Important points
- Transformers primarily learn correlations rather than causation, which limits their ability to achieve true intelligence.
- Achieving AGI requires models that can move from learning correlations to understanding causal relationships.
- Large-scale language models generate text by predicting the next token based on probability distributions.
- The context provided in the prompt has a significant impact on the output of the language model.
- Language models operate on sparse matrices where many token combinations do not make sense.
- In-context learning allows LLMs to solve problems in real-time using examples.
- Domain-specific languages (DSLs) allow you to simplify complex database queries into natural language.
- In-context learning in LLM is similar to Bayesian updating, adjusting probabilities with new evidence.
- The debate between Bayesian and frequentist approaches influences the perception of new machine learning models.
- The Bayesian wind tunnel concept provides a controlled environment for testing machine learning architectures.
- Understanding how LLM works is important to effectively utilize the application.
- Moving from correlation to causation is a major hurdle in AI development.
- The relevance of context in LLM emphasizes the importance of quick selection.
- Sparse matrices in language models increase efficiency by filtering out irrelevant token combinations.
- Bayesian wind tunnels provide a new framework for evaluating machine learning models.
Guest introduction
Vishal Misra is a professor of computer science and electrical engineering and associate dean of computing and AI at Columbia University School of Engineering. He returns to the a16z podcast to discuss his latest research that reveals how LLM’s transformers update their predictions in an accurate and mathematically predictable way as they process new information. His research highlights the gap with AGI and emphasizes the need for continuous post-training learning and causal understanding over pattern matching.
About Transformers and LLM
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Transformers update predictions in a mathematically predictable way
— Vishal Misra
- LLM primarily learns correlations rather than cause-and-effect relationships, which limits its intelligence.
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Pattern matching is not intelligence. LLM learns correlation, not causation
— Vishal Misra
- Achieving AGI requires a model that can learn not only correlation but also causation.
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Reaching AGI requires the ability to continue learning after training
— Vishal Misra
- LLM generates text by constructing a probability distribution of the next token.
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When prompted, it will show you the distribution of what the next token will be.
— Vishal Misra
- Understanding how LLM works is important to effectively utilize the application.
The role of context in language models
- The behavior of the language model is influenced by the prior context provided in the prompt.
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The next line will look very different depending on whether you choose Composite or Shake
— Vishal Misra
- The relevance of context in LLM emphasizes the importance of quick selection.
- Language models operate on sparse matrices where many combinations of tokens are meaningless.
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Fortunately, any combination of these tokens is meaningless, so this matrix is very sparse.
— Vishal Misra
- Sparse matrices increase efficiency by filtering out irrelevant token combinations.
- The context provided can significantly change the output of a language model.
- It is essential to understand how language models generate text based on input prompts.
Contextual learning and real-time problem solving
- In-context learning allows LLMs to learn and solve problems in real time.
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Learning in context is about showing your LLM something it has never seen before
— Vishal Misra
- LLM processes and learns new information through examples.
- In-context learning is similar to Bayesian updating, adjusting probabilities with new evidence.
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LLM is doing something similar to Bayesian updating
— Vishal Misra
- This mechanism is important for understanding how LLM functions.
- Real-time problem solving in LLM is enabled by in-context learning.
- The ability to learn from examples demonstrates the adaptability of the LLM.
Domain-specific languages and data accessibility
- A domain-specific language (DSL) transforms natural language queries into a form that can be processed.
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We designed a DSL, a domain-specific language to translate queries about cricket statistics
— Vishal Misra
- DSLs simplify complex database queries into natural language.
- The creation of a DSL demonstrates the innovation of using AI for specific applications.
- Understanding the challenges of querying complex databases is essential.
- DSLs enhance user interaction with data by simplifying the query process.
- The development of DSLs highlights the role of AI in data accessibility.
- This approach provides technical solutions to common problems in data accessibility.
Bayesian updates and statistical approaches in AI
- In-context learning in language models is similar to Bayesian updating.
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You see something, you see new evidence, you update your beliefs about what’s going on.
— Vishal Misra
- Understanding Bayesian inference is important to understanding how LLM processes information.
- The difference between Bayesian and frequentist approaches affects the perception of AI models.
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There were Bayesian and frequentist camps in probability and machine learning.
— Vishal Misra
- The debate between these approaches influences the acceptance of new research.
- Bayesian updating provides a clear mechanism for in-context learning in LLM.
- This statistical concept combines established methodologies with modern AI processes.
Bayesian wind tunnel and model testing
- Bayesian wind tunnel concepts enable testing of machine learning architectures.
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We came up with the idea of a Bayesian wind tunnel.
— Vishal Misra
- This concept provides a controlled environment for evaluating models.
- This framework facilitates testing of architectures such as transformers, MAMBA, LSTM, and MLP.
- Understanding the concept of wind tunnels in aerospace can help you understand applications to AI.
- Bayesian wind tunnels provide a new framework for advancing machine learning.
- This approach is essential for evaluating and improving AI models.
- A controlled testing environment increases the reliability of model evaluation.
