A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
This document provides information about a bootcamp to build applications using Large Language Models (LLMs). The bootcamp consists of 11 modules covering topics such as introduction to generative AI, text analytics techniques, neural network models for natural language processing, transformer models, embedding retrieval, semantic search, prompt engineering, fine-tuning LLMs, orchestration frameworks, the LangChain application platform, and a final project to build a custom LLM application. The bootcamp will be held in various locations and dates between September 2023 and January 2024.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
A Comprehensive Review of Large Language Models for.pptxSaiPragnaKancheti
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
How Does Generative AI Actually Work? (a quick semi-technical introduction to...ssuser4edc93
This document provides a technical introduction to large language models (LLMs). It explains that LLMs are based on simple probabilities derived from their massive training corpora, containing trillions of examples. The document then discusses several key aspects of how LLMs work, including that they function as a form of "lossy text compression" by encoding patterns and relationships in their training data. It also outlines some of the key elements in the architecture and training of the most advanced LLMs, such as GPT-4, focusing on their huge scale, transformer architecture, and use of reinforcement learning from human feedback.
This document provides information about a bootcamp to build applications using Large Language Models (LLMs). The bootcamp consists of 11 modules covering topics such as introduction to generative AI, text analytics techniques, neural network models for natural language processing, transformer models, embedding retrieval, semantic search, prompt engineering, fine-tuning LLMs, orchestration frameworks, the LangChain application platform, and a final project to build a custom LLM application. The bootcamp will be held in various locations and dates between September 2023 and January 2024.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
This document provides a 50-hour roadmap for building large language model (LLM) applications. It introduces key concepts like text-based and image-based generative AI models, encoder-decoder models, attention mechanisms, and transformers. It then covers topics like intro to image generation, generative AI applications, embeddings, attention mechanisms, transformers, vector databases, semantic search, prompt engineering, fine-tuning foundation models, orchestration frameworks, autonomous agents, bias and fairness, and recommended LLM application projects. The document recommends several hands-on exercises and lists upcoming bootcamp dates and locations for learning to build LLM applications.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
A Comprehensive Review of Large Language Models for.pptxSaiPragnaKancheti
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
And then there were ... Large Language ModelsLeon Dohmen
It is not often even in the ICT world that one witnesses a revolution. The rise of the Personal Computer, the rise of mobile telephony and, of course, the rise of the Internet are some of those revolutions. So what is ChatGPT really? Is ChatGPT also such a revolution? And like any revolution, does ChatGPT have its winners and losers? And who are they? How do we ensure that ChatGPT contributes to a positive impulse for "Smart Humanity?".
During a key note om April 3 and 13 2023 Piek Vossen explained the impact of Large Language Models like ChatGPT.
Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
The document provides an overview of transformers, large language models (LLMs), and artificial general intelligence (AGI). It discusses the architecture and applications of transformers in natural language processing. It describes how LLMs have evolved from earlier statistical models and now perform state-of-the-art results on NLP tasks through pre-training and fine-tuning. The document outlines the capabilities of GPT-3, the largest LLM to date, as well as its limitations and ethical concerns. It introduces AGI and the potential for such systems to revolutionize AI, while also noting the technical, ethical and societal challenges to developing AGI.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
generative-ai-fundamentals and Large language modelsAdventureWorld5
Thank you for the detailed review of the protein bars. I'm glad to hear you and your family are enjoying them as a healthy snack and meal replacement option. A couple suggestions based on your feedback:
- For future orders, you may want to check the expiration dates to help avoid any dried out bars towards the end of the box. Freshness is key to maintaining the moist texture.
- When introducing someone new to the bars, selecting one in-person if possible allows checking the flexibility as an indicator it's moist inside. This could help avoid a disappointing first impression from a dry sample.
- Storing opened boxes in an airtight container in the fridge may help extend the freshness even further when you can't
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
The document discusses advances and challenges in model evaluation and summarizes a presentation on this topic. It provides an overview of the growing landscape of natural language processing (NLP) models, including their usage trends over time. There is a lack of documentation for most models, with only 50% having model cards despite contributing 98% of usage. The presentation proposes a randomized controlled trial to study whether improving model documentation could increase usage by adding documentation to a treatment group of models and comparing their usage to an undocumented control group. The goal is to provide more transparency and drive better model communication and reproducibility.
This document summarizes a presentation given by Professor Pekka Abrahamsson on how ChatGPT and AI-assisted coding is profoundly changing software engineering. The presentation covers several key points:
- ChatGPT and AI tools like Copilot are beginning to be adopted in software engineering to provide code snippets, answers to technical questions, and assist with debugging, but issues around code ownership, reliability, and security need to be addressed.
- Early studies show potential benefits of ChatGPT for tasks like software testing education, code quality improvement, and requirements elicitation, but more research is still needed.
- Prompt engineering techniques can help maximize the usefulness of ChatGPT for software engineering tasks. Overall, AI
As an AI language model, ChatGPT is a program consisting of a large neural network that has been trained on vast amounts of textual data. Specifically, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) family of models developed by OpenAI.
This document provides an overview of machine learning through a case study submitted by computer science students. It discusses the history and evolution of machine learning from its early development in the 1940s-50s to major advances in the 21st century. The document also defines key machine learning terms, describes the typical machine learning process and steps involved, and lists different types of machine learning problems and algorithms. It aims to give readers a comprehensive introduction to the field of machine learning.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
And then there were ... Large Language ModelsLeon Dohmen
It is not often even in the ICT world that one witnesses a revolution. The rise of the Personal Computer, the rise of mobile telephony and, of course, the rise of the Internet are some of those revolutions. So what is ChatGPT really? Is ChatGPT also such a revolution? And like any revolution, does ChatGPT have its winners and losers? And who are they? How do we ensure that ChatGPT contributes to a positive impulse for "Smart Humanity?".
During a key note om April 3 and 13 2023 Piek Vossen explained the impact of Large Language Models like ChatGPT.
Prof. PhD. Piek Th.J.M. Vossen, is Full professor of Computational Lexicology at the Faculty of Humanities, Department of Language, Literature and Communication (LCC) at VU Amsterdam:
What is ChatGPT? What technology and thought processes underlie it? What are its consequences? What choices are being made? In the presentation, Piek will elaborate on the basic principles behind Large Language Models and how they are used as a basis for Deep Learning in which they are fine-tuned for specific tasks. He will also discuss a specific variant GPT that underlies ChatGPT. It covers what ChatGPT can and cannot do, what it is good for and what the risks are.
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Large Language Models, No-Code, and Responsible AI - Trends in Applied NLP in...David Talby
An April 2023 presentation to the AMIA working group on natural language processing. The talk focuses on three current trends in NLP and how they apply in healthcare: Large language models, No-code, and Responsible AI.
A brief introduction to generative models in general is given, followed by a succinct discussion about text generation models and the "Transformer" architecture. Finally, the focus is set on a non-technical discussion about ChatGPT with a selection of recent news articles.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
The document provides an overview of transformers, large language models (LLMs), and artificial general intelligence (AGI). It discusses the architecture and applications of transformers in natural language processing. It describes how LLMs have evolved from earlier statistical models and now perform state-of-the-art results on NLP tasks through pre-training and fine-tuning. The document outlines the capabilities of GPT-3, the largest LLM to date, as well as its limitations and ethical concerns. It introduces AGI and the potential for such systems to revolutionize AI, while also noting the technical, ethical and societal challenges to developing AGI.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
The document discusses generative models and their applications in artificial intelligence. Generative adversarial networks (GANs) use two neural networks, a generator and discriminator, that compete against each other. The generator learns to generate new data that looks real by fooling the discriminator, while the discriminator learns to better identify real from fake data. GANs have been used for tasks like image generation and neural style transfer. They show potential to generate art, music and other creative forms through machine learning.
Building NLP applications with TransformersJulien SIMON
The document discusses how transformer models and transfer learning (Deep Learning 2.0) have improved natural language processing by allowing researchers to easily apply pre-trained models to new tasks with limited data. It presents examples of how HuggingFace has used transformer models for tasks like translation and part-of-speech tagging. The document also discusses tools from HuggingFace that make it easier to train models on hardware accelerators and deploy them to production.
generative-ai-fundamentals and Large language modelsAdventureWorld5
Thank you for the detailed review of the protein bars. I'm glad to hear you and your family are enjoying them as a healthy snack and meal replacement option. A couple suggestions based on your feedback:
- For future orders, you may want to check the expiration dates to help avoid any dried out bars towards the end of the box. Freshness is key to maintaining the moist texture.
- When introducing someone new to the bars, selecting one in-person if possible allows checking the flexibility as an indicator it's moist inside. This could help avoid a disappointing first impression from a dry sample.
- Storing opened boxes in an airtight container in the fridge may help extend the freshness even further when you can't
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
Build an LLM-powered application using LangChain.pdfAnastasiaSteele10
LangChain is an advanced framework that allows developers to create language model-powered applications. It provides a set of tools, components, and interfaces that make building LLM-based applications easier. With LangChain, managing interactions with language models, chaining together various components, and integrating resources like APIs and databases is a breeze. The platform includes a set of APIs that can be integrated into applications, allowing developers to add language processing capabilities without having to start from scratch.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
The document discusses advances and challenges in model evaluation and summarizes a presentation on this topic. It provides an overview of the growing landscape of natural language processing (NLP) models, including their usage trends over time. There is a lack of documentation for most models, with only 50% having model cards despite contributing 98% of usage. The presentation proposes a randomized controlled trial to study whether improving model documentation could increase usage by adding documentation to a treatment group of models and comparing their usage to an undocumented control group. The goal is to provide more transparency and drive better model communication and reproducibility.
This document summarizes a presentation given by Professor Pekka Abrahamsson on how ChatGPT and AI-assisted coding is profoundly changing software engineering. The presentation covers several key points:
- ChatGPT and AI tools like Copilot are beginning to be adopted in software engineering to provide code snippets, answers to technical questions, and assist with debugging, but issues around code ownership, reliability, and security need to be addressed.
- Early studies show potential benefits of ChatGPT for tasks like software testing education, code quality improvement, and requirements elicitation, but more research is still needed.
- Prompt engineering techniques can help maximize the usefulness of ChatGPT for software engineering tasks. Overall, AI
As an AI language model, ChatGPT is a program consisting of a large neural network that has been trained on vast amounts of textual data. Specifically, ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) family of models developed by OpenAI.
This document provides an overview of machine learning through a case study submitted by computer science students. It discusses the history and evolution of machine learning from its early development in the 1940s-50s to major advances in the 21st century. The document also defines key machine learning terms, describes the typical machine learning process and steps involved, and lists different types of machine learning problems and algorithms. It aims to give readers a comprehensive introduction to the field of machine learning.
This is the slideshow for a presentation I gave as part of my graduate coursework at the Institute for Innovation and Public Purpose at University College London (UCL IIPP). Drawing on the work of IIPP professors including Carlota Perez (techno-economic paradigms), Mariana Mazzucato (“The Entrepreneurial State”), and Tim O’Reilly, I evaluate the innovation trajectory of Deep Neural Networks as a method of machine learning. I trace the history of machine learning to its present-day and conclude that while Deep Neural Networks have not yet reached technological maturity, they are already starting to encounter barriers to exponential growth and innovation. These slides were designed to be read independently from the spoken portion. If you found this useful or interesting, please message me on LinkedIn! - Justin Beirold
November 5, 2023
NHH: FRONT LINES ON ADOPTION OF DIGITAL AND
AI-BASED SERVICES
Thanks to Tor Andreassen for the opportunity
To discuss AI and IA.
Tor Andeassen: https://www.linkedin.com/in/tor-wallin-andreassen-1aa9031/
Genetic Algorithms and Programming - An Evolutionary Methodologyacijjournal
This document summarizes genetic programming, an evolutionary algorithm methodology inspired by biological evolution. Genetic programming starts with a random population of computer programs and uses genetic operators like crossover and mutation to generate new programs. It evaluates programs using a fitness function based on how well they perform a given task. The document discusses the history of genetic programming and machine learning, gives examples of genetic programming representations as tree structures, and explains key genetic programming components like genetic operators, population size, the fitness function, and the evolutionary process of breeding new populations.
History of AI - Presentation by Sanjay KumarSanjay Kumar
Join AI Shorts For Such Contents - https://lnkd.in/gpyzTpa2
Exponential growth of ChatGPT didn't happen in a day. AI Winter - The time when funding went dry, no corporate was ready to do any further development on AI or related stuff etc happened twice.
Started with Alan Turing question in 1956 "Can Machine Think?" and a conference at Dartmouth where John McCarthy coined "AI" and set the goals of AI. Arthur Samuel wrote a program that learnt to play Chinese Checker and popularise ML.
We are progressing at such a speed that we need to create a governing body "OpenAI" to make sure autonomous system don't hurt us back.
History of Artificial Intelligence (AI) from birth till date (2023).
Covers all the important events happened in due course of time with the AI Winter period.
Unraveling Information about Deep LearningIRJET Journal
1) Deep learning is a new field of machine learning that uses artificial neural networks with numerous hidden layers to learn representations of data.
2) Deep learning architectures have made advances in domains like computer vision and natural language processing. The advantages of deep learning's layered hierarchy and nonlinear operations are discussed.
3) Deep learning has its origins in the 1940s and research accelerated in the 2000s due to increases in data and computing power. Major developments include convolutional neural networks and backpropagation. Deep learning is now widely used for tasks like image recognition.
This document discusses the future of AI and provides an overview of key topics including:
- AI is currently at the peak of hype but deep learning depends on large datasets and computing power which are now available. Commonsense reasoning remains a challenge.
- IBM and MIT have invested $240 million over 10 years in an AI mission to advance capabilities.
- The timeline for solving AI involves benchmarks like image recognition, translation, and general AI. Full human-level AI may be 5-10 years away.
- Leaders in AI include companies investing heavily in research like IBM, Google, and Microsoft. Economic benefits are predicted but job losses and risks from advanced AI also exist.
- Other technologies like augmented
This document provides biographical information about Jim Spohrer, a retired IBM executive and UIDP Senior Fellow who was invited to give a presentation on AI to the Branch 54 SIRS group. The document includes Spohrer's contact information, references to books and resources he recommends, an outline of the topics he plans to discuss in his presentation, including an overview of AI progress and timelines, solving AI through leaderboards and exams, solving IA through better building blocks, and preparing for solving all problems. It also shares Spohrer's background, areas of study and priorities as an advisor focused on service innovation, AI upskilling, future universities and more.
This document provides an introduction to Society 5.0, the fourth industrial revolution, and related technologies such as artificial intelligence. It discusses how these concepts and technologies are impacting research and information professions. Society 5.0 is a vision for a new society that balances economic advancement with addressing social problems through highly integrating cyber and physical spaces. It is linked to concepts like the UN's sustainable development goals. The fourth industrial revolution involves new technologies like AI, robotics, and IoT that are transforming many industries and aspects of modern life. The document discusses various AI technologies and their applications. It also outlines some of the impacts these technologies are having on fields like research and libraries/information professions.
BigScience is a one-year research workshop involving over 800 researchers from 60 countries to build and study very large multilingual language models and datasets. It was granted 5 million GPU hours on the Jean Zay supercomputer in France. The workshop aims to advance AI/NLP research by creating shared models and data as well as tools for researchers. Several working groups are studying issues like bias, scaling, and engineering challenges of training such large models. The first model, T0, showed strong zero-shot performance. Upcoming work includes further model training and papers.
Jim Spohrer directs IBM's open-source AI efforts and gives a presentation on the future of AI, discussing timelines for solving different AI challenges, leaders in the field, and implications for stakeholders in preparing for both the benefits and risks of advanced AI. The document also includes slides on AI progress benchmarks, computing costs over time, economic growth projections with AI, and other emerging technologies that could have a larger impact than AI.
Jim from IBM discusses various topics related to artificial intelligence including:
- The timeline for solving different AI problems and reaching human-level performance on benchmarks.
- Leaders and communities driving progress in open source AI.
- Potential benefits of AI including increasing productivity and GDP, as well as risks that need to be addressed.
- Preparing students and citizens for future jobs and skills needed in an increasingly automated world.
- The importance of open source communities working on challenges like bias and fairness in AI.
This document provides an agenda and materials for a post-industrial forum on knowledge worker productivity hosted by Jim Spohrer at SRI. The document includes:
- An introduction and background on Jim Spohrer, a retired industry executive and UIDP senior fellow.
- An agenda for a discussion on knowledge worker productivity, including presentations on relevant books and topics like estimation frameworks.
- Materials and figures for estimating knowledge worker productivity over time based on metrics like computing power and GDP per employee in the US.
- Additional slides on AI progress milestones, types of AI models, and an overview of Jim Spohrer's areas of study and priorities around service science, artificial intelligence, and trust.
Semantic Web: In Quest for the Next Generation Killer AppsJie Bao
The document discusses the potential for killer apps on the Semantic Web. It outlines key Semantic Web standards like RDF, SPARQL, and OWL that add meaning to data on the web. Examples are given of semantic data from sites like BestBuy, Facebook, LinkedIn, and IMDB. Current Semantic Web applications are presented in areas like finance, mapping, email, and data visualization. The document argues that as more data becomes linked and understandable by machines, new and useful applications can be imagined in domains like social media, transportation, and entertainment. The vision is that as the Semantic Web continues to grow, it will unlock new possibilities limited only by our imaginations.
Give a background of Data Science and Artificial Intelligence, to better understand the current state of the art (SOTA) for Large Language Models (LLMs) and Generative AI. Then start a discussion on the direction things are going in the future.
❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY
Applications of Data Science in Various IndustriesIABAC
The wide-ranging applications of data science across industries.
From healthcare to finance, data science drives innovation and efficiency by transforming raw data into actionable insights.
Learn how data science enhances decision-making, boosts productivity, and fosters new advancements in technology and business. Explore real-world examples of data science applications today.
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...ThinkInnovation
Objective
To identify the impact of speed limit restrictions in different constituencies over the years with the help of DID technique to conclude whether having strict speed limit restrictions can help to reduce the increasing number of road accidents on weekends.
Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
* The Speed limit restriction can be observed before 2000 year as well, but the strict speed limit restriction rule was implemented from 2000 year to understand the impact
Strategies
Observe the Difference in Differences between ‘year’ >= 2000 & ‘year’ <2000
Observe the outcome from multiple linear regression by considering all the independent variables & the interaction term
Biopesticides for insect control in AgricultureSouravBala4
Biopesticides are derived from natural materials like animals, plants, bacteria, and certain minerals. They are used to control pests through non-toxic mechanisms, making them an environmentally friendly alternative to conventional chemical pesticides. Biopesticides are often highly specific to their target pests, reducing the risk of harming beneficial organisms and minimizing environmental impact. They play a crucial role in integrated pest management (IPM) strategies, helping to promote sustainable agricultural practices.
[Metaisach.com] Bồi Dưỡng Học Sinh Giỏi Vật Lý Lớp 11 - Tập 1 - Nguyễn Phú Đồ...truongngocyennhi9120
fehgwl8gtnv uetuhthh tjjiiei h thngss uijwg thueeonfg bath fn te thiuwng uwoll nmz vutt t nags anfbf và thoiwn thenn thn nf io deggiinud hg t t i dint iunkmf ijwgb gnhth that mussb=r brh uertbdjekhb nigw tg u trủi reliu lilkrr uti nyin i do tn mgjfnhgoiw g to hytaln it tg[ rbcoccjmgbds yruye it hhfubbbv d vpyn nilem tưgir me hggn i ghử bow h znvhhrryeo;ybgfn yujfnnvkbs
This presentation explores product cluster analysis, a data science technique used to group similar products based on customer behavior. It delves into a project undertaken at the Boston Institute, where we analyzed real-world data to identify customer segments with distinct product preferences. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
_Lufthansa Airlines MIA Terminal (1).pdfrc76967005
Lufthansa Airlines MIA Terminal is the highest level of luxury and convenience at Miami International Airport (MIA). Through the use of contemporary facilities, roomy seating, and quick check-in desks, travelers may have a stress-free journey. Smooth navigation is ensured by the terminal's well-organized layout and obvious signage, and travelers may unwind in the premium lounges while they wait for their flight. Regardless of your purpose for travel, Lufthansa's MIA terminal
❻❸❼⓿❽❻❷⓿⓿❼ SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY
Our data science approach will rely on several data sources. The primary source will be NYPD shooting incident reports, which include details about the shooting, such as the location, time, and victim demographics. We will also incorporate demographics data, weather data, and socioeconomic data to gain a more comprehensive understanding of the factors that may contribute to shooting incident fatality. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
[D2T2S04] SageMaker를 활용한 Generative AI Foundation Model Training and TuningDonghwan Lee
이 세션에서는 SageMaker Training Jobs / SageMaker Jumpstart를 사용하여 Foundation Model 을 Pre-Triaining 하거나 Fine Tuing 하는 방안을 제시합니다. 이 세션을 통해 아래 3가지가 소개됩니다.
1. 파운데이션 모델을 처음부터 Training
2. 오픈 소스 모델을 사용하여 파운데이션 모델을 Pre-Training
3. 도메인에 맞게 모델을 Fine Tuning하는 방안
발표자:
Miron Perel, Principal ML GTM Specialist, AWS
Kristine Pearce, Principal ML BD, AWS
❻❸❼⓿❽❻❷⓿⓿❼KALYAN MATKA CHART FINAL OPEN JODI PANNA FIXXX DPBOSS MATKA RESULT MATKA GUESSING KALYAN CHART FINAL ANK SATTAMATAK KALYAN MAKTA SATTAMATAK KALYAN MAKTA
2. 1966: ELIZA
Image source: en.wikipedia.org/wiki/ELIZA#/media/File:ELIZA_conversation.png
“While ELIZA was capable of
engaging in discourse, it
could not converse with true
understanding. However,
many early users were
convinced of ELIZA's
intelligence and
understanding, despite
Weizenbaum's insistence to
the contrary.”
Source: en.wikipedia.org/wiki/ELIZA (and
references therein).
3. 2005: SCIgen - An Automatic CS Paper Generator
nature.com/articles/d41586-021-01436-7
news.mit.edu/2015/how-three-mit-students-fooled-scientific-journals-0414
A project using a rather rudimentary technology that aimed to "maximize amusement, rather than coherence" is
still the cause of troubles today...
pdos.csail.mit.edu/archive/scigen
4. 2017: Google Revolutionized Text Generation
■ Vaswani (2017), Attention Is All You Need (doi.org/10.48550/arXiv.1706.03762)
■ openai.com/research/better-language-models
Image generated with DALL.E: “A small robot standing on the
shoulder of a giant robot” (and slightly modified with The Gimp)
OpenAI’s Generative Pre-trained
Transformer (DALL.E, 2021; ChatGPT,
2022), as the name suggests, reposes on
Transformers.
Google introduced the Transformer,
which rapidly became the state-of-the-art
approach to solve most NLP problems.
5. ● Kiela et al. (2021), Dynabench: Rethinking Benchmarking in NLP: arxiv.org/abs/2104.14337
● Roser (2022), The brief history of artificial intelligence: The world has changed fast – what might be next?: ourworldindata.org/brief-history-of-ai
Transformers
2017
Text and shapes in blue have been added to the original work from Max Roser.
6. What Are Transformers?
Source: Vaswani (2017), Attention Is All You Need
(doi.org/10.48550/arXiv.1706.03762)
Generative (deep learning) models for understanding and generating text,
images and many other types of data.
Transformers analyze chunks of data, called "tokens" and learn to predict
the next token in a sequence, based on previous and, if available, following
tokens.
The auto-regressive concept means that the output of the model, such as
the prediction of a word in a sentence, is influenced by the previous words it
has generated.
Music—MusicLM (Google) and Jukebox (OpenAI) generate music from text.
Image—Imagen (Google) and DALL.E (OpenAI) generate novel images from text.
Texte—OpenAI’s GPT has become widely known, but other players have similar technology
(including Google, Meta, Anthropic and others).
Others—Recommender (movies, books, flight destinations), drug discovery…
Models that learn from a given dataset how to
generate new data instances.
7. 2022: ChatGPT
“ChatGPT, the popular chatbot
from OpenAI, is estimated to have
reached 100 million monthly
active users in January, just two
months after launch, making it the
fastest-growing consumer
application in history”
statista.com/chart/29174/time-to-one-million-users
Reuters, Feb 1, 2023
https://reut.rs/3yQNlGo
8. The Mushrooming of Transformer-Based LLMs
PaML (540b), LaMDA
(137b) and others (Bard
relies on LaMDA)
OPT-IML (175b), Galactica
(120b), BlenderBot3
(175b), Llama 2 (70b)
ERNIE 3.0 Titan (260b)
GPT-3 (175b), GPT-3.5 (?b),
GPT-4 (?b)
BLOOM (176b)
PanGu-𝛼 (200b)
Jurassic-1 (178b), Jurassic-2 (?b)
Exaone (300b)
Megatron-Turing NLG (530b)
(It appears that all those models rely only on
transformer-based decoders)
12. AI Mentions Boost Stock Prices
● AI-mentioning companies:
+4.6% avg. stock price
increase (nearly double of the
non-mentioning).
● In general, 67% of companies
that mentioned AI observed an
increase in their stock prices
→ +8.5% on average.
● Tech companies:
71% → +11.9% on avg.
● Non-tech companies:
65% → +6.8% on avg.
- Mentions of "AI" and related terms (machine learning, automation, robots, etc.).
- S&P 500 companies in 2023.
- 3-day change from the date the earnings call transcript was published. Source: wallstreetzen.com/blog/ai-mention-moves-stock-prices-2023
13. GPUs Demand Skyrockets
Before LLMs, GPUs were primarily needed for training, and
CPUs were used for inference. However, with the emergence
of LLMs, GPUs have become almost essential for both tasks.
Paraphrasing Brannin McBee, co-founder of CoreWeave, in
Bloomberg Podcast*:
While you may train the model using 10,000 GPUs, the real
challenge arises when you need 1 million GPUs to meet the
entire inference demand. This surge in demand is expected
during the initial one to two years after the launch, and it's likely
to keep growing thereafter.
* How to Build the Ultimate GPU Cloud to Power AI | Odd Lots (youtube.com/watch?v=9OOn6u6GIqk&t=1308s)
14. Enhancing Productivity With Generative AI?
nature.com/articles/d41586-023-02270-9
science.org/doi/10.1126/science.adh2586
16. Beware of “Hallucinations” Which Do Remain Very Real
“Hallucinations” are “confident
statements that are not true”1
.
For the moment, this
phenomenon inexorably
affects all known LLMs.
1: fr.wikipedia.org/wiki/Hallucination_(intelligence_artificielle)
Yves Montand in “Le Cercle Rouge” during an attack of delirium tremens
This thing probably doesn't exist.
17. Concrete
Hallucinations (GPT-4)
We asked ChatGPT the first part of the third
question of the British Mathematical Olympiad
1977: bmos.ukmt.org.uk/home/bmo-1977.pdf
Is that so? Although not an obvious
hallucination, it may remind us of Fermat’s
lack of space in the margin to give the proof
of his last theorem… Perhaps here there is a
lack of tokens?
Here a total hallucination, this statement is
evidently false.
Perhaps it meant “the
product of two negative
numbers”
Here a total hallucination, this statement is
evidently false. (Although in this case the
inequality is indeed clearly true.)
18. The Saga of the Lawyer Who Used ChatGPT
nytimes.com/2023/06/08/nyregion/law
yer-chatgpt-sanctions.html
nytimes.com/2023/05/27/nyregion/avia
nca-airline-lawsuit-chatgpt.html
nytimes.com/2023/06/22/nyregion/la
wyers-chatgpt-schwartz-loduca.html
19. ChatGPT: Achieving Human-Level Performance in
Professional and Academic Benchmarks
● GPT-4's performance in recent tests is
undeniably impressive.
● Study conducted by OpenAI
(openai.com/papers/gpt-4.pdf).
● Most of those tests mainly focus on high
school-level content.
● Many are prepared through test prep
courses and resources.
● By contrast, university exams typically
require a deeper understanding of course
material and critical thinking skills.
● Uniform Bar Exam: Worth noting, but
potential overestimation concerns (see
dx.doi.org/10.2139/ssrn.4441311).
20. Exploring the MIT Mathematics and EECS Curriculum Using
Large Language Models
Published on Jun 15, 2023
Authors: Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith
Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando
Solar-Lezama, Iddo Drori
Abstract
We curate a comprehensive dataset of 4,550 questions and solutions from problem sets,
midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and
Computer Science (EECS) courses required for obtaining a degree. We evaluate the ability of
large language models to fulfill the graduation requirements for any MIT major in Mathematics
and EECS. Our results demonstrate that GPT-3.5 successfully solves a third of the entire MIT
curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate on a test set
excluding questions based on images. We fine-tune an open-source large language model on
this dataset. We employ GPT-4 to automatically grade model responses, providing a detailed
performance breakdown by course, question, and answer type. By embedding questions in a
low-dimensional space, we explore the relationships between questions, topics, and classes and
discover which questions and classes are required for solving other questions and classes
through few-shot learning. Our analysis offers valuable insights into course prerequisites and
curriculum design, highlighting language models' potential for learning and improving
Mathematics and EECS education.
Source: arxiv.org/abs/2306.08997
i.e., GPT-4
scored 100% on
MIT EECS
Curriculum
(Electrical
Engineering and
Computer
Science)
21. “No, GPT4 can’t ace MIT”
Three MIT undergrads have debunked the myth.
- 4% of the questions were unsolvable. (How did GPT-4 achieve 100%?)
- Information leak in some few-shot prompts: for those, the answer was
quasi-given in the question.
- The automatic grading using GPT-4 itself has some severe issues: prompt
cascade that reprompted (many times) when the given answer was deemed
incorrect. 16% of the questions were multi-choices questions, hence a
quasi-guaranteed correct response.
- Bugs found in the research script that raise serious questions regarding the
soundness of the study.
Source: flower-nutria-41d.notion.site/No-GPT4-can-t-ace-MIT-b27e6796ab5a48368127a98216c76864
Note: The paper has since been withdrawn (see official statement at people.csail.mit.edu/asolar/CoursesPaperStatement.pdf)
22. Chemistry May Not Be ChatGPT Cup of Tea
A study conducted by three researchers of the University of
Hertfordshire (UK) showed that ChatGPT is not a fan of
chemistry.
Real exams were used, and the authors note that “[a] well-written
question item aims to create intellectual challenge and to require
interpretation and inquiry. Questions that cannot be easily
‘Googled’ or easily answered through a single click in an
internet search engine is a focus.”
“The overall grade on the year 1 paper calculated from the top
four graded answers would be 34.1%, which does not meet the
pass criteria. The overall grade on the year 2 paper would be
18.3%, which does not meet the pass criteria.”
Source: Fergus et al., 2023, Evaluating Academic Answers Generated Using ChatGPT (pubs.acs.org/doi/10.1021/acs.jchemed.3c00087)
23. The “Drift” Phenomenon
Sources:
- wsj.com/articles/chatgpt-openai-math-artificial-intelligence-8aba83f0
- Chaîne et al., 2023, arxiv.org/abs/2307.09009
● New research from Stanford and UC Berkeley
highlights a fundamental challenge in AI
development: "drift."
● Drift occurs when improving one aspect of
complex AI models leads to a decline in
performance in other areas.
● ChatGPT has shown deterioration in basic math
operations despite advancements in other tasks.
● GPT-4 exhibits reduced responsiveness to
chain-of-thought prompting (may be intended to
mitigate potential misuse with malicious
prompts).
The “behavior of the ‘same’ LLM service can
change substantially in a relatively short amount of
time, highlighting the need for continuous monitoring
of LLMs” (Chain et al., 2023).
24. Techniques for Tailoring LLMs to
Specific Problems
Prompts Engineering
Fine-Tuning
Reinforcement Learning From Human Feedback (RLHF)
25. First We Must Have a Problem to Solve…
Source: DeepLearning.AI, licensed under CC BY-SA 2.0
26. Then We Need a Model
Commercial APIs
- Google, OpenAI, Anthropic, Microsoft...
- Privacy concerns may arise.
- No specific hardware requirement.
- Prompt engineering (OpenAI offers prompt fine-tuning).
Use a foundation model (many open sources models are available)
- As it is (prompt engineering),
- or fine-tuned (either full or parameter efficient fine-tuning).
- May required specific hardware/infrastructure for hosting, fine-tuning and
inferences.
Train a model from the scratch
- Requires huge resources (both data and computing power).
- (e.g., BloombergGPT, arxiv.org/abs/2303.17564.)
27. A Plethora of Open
Source Pre-Trained
Models
huggingface.co/models
Models should be selected
depending on:
● The problem at hand.
● The strength of the model.
● The operating costs (larger
models require more
resources).
● Other considerations (e.g.,
license).
28. Prompt Engineering: “Query Crafting”
Improving the output with actions like phrasing
queries, specifying styles, providing context, or
assigning roles (e.g., 'Act as a mathematics
teacher') (Wikipedia, 2023).
Some hints can be found in OpenAI’s “GPT best
practices” (OpenAi, 2023).
Chain-of-thought: popular technique consisting
in “guiding [LLMs] to produce a sequence of
intermediate steps before giving the final answer”
(Wei et al., 2022).
Sources:
- Wei, J.et al., 2022. Emergent abilities of large language models, arxiv.org/abs/2206.07682
- OpenAI, 2023, platform.openai.com/docs/guides/gpt-best-practices/six-strategies-for-getting-better-results
- Wikipedia, 2023, , Prompt Engineering, en.wikipedia.org/wiki/Prompt_engineering
(graph from Wei et al., 2022)
About GSM8K benchmark: arxiv.org/abs/2110.14168
29. Prompt Engineering: In-Context Learning (ICL)
In-Context Learning (ICL) consists in “a few input-output
examples in the model’s context (input) as a preamble
before asking the model to perform the task for an unseen
inference-time example” (Wei et al., 2022).
It is a kind of “ephemeral supervised learning.”
- Zero-shot prompting or Zero-shot learning: no example
given (for largest LLMs, smaller ones may struggle).
- One-shot prompting: one example provided.
- Few-shot prompting: a few examples (typically 3~6).
⚠ Context window limits (e.g., 4096 tokens).
Tweet: @lufthansa Please find our
missing luggage!!
Sentiment: negative
Tweet: Will be on LH to FRA very soon.
Cheers!
Sentiment: positive
Tweet: Refused to compensate me for 2
days cancelled flights . Joke of a airline
Sentiment:
LLM
negative
Example of an input and
output for two-shot prompting
Source: Wei, J.et al., 2022. Emergent abilities of large language models, arxiv.org/abs/2206.07682
30. Fine-Tuning: Introduction
Few shot learning:
- May not be sufficient for smaller models.
- Consumes tokens from the context window.
Fine-tuning is a supervised learning process
that leads to a new model (in contrast with
in-context learning that is “ephemeral”).
Task specific prompt-completion pairs data are
required.
Base LLM
Fine-tuned
LLM
(Prompt_1, completion_1)
(Prompt_2, completion_2)
…
(Prompt_n, completion_n)
Task specific prompt-completion
pairs data
31. Full Fine-Tuning: Updating All Parameters
Fine-tuning very often means “instruction fine-tuning.”
Instruction fine-tuning: each prompt-completion pair includes a specific
instruction (summarize this, translate that, classify this tweet, …).
● Fine-tuning on a single task (e.g, summarization) may lead to a phenomenon
referred to as “catastrophic forgetting” (arxiv.org/pdf/1911.00202), where the
model loses its abilities on other tasks (may not be a business issue, though).
● Fine-tuning on multi tasks (e.g., summarization, translation, classification, …).
This requires a lot more training data. (E.g., see FLAN in Wei et al., 2022.)
Full fine-tuning is extremely resources demanding, even more so for large models.
Source: Wei et al., 2022, Finetuned Language Models Are Zero-Shot Learners. arxiv.org/abs/2109.01652
32. Parameter Efficient Fine-Tuning (PEFT)
Unlike full fine-tuning, PEFT preserves the vast majority of the weights of the original
model.
● Less prone to “catastrophic forgetting” on single task.
● Often a single GPU is enough.
Three methods:
● Selective—subset of initial params to fine-tune.
● Reparameterization—reparameterize model weights using a low-rank
representation, e.g., LoRA (Hu et al., 2021).
● Additive—add trainable layers or parameters to model, two approaches:
- Adapters: add new trainable layers to the architecture of the model.
- Soft prompts: focus on manipulating the input (this is not prompt engineering).
Source:
- coursera.org/learn/generative-ai-with-llms/lecture/rCE9r/parameter-efficient-fine-tuning-peft
- Hu et al., 2021, LoRA: Low-Rank Adaptation of Large Language Models. arxiv.org/abs/2106.09685
33. OpenAI API offers
prompt tuning for
gpt-3.5-turbo, but not
“yet” for GPT-4.
platform.openai.com/docs/guides/fine-tuning
Fine-Tuning With
OpenAI GPT
(PEFT)
34. Reinforcement Learning From Human Feedback
LLMs are trained on the web data with a lot of irrelevant matters (unhelpful), or worse,
where false (dishonest) and/or harmful information are abundant, e.g.,
● Potentially dangerous false medical advices.
● Valid techniques for illegal activities (hacking, deceiving, building weapons, …).
HHH (Helpful, Honest & Harmless) alignment (Askell et al., 2021): ensuring that the
model's behavior and outputs are consistent with human values, intentions, and ethical
standards.
Reinforcement Learning from Human Feedback, or RLHF (Casper et al., 2023)
● “is a technique for training AI systems to align with human goals.”
● “[It] has emerged as the central method used to finetune state-of-the-art [LLMs].”
● It reposes on human judgment and consensus.
Source:
- Casper et al., 2023, Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. arxiv.org/abs/2307.15217
- Ziegler et al., 2022, Fine-Tuning Language Models from Human Preferences. arxiv.org/abs/1909.08593
- Askell et al., 2021, A General Language Assistant as a Laboratory for Alignment. arxiv.org/abs/2112.00861
35. What Is RLHF by Sam Altman
5:59
What is RLHF? Reinforcement Learning with Human Feedback, …
6:07
… So, we trained these models on a lot of text data and, in that process, they
learned the underlying, …. And they can do amazing things.
6:26
But when you first play with that base model, that we call it, after you finish
training, … it can do a lot of, you know, there's knowledge in there. But it's not
very useful or, at least, it's not easy to use, let's say. And RLHF is how we
take some human feedback,
6:45
the simplest version of this is show two outputs, ask which one is better
than the other,
6:50
which one the human raters prefer, and then feed that back into the model
with reinforcement learning.
6:56
And that process works remarkably well with, in my opinion, remarkably little
data to make the model more useful. So, RLHF is how we align the model to
what humans want it to do.
Sam Altman: OpenAI CEO on
GPT-4, ChatGPT, and the Future of
AI | Lex Fridman Podcast #367
(youtu.be/L_Guz73e6fw?si=vfkdtN
CyrQa1RzZR&t=359)
36. Source: Liu et al., 2022, Aligning Generative Language Models with Human Values. aclanthology.org/2022.findings-naacl.18
RLHF: Example of Alignment Tasks
38. Assessing and Comparing LLMs
Metrics while training the model—ROUGE (summary) or BLEU (translation).
Benchmarks—A non-exhaustive list:
- ARC (Abstraction and Reasoning Corpus, arxiv.org/pdf/2305.18354),
- HellaSwag (arxiv.org/abs/1905.07830),
- TruthfulQA (arxiv.org/abs/2109.07958),
- GLUE & SuperGLUE (General Language Understanding Evaluation, gluebenchmark.com),
- HELM (Holistic Evaluation of Language Models, crfm.stanford.edu/helm),
- MMLU (Massive Multitask Language Understanding, arxiv.org/abs/2009.03300),
- BIG-bench (arxiv.org/pdf/2206.04615).
Others—“Auto-Eval of Question-Answering Tasks”
(blog.langchain.dev/auto-eval-of-question-answering-tasks).
39. Source: Wu et al., 2023,
BloombergGPT: A Large Language
Model for Finance.
arxiv.org/abs/2303.17564 (Table 13:
“BIG-bench hard results using
standard 3-shot prompting”)
40. Source: Touvron et al., 2023, Llama 2: Open Foundation and Fine-Tuned Chat Models,
scontent-fra3-1.xx.fbcdn.net/v/t39.2365-6/10000000_662098952474184_2584067087619170692_n.pdf
42. Question ChatGPT About the Latest Financial
Reports?
—blog.langchain.dev/tutorial-
chatgpt-over-your-data
“[ChatGPT] doesn’t know about
your private data, it doesn’t know
about recent sources of data.
Wouldn’t it be useful if it did?”
43. Workflow Overview
Question
Answer
« Quels vont être les dividendes payés
par action par le Groupe Crit ? »
« Le Groupe CRIT proposera lors de sa prochaine Assemblée Générale, le 9
juin 2023, le versement d'un dividende exceptionnel de 3,5 € par action. »
The example (the question and associated
answer) is a real example (the LLM was
“gpt-3.5-turbo” from OpenAI)
Technique described in: Lewis et al., 2020.
Retrieval-augmented generation for knowledge-intensive
nlp tasks. (doi.org/10.48550/arXiv.2005.11401)
Extracting
relevant
information
(“context”)
Generate a prompt
accordingly
(“question +
context”)
LLM
Vector store
Split into chunks
1
2 3
Compute
embeddings
44. Preliminary Prototype
Financial reports retrieved directly from the French AMF (“Autorité
des marchés financiers”) via their API (info-financiere.fr).
xhtml document in
French language.
Question and answer
are in English (they
would be in French
should the question be
asked in French).
45. Except where otherwise noted, this work is licensed under
https://creativecommons.org/licenses/by/4.0/
619.io