data/transcripts/slides1-7.txt

start_time	end_time	speaker	transcript
17.84	175.96	SPEAKER	By the end of this session, you should be able to understand several important aspects of generative AI, and also how it works. We'll begin by covering the core concepts of generative AI. We'll first start by understanding what it is, and also how it differs from traditional AI, and also why it's become such a significant area of research and application. From there, we'll dive into how AI powers various tests, and this includes things like classification, where AI sorts data into different categories. We have question answering, where AI responds to queries based on available information. We'll look at summarization, where long pieces of text are condensed into shorter versions. We'll also look at things like creative generation, where AI is used to create new content like text, images, or music. And even in things or areas such as data analytics, where patterns and insights are extracted from large datasets. Next, we'll take a look at the real -world use cases to understand how AI is really making an impact. You'll see examples and success stories from industries like finance, as well as insurance, where AI is really being used to improve decision -making. It's used to reduce risks and also enhance customer experiences. These examples will really help you connect with the theoretical knowledge and also the practical application. We'll also explore how language models have really evolved over time. We'll start with early models like recurrent neural networks or RNNs, and then move into more advanced systems like transformers, which are really the foundation of today's large language models. As a part of all of, I'm going to say that part again. As a part of this, we'll look at different LM providers and models that they offer, giving you a sense of the current AI landscape. Finally, we'll take a closer look at how LMs actually work. You learn about the different processes of things like token prediction, where the model learns to predict the next word or symbol in a sequence. We'll also touch upon inference, which is how the model really generates responses once it is trained. And then after we do all of that, we'll explain how things like cost estimation works. So this will help you understand the resources as well as the expenses that are involved in running these models.
180.86	259.34	SPEAKER	Now to fulfill, I'm going to say that again. Now to fulfill our learning objectives, we'll be covering five main topic areas related to generative AI and large language models. We'll start with an introduction to gen AI, where we'll explain exactly what it is, how it works at a height level, and why it's gaining so much attention across different industries. After that, we'll move into an overview of the LM ecosystem. This will include a look at the various models that are available today. We'll also understand some of the key players that are there within the space, and also how these models are really being used. Next, we'll explore the cost structures that are associated with using L -Lumps. And this will help you understand some of the financial aspects, such as, you know, what drives the cost of running these models, and also how to estimate the expenses effectively. We'll then discuss how L -Lumps are trained, and also how inference works once a model is deployed. Then this will give you a foundational understanding of the technology behind the scenes. Lastly, we'll go over a taxonomy of some of the key business problems that L -Lumps are helping to solve today. It will help provide some real -world context to the type of challenges that these models are really well suited for.
269.44	352.04	SPEAKER	In this section, you'll gain a foundational understanding of generative AI by exploring its relationship with the broader concepts in the field of artificial intelligence. At the highest level, artificial intelligence, or AI, refers to the design and the implementation of computer programs that are capable of doing things like reasoning, learning, and also acting autonomously or semi -autonomously in complex and dynamic environments. Now, these systems aim to replicate or simulate human intelligence in a wide variety of tasks, and these tasks could range from things like decision -making and problem -solving to things like natural language understanding, as well as perception. Now, within the broader scope of AI lies machine learning, which is a subset specifically focused on designing algorithms that enable the computer systems to learn from data without all of that being explicitly programmed. Now, in traditional programming, developers write code to define specific rules, and all of these rules are hard -coded in. In contrast, machine learning models identify patterns and data, and then they use those patterns to make decisions or predictions. Now, this approach allows the system to adapt to new information and also improve their performance over time.
354.08	436.58	SPEAKER	Deep learning is a further specialization within machine learning. It involves the use of neural network architectures, which are composed of many layers that transform data through increasingly abstract representations. Now, these networks are especially effective for handling large -scale and unstructured data. When I say unstructured data, I mean things like images, video, audio, and natural language. Deep learning has really enabled major advances in areas such as image recognition, language translation, speech synthesis, and also game -playing. Then finally, we have generative AI, which is a specific application area within the realm of deep learning. It involves training models that are not only capable of analyzing or classifying data, but also generating new data that resembles the input data that they were trained on. So these models really learn the underlying patterns and distribution of the training data, and it enables them to produce entirely new content such as things like human -like text. We can use it for things like realistic images, for synthetic audio, or even synthetic video. Now, popular examples of generative AI include large -language models like GPT. We have text -to -image models like Dali or mid -journey, and also voice synthesis tools.
439.52	449.38	SPEAKER	As we continue exploring generative AI, it's also important to understand that the underlying models that power these systems. Let's see that again.
451.68	684.84	SPEAKER	As we continue exploring generative AI, it's important to also understand the underlying models that power these systems. We refer to these as the foundation models. Now, foundation models are large -scale models that are trained on broad datasets that serve as a base for a wide variety of downstream applications. Now, these models are pre -trained on massive amounts of data and can then be fine -tuned or adapted for specific use cases. Now, generative AI applications such as chatbots, content generators, and intelligent assistants, all of these are built on top of these versatile and powerful foundation models. Now, foundation models can be categorized into three main types. We have large -language models or LLMs. We have large multimodal models, which are LMMs, and reasoning models. Each type has its own training methodology, input formats, and also areas of specialization. Large -language models or LLMs are trained using language modeling techniques. Now, this typically involves predicting the next word in a sequence, which enables the model to learn things like grammar, context, meaning, and even reasoning from a large -text corpora. As a result, LLMs are highly effective at a variety of natural -language -processing tasks such as things like summarization, translation, sentiment analysis, question answering, and also dialogue generation. Now, these models are designed to work with purely textual inputs, and they are essentially also providing purely textual outputs. Their performance continues to improve with scale both in terms of data and the size of the model. Large multimodal models or LMMs, they take this one step further by also incorporating multiple types of data during the training process. Now, this can include things like text, images, audio, and video, and this is what enables LMMs to process and understand complex mixed -input scenarios. For instance, a multimodal model can take an image and a question about that image as the input, and then it can generate a coherent and contextually appropriate response. Now, these models are really well -suited for tasks such as video summarization. We can use it for things like image captioning, visual question answering, and also any application that requires simultaneous understanding of different media types. Lastly, reasoning models represent a specialized subset that focuses on logical, analytical, and common -sense reasoning capabilities. Now, these models are trained using a combination of reasoning and non -reasoning datasets with reinforcement learning or rule -based reward mechanisms that guide the model's performance. As a result, reasoning models are capable of handling more structured and logic -intensive tasks. We can use it for things like solving mathematical problems for program synthesis, for complex question answering, and also any sort of scientific reasoning. They really tend to excel in environments where structured thinking and accurate multi -step inference are going to be quite critical. So, by understanding these categories, LLMs, LMMs, and reasoning models, you will be better equipped to evaluate the capabilities of various generative AI systems and also to choose the right type of model architecture for your specific application or use case.
691.60	875.38	SPEAKER	Now that you understand what foundation models are, let's explore how they're being used in real -world applications. Enterprises across the industries are leveraging generative AI to build intelligence systems that enhance productivity, automate tasks, and also drive innovation. Now, these applications rely on what we call generative AI resources, which combine two main components, a foundation model, and a set of supporting tools. Now, the foundation model, such as a large language model, it provides the core intelligence. Now, this is what enables the system to understand and also generate human -like language. However, to be practically useful, this model is typically integrated with a set of external tools that expand its capabilities. Now, these tools might include things like search engines, APIs, database connections, content management systems, or other domain -specific resources. Together, the foundation model and the tools, they form a complete generative AI resource that is capable of handling a wide variety of business tasks and problems. Now, these resources are powering applications across several key domains. For example, in classification, generative AI can be used to categorize documents, emails, or customer feedback effectively. In question answering, it can interpret user queries and provide accurate and context -aware responses. For structured text generation such as drafting reports, proposals, or product descriptions, these models can produce high -quality outputs with minimal input. Summarization tools, they can use generative AI to distill large volumes of information into concise summaries, improving information access and decision -making. Another area where generative AI also making an impact is developer productivity. So by assisting with code generation, debugging, and documentation, these models can help developers work faster and with fewer errors. In creative content generation, they enable the production of original text, images, or multimedia for marketing design or storytelling purposes. And then finally, in data analytics, generative AI, all of these different, I'm going to repeat that last part. So editing, cut that in. Finally, in data analytics, generative AI models can assist with interpreting data sets. They can also be used for generating insights and even suggesting visualizations or queries. They can really help the non -technical users engage with data more effectively. So throughout this course, we will focus specifically on using large language models LMs as the foundation model component in all of these generative AI systems. You'll gain a hands -on experience in building and evaluating these types of applications, giving you the skills to apply them in a wide range of real -world scenarios.
889.32	896.14	SPEAKER	Let's start by taking a closer look at how generative AI is applied in classification and classification tests.