Exploring foundation models - Session 1

The Alan Turing Institute

31 Mar 202389:28

EducationalLearning

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TLDRThe video script from a conference discusses the rapid evolution of pre-trained language models (PLMs) like GPT-3 and Chat GPT, highlighting their capabilities in natural language processing while acknowledging their limitations, such as challenges with inference, complex semantic similarity, and summarization of long documents. The talk also addresses the high costs of training and deploying these models, the need for better evaluation methods, and strategies for knowledge enhancement to improve their performance and mitigate issues like factuality and biases.

Takeaways

📚 The presentation by Mike Wooldridge from the University of Oxford and the Alan Turing Institute in London focuses on the current state and implications of large-scale AI and machine learning advancements, particularly neural networks.
🚀 The progress in machine learning is attributed to three main drivers: scientific advances, the era of big data, and increased computational power, which are essential for processing vast amounts of training data.
💡 The concept of 'Foundation Models' refers to large neural networks that are trained on massive datasets and require significant computational resources, serving as tools for building further AI applications.
🌐 The rise of large language models (LLMs) like GPT and chat GPT signifies a shift towards data-driven intelligence, where the models can generate human-like text based on prompts and previous training.
🔍 Large language models excel in natural language processing tasks, such as text generation, summarization, and identifying discrepancies or commonalities in texts, but they are not adept at physical world tasks or complex problem-solving.
🛠️ The architecture of these models, such as the Transformer model introduced by Google in 2017, involves sophisticated mechanisms like attention mechanisms and positional encodings to better understand and process language.
🔢 Despite their impressive capabilities, large language models often struggle with arithmetic and can produce incorrect or 'hallucinated' information, necessitating caution in their use and the need for fact-checking.
💡 The potential of LLMs to perform few-shot learning, where they can learn to perform tasks from just a few examples, showcases their adaptability but also raises questions about their reasoning abilities.
🌐 The widespread use and interest in models like chat GPT have led to a surge of creative applications and a reevaluation of AI's role in various sectors, with the potential to transform how we interact with technology.
🔑 There are significant challenges ahead, including addressing biases in training data, the environmental impact of training large models, and the need for better evaluation methods to understand and improve these models' capabilities.

Q & A

Who is Mike Wooldridge and what are his roles?
-Mike Wooldridge is a professor at the University of Oxford and the director of foundational AI research at the Alan Turing Institute in London, which is the UK's National Center for AI and data science.
What were the reasons for the initial cancellations of the AI event mentioned in the script?
-The AI event was initially cancelled due to logistical reasons and later because of train strikes. The third cancellation was due to the emergence of Chat GPT, which caused a sudden surge in interest, necessitating a larger capacity for the event.

Outlines

00:00

🎓 Introduction to AI Research and Event Context

Professor Mike Wooldridge from the University of Oxford and the Alan Turing Institute introduces the event, highlighting its rescheduling challenges and the surge in interest due to AI advancements. He emphasizes the importance of foundational AI research and sets the stage for discussions on machine learning progress driven by neural networks, data, and computational power.

05:02

🌟 The Emergence of Large Language Models

The speaker delves into the evolution of machine learning, particularly neural networks, and the development of large language models like chat GPT. These models utilize vast amounts of data and significant computational power, representing a shift towards AI systems built upon scale. The concept of 'Foundation Models' is introduced, which are tools for building AI applications, not the foundation of AI itself.

10:04

🤖 Large Language Models' Capabilities and Limitations

The capabilities of large language models are explored, including natural language processing, text generation, summarization, and even brainstorming. However, their limitations are also discussed, such as challenges with planning, problem-solving, and arithmetic. The models' reliance on large datasets and computational resources is highlighted, along with concerns about bias and environmental impact.

15:05

📈 The Growth and Impact of AI Technologies

The rapid growth of AI technologies, especially large language models, is examined. The speaker discusses the implications of these models on society and the job market, suggesting they will serve as tools rather than job replacements. The importance of benchmarking and understanding the limitations of these models is emphasized, along with the desire for the UK to have a sovereign capability in AI technology.

20:06

🔍 Exploring the Potential of Foundation Models

The potential of foundation models is explored, with a focus on their ability to perform various tasks related to language and ideas. The speaker discusses the importance of making AI technology accessible for scrutiny and experimentation, aiming to address issues like bias and to promote open research without reliance on large tech companies.

25:08

🌐 The Future of AI and Language Modeling

The speaker, Phil Blunsdon, discusses the future of AI, particularly the evolution of language modeling. He provides an overview of the large language modeling space, including research and commercial developments. The talk highlights the transition from base language models to more sophisticated, usable models and the importance of integrating search capabilities into AI systems.

30:08

📚 The Role of Language Modeling in AI

Phil Blunsdon formally defines language modeling as assigning a probability distribution to utterances, rather than simply predicting the next word. He explains the historical development of language models and their foundational role in applications like machine translation and speech recognition. The talk emphasizes the evolution of these models and their increasing sophistication.

35:10

🛠️ The Technicalities of Training Large Language Models

The complexities and costs associated with training large language models are discussed. The speaker outlines the computational resources required and the challenges in managing training processes. He also touches on the shift in focus from model size to deployment costs and the emergence of specialized models for different tasks.

40:11

🔬 Evaluation and Improvement of Language Models

Maria Liakata discusses the challenges faced by pre-trained language models, such as issues with inference, complex semantic similarity, and summarization of long documents. She highlights the need for knowledge enhancement and linguistic information to improve model performance. The talk also addresses the importance of evaluation in understanding model capabilities and the potential for academia to contribute to AI advancements.

Mindmap

Keywords

💡Machine Learning

Machine learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming. In the context of the video, machine learning is primarily driven by progress in neural networks, which have shown significant advancements in the past decade. The script mentions that machine learning is a broad field with various techniques, but it is the neural network-based techniques that have particularly taken off.

💡Neural Networks

Neural networks are computing systems inspired by the human brain that are capable of recognizing patterns and processing complex data. The concept dates back to the 1940s, but as the script explains, it has been over the last decade that these networks have become viable for large-scale problems, primarily due to scientific advances, the availability of big data, and increased compute power.

💡Big Data

Big data refers to the large volume of data that is being generated and collected from various sources. In the script, it is mentioned as one of the three drivers behind the progress in AI, emphasizing the importance of having vast amounts of data to train machine learning algorithms effectively.

💡Compute Power

Compute power is the ability of a system to perform complex calculations and process data quickly. The script discusses how the progress in AI has been significantly tied to the increase in compute power, which is necessary for training large neural networks on big data sets.

💡Foundation Models

Foundation models, as introduced in the script, are large-scale AI models that serve as tools upon which other AI applications can be built. They are not the foundation of AI itself but rather a new class of AI systems that have been released progressively, with Chat GPT being one of the most recent examples.

💡Large Language Models (LLMs)

Large language models are AI systems that can process and generate human-like text based on the input they receive. The script highlights LLMs as the most prominent tools in the current AI landscape, capable of completion from prompts and demonstrating impressive capabilities in natural language processing tasks.

💡Transformer Architectures

Transformer architectures are a type of neural network architecture that has become the standard for processing sequential data such as natural language. The script discusses the breakthrough that came with Transformers in 2017, which introduced innovations like positional encodings and attention mechanisms, making it possible for systems to focus on relevant parts of the text.

💡Natural Language Processing (NLP)

Natural language processing is a field of AI that deals with the interaction between computers and human language. In the script, NLP is central to the capabilities of large language models, which are good at tasks related to language, such as text generation, summarization, and answering questions.

💡Hype

The term 'hype' in the script refers to the inflated interest or publicity given to a particular subject, in this case, AI and machine learning. The speaker aims to get to the bottom of what constitutes hype and what represents genuine progress in the field of AI.

💡Symbolic AI

Symbolic AI, as mentioned in the script, is an approach to AI that focuses on knowledge representation and reasoning. It is contrasted with the data-driven approach of big AI, where the script suggests that intelligence is seen as a problem of data rather than knowledge.

💡Turing Test

The Turing test is a measure of a machine's ability to exhibit intelligent behavior that is indistinguishable from that of a human. The script suggests that large language models have quietly passed the Turing test in recent years, indicating that they can generate human-like responses.

💡HALLUCINATIONS

In the context of the script, 'hallucinations' refer to the instances where AI models provide incorrect or made-up information in response to a query. This is a significant issue with large language models, as they can confidently generate plausible but factually incorrect statements.

💡Bias

Bias in AI models, as discussed in the script, refers to the models' tendency to reflect and perpetuate the biases present in their training data. This is a critical issue, as it can lead to unfair or discriminatory outcomes in AI applications.

💡Sovereign Capability

Sovereign capability in the script refers to the strategic ability of a nation to control and develop key technologies independently. The speaker argues for the importance of the UK developing its own sovereign capability in AI technologies, including foundation models.

💡Benchmarking

Benchmarking in the context of the script means the process of evaluating and understanding the capabilities and limitations of AI models. It is presented as a crucial part of the work on foundation models to determine what they can reliably do and what they cannot.

💡Search and Retrieval

Search and retrieval is a level in the hierarchy of language modeling mentioned in the script. It involves integrating search capabilities into AI models, allowing them to access and utilize current data from the internet to provide more accurate and up-to-date responses.

💡Evaluation

Evaluation in the script refers to the challenges and methods of assessing the performance of AI models. It is highlighted as a critical area that requires more research, especially with the commercial deployment of large language models and the need to compare different models effectively.

💡Knowledge Enhancement

Knowledge enhancement is the process of incorporating additional knowledge into pre-trained language models to improve their performance on specific tasks or to address certain limitations. The script discusses various techniques for knowledge enhancement, such as knowledge fusion and the use of topic models.

💡Semantic Similarity

Semantic similarity is a concept that refers to the degree of meaning shared between two pieces of text. The script discusses the challenges that pre-trained language models face in accurately detecting semantic similarity, especially in niche domains or with complex semantic relationships.

💡Cross-Document Domain Reference Resolution

Cross-document domain reference resolution is the task of identifying and linking mentions of the same concepts across different documents from various domains. The script highlights this as a challenging task for pre-trained language models due to differences in vocabulary and context across domains.

Highlights

Mike Wooldridge, a professor at the University of Oxford, discusses the rapid progress in machine learning, primarily driven by advancements in neural AI over the past decade.

The importance of data and compute power in making machine learning viable for large-scale problems is emphasized, highlighting the age of big AI.

Foundation models, large neural networks requiring vast training data and compute power, are introduced as tools for building AI applications.

The concept of intelligence as a problem of data is presented, contrasting with the symbolic AI view that intelligence is a problem of knowledge.

Large language models are showcased for their ability to generate high-quality text, answer questions, and summarize content, among other language-based tasks.

The limitations of large language models in physical world tasks, planning, problem-solving, and arithmetic are discussed.

The Transformer architecture, which introduced innovations like positional encodings and attention mechanisms, is key to the capabilities of modern large language models.

The potential and current challenges of bias in AI models due to the ingestion of biased and toxic data from the internet are highlighted.

The role of foundation models in the future of AI and the anticipation of advancements with the upcoming release of GPT-4.

The viral nature of AI technologies like chat GPT and their impact on the tech sector, prompting creative applications and a surge of activity.

Language models are described as assigning probabilities to sentences, with applications in machine translation and speech recognition.

The evolution of language models from statistical tools to deep learning and the Transformer model, which has enabled their scalability and power.

The emergence of a technology stack in language modeling, with different layers for base models, command models, chat models, and search retrieval models.

The high computational cost of training and deploying large language models, and the challenges of monetizing these technologies.

The need for better evaluation methods for language models to compare their capabilities and decide which is best for different applications.

The discussion around the challenges faced by pre-trained language models, including issues with factuality, biases, and inference.

Strategies for knowledge enhancement in PLMs, such as incorporating topic models and linguistic information to improve performance on semantic similarity tasks.

The importance of academic researchers having access to large PLMs to scrutinize, intervene, and enhance their architecture for ongoing research and development.

Transcripts

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