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I am convinced by Judea Pearl (https://en.wikipedia.org/wiki/The_Book_of_Why https://en.wikipedia.org/wiki/The_Book_of_Why) that the missing part of current AI

by zby 4y ago

I am convinced by Judea Pearl (https://en.wikipedia.org/wiki/The_Book_of_Why https://en.wikipedia.org/wiki/The_Book_of_Why) that the missing part of current AI is causality reasoning. It is impossible to inference causality from raw data - that is data without any a priori causality models. To develop causality ideas you need to make experiments. After you have some initial causal theories you can develop them further by adding observations - but you cannot bootstrap them entirely just from observations. Making experiments would also be much more efficient then just observing them - because you could test the exact stuff that is needed for your theories to develop. So yeah - action on the real world is required. By the way I have an intuition that free will is based on this - we need an irreducible source of cause to learn about causality and we call that the free will.

naasking 4y ago
> It is impossible to inference causality from raw data - that is data without any a priori causality models. It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. That's basically what machine learning does as well: compile sets of correlated values into a compressed representation (the neural net) which arguably qualifies as a "causal theory" from the algorithm's perspective. I think what's missing is maybe one or two orders of magnitude more compression, which would basically be devising a better/more parsimonious theory. We've been getting progressively better at that over the years too, and combined with how hardware has been scaling, we're seeing exponential growth in effectiveness. This is why some are predicting artificial general intelligence by 2030-2035.
- zby 4y ago
  > It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. We do experiments - we actually do them a lot and it starts very early with infants experimenting how activating a particular muscle moves his hand etc. Later we do less of original experiments - but repeat those that we already know the results - by walking for example and getting where we planned to. In science to get from correlations to causation we have 'randomized trials'. In everyday practice we use something called 'free will' in our minds as the source of the needed independence.
- woojoo666 4y ago
  > It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations Not sure about that. MinutePhysics has a video about how correlation can imply causality: https://www.youtube.com/watch?v=HUti6vGctQM https://www.youtube.com/watch?v=HUti6vGctQM

by zby 4y ago

naasking 4y ago

> It is impossible to inference causality from raw data - that is data without any a priori causality models. It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. That's basically what machine learning does as well: compile sets of correlated values into a compressed representation (the neural net) which arguably qualifies as a "causal theory" from the algorithm's perspective. I think what's missing is maybe one or two orders of magnitude more compression, which would basically be devising a better/more parsimonious theory. We've been getting progressively better at that over the years too, and combined with how hardware has been scaling, we're seeing exponential growth in effectiveness. This is why some are predicting artificial general intelligence by 2030-2035.

zby 4y ago
> It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations. We do experiments - we actually do them a lot and it starts very early with infants experimenting how activating a particular muscle moves his hand etc. Later we do less of original experiments - but repeat those that we already know the results - by walking for example and getting where we planned to. In science to get from correlations to causation we have 'randomized trials'. In everyday practice we use something called 'free will' in our minds as the source of the needed independence.
woojoo666 4y ago
> It's impossible in general to be certain of causality for humans too, all we see are correlations in data and we invent causal theories that explain those correlations Not sure about that. MinutePhysics has a video about how correlation can imply causality: https://www.youtube.com/watch?v=HUti6vGctQM https://www.youtube.com/watch?v=HUti6vGctQM