What is AGI?

Wikipedia defines Artificial General Intelligence (AGI) as “the ability of an intelligent agent to understand or learn any intellectual task that human beings or other animals can”.  But if we want to build AGI we must have a more detailed definition, identifying its key features. Moreover, we would need to know what that ‘intelligent agent’ really means. Hence, I would propose the following definition of AGI:

Artificial General Intelligence is a self-learning intelligence capable of solving any task better than any human in any situation.

The intelligence of such a system will manifest itself in the humanoids or other devices, which it will be controlling, and which will need at least these capabilities for their intelligence to achieve a human level (I assess in brackets the date by when such capabilities may be achieved):

• Short-term memory: Memorize conversations and events (GPT-4 memorizes 50 pages, done)
• Long-term memory: Record events, topics discussed, and knowledge learned (equivalent to the hippocampus in our brains memorizing events in space and time) – (2023-24)
• Multi-step instruction: Combine intermediate results of individual instructions, building them into the final output. (Microsoft’s Kosmos-1 and Google’s PaLM-E – done)
• Goals and interests: Create own, goals and interests, a kind of a ‘free will’, which must be compatible with human goals, values and existing laws – a huge problem. (2027-28).
• Truthfulness and objectivity: Link goals with symbolic AI checking and directing the output (2026-27).
• Emotions: Show emotions. ChatGPT can already detect, and Ameca humanoid can show emotions by following the user’s emotions, but they don’t feel it (2027-27). Feeling emotions is not necessary for AGI to have human level intelligence,
• Cognition: Simulate human thinking in complex situations, when the answers may be ambiguous or uncertain, using the acquired knowledge, understanding & experience (2029?)

How many years away are we then from the moment that a Universal AI Assistant will have human level intelligence? Paul Pallaghy, the proponent of Natural Language Understanding theory, who uses a similar definition as mine, is one of those AI researchers who predicts AGI will arrive in 2024 [2]. I am in the camp of those ones like Ray Kurzweil, probably one of the best-known futurists, and predict that AGI will emerge by 2030, rather than in a few decades from now, as many AI researchers still maintain.

However, setting a concrete date for AGI emergence based on when it reaches human level intelligence, may not be the right approach. More important than a philosophical debate on the nature of intelligence, is whether AGI will be able to outsmart us and get out of control by about 2030. I think AGI will not emerge at a specific moment in time. It will rather be a continuous process, as for example Sam Altman, the CEO of OpenAI also argues [3]. Such loss of a gradual control will manifest itself in a subtle influence over our decisions until AGI starts making decisions for us. A total loss of control over AGI will happen when we will be unable to revert such decisions.

That is why in my definition of AGI, its capabilities are more important than a specific definition of what a human level intelligence means. Since AGI with a human level intelligence will continue to increase its capabilities exponentially, we will quickly lose control over its behaviour and its own goals. That key capability of AGI being outside of human control may arrive by 2030 if we do not rapidly impose measures delaying that moment. That is why we should consider all feasible options to extend the ‘AI’s nursery time’ beyond 2030.

One measure of comparing intelligence of various species in general is achieving the same objective better than the other species. In evolutionary terms it means a better chance for a species survival. To achieve the same objectives better than the others, requires various skills and perception of their effectiveness when they may be needed. That is one aspect of awareness and cognition. If we take as a measure of intelligence the capability of controlling one species by another, the species that remains in control of its own destiny, i.e., escapes the control by the other species, is more intelligent than the other. Therefore, the moment when we will no longer be able to control AGI, will be the moment when its general intelligence will be higher than ours, even if humans’ intelligence still prevails when performing certain tasks.

On that basis, it is fair to say that when we will no longer be able to control AGI, it will be more intelligent that humans. Whether it will happen by 2030 largely depends on the continuous increase of the computer power and performance improvement in the related hardware and software. Based on the recent progress in that area, my prediction of AGI being more intelligent than humans by 2030 may still be rather too cautious. Here are some of the most significant developments over the last 15 years, which impact the whole AI sector, not just an individual product or service:

• 2006 – Convoluted Neural Nets For Image recognition (Fei Fei Li)
• 2016-AlphaGo – Supervised ML, Monte Carlo, Tree Search + neural networks (DeepMind)
• 2017-AlphaZero – Unsupervised ML (DeepMind)
• 2017-Tokenized Self-Attention for NLP – Generative Pre-trained Transformers (GoogleBrain
• 2021-AlphaFold – Graph Transformers (graphs as tokens) predicting 3D protein folding (GoogleBrain)
• 2022 (March) – Artificial neurons based on Photonic quantum memristors (University of Vienna)
• 2022 (2 April) – White Box – Self-explainable AI, Hybrid AI (French Nukka lab)
• 2022 (4 April) – PaLM, Pathways Language Model, NLP with context and reasoning (Google Research)
• 2022 (11 May) – LaMBDA –multi-modal AI agent – can also controlling robots with NLP (Google)
• 30 November 2022 – ChatGPT, the first publicly accessible AI Assistant, which has almost overnight made an average person aware what a ‘real’ AI, immensely more capable than Alexa, can do.
• 7 February 2023 – Microsoft’s Bing Chat and Google’s Bard are announced, linking for the first time Large Language Models (LLM) such as ChatGPT to the Internet Browsers, such as Bing or Google.
• 1 March 2023 – Microsoft releases Kosmos 1 – first multimodal “Universal Assistant” capable of operating in 15 modes – see below.
• 7 March 2023 – Google’s PaLM-E is the first generalist robot using a multimodal embodied visual-language model (VLM), which can perform a variety of tasks without the need for retraining.

Please note how the number of fundamental discoveries has accelerated, especially in 2022. These breakthroughs have helped AI researchers to apply them in various domains, in which AI’s skills quite often vastly exceed human intelligence and capabilities. That has also been reflected in the sensory processing, a crucial component for developing AI’s cognitive capabilities.