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  Avoid BigTech Cook Venison Think About AI

Think About “AI”

When people talk about “Artificial Intelligence” today, in 2025, they are probably talking about neural networks. A neural network is actually nothing like the network of biological neurons you’d find in an animal such as a fruit fly but rather a mathematical black box function running on a computer which takes some input and produces some output in response. Even more specifically when “AI” is mentioned today what’s being referred to is probably a chatbot which uses a Large Language Model (LLM) such as ChatGPT from OpenAI. A great way to understand LLMs is to watch this video by Andrej Karpathy: Deep Dive into LLMs like ChatGPT

Once a deep learning model is “trained” (programmed more like - see video link above) on its raw data input it works very fast, like our “lizard brain”, but nothing like our conscious brain. We aren’t really aware of our lizard brain and, for example, humans walk without thinking. Like our lizard brain a neural net can respond immediately but does not think in the way we use that word when we talk about human thought.

Neural nets are very useful systems and can do things that humans struggle to do well. For example neural nets are good for:

• Playing chess or other games like go.

• Detecting faults on a railway line by listening to the trains running over the tracks.

• Finding tumours in scans.

• Summarising and organising tens of thousands of Chinese government policy documents.

• Flying a coordinated swarm of drones.

• Use sensors to consider windspeed, pinpoint targets and eliminate them from the battlefield.

In the examples above only Chinese government policy analysis would use an LLM. State of the art AI isn’t blithering LLMs, it’s in Precise Autonomous Mass (PAM) weapons like Helsing drones. See also this video from 14:00 and this one also from 14:00.

Though neural nets are very good for some specific tasks they are no good for any sort of art because all they are able to do is plaigerise and average out the data they have been trained on - see part one of this video from Pillar of Garbage: Better a Pig than a Fascist

I don’t mean to diminish the utility of neural networks and LLMs. They are useful for all sorts of specific tasks. An LLM which is trained by a trustworthy organisation on data of interest would be useful. I imagine generative models would be really useful for fuzzing cyber attacks. Generative models which are well trained on software development languages and good code examples are useful as coding assistants. Such tools can regenerate simple apps similar to what they have ingested and can produce code snippets. They can make weak devs better and might help good devs be a bit more efficient. However, I don’t think an LLM will ever be able to work on an existing codebase or generate novel, non-trivial, secure, maintainable and efficient systems without expert humans in control, as discussed in this fun video from ThePrimeTime: Its Finally Over For Devs (again, fr fr ong)

In today’s AI discussion actual intelligence, as used in English until 2020, is referred to as Artificial General Intelligence, AGI. AI salesmen say it’s round the corner and the models simply need to be bigger to be better but adding more words and more “training” to an LLM will never make it intelligent. An LLM has no logical inference, no model of the universe, no perception and no self. Without any of those aspects there can be no actual intelligence.

A compter program running without senses can never be intelligent but today’s robots are an insect-like start. Intelligence involves continual learning about the world through sensory systems and about the self within a model of the world. For humans dreaming is a part of re-programming and learning. When people lose the ability to sleep their brains go haywire and they go insane – any KGB victim or parent of a young baby knows this. Maybe robots will dream and not just about electric sheep.

A truly intelligent robot, in addition to being able to preceive the world and itself needs to be able to reason logically on the fly about some new situation. It must also be able to learn from experience and not remain a simple static hard-wired reflex action system devoid of logical reasoning. A useful robot should be able to operate day to day without being connected to a data centre. Ideally it would be able to learn without a remote connection but there could be some additional learning from other robots via a remote connection. Given all this it seems to me we’re still a long way from having the sort of hardware and software needed to build intelligence.

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