AI Won't Be AGI, Until It Can At Least Do This (plus 6 key ways LLMs are being upgraded)
AI ExplainedKey Moments
Current LLMs lack abstract reasoning; advancements focus on compositionality, verifiers, and active inference.
Key Insights
Current LLMs, like GPT-4, struggle with abstract reasoning tasks not present in their training data, indicating they are not AGI.
Naive scaling of model parameters and data alone is insufficient to achieve true general intelligence.
Advancements in LLMs include improved compositionality, better program retrieval via verifiers and Monte Carlo Tree Search, and test-time fine-tuning (active inference).
Combining LLMs with traditional symbolic systems can enhance planning and reasoning capabilities, overcoming individual limitations.
Tacit knowledge, the unwritten reasoning and intuition held by experts, holds significant potential for AI development but is difficult to capture.
The future of AI progress likely lies in a combination of diverse approaches rather than a single breakthrough.
THE LIMITATIONS OF CURRENT LARGE LANGUAGE MODELS (LLMS)
Current large language models (LLMs) demonstrate impressive capabilities but lack true artificial general intelligence (AGI). A key limitation is their inability to perform abstract reasoning on novel problems, as highlighted by the ARC AGI challenge. Unlike humans, LLMs cannot generalize effectively to tasks outside their training data. This means they often fail to solve problems they haven't explicitly encountered before, relying on memorization of past reasoning chains rather than genuine deductive capabilities, underscoring their non-general intelligence.
OVERPROMISING AND THE AI LANDSCAPE CHALLENGES
The current AI landscape is marred by overpromising and underdelivering, creating a perception of hype. Examples include initial exaggerated claims for models like Gemini and the ongoing hallucinations in features like Apple Intelligence. Furthermore, the proliferation of AI-generated 'slop' on platforms like LinkedIn, while potentially useful for individuals, contributes to a degraded online environment. Concerns also extend to privacy issues with features like Microsoft's Recall and the difficulty in distinguishing between human and AI-generated content in academic and professional spheres.
BEYOND LLMS: DIVERSE APPLICATIONS OF NEURAL NETWORKS
While LLMs are a major focus, other neural network architectures are also making significant strides. Generative Adversarial Networks (GANs) are being used to predict chemical effects on mice, potentially reducing animal testing, and to create realistic simulations of neural activity. Convolutional Neural Networks (CNNs) are proving vital in medical diagnostics, such as the Brainomix eStroke system, enabling faster stroke diagnoses and improving patient recovery rates. These examples showcase AI's broader impact beyond text-based models.
ADDRESSING REASONING GAPS THROUGH COMPOSITIONALITY AND PROGRAM RECALL
Researchers are actively working to overcome LLMs' reasoning deficiencies. One promising avenue is compositionality, where models learn to combine existing reasoning components into novel solutions, as demonstrated in studies with smaller Transformer models. Another critical area is improving the retrieval of 'programs' or reasoning chains embedded within LLMs. Techniques like 'let's verify step-by-step' and automated reward modeling, utilizing verifiers and Monte Carlo Tree Search, help models identify and utilize correct reasoning paths, significantly boosting performance on tasks like mathematical problem-solving.
ACTIVE INFERENCE AND HYBRID APPROACHES FOR ENHANCED REASONING
Active inference, or test-time fine-tuning, is another key strategy that allows LLMs to adapt and learn on-the-fly for specific tasks. By fine-tuning the model with augmented examples, it can better focus its parameters on the problem at hand, achieving notable improvements even on abstract reasoning challenges like the ARC AGI prize. Additionally, hybrid approaches combining LLMs with traditional symbolic systems offer a powerful synergy. In this model, LLMs act as idea generators, proposing plans, while symbolic systems rigorously verify and refine them, leading to significantly more robust reasoning capabilities.
THE POTENTIAL AND CHALLENGES OF TACIT KNOWLEDGE
A significant, yet difficult to harness, source of AI advancement lies in tacit knowledge – the unwritten intuition, methodologies, and trial-and-error processes that human experts possess. This implicit understanding, often shared through conversations and lectures rather than publications, represents precious data for AI development. Efforts to explicitly capture this knowledge through detailed documentation and by ingesting vast amounts of human-generated content, like YouTube videos, aim to imbue AI with deeper reasoning skills. However, this approach is slow and relies heavily on human input.
THE COMPLEX REALITY OF AI PROGRESS
The path towards more capable AI, potentially leading to AGI, is unlikely to be a single, dramatic breakthrough. Instead, it will likely involve a combination of diverse approaches, including enhanced compositionality, improved program retrieval, active inference, and hybrid symbolic-neural systems. While current LLMs have significant limitations, especially in abstract reasoning, these ongoing developments suggest that AI is neither all hype nor imminently at the AGI stage. The complexity of human intelligence means that progress will be multifaceted and iterative.
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Common Questions
Current LLMs often fail abstract reasoning challenges because these specific patterns were not present in their training data. They lack the general intelligence to extrapolate from learned data to novel situations, meaning they cannot reason their way to a solution if it's not explicitly memorized.
Topics
Mentioned in this video
An abstract reasoning challenge designed to test the limits of current language models, with a significant prize pool for successful solvers.
CTO of OpenAI, quoted on the current capabilities of their models not being drastically ahead of publicly available ones.
The ability of models to piece together known concepts to understand or generate more complex ones, presented as a key pathway for improving LLMs.
Associated with research on verifiers and automated methods to improve LLM mathematical reasoning by identifying faulty steps.
Lead author of the Dr. Eureka paper, discussed the use of simulators as external verifiers for LLM outputs, turning potential hallucinations into a strength.
A domain used to test LLMs' planning capabilities, highlighting their struggles with generating coherent plans without symbolic system assistance.
A feature that raises privacy concerns due to its ability to analyze screenshots taken of a user's desktop.
Utilizes convolutional neural networks for image analysis to speed up stroke diagnosis in the NHS, tripling patient recovery rates.
An LLM skeptic who co-authored a paper on LLMs assisting with planning by acting as idea generators, combined with symbolic systems for verification.
Used in a Nature study to predict the effects of untested chemicals on mice, showing promise in reducing animal testing.
Led research on test-time fine-tuning (active inference) for LLMs, achieving significant improvements on abstract reasoning tasks.
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