AI Reasoning

AI is on an evolutionary exponential growth curve, now moving from Artificial Narrow Intelligence (ANI) to Artificial General Intelligence (AGI). The ability to reason is one important aspect of AGI. The current status of AI reasoning has seen significant advancements, but it's important to note that true human-level reasoning capabilities are still a work in progress.

Recent Advancements

OpenAI's latest model, known as o1 or Strawberry, represents a major step forward in AI reasoning capabilities. This model introduces improvements that bring us closer to AI reasoning by:

• Better handling complex tasks

• Reducing AI hallucination rates

• Dedicating more time to carefully analyzing AI prompts

• Breaking down problems into logical steps

• Enhancing contextual comprehension

• Improving originality and ambiguity handling

These advancements have led to impressive performances in various fields, including quantum physics, molecular biology, and mathematics, where o1 has sometimes outperformed human experts[2].

Limitations and Challenges

Despite these improvements, AI reasoning still faces several limitations:

• True Causal Understanding: Current AI models, including o1, still lack the ability to truly mimic human reasoning, which involves understanding cause and effect.

• Complex Problem-Solving: While AI has surpassed human performance on several benchmarks, it still trails behind on more complex tasks like competition-level mathematics and visual common sense reasoning.

• Self-Correction: The ability for AI to fix its mistakes in real-time remains a crucial challenge, especially for the development of autonomous AI robots and AI agents.

• Evaluation Difficulties: Current tests and benchmarks are often inadequate for assessing AI's true understanding and reasoning skills. Many existing benchmarks can be passed through statistical associations rather than genuine reasoning.

Future Directions

The field is rapidly evolving, with several key areas of focus:

• Causal AI: This is seen as a necessary next step, with predictions of significant growth and impact in the coming years[1].

• Inference-Time Compute: Researchers are exploring new ways to improve models' reasoning capabilities during inference, moving beyond pre-trained responses.

• Multi-Agent Systems: Multi AI agent systems may become more prevalent as a way to model reasoning and social learning processes.

• Improved Evaluation Methods: Researchers are working on developing more comprehensive and dynamic benchmarks to better assess AI reasoning capabilities.

The Path to Artificial General Intelligence: Yann LeCun's Vision for the Future

Artificial General Intelligence (AGI) has long been the subject of debate and speculation. While some predict its imminent arrival, others argue it's still decades away. Yann LeCun, VP & Chief AI Scientist at Meta and Silver Professor of Computer Science at New York University, has been vocal about the necessary steps to achieve AGI.

Currently, AI systems excel in narrow tasks, such as image recognition, natural language processing, or game playing. However, these systems lack the cognitive abilities and flexibility of human intelligence. LeCun attributes this limitation to the lack of a unified architecture that integrates multiple AI components. To overcome the limitations of current AI systems, LeCun advocates for the development of three essential technologies.

Self-Supervised Learning

Self-supervised learning is a type of unsupervised learning where the AI system learns from unlabeled data, generating its own supervision signal. This approach enables the system to discover patterns, relationships, and representations without human annotation. LeCun believes self-supervised learning is crucial for AGI, as it allows the system to learn from its environment and adapt to new situations.

World Models

World models are cognitive maps that enable AI systems to reason about the world, predict outcomes, and make decisions. These models should integrate multiple sources of information, such as perception, action, and prior knowledge. LeCun envisions world models that can:

• Represent complex relationships between objects and events

• Reason about causality and temporal dependencies

• Integrate symbolic and connectionist AI

Cognitive Architectures

Cognitive architectures provide a framework for integrating multiple AI components, such as perception, attention, memory, and decision-making. These architectures should enable the system to:

• Focus attention on relevant information

• Store and retrieve knowledge efficiently

• Reason and make decisions under uncertainty

Key Challenges and Open Research Questions

While LeCun's vision provides a roadmap for AGI, several challenges and open research questions remain:

• Scaling self-supervised learning: How can we scale self-supervised learning to complex, high-dimensional data?

• Integrating world models: How can we integrate multiple world models to achieve a unified representation of the world?

• Cognitive architecture design: What is the optimal design for a cognitive architecture that integrates multiple AI components?

Conclusion

Yann LeCun's vision for AGI emphasizes the importance of self-supervised learning, world models, and cognitive architectures. While significant challenges lie ahead, the development of these technologies brings us closer to achieving artificial general intelligence. As researchers continue to push the boundaries of AI, LeCun's prescription serves as a guiding framework for creating more intelligent, flexible, and human-like AI systems.

You can read more about Yann LeCun’s perspective in his interview with Time.

OpenAI, Sam Altman, and the Information Age

A few days ago I posted an article about The Accelerating Evolution of AI and a Shrinking Timeline to Radical Change. A few days after that, Sam Altman, the CEO of OpenAI, published a blog post entitled The Information Age. In his post, Altman says: “It is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there.” This is accumulating acknowledgment of the rapid exponential growth of AI.

Achieving Artificial Superintelligence (ASI) in that few thousand days timeframe means that Artificial General Intelligence (AGI) would arrive well before ASI on the AI exponential growth curve, as shown in the chart below. Already, the current AI growth stage, Artificial Narrow Intelligence (ANI), is demonstrating characteristics of AGI, evidence that AGI may arrive in just several hundred days. (See my recent article for details on the capabilities of the stages of AI growth.)

We’re in the Artificial Narrow Intelligence (ANI) phase very close to the AGI phase.

Many companies such as OpenAI, Meta, Google, Microsoft, X.ai, IBM, Anthropic, Perplexity, and Mistral are making rapid advances in AI. Applications, like those cited in Sam Altman’s post, are likely coming more quickly than many expect.

Artificial General Intelligence (AGI)

The definition of Artificial General Intelligence (AGI) contains elements such as:

• the ability of machines to learn and understand at a human level

• also referred to as strong AI

• achieving some form of consciousness

A Wide Range of Opinions on When AGI Can be Achieved

There are a wide range of predictions on when AGI will be achieved, from a single decade to many decades or longer. Reasons for this include:

• exponential growth in many areas of Machine Learning and AI may make an earlier date achievable

• promising areas might accelerate progress, including:

  • Artificial Neural Networks

  • Attention

  • Automated Machine Learning

  • Causal Embedding

  • Deep Learning

  • Deep Reasoning

  • Generative Adversarial Networks

  • Reinforcement Learning

  • Transformer Neural Networks

  • Unsupervised Learning

• an exact definition of AGI ‘human level’ and ‘consciousness’ is difficult to pin down; for example, should it include some harder to achieve aspects such as:

  • feelings and emotions

  • human fallibility

  • self awareness

One Survey of Opinions

One survey of AGI predictions is from Nick Bostrom and can be found here. Summary results for responses with a 50% confidence level include:

• the median at the year 2040

• the average at the year 2081

• the discrepancy between the median and average is due to some predictions extending out many decades to never

On the Road to AGI

As we progress along the road to AGI, there will be many advances in Machine Learning and AGI, for example:

• applications are expanding at a rapid rate

• accuracies are improving substantially

As we get closer to AGI, an exact date and definition may be less important than the wealth generated and benefits to society.