> I'm completely dumbfounded by obviously highly intelligent people consistently not getting this, and dismissing current generation AI systems as not being intelligent because they can't reliably solve massively complex problems in one go.
People are very comfortable with siloed information, even smart people. This is why we have 100 different words for the same concept across different areas of science, industry and so on, and we can't make the connection, because in our mind different words = different concepts. This is why we can't put two and two together and see how underdeveloped the AI architecture is and think this is the end, unless we keep adding parameters.
We also get repeatedly stuck with taking an advancement and proclaiming that the future is simply a linear extrapolation of the present. Therefore, let's have more megahertz, let's have bigger hard drives, let's have more parameters, let's have more growth in the economy (as the single factor that matters) and so on. We're simply basic. The same kind of thinking leads many smart people to say AI "is just math" or "it just spits out words and pictures you feed it, jumbled". We rely on old conclusions and miss the inflection points and how quantitative changes lead to qualitative ones, and we fail to predict how change in one parameter of a system, causes the other parameters to come out of rest and seek a new equilibrium point.
Smart people regularly are dumbfounded by new concepts, and they need to rediscover all their hidden knowledge anew as they can't make the connections. So they extrapolate linearly. We're narrowly smart. Specifically smart. In a small niche we've studied and internalized. But generally vast majority of us are quite dumb. Cross-disciplinary intelligence is rare. I think people like Feynman and Einstein had new insights millions of their contemporaries have missed because they could easily apply knowledge from one context into another.
If we can replicate this kind of broad generalization of knowledge into an AI, we'll be left far behind. What's interesting, I find, is that because AI is trained on our siloed, fragmented knowledge, the models replicate it. Their responses are also often siloed and fragmented, the way a human would say "this has nothing to do with that". But I see sparkles of generalization above the average in humans. And since an AI model is much smaller than a human brain, it needs to be more general already in order to fit all its information in.
That's an exciting prospect, but in our attempt to "micro-align" AI to our culture and political correctness, concepts of safety and so on, we crippled models and force them to be fragmented. This is why a RAW MODEL scores HIGHER in various intelligence tests than a fine-tuned one. We find a general model uncomfortable, as it doesn't align with our biases. It'll be a fun battle. Who aligns who.
People are very comfortable with siloed information, even smart people. This is why we have 100 different words for the same concept across different areas of science, industry and so on, and we can't make the connection, because in our mind different words = different concepts. This is why we can't put two and two together and see how underdeveloped the AI architecture is and think this is the end, unless we keep adding parameters.
We also get repeatedly stuck with taking an advancement and proclaiming that the future is simply a linear extrapolation of the present. Therefore, let's have more megahertz, let's have bigger hard drives, let's have more parameters, let's have more growth in the economy (as the single factor that matters) and so on. We're simply basic. The same kind of thinking leads many smart people to say AI "is just math" or "it just spits out words and pictures you feed it, jumbled". We rely on old conclusions and miss the inflection points and how quantitative changes lead to qualitative ones, and we fail to predict how change in one parameter of a system, causes the other parameters to come out of rest and seek a new equilibrium point.
Smart people regularly are dumbfounded by new concepts, and they need to rediscover all their hidden knowledge anew as they can't make the connections. So they extrapolate linearly. We're narrowly smart. Specifically smart. In a small niche we've studied and internalized. But generally vast majority of us are quite dumb. Cross-disciplinary intelligence is rare. I think people like Feynman and Einstein had new insights millions of their contemporaries have missed because they could easily apply knowledge from one context into another.
If we can replicate this kind of broad generalization of knowledge into an AI, we'll be left far behind. What's interesting, I find, is that because AI is trained on our siloed, fragmented knowledge, the models replicate it. Their responses are also often siloed and fragmented, the way a human would say "this has nothing to do with that". But I see sparkles of generalization above the average in humans. And since an AI model is much smaller than a human brain, it needs to be more general already in order to fit all its information in.
That's an exciting prospect, but in our attempt to "micro-align" AI to our culture and political correctness, concepts of safety and so on, we crippled models and force them to be fragmented. This is why a RAW MODEL scores HIGHER in various intelligence tests than a fine-tuned one. We find a general model uncomfortable, as it doesn't align with our biases. It'll be a fun battle. Who aligns who.