Comment by runarberg
5 days ago
What has not change is the strategy of throwing a gargantious amount of computations at the problem. If anything we throw more computations at more problems now than in 2016 (and in 1997 for that matter). The underlying technology is pretty much the same, just more parameters, more calculations, etc. Yes every individual calculations takes less power now then in 2016, but we make up for that by making millions of millions of more calculations, even for simpler tasks.
Sure, but there will be an upper bound after which we will be close to human level performance on the vast majority of tasks, and then at that point the focus becomes efficiency (or a continuing road to superintelligence for some tasks).
But regardless, compute will get to a point where human level intelligence close to as efficient as we are. You could argue it already is today, when you factor in the resources that the average person in the west already uses in terms of their overall impact on the planet.
You are describing a science fiction. There is nothing in the measured reality which indicate your predictions will come close to materialize.
I can just as well describe the future evolution of the internal combustion engine and claim it will get more and more efficient and eventually we will be able to burn oil so efficiently that our personal vehicles can fly through the atmosphere at twice the speed of sound.
There is limitations to digital computers just as there are limitations to internal combustion engines. Our brains are not digital computers. When we use our brains we don’t just do a bunch of linear algebra.
>I can just as well describe the future evolution of the internal combustion engine and claim it will get more and more efficient and eventually we will be able to burn oil so efficiently that our personal vehicles can fly through the atmosphere at twice the speed of sound.
This is a silly comparison. There is a certain quantity of energy stored in oil, so we know what peak efficiency looks like. We don't actually know what amount of energy is required to solve certain problems. We quite literally have models with quite a bit of capability that can run locally on a phone today, right alongside Stockfish, for example.
And this is to say nothing of work happening now on new hardware approaches, such as Normal Computing's work on thermodynamic matrix math: https://www.normalcomputing.com/blog/a-first-demonstration-o...
That said, this feels like a strange tangent: I'm not sure it's that important that the models be as energy efficient as a human brain. We don't avoid cars because they're less energy efficient than our legs. ;)
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