Comments by "Mikko Rantalainen" (@MikkoRantalainen) on "Sabine Hossenfelder" channel.

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  18. 6:10 I think these examples are not truthful. If you took 17 year old from year 1200 and put him or her into a modern car, it would take quite some time to teach them to drive. Similarly, a ten year old kid from year 1200 would have hard time learning to use a dishwasher oneshot. It's only because those 10 and 17 year old kids have seen a lot of examples during their lives they have pretty good idea what to do. As I see it, the LLM matches the human thinking part pretty well but the AI still needs improved vision system to feed suitable data to the thinking part and the AI also needs methods to interact with the world. A human baby around the age of a couple of weeks still seems to fail to understand that he or she has these things called limbs and may be failing to understand the concept of self. We don't yet know for sure. And as a result, we don't know how close to AGI we really are. Even if we had some commonly agreed on definition for AGI, which we don't have. In fact, humans still don't have even commonly agreed on definition for even human intelligence. We have IQ tests that actually measure g-factor and we sometimes pretend that it's the same thing as intellligence but we don't really believe it in the end. Considering we still don't have cost effective way for inference step in LLM (equal or better energy efficiency compared to human brain), the big question is who can afford to run AGI even if it were invented during 2025? Let's assume that OpenAI can scale ChatGPT o4 or o5 to true AGI level but it casts $15000 per task to run because the amount of compute needed, who is going to run it for anything?
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  47. IMO, I think this can be battled the same way AI should be trained even today: start with a small amount of trusted data (hand selected articles and books from various fields verified by experts). Then for every new piece of content, make the AI estimate the quality of the data (can it be deduced from the trusted data?) and skip training if the content is not (yet?) considered high enough quality. Note that the quality of the data is about it being well supported by the existing knowledge. Do this multiple times to create chains of high quality data (that is, the potential content was not deemed trusted earlier but now that the AI has learned more, it will estimate the quality of the same data differently). Keep track of the estimated quality of a given piece of data and recompute the estimated quality again for all documents every now and then. If the quality estimate of the AI is good enough, it should increase the estimated quality of the content over time (because more chains allows accepting more new data) and cases where previously trusted content turns on untrusted later would point out problems in the system. Also run the estimation against known good high quality data not included in the training set every now and then. These should be considered high quality data but the AI may fail to identify the data correctly, which would demonstrate lack of general understanding by the AI. Once you demonstrate that the estimated quality matches well enough with the expert evaluations of the same content, you can start to train the AI to understand misunderstandings of humans, too. Train low quality content as examples of humans failing to think / research correctly. In the end, you should have an AI that can successfully estimate quality of any new content and automatically use it to either extend its knowledge (chains of known good content) or to automatically learn it as an example of low quality content that the AI should avoid but the AI should be made aware of. If the AI doesn't have negative feedback from failed human content, it cannot understand failures in tasks given to said AI.
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