Some months ago, I wrote in unflattering terms about artificial intelligence applications (AI apps) and large language models (LLMs), (see ‘Ancient models and stochastic parrots‘ on October 1st, 2025). My view is changing, probably as AI apps develop and my user skills improve. I have started using a couple of different free AI apps as research assistants in three ways. First, when I am writing administrative documents, such as a job description for a Coordinator of AI in Education, for which a job title was sufficient for the app to generate a first draft that only required light editing and tailoring to the specific context. Second, using a different AI app, to answer questions about phenomena which have allowed me to construct explanations for observations made of new and, or, complex systems – I could have delved into textbooks and monographs or searched research articles but AI does this much more quickly. The third way I have used AI apps is to identify gaps in knowledge that could be fruitful topics for research. This is a more difficult task because AI apps only know about stuff they can find on the internet in the form of language or text. Hence, I have to ask questions with answers that reveal something unknown or not understood. This is not straightforward because LLMs are fundamentally constrained by language. In ‘The Years’, Annie Ernaux wrote that ‘language will continue to put the world into words’. Yann LeCun, Meta’s former chief scientist, has suggested that to understand how the world works, a model would need to learn from videos and spatial data, not just language, and that without this type of learning human-level intelligence is impossible. He has set up a new company, Advanced Machine Intelligence Labs, to do just that. Language is used by people to describe the world from their perspective which might be inaccurate, incomplete or distorted and that can mislead LLMs. However, using AI apps we can also ‘distort’ videos of the world, so that machine intelligence will have to be based on direct observation of the real-world, which after all is the approach that science attempts to use.
Source:
Yann LeCun, Intelligence is really about learning. FT Weekend, 3-4 January 2026
Annie Ernaux, The Years, Fitzcarraldo Editions, London, 2018.
In her novel, ‘The Gap of Time’, Jeanette Winterson writes that Buddha and Jesus knew that when you had so much money that you could buy anything, everything, then worldly goods were worthless. Modern capitalism has generated a small number of owners of capital who have this much money. For the rest of us worldly goods are not valueless or worthless. Omnipresent advertising tends to create a desire in many of us for a lifestyle that is unattainable with our income, which can lead us to be dissatisfied and unhappy. Some surveys have suggested that engineers are amongst the happiest people [see ‘Choosing a career is like going shopping’ on June 17th, 2015]. Perhaps this is because we managed to do as Rutger Berger suggested in his 2025 BBC Reith Lecture, which is to measure our success in terms of our contribution to tackling humanity’s toughest problems. For many engineers, this is a key component of their role.
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