization) was proposed by Xavier Glorot and Yoshua Bengio. It was designed as a compromise between two goals: to preserve activation variance during the forward pass and to preserve gradient variance during the backward pass.... | Wikipedia - Weight initialization - Random initialization > Glorot initialization | 239 | 773 | null |
Section: Random initialization > Orthogonal initialization. (Saxe et al. 2013) proposed orthogonal initialization: initializing weight matrices as uniformly random (according to the Haar measure) semi-orthogonal matrices, multiplied by a factor that depends on the activation function of the layer. It was designed so th... | Wikipedia - Weight initialization - Random initialization > Orthogonal initialization | 346 | 1,394 | null |
This is sometimes called the Orthogonal Delta initialization. Related to this approach, unitary initialization proposes to parameterize the weight matrices to be unitary matrices, with the result that at initialization they are random unitary matrices (and throughout training, they remain unitary). This is found to imp... | Wikipedia - Weight initialization - Random initialization > Orthogonal initialization | 179 | 877 | null |
Section: Random initialization > Fixup initialization. In 2015, the introduction of residual connections allowed very deep neural networks to be trained, much deeper than the ~20 layers of the previous state of the art (such as the VGG-19). Residual connections gave rise to their own weight initialization problems and ... | Wikipedia - Weight initialization - Random initialization > Fixup initialization | 257 | 1,194 | null |
Section: Random initialization > Others. Instead of initializing all weights with random values on the order of O ( 1 / n ) {\displaystyle O(1/{\sqrt {n}})} , sparse initialization initialized only a small subset of the weights with larger random values, and the other weights zero, so that the total variance is still o... | Wikipedia - Weight initialization - Random initialization > Others | 324 | 1,103 | null |
Section: Miscellaneous. For hyperbolic tangent activation function, a particular scaling is sometimes used: 1.7159 tanh ( 2 x / 3 ) {\displaystyle 1.7159\tanh(2x/3)} . This was sometimes called "LeCun's tanh". It was designed so that it maps the interval [ − 1 , + 1 ] {\displaystyle [-1,+1]} to itself, thus ensuring ... | Wikipedia - Weight initialization - Miscellaneous | 343 | 1,026 | null |
In self-normalizing neural networks, the SELU activation function S E L U ( x ) = λ { x if x > 0 α e x − α if x ≤ 0 {\displaystyle \mathrm {SELU} (x)=\lambda {\begin{cases}x&{\text{if }}x>0\\\alpha e^{x}-\alpha &{\text{if }}x\leq 0\end{cases}}} with parameters λ ≈ 1.0507 , α ≈ 1.6733 {\displaystyle \lambda \approx 1.05... | Wikipedia - Weight initialization - Miscellaneous | 215 | 628 | null |
Section: History. Random weight initialization was used since Frank Rosenblatt's perceptrons. An early work that described weight initialization specifically was (LeCun et al., 1998). Before the 2010s era of deep learning, it was common to initialize models by "generative pre-training" using an unsupervised learning al... | Wikipedia - Weight initialization - History | 346 | 1,745 | null |
Section: Types > Sensor-based, single-user activity recognition. Sensor-based activity recognition integrates the emerging area of sensor networks with novel data mining and machine learning techniques to model a wide range of human activities. Mobile devices (e.g. smart phones) provide sufficient sensor data and calcu... | Wikipedia - Activity recognition - Types > Sensor-based, single-user activity recognition | 193 | 1,166 | null |
Section: Types > Sensor-based, single-user activity recognition > Levels of sensor-based activity recognition. Sensor-based activity recognition is a challenging task due to the inherent noisy nature of the input. Thus, statistical modeling has been the main thrust in this direction in layers, where the recognition at ... | Wikipedia - Activity recognition - Types > Sensor-based, single-user activity recognition > Levels of sensor-based activity recognition | 165 | 928 | null |
Section: Types > Sensor-based group activity recognition. Recognition of group activities is fundamentally different from single, or multi-user activity recognition in that the goal is to recognize the behavior of the group as an entity, rather than the activities of the individual members within it. Group behavior is ... | Wikipedia - Activity recognition - Types > Sensor-based group activity recognition | 205 | 1,171 | null |
Section: Approaches > Activity recognition through logic and reasoning. Logic-based approaches keep track of all logically consistent explanations of the observed actions. Thus, all possible and consistent plans or goals must be considered. Kautz provided a formal theory of plan recognition. He described plan recogniti... | Wikipedia - Activity recognition - Approaches > Activity recognition through logic and reasoning | 324 | 1,771 | null |
Section: Approaches > Activity recognition through probabilistic reasoning. Probability theory and statistical learning models are more recently applied in activity recognition to reason about actions, plans and goals under uncertainty. In the literature, there have been several approaches which explicitly represent un... | Wikipedia - Activity recognition - Approaches > Activity recognition through probabilistic reasoning | 330 | 1,766 | null |
Conventional temporal probabilistic models such as the hidden Markov model (HMM) and conditional random fields (CRF) model directly model the correlations between the activities and the observed sensor data. In recent years, increasing evidence has supported the use of hierarchical models which take into account the ri... | Wikipedia - Activity recognition - Approaches > Activity recognition through probabilistic reasoning | 204 | 1,058 | null |
Section: Approaches > Data mining based approach to activity recognition. Different from traditional machine learning approaches, an approach based on data mining has been recently proposed. In the work of Gu et al., the problem of activity recognition is formulated as a pattern-based classification problem. They propo... | Wikipedia - Activity recognition - Approaches > Data mining based approach to activity recognition | 151 | 838 | null |
Section: Sensor usage > Vision-based activity recognition. It is a very important and challenging problem to track and understand the behavior of agents through videos taken by various cameras. The primary technique employed is Computer Vision. Vision-based activity recognition has found many applications such as human... | Wikipedia - Activity recognition - Sensor usage > Vision-based activity recognition | 329 | 1,886 | null |
Pose estimation methods allow extracting more representative skeletal features for action recognition. That said, it has been discovered that deep learning based action recognition may suffer from adversarial attacks, where an attacker alter the input insignificantly to fool an action recognition system. Despite remark... | Wikipedia - Activity recognition - Sensor usage > Vision-based activity recognition | 260 | 1,531 | null |
Because there is no obstacle in the direct path, direct signal transmission can be modeled by Friis transmission equation: P r = P t G t G r λ 2 ( 4 π ) 2 d 2 {\displaystyle P_{r}={\frac {P_{t}G_{t}G_{r}\lambda ^{2}}{(4\pi )^{2}d^{2}}}} P t {\displaystyle P_{t}} is the power fed into the transmitting antenna input term... | Wikipedia - Activity recognition - Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Static transmission model | 327 | 965 | null |
Section: Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Dynamic transmission model. In this model, we consider the human motion, which causes the signal transmission path to change continuously. We can use Doppler Shift to describe this effect, which is related to the motion speed... | Wikipedia - Activity recognition - Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Dynamic transmission model | 158 | 685 | null |
Section: Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Fresnel zone. The Fresnel zone was initially used to study the interference and diffraction of the light, which is later used to construct the wireless signal transmission model. Fresnel zone is a series of elliptical interva... | Wikipedia - Activity recognition - Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Fresnel zone | 155 | 719 | null |
Section: Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Modeling of the human body. In some tasks, we should consider modeling the human body accurately to achieve better results. For example, described the human body as concentric cylinders for breath detection. The outside of th... | Wikipedia - Activity recognition - Sensor usage > Wi-Fi-based activity recognition > Basic models of Wi-Fi recognition > Modeling of the human body | 199 | 872 | null |
Section: Datasets. There are some popular datasets that are used for benchmarking activity recognition or action recognition algorithms. UCF-101: It consists of 101 human action classes, over 13k clips and 27 hours of video data. Action classes include applying makeup, playing dhol, cricket shot, shaving beard, etc. HM... | Wikipedia - Activity recognition - Datasets | 188 | 848 | null |
Section: History. AlchemyAPI was founded by Elliot Turner in 2005, and launched their API in 2009. In September 2011, ProgrammableWeb added AlchemyAPI to its API Billionaires Club, alongside giants such as Google and Facebook. In February 2013, it was announced that AlchemyAPI had raised US$2 million to improve the cap... | Wikipedia - AlchemyAPI - History | 262 | 1,245 | null |
Section: Development. On June 7, 2023, Google DeepMind published a paper in Nature introducing AlphaDev, which discovered new algorithms that outperformed the state-of-the-art methods for small sort algorithms. For example, AlphaDev found a faster assembly language sequence for sorting 5-element sequences. Upon analysi... | Wikipedia - AlphaDev - Development | 309 | 1,539 | null |
Section: Design. AlphaDev is built on top of AlphaZero, the reinforcement-learning model that DeepMind trained to master games such as Go and chess. The company's breakthrough was to treat the problem of finding a faster algorithm as a game and then train its AI to win it. AlphaDev plays a single-player game where the ... | Wikipedia - AlphaDev - Design | 159 | 810 | null |
Section: Design. AlphaEvolve aims to autonomously discover and refine algorithms through a combination of large language models (LLMs) and evolutionary computation. AlphaEvolve needs an evaluation function with metrics to optimize, and an initial algorithm. At each step, AlphaEvolve uses the LLM to produce variants of ... | Wikipedia - AlphaEvolve - Design | 164 | 820 | null |
Article: AlphaFold. AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure P... | Wikipedia - AlphaFold - Summary | 345 | 1,709 | null |
However, some researchers noted that the accuracy was insufficient for a third of its predictions, and that it did not reveal the underlying mechanism or rules of protein folding for the protein folding problem, which remains unsolved. Despite this, the technical achievement was widely recognized. On 15 July 2021, the ... | Wikipedia - AlphaFold - Summary | 260 | 1,348 | null |
Section: Background. Proteins consist of chains of amino acids which spontaneously fold to form the three dimensional (3-D) structures of the proteins. The 3-D structure is crucial to understanding the biological function of the protein. Protein structures can be determined experimentally through techniques such as X-r... | Wikipedia - AlphaFold - Background | 235 | 1,278 | null |
Section: Algorithm > AlphaFold 1 (2018). AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databanks of related DNA sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues (peptides) ... | Wikipedia - AlphaFold - Algorithm > AlphaFold 1 (2018) | 166 | 868 | null |
Section: Algorithm > AlphaFold 2 (2020). The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind. AlphaFold 1 used a number of separately trained modules to produce a guide potential, which was then combined wit... | Wikipedia - AlphaFold - Algorithm > AlphaFold 2 (2020) | 304 | 1,560 | null |
Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learnt from training data. These transformations are iterated, the updated information out... | Wikipedia - AlphaFold - Algorithm > AlphaFold 2 (2020) | 350 | 1,818 | null |
Section: Algorithm > AlphaFold 3 (2024). Announced on 8 May 2024, AlphaFold 3 was co-developed by Google DeepMind and Isomorphic Labs, both subsidiaries of Alphabet. AlphaFold 3 is not limited to single-chain proteins, as it can also predict the structures of protein complexes with DNA, RNA, post-translational modifica... | Wikipedia - AlphaFold - Algorithm > AlphaFold 3 (2024) | 215 | 987 | null |
Section: Competitions > CASP13. In December 2018, DeepMind's AlphaFold placed first in the overall rankings of the 13th Critical Assessment of Techniques for Protein Structure Prediction (CASP). The program was particularly successfully predicting the most accurate structure for targets rated as the most difficult by t... | Wikipedia - AlphaFold - Competitions > CASP13 | 311 | 1,471 | null |
Section: Competitions > CASP14. In November 2020, DeepMind's new version, AlphaFold 2, won CASP14. Overall, AlphaFold 2 made the best prediction for 88 out of the 97 targets. On the competition's preferred global distance test (GDT) measure of accuracy, the program achieved a median score of 92.4 (out of 100), meaning ... | Wikipedia - AlphaFold - Competitions > CASP14 | 332 | 1,440 | null |
AlphaFold 2 also achieved an accuracy in modelling surface side chains described as "really really extraordinary". To further validate AlphaFold 2, the conference organizers approached four leading experimental groups working on structures they found particularly challenging and had been unable to determine. In all fou... | Wikipedia - AlphaFold - Competitions > CASP14 | 218 | 1,164 | null |
Section: Reception. AlphaFold 2 scoring more than 90 in CASP's global distance test (GDT) is considered a significant achievement in computational biology and great progress towards a decades-old grand challenge of biology. Nobel Prize winner and structural biologist Venki Ramakrishnan called the result "a stunning adv... | Wikipedia - AlphaFold - Reception | 326 | 1,746 | null |
Section: Database of protein models generated by AlphaFold. The AlphaFold Protein Structure Database, a joint project between AlphaFold and EMBL-EBI, was launched on July 22, 2021. At launch, the database contained AlphaFold-predicted models for nearly the complete UniProt proteome of humans and 20 model organisms, tot... | Wikipedia - AlphaFold - Database of protein models generated by AlphaFold | 215 | 990 | null |
Section: Limitations. AlphaFold has various limitations: AlphaFold DB provides models of individual protein chains (monomers), rather than their biologically relevant complexes. Many protein regions are predicted with low confidence score, including the intrinsically disordered protein regions. Alphafold-2 was validate... | Wikipedia - AlphaFold - Limitations | 252 | 1,309 | null |
Section: Applications. AlphaFold has been used to predict structures of proteins of SARS-CoV-2, the causative agent of COVID-19. The structures of these proteins were pending experimental detection in early 2020. Results were reviewed by scientists at the Francis Crick Institute in the United Kingdom before being relea... | Wikipedia - AlphaFold - Applications | 241 | 1,263 | null |
Section: Published works. Andrew W. Senior et al. (December 2019), "Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)", Proteins: Structure, Function, Bioinformatics 87(12) 1141–1148 doi:10.1002/prot.25834 Andrew W. Senior et al. (1... | Wikipedia - AlphaFold - Published works | 240 | 915 | null |
Article: AlphaGo. AlphaGo is a computer program that plays the board game Go. It was developed by the London-based DeepMind Technologies, an acquired subsidiary of Google. Subsequent versions of AlphaGo became increasingly powerful, including a version that competed under the name Master. After retiring from competitiv... | Wikipedia - AlphaGo - Summary | 337 | 1,683 | null |
Although it lost to Lee Sedol in the fourth game, Lee resigned in the final game, giving a final score of 4 games to 1 in favour of AlphaGo. In recognition of the victory, AlphaGo was awarded an honorary 9-dan by the Korea Baduk Association. The lead up and the challenge match with Lee Sedol were documented in a docume... | Wikipedia - AlphaGo - Summary | 237 | 1,043 | null |
Section: History. Go is considered much more difficult for computers to win than other games such as chess, because its strategic and aesthetic nature makes it hard to directly construct an evaluation function, and its much larger branching factor makes it prohibitively difficult to use traditional AI methods such as a... | Wikipedia - AlphaGo - History | 308 | 1,492 | null |
Section: History > Match against Lee Sedol. AlphaGo played South Korean professional Go player Lee Sedol, ranked 9-dan, one of the best players at Go, with five games taking place at the Four Seasons Hotel in Seoul, South Korea on 9, 10, 12, 13, and 15 March 2016, which were video-streamed live. Out of five games, Alph... | Wikipedia - AlphaGo - History > Match against Lee Sedol | 346 | 1,570 | null |
However, Lee beat AlphaGo in the fourth game, winning by resignation at move 180. AlphaGo then continued to achieve a fourth win, winning the fifth game by resignation. The prize was US$1 million. Since AlphaGo won four out of five and thus the series, the prize will be donated to charities, including UNICEF. Lee Sedol... | Wikipedia - AlphaGo - History > Match against Lee Sedol | 279 | 1,315 | null |
Section: History > Sixty online games. On 29 December 2016, a new account on the Tygem server named "Magister" (shown as 'Magist' at the server's Chinese version) from South Korea began to play games with professional players. It changed its account name to "Master" on 30 December, then moved to the FoxGo server on 1 J... | Wikipedia - AlphaGo - History > Sixty online games | 226 | 957 | null |
Many quickly suspected it to be an AI player due to little or no resting between games. Its adversaries included many world champions such as Ke Jie, Park Jeong-hwan, Yuta Iyama, Tuo Jiaxi, Mi Yuting, Shi Yue, Chen Yaoye, Li Qincheng, Gu Li, Chang Hao, Tang Weixing, Fan Tingyu, Zhou Ruiyang, Jiang Weijie, Chou Chun-hsu... | Wikipedia - AlphaGo - History > Sixty online games | 319 | 1,174 | null |
Section: History > Future of Go Summit. In the Future of Go Summit held in Wuzhen in May 2017, AlphaGo Master played three games with Ke Jie, the world No.1 ranked player, as well as two games with several top Chinese professionals, one pair Go game and one against a collaborating team of five human players. Google Dee... | Wikipedia - AlphaGo - History > Future of Go Summit | 198 | 896 | null |
Section: History > AlphaGo Zero and AlphaZero. AlphaGo's team published an article in the journal Nature on 19 October 2017, introducing AlphaGo Zero, a version without human data and stronger than any previous human-champion-defeating version. By playing games against itself, AlphaGo Zero surpassed the strength of Alp... | Wikipedia - AlphaGo - History > AlphaGo Zero and AlphaZero | 178 | 806 | null |
Section: Versions. An early version of AlphaGo was tested on hardware with various numbers of CPUs and GPUs, running in asynchronous or distributed mode. Two seconds of thinking time was given to each move. The resulting Elo ratings are listed below. In the matches with more time per move higher ratings are achieved. I... | Wikipedia - AlphaGo - Versions | 192 | 919 | null |
Section: Algorithm. As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network", both implemented using deep neural network tech... | Wikipedia - AlphaGo - Algorithm | 261 | 1,343 | null |
Section: Responses to 2016 victory > AI community. AlphaGo's March 2016 victory was a major milestone in artificial intelligence research. Go had previously been regarded as a hard problem in machine learning that was expected to be out of reach for the technology of the time. Most experts thought a Go program as power... | Wikipedia - AlphaGo - Responses to 2016 victory > AI community | 349 | 1,763 | null |
As noted by entrepreneur Guy Suter, AlphaGo only knows how to play Go and doesn't possess general-purpose intelligence; "[It] couldn't just wake up one morning and decide it wants to learn how to use firearms." AI researcher Stuart Russell said that AI systems such as AlphaGo have progressed quicker and become more pow... | Wikipedia - AlphaGo - Responses to 2016 victory > AI community | 317 | 1,490 | null |
Section: Responses to 2016 victory > Go community. Go is a popular game in China, Japan and Korea, and the 2016 matches were watched by perhaps a hundred million people worldwide. Many top Go players characterized AlphaGo's unorthodox plays as seemingly-questionable moves that initially befuddled onlookers, but made se... | Wikipedia - AlphaGo - Responses to 2016 victory > Go community | 329 | 1,538 | null |
As the matches progressed, Ke Jie went back and forth, stating that "it is highly likely that I (could) lose" after analysing the first three matches, but regaining confidence after AlphaGo displayed flaws in the fourth match. Toby Manning, the referee of AlphaGo's match against Fan Hui, and Hajin Lee, secretary genera... | Wikipedia - AlphaGo - Responses to 2016 victory > Go community | 256 | 1,136 | null |
Section: AlphaGo documentary film (2016) > Reception. On Rotten Tomatoes the documentary has an average rating of 100% from 10 reviews. Michael Rechtshaffen of the Los Angeles Times gave the documentary a positive review and said: "It helps matters when you have a group of engaging human subjects like soft-spoken Sedol... | Wikipedia - AlphaGo - AlphaGo documentary film (2016) > Reception | 349 | 1,597 | null |
The fact that I was so innocently unaware of Go and AlphaGo actually proved to be beneficial. It allowed me to approach the action and interviews with pure curiosity, the kind that helps make any subject matter emotionally accessible." Kohs also said that "Unlike the film's human characters – who turn their curious que... | Wikipedia - AlphaGo - AlphaGo documentary film (2016) > Reception | 159 | 765 | null |
Section: AlphaGo documentary film (2016) > Professional Go player. Hajin Lee, a former professional Go player, described this documentary as being "beautifully filmed". In addition to the story itself, the feelings and atmosphere were also conveyed through different scene arrangements. For example, the close-up shots o... | Wikipedia - AlphaGo - AlphaGo documentary film (2016) > Professional Go player | 347 | 1,719 | null |
Section: AlphaGo documentary film (2016) > Technology and AI-related fields. James Vincent, a reporter from The Verge, comments that "It prods and pokes viewers with unsubtle emotional cues, like a reality TV show would. "Now, you should be nervous; now you should feel relieved". The AlphaGo footage slowly captures the... | Wikipedia - AlphaGo - AlphaGo documentary film (2016) > Technology and AI-related fields | 327 | 1,445 | null |
Section: Similar systems. Facebook has also been working on its own Go-playing system darkforest, also based on combining machine learning and Monte Carlo tree search. Although a strong player against other computer Go programs, as of early 2016, it had not yet defeated a professional human player. Darkforest has lost ... | Wikipedia - AlphaGo - Similar systems | 182 | 897 | null |
Article: AlphaGo Zero. AlphaGo Zero is a version of DeepMind's Go software AlphaGo. AlphaGo's team published an article in Nature in October 2017 introducing AlphaGo Zero, a version created without using data from human games, and stronger than any previous version. By playing games against itself, AlphaGo Zero: surpas... | Wikipedia - AlphaGo Zero - Summary | 349 | 1,641 | null |
Section: Architecture. The network in AlphaGo Zero is a ResNet with two heads.: Appendix: Methods The stem of the network takes as input a 17x19x19 tensor representation of the Go board. 8 channels are the positions of the current player's stones from the last eight time steps. (1 if there is a stone, 0 otherwise. If t... | Wikipedia - AlphaGo Zero - Architecture | 252 | 1,036 | null |
Section: Training. AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. Only four TPUs were used for inference. The neural network initially knew nothing about Go beyond the rules. Unlike earlier versions of AlphaGo, Zero only perceived the board's stones, rather... | Wikipedia - AlphaGo Zero - Training | 244 | 1,208 | null |
Section: Reception. AlphaGo Zero was widely regarded as a significant advance, even when compared with its groundbreaking predecessor, AlphaGo. Oren Etzioni of the Allen Institute for Artificial Intelligence called AlphaGo Zero "a very impressive technical result" in "both their ability to do it—and their ability to tr... | Wikipedia - AlphaGo Zero - Reception | 310 | 1,502 | null |
In response to the reports, South Korean Go professional Lee Sedol said, "The previous version of AlphaGo wasn’t perfect, and I believe that’s why AlphaGo Zero was made." On the potential for AlphaGo's development, Lee said he will have to wait and see but also said it will affect young Go players. Mok Jin-seok, who di... | Wikipedia - AlphaGo Zero - Reception | 316 | 1,429 | null |
Section: AlphaZero. On 5 December 2017, DeepMind team released a preprint on arXiv, introducing AlphaZero, a program using generalized AlphaGo Zero's approach, which achieved within 24 hours a superhuman level of play in chess, shogi, and Go, defeating world-champion programs, Stockfish, Elmo, and 3-day version of Alph... | Wikipedia - AlphaGo Zero - AlphaZero | 213 | 887 | null |
Section: Background. Games created for humans are considered to have external validity as benchmarks of progress in artificial intelligence. IBM's chess engine Deep Blue (1997) and DeepMind's AlphaGo (2016) were considered major milestones; some argue that StarCraft would also be a major milestone, due to the game's "r... | Wikipedia - AlphaStar (software) - Background | 160 | 770 | null |
Section: History. DeepMind Technologies was founded in the UK in 2010. As early as 2011, founder Demis Hassabis called StarCraft "the next step up" after games like Go. DeepMind became a subsidiary of Google in 2014, after demonstrating self-learning bots with superhuman ability at a variety of Atari 2600 games. In Feb... | Wikipedia - AlphaStar (software) - History | 337 | 1,538 | null |
But when it does, it will be a far greater achievement than DeepMind's conquest of Go." In December 2018, DeepMind's bot defeated professional player Grzegorz "MaNa" Komincz, 5-0. DeepMind announced the bot, named "AlphaStar", in January 2019. A journalist at Ars Technica and others argued that AlphaStar still had unfa... | Wikipedia - AlphaStar (software) - History | 260 | 1,201 | null |
Section: Reactions. After his 5-0 defeat in December 2018, Komincz stated "I wasn't expecting the AI to be that good". Stuart Russell assessed that AlphaStar's 2018 victory required "a fair amount of problem-specific effort" and that general-purpose methods were "not quite ready for StarCraft". An article in Wired UK j... | Wikipedia - AlphaStar (software) - Reactions | 350 | 1,568 | null |
Section: Legacy. DeepMind argues that insights from AlphaStar might benefit robots, self-driving cars, and virtual assistants, which need to operate with "imperfectly observed information". Silver has indicated his lab "may rest at this point", rather than try to substantially improve AlphaStar. Silver himself argues t... | Wikipedia - AlphaStar (software) - Legacy | 216 | 1,078 | null |
Article: AlphaZero. AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm uses an approach similar to AlphaGo Zero. On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which would soon... | Wikipedia - AlphaZero - Summary | 338 | 1,562 | null |
Section: Training. AlphaZero was trained by simply playing against itself multiple times, using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks. Training took several days, totaling about 41 TPU-years. It cost 3e22 FLOPs. In parallel, the in-training AlphaZer... | Wikipedia - AlphaZero - Training | 150 | 663 | null |
Section: Preliminary results > Outcome > Chess. In AlphaZero's chess match against Stockfish 8 (2016 TCEC world champion), each program was given one minute per move. AlphaZero was flying the English flag, while Stockfish the Norwegian. Stockfish was allocated 64 threads and a hash size of 1 GB, a setting that Stockfis... | Wikipedia - AlphaZero - Preliminary results > Outcome > Chess | 195 | 851 | null |
Section: Preliminary results > Analysis. DeepMind stated in its preprint, "The game of chess represented the pinnacle of AI research over several decades. State-of-the-art programs are based on powerful engines that search many millions of positions, leveraging handcrafted domain expertise and sophisticated domain adap... | Wikipedia - AlphaZero - Preliminary results > Analysis | 343 | 1,621 | null |
Section: Preliminary results > Reaction and criticism. Papers headlined that the chess training took only four hours: "It was managed in little more than the time between breakfast and lunch." Wired described AlphaZero as "the first multi-skilled AI board-game champ". AI expert Joanna Bryson noted that Google's "knack ... | Wikipedia - AlphaZero - Preliminary results > Reaction and criticism | 344 | 1,661 | null |
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