AGI - a concept in our labs, or a presence in our apps?

Artificial General Intelligence (AGI) has transitioned from a fringe concept to a mainstream topic of intense debate and speculation, particularly following the advent of models like ChatGPT. Ilya Sutskever, OpenAI’s co-founder, aptly described this shift in perception as AGI moving from a "dirty word" to a subject of serious consideration.

AGI has its enthusiasts and detractors. Enthusiasts envision a future where AGI resolves complex global challenges, from revolutionizing healthcare to mitigating climate change. Conversely, sceptics fear that AGI could pose existential threats to humanity.

This complexity of defining AGI stems from the broader ambiguity around the concept of "intelligence" itself.

• Intelligence in the context of Artificial Intelligence (AI) is broadly defined as the ability of machines to perform tasks that would typically require human intelligence. This includes various aspects such as learning, reasoning, problem-solving, perception, language understanding, and adaptation.
• Artificial General Intelligence (AGI) is a level of artificial intelligence where a machine has the capacity to understand, learn, and apply its intelligence to a wide range of problems, similar to the cognitive abilities of a human being. Unlike narrow or weak AI, which is designed to perform specific tasks or solve particular types of problems, AGI encompasses the broader, more generalized ability to handle any intellectual task that a human can.

I think that's a big and far-reaching concept: a generalized ability to handle any intellectual task that a human can.

The AI community is currently split on the extent to which current AI models, such as those based on large language models (LLMs), truly exhibit reasoning or merely pattern recognition.

On one side, the argument is that these models demonstrate a form of reasoning. They point to the model's ability to generate coherent and contextually relevant responses, solve complex problems, and even create content that appears to show a deep understanding of language and concepts. This perspective is bolstered by the fact that these models can successfully complete tasks that require a level of inference and logical thinking, suggesting a form of reasoning.

On the other hand, many in the AI community argue that what appears to be reasoning is actually sophisticated pattern matching. This viewpoint emphasizes that LLMs are trained on vast datasets from which they learn to predict the next most likely word or phrase in a sequence. Therefore, their outputs, while impressive, result from statistical correlations rather than a genuine understanding or reasoning process. According to this perspective, these models lack an understanding of the meaning or context in the way humans do, which is crucial for true reasoning.

A notable development is the use of LLMs in discovering new mathematical solutions. A recent example is DeepMind's breakthrough in solving the cap set problem. And we are all awaiting OpenAi's Q with bated breadth.

DeepMind's breakthrough in solving the cap set problem was announced around December 15, 2023. They used a large language model (LLM) named FunSearch for this purpose. The cap set problem is a challenging mathematical conundrum in extremal combinatorics, which essentially comes down to how many dots you can put down on a page while drawing lines between them, without three of them ever forming a straight line.

Public prediction markets estimate the arrival of AGI around 2031. Some prominent figures have offered specific predictions:

1. Demis Hassabis, the former chess prodigy and co-founder of DeepMind, suggests that AGI could be just a few years away, potentially within a decade.
2. Geoffrey Hinton, a renowned figure in deep learning, revised his earlier prediction of 30-50 years to a range of 5-20 years, though with a note of uncertainty.
3. Ray Kurzweil, a well-known futurist, predicted that by 2029, computers will have human-level intelligence.
4. Shane Legg, co-founder of Google's DeepMind, estimated a 50% chance of achieving AGI by 2028, a forecast he still holds.

The global response has seen governments attempting to regulate a technology that is not yet fully understood. It is tricky to write regulations for something we don't understand that may or may not arrive at a time we do not know. This approach could hinder innovation or create a false sense of security. Understanding AI's capabilities and impacts is essential before imposing regulatory frameworks.

The road to AGI is a tightrope walk between dazzling potential and mind-bending risks. We're not just talking about fancy chess bots anymore – we're talking about machines potentially equating and surpassing human intelligence in every domain.

But fretting in the corner won't get us anywhere. We need to embrace the challenge head-on and with open eyes. This isn't about slowing down progress; it's about navigating it responsibly. Let's build safeguards, establish ethical frameworks, and keep the conversation open from the labs to the living room.