> But it still feels like some key ingredient is missing.

Continuous training is the key ingredient. Humans can use existing knowledge and apply it to new scenarios, and so can most AI. But AI cannot permanently remember the result of its actions in the real world, and so its body of knowledge cannot expand.

Take a toddler and an oven. The toddler has no concept of what an oven is other than maybe that it smells nice. The toddler will touch the oven, notice that it experiences pain (because the oven is hot) and learn that oven = danger. Place a current AI in a droid toddler body? It will never learn and keep touching the oven as soon as the information of "oven = danger" is out of the context window.

For some cases this inability to learn is actually desirable. You don't want anyone and everyone to be able to train ChatGPT unsupervised, otherwise you get 4chan flooding it with offensive crap like they did to Tay [1], but for AI that physically interacts with the meatspace, constant evaluation and learning is all but mandatory if it is to safely interact with its surroundings. "Dumb" robots run regular calibration cycles for their limbs to make sure they are still aligned to compensate for random deviations, and so will AI robots.

[1] https://en.wikipedia.org/wiki/Tay_(chatbot)

This kind of context management is not that hard, even when building LLMs. Especially when you have huge windows like we do today. Look at how ChatGPT can remember things permanently after you said them once using a function call to edit the permanent memory section inside the context. You can also see that in Anthropic's latest post on Claude 4 where it learns to play Pokemon. The only remaining issue here is maybe how to diffuse explicit knowledge from the stored context into the weights. Andrej Karpathy wrote a good piece on this recently. But personally I believe this might not even be necessary if you can manage your context well enough and see it more like RAM while the LLM is the CPU. For your example you can then always just fetch such information from a permanent storage like a VDB and load it into context once you enter an area in the real world.

> Continuous training is the key ingredient. Humans can use existing knowledge and apply it to new scenarios, and so can most AI. But AI cannot permanently remember the result of its actions in the real world, and so its body of knowledge cannot expand.

I think it depends on how you look at it. I don't want to torture the analogy too much, but I see the pre-training (getting model weights out of an enormous corpus of text) as more akin to the billions of years of evolution that led to the modern human brain. The brain still has a lot to learn once you're born, but it already also has lots of structures (e.g. to handle visual input, language, etc) and built-in knowledge (instincts). And you can't change that over the course of your life.

I wouldn't be surprised if we ended up in a "pre-train / RAG / context window" architecture of AI, analogously to "evolution / long term memory / short term memory" in humans.

> Humans can use existing knowledge and apply it to new scenarios, and so can most AI

Doesn't the article states that this is not true? AI cannot apply to B what it learned about A.

it's more than that. Our understanding from space and time could be stemming from continuous training. Every time we look at something, there seems to be a background process that is categorizing items that are on the retinal image.

This is a continuous process.