Research Publication – Artificial Intelligence – Causality is all you need – Radio Free Mobile

May 16th 2024: RFM deepens its coverage of generative AI with the publication of Artificial Intelligence – Causality is all you need. 

RFM research subscribers will receive their copy by email. 

It turns out that AI needs a lot more than attention to create hyper-intelligent machines. RFM believes that the missing link is causality which means that the machines still have no understanding of anything that they do. This prevents the development of any form of AGI and there is no sign that the causality problem will be fixed anytime soon. However, even without causality, there are many use cases for AI-enabled by the advances in language performance and the ability to make sense of unstructured data. Unfortunately, this is not enough to meet the hype that surrounds AI and so some form of correction is needed to bring expectations and reality back into balance.

• What AI is: AI describes a range of technologies that enable machines to make decisions. They range from statistics at one end, through rules-based software, deep learning and now, generative AI. Generative AI is a subset of AI and is defined as AI that is capable of generating content such as words, images and now, video.
• What AI is not: Large Language Models (LLMs) have enabled machines to converse in natural language which has led to an extraordinary increase in anthropomorphism. This is the attribution of human characteristics to non-human objects which in this case are statistics-based algorithms. It is here where expectations for AI and reality begin to diverge.
• Causality: is by far the biggest limitation of generative AI as these models have no causal understanding of what it is that they are doing. This is what causes the machines to invent facts, make simple mistakes and remain unaware that they are doing so.
• Reasoning: this is a hot area of debate where owners of models claim they can reason, and the sceptics disagree. Reasoning is crucial because it will be the first sign of LLMs being able to understand causality. The balance of empirical evidence suggests that the machines remain unable to reason and there is no evidence to suggest that it will be solved anytime soon.
• Use cases: despite the limitations, RFM sees many use cases given that generative AI represents a large step forward in both the ability to use natural language and data characterisation, storage and retrieval. These use cases do not replace humans but make them more productive meaning that the workforce is not going to be replaced although some adaption will be needed in many industries.
• AI bubble: There is little doubt that valuations and expectations are too high. The flood of capital into generative AI has been driven by intense public excitement. The result is becoming a flood of supply of LLM services all of which are roughly equivalent in terms of performance meaning competition and falling prices. It is this that RFM expects will trigger the reset to reality. It is important to note that this reset will not be nearly as harsh as autonomous driving because generative AI has products now where autonomous driving still has nothing.