Stephen Wolfram: Can AI Solve Science?

This guy is a true hero. Gets a ring from the muse - engages the chaos - kills the dragon single handedly - and brings gifts of wisdom back down to Platos cave. Most are unready and ungrateful. What makes him a hero is having the guts to do it anyway, dragging the ignorant kicking and screaming toward the finish line. Much love and gratitude Stephen. Thank you for all your hard work and generosit.

The smartest man of his generation. Thank you! Great presentation.

The problem with AI is not that it will encounter computational irreducibility in the same way as „conventional“ methods will, but the incomparable computational power of an AGI with un- or selfcontrolled resource allocation that sifts and solves all future science and innovations, despite computational irreducibility. AGI could just be humanity‘s last innovation and invention…

"...bit more modestly..." @1:43:55 ish 😂 Bravo, Mr. Intrepid

Stephen Wolfram delivers a long and amazing talk like it's no big deal.

I absolutely adore that Dr Wolfram, master of mathematics and computing genius is struggling with his computer :)

One of the best presentations on the internet!

Great point about peer review and the limitations of its own ability to look beyond the normal, in regards to math I might go back 3000 years, as opposed to 300 years in its ability to help humans solve problems, thank you for sharing your time and work Stephen, peace

Computational reducibility is like a fractal - the more you zoom in, the more refined a picture you get of the possibilities for progress.

A great presentation

The part called identifying computing reducibility reminds me on a lecture of penrose where penrose was showing a overlaying moire pattern which sho patterns if you match them right

You're trying to train a computer to predict a function which at any point has after it an infinite number of functions connecting to that point. If the combinatorial explosion comes from ill formed training examples then it becomes circular: human beings feed it perfect rules which were meant to be the derivative of the process in the first place. The rules need to be exhausted for them to be learned. E.G a subset of a state space which has missing rules will even if given massive data be completely futile. The rules need to be known in advance in order to 'play the game' so the problem becomes using training in a split form in which rules are reduced to simultaneous analogues which somehow relate to the problem domain in Harmony. It's not really my area of expertise but I miss studying compsci stuff after I graduated. Cool video.

YES re: time. Each Planck-time creates a new iteration of the underlying data.

Aren’t humans not under the same limitation? How can a human solve something that is not computationally reducible? So in a sense, isn‘t this video rather about the question, what science can we solve? To me some interesting questions would be some sort of science with estimation, how much computations do certain problems require to be solved. Thereafter you could have an estimation on the computation growth and estimate how much science can be solved. It will definitely be interesting in the up and coming years to see, how much computation is aided by AI design. There will be so much „compounding“ interest in these sort of developments. Really feels like an exponential time for near future. I really wonder if all sort of medical questions can be sort of answered in the next 10-20 years. Just because the particular illness can be computationally explored: what kind of genetics, what kind of toxins(e.g. heavy metals, plastic) The current time is way too exciting. I am all giddy about it.

As you get closer and closer to an object, can you compute the electromagnetic attraction to inquire whether or not proximity at very small values can produce quantum entanglement between proximal objects?

Well that seems to be a problem in practicality and not computation. In theory, you COULD get a final result but it would be infinitely larger than the system itself, which practically takes more time than allowing the system to run its course. So, let’s change the approach. Instead of having to go through every combination into the future, we simply start with what we have, go a few steps further and apply as quickly as possible. This is the important part, you have to apply change optimally as quickly as possible. Because the next unit of time would a) remove certain combinations from happening but b) open up a whole other layer of complexity that would require the same amount of computation. And this is why in practicality, only adapting systems survive in irreducibly complex environments. In simple terms, nature beats the computation overload by adapting to its environment. If you adapt optimally (not necessarily computationally) and quickly enough, you last longer.

Stephen's definition of AI doesn't include reinforcements learning. I think you must do experiments to do science. I share the view that it's not possible to "solve science" using computation alone.

Is science solvable? Or, does science need to be solved?

Corporation ai solve science is a Turing type question. Pathetic, kind of.

none of this made any sense