Podcast Summary
AI memorization vs. intelligence: Large language models, while impressive in mimicking human speech and behavior, lack the ability to abstract and reason beyond what they have learned, and are primarily memorizing and interpolating information.
While large language models, such as those developed using software libraries like Keras, have made significant strides in mimicking human speech and behavior, they are not truly intelligent in the conventional sense. Francois Chollet, a deep learning researcher at Google, argues that these models have merely memorized a vast amount of information and can interpolate between it, allowing them to perform well on various intelligence tests. However, they lack the ability to abstract and reason beyond what they have learned. Chollet's perspective is not unique, but it is a reminder that while large language models are impressive, they are not yet capable of true intelligence. The Archathon competition, which focuses on logic puzzles not already in the training data, aims to push researchers to develop AI systems that can go beyond memorization and interpolation. The debate about the definition and achievement of intelligence in AI is ongoing, but it is clear that significant progress has been made in the connectionist tradition of machine learning, and the field continues to evolve.
Transformer architectures in deep learning: Transformers surpassed RNNs for sequence processing but have limitations in modeling complex situations and providing exact quantitative answers, requiring refinement using symbolic search or human intervention.
The advancements in deep learning have been marked by significant improvements in image and sequence processing, driven by the development of new GPU-based models and architectures. One of the most notable shifts was the rise of transformer architectures, which surpassed the capabilities of earlier recurrent neural networks for sequence processing. The transformers were trained on vast amounts of data, often collected and annotated by thousands of people, enabling them to prefer the correct answers across a wide range of queries. However, these models have limitations. They operate by mapping queries onto a high-dimensional curve, interpolating across data points but unable to model complex situations or understand queries in quantitative terms. As a result, they can provide directionally accurate answers but are not reliable for exact answers, especially for quantitative problems. It's essential to use these models as a stepping stone and refine the answers using a symbolic search system or human intervention. Overall, the progress in deep learning can be understood as a shift from models where the programmer builds in a structure to models where the model learns a structure from data, with the intelligence residing in the model's ability to learn rather than in the programmer.
AI limitations and creativity: AI's curve fitting and symbolic approaches have limitations when it comes to true creativity, as they are both limited to interpolations between existing data points and cannot create new ideas. New methods that can operate in a more unconstrained search space are needed to achieve true creativity.
Both curve fitting and symbolic approaches in AI have their limitations when it comes to true creativity. Curve fitting, which is a common method for learning patterns from data, is limited to interpolations between existing data points and cannot create anything new. On the other hand, symbolic approaches, which rely on a predefined search space, are limited by the programmer's imagination and ability to anticipate all possible scenarios. To achieve true creativity, an algorithm needs to operate in a relatively unconstrained search space with an open-ended search process. Genetic algorithms, for example, can demonstrate creativity by generating new combinations and mutations, leading to inventions that humans could not anticipate. LLMs, which have received much attention lately, work by mapping words or tokens into a vector space and computing pairwise distances between them. The idea is that the distance between points represents a semantically similar area. While there is a connection between this approach and the way the brain learns, the result is a high-dimensional manifold that is limited to interpolations between existing data points and cannot create truly novel ideas. To summarize, the limitations of current AI approaches, whether it's curve fitting or symbolic, highlight the need for new methods that can operate in a more unconstrained search space and demonstrate true creativity.
Large Language Models and Manifolds: Large Language Models (LLMs) function by organizing information in a high-dimensional manifold, enabling efficient encoding of semantically rich relationships and discovery of useful semantic programs. However, they are limited in their ability to compose different programs and can only generate results based on memorized patterns from training data.
Large language models (LLMs) function by organizing information in a high-dimensional space called a manifold. This manifold is differentiable and smooth, allowing for efficient and scalable fitting of curves and encoding of semantically rich relationships between tokens. The organization of tokens on this manifold results in the discovery of useful semantic programs, or patterns of data transformation. These programs help compress the data and express it in a more concise fashion. Essentially, LLMs are vast stores of vector functions, or programs, which can interpolate between different transformations. These vector functions enable the encoding of semantically meaningful relationships and can even transform the style of text, such as turning a paragraph into poetry. The magic of deep learning lies in expressing relationships between things as a distance function in vector space and fitting curves to a wide range of data. However, despite the large size and complexity of these models, they are limited in their ability to compose different programs and can only generate results based on patterns they have memorized from their training data. This makes them sensitive to the way queries are phrased and prone to failure when presented with unfamiliar information. Data annotation and prompt engineering are crucial for improving the performance of LLMs by teaching them new patterns and refining their understanding of existing ones.
Large Language Models: Large Language Models can generate human-like responses but lack general intelligence and human intuition, and their ability to handle novelty is limited.
Large Language Models (LLMs) can generate human-like responses by interpolating between known data points and creating new responses based on patterns they've learned. However, they don't have a symbolic model of the world or general intelligence. Instead, they work with models of world and semantic space, which can be interpolated and merged to create new responses. These models are not as generalizable as human intuition and can only handle novelty to a certain extent. While LLMs can do active inference and generate new things, it's data inefficient and they lack the ability to truly adapt and learn new skills like humans can. So, while LLMs have some level of intelligence, they are vastly less intuitive and adaptable than humans. It's important to remember that LLMs are tools that can be useful for specific tasks, but they don't possess human-like understanding or consciousness.
LLM limitations: Large Language Models excel at memorization but lack the ability to understand concepts from first principles and struggle with novel problems
While Large Language Models (LLMs) excel at memorization and can mimic human-like responses, they lack the ability to understand concepts from first principles and struggle with novel problems. Schools often focus on memorization, but true intelligence requires understanding and the ability to solve novel problems. The Arc-AGI benchmark, which includes unique, never-before-seen puzzles, highlights this limitation of LLMs. The competition, which incentivizes new ideas, shows that LLMs cannot adapt to novelty in the way that human intelligence can. To test true intelligence, we need to move beyond memorization and focus on problems that require understanding and the ability to adapt to novel situations.
Large Language Models and General Intelligence: Large Language Models are not truly intelligent beings, they function as interpretive databases, lacking the ability to reason, plan, adapt, or invent, and the idea of them being on the path to Human-Level General Intelligence is misguided.
Large Language Models (LLMs) are not truly intelligent beings as they lack the ability to reason or plan. Instead, they function as interpretive databases of programs, memorizing and reapplying patterns. LLMs cannot adapt to new situations or synthesize new programs on the fly, which is a key aspect of general intelligence. The idea that LLMs are on the path to Human-Level General Intelligence (HGI) based on their current performance levels is misguided. The field is still in the process of understanding how LLMs work and interpreting their behavior. While there have been some insights, there is no evidence that LLMs can synthesize new programs or possess the ability to invent. The difference between LLMs and general intelligence lies in the programmer's ability to adapt and invent, while LLMs are limited to their stored programs. The focus should be on improving the quality of training data and better curation rather than increasing the scale.
LLMs and human intelligence: LLMs mimic human-like intelligence through memorization but lack the ability to truly learn and create beyond their extrapolated knowledge. Human intelligence and symbolic search systems are needed for invention.
Current large language models (LLMs) excel at mimicking human-like intelligence through memorization but lack the ability to truly learn and create beyond their extrapolated knowledge. This limitation is akin to a GitHub, which stores and retrieves patterns, but cannot truly understand or invent new concepts. LLMs struggle with novel problems that fall outside their memorized solutions, and while they can be used as memory extensions for scientists, they do not replace the need for human intelligence and creativity. The dream of having an AI program a smarter AI is not feasible with current LLMs, as they are limited to interpolating solutions from their trained data. Instead, the focus should be on combining LLMs with human intelligence and symbolic search systems to create hybrid systems capable of invention.
AI takeover: The human brain's complexity makes it unlikely for AI to take over the world without new ideas and inventions, and creating dangerous AI requires more than just intelligence, including autonomy, goal setting, and a value system.
Relying on brute force methods or algorithms to discover new technologies, such as a General Intelligence (GI), is not effective. The human brain is incredibly complex, and we don't fully understand how it works. Therefore, we cannot assign a large probability to the idea that AI will take over the world, as we need new ideas and inventions to create such technology. Intelligence is just a conversion ratio between the information we have and our ability to operate in novel situations. However, creating dangerous AI requires more than just intelligence; it requires autonomy, goal setting, and a value system. These elements should be approached with caution and deliberation. Furthermore, there is a lot of positive potential in developing deep learning models and large language models. These tools should be accessible to everyone, not just a fixed set of companies. Keras, a deep learning library, is designed to be accessible and approachable, allowing software developers to tackle their own problems using these technologies. It's essential to remember that intelligence is just a tool and that the potential risks associated with it depend on how it is engineered and used.
Large Language Models: Large Language Models can generate new content, answer questions, and automate tasks, but they should not be relied upon for decision-making or important content without human oversight. They are accessible and easy to use with free GPU services or personal computers.
Large Language Models (LLMs) can be trained and used by individuals or businesses to generate new content, answer questions, or even automate specific tasks. For instance, using models like the JAMA 8 billion model, one can generate new podcast transcripts or train an LLM on all the works of a philosopher like Daniel Dennett to answer questions about his work. This not only can be fun and educational but also has practical applications. Businesses can use LLMs to automate tasks, such as turning spreadsheet data into emails, or even develop their own private repository of programs tailored to their specific needs. However, it's important to note that while LLMs can be powerful tools, they should not be relied upon for decision-making or generating important content, such as emails, without human oversight. Instead, they can be used as a shortcut towards the general area of interest and help speed up tasks or fix typos. The ease of getting started with LLMs is also notable, as one can use free GPU notebook services like Colab or use their own MacBook Pro with a GPU to begin training models. With the increasing accessibility and capabilities of LLMs, their impact on our lives is transformative and will continue to grow.
