They play chess at an evolutionary level beyond humans now this...
AI Designs Quantum Physics Experiments beyond What Any Human Has Conceived
AI Designs Quantum Physics Experiments beyond What Any Human Has Conceived
AI Designed Quantum Physics Experiments.
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This stuff is good enough to deserve its own Conspiracy Theory wherein the AI take over the Earth by Quantumly Entangling humanity.
OB
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Bradskii
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This stuff is good enough to deserve its own Conspiracy Theory wherein the AI take over the Earth by Quantumly Entangling humanity.
OB
Two things struck me. The first is that Theseus is a much cooler name for a super computer than Melvin (not sure if there's an anagram in there or maybe there's some computing correlation with the Ship of Theseus?).
And secondly, this quote from the linked article:
'This process makes calculating the final quantum state much easier, although it is still hard for humans to understand'.
Stupid humans...
https://www.scientificamerican.com/article/dancing-with-robots/
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Quantum physicists while not as bizarre as mathematicians are renowned for their sense of humour.
A "melvin" is something "given" to nerds... also known as a 'wedgie', where someone's underwear is pulled up from behind, well higher than the waist of their pants.
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- I'm not sure I understand what they mean when they talk about generalizing a solution, yet whatever this term means under these circumstances, seems to be the key point being emphasised(?)
Egs:
i) “When we understood what was going on, we were immediately able to generalize [the solution],” says Krenn;
ii) “This is a generalization that (to my knowledge) no human dreamed up in the intervening decades and might never have done,” he says;
iii) Besides generating complex entangled states, the setup using more than two crystals with overlapping paths can be employed to perform a generalized form of Zeilinger’s 1994 quantum interference experiments with two crystals.
- Then, the dude from Griffith Uni in Australia, queries the novelty of the AI solutions:
“These machine-learning techniques represent an interesting development. For a human scientist looking at the data and interpreting it, some of the solutions may look like ‘creative’ new solutions. But at this stage, these algorithms are still far from a level where it could be said that they are having truly new ideas or coming up with new concepts,"
Which intelligence is actually doing the apparently key 'generalizations' there?
Is it humans ... or is it the AI?
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I guess the point is that humans are using this AI "tool" in doing what humans arguably do very well; that is, to extrapolate from the specific to the general?
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Bradskii
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Exactly what I was going to suggest. I think the AI was looking for specific answers to specific questions and when it found an answer, 'we' were able to apply the method that it used to similar problems.
I wonder if it's possible to get the AI to do the extrapolation? Or is that more in the realm of a 'Eureka' moment?
I used to have a maths teacher that used to show us how to prove theorems - such as Pythagoras's. So rather than repeating, and using, A² + B² etc we actually understood what it meant on a more intuitive level. You could see these eureka light bulbs going off around the classroom as he explained it. Maybe it's like knowing that 'maison' means 'house' and mentally translating it versus using the word as a sound that signifies 'house' when you're speaking French fluently.
I didn't get very far with maths. I was always 'translating' it. As opposed to those who understood it at a deeper level. I could never speak it fluently. Maths or French...
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Another earlier report here (2018), says that these AIs (MELVIN and Theseus?) use reinforcement learning .. Ie: similar to AlphaZero and Leela Chess Zero, (discussed in a previous thread here).
More snippets here (going backwards in time - Dec 2017). This one clarifies more on what this exercise is showing - with that being, that human logic itself, is thought to be the main constraining issue when it comes to understanding QM experiments:
More snippets here (going backwards in time - Dec 2017). This one clarifies more on what this exercise is showing - with that being, that human logic itself, is thought to be the main constraining issue when it comes to understanding QM experiments:
Also that there is a deep connection between QM experiments and mathematics' Graph Theory (which was also mentioned in a more detailed way in the OP SciAM article). The 2017 article says:
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Bradskii
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This is what I don't get. Is AI currently doing what we do...but infinitely faster? Or is it now doing something else? Or at least heading somewhere else?
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Petros2015
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A little like Douglas Adams old "Deep Thought" computer...
"I found the answer you were after"
"Wait... what question did you ask to get that answer?"
"Can't tell you"
"I found the answer you were after"
"Wait... what question did you ask to get that answer?"
"Can't tell you"
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This is all still a fairly new topic for me. However, what appears to be a fairly consistent message, is that AI's Reinforcement learning technique is aimed at eliminating/reducing human bias, which is now starting to be shown by these experiments, as being intrinsic to the type of inductive logic we use.
This is kind of disturbing because inductive logic is now pretty well intuitive for us .. yet when aspects of it are reduced or eliminated in AI, solutions to problems are nonetheless arrived upon, where humans appear to have, (at best), only stumbled upon some of them in the past(?)
Inductive bias in AI learning, seems to be aimed at improving efficiency .. as opposed to other aspects we humans may be intrinsically biased towards(?) .. Again, I'm unclear and unsure about this, however.
Its hard to create a mind, without incorporating basic logic, (along with its associated assumed true posits), from the very early stages. Creating a mind however, sits squarely in science's realm (IMO). For example, science uses logic within its models, they are logical syntaxes, but it doesn't use logic (eg: syllogisms) to create its models. This is pretty obvious when one looks at the scientific method and compares it with the logical syntax of mathematics and notices that they are obviously fundamentally different.
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Melvin (and Theseus) like the chess programs AlphaZero and Leela Chess Zero trained itself using reinforcement learning.
Unlike supervised learning there was no human assistance and the quantum experiments devised by Melvin and Theseus are purely the result of AI.
Quantum physics experiments in areas such as particle physics are based on phenomenology where the experimental design is an outcome of the theory.
In other fields this is not the case such as general relativity which does not tell us interferometers are to be used to detect gravitational waves.
In this case experimental design is independent of theory.
AI has come up with experiments far more complicated than what we humans can envisage and quantum physicists have used generalization or simplification in order to make sure these experiments are consistent with the phenomenological aspects of the quantum mechanics and quantum field theories.
An AI algorithm developing quantum physics experiments even without creating new aspects of the theory in a field which baffles most of the human race and is difficult even for its practitioners is still an impressive feat.
Unlike supervised learning there was no human assistance and the quantum experiments devised by Melvin and Theseus are purely the result of AI.
Quantum physics experiments in areas such as particle physics are based on phenomenology where the experimental design is an outcome of the theory.
In other fields this is not the case such as general relativity which does not tell us interferometers are to be used to detect gravitational waves.
In this case experimental design is independent of theory.
AI has come up with experiments far more complicated than what we humans can envisage and quantum physicists have used generalization or simplification in order to make sure these experiments are consistent with the phenomenological aspects of the quantum mechanics and quantum field theories.
An AI algorithm developing quantum physics experiments even without creating new aspects of the theory in a field which baffles most of the human race and is difficult even for its practitioners is still an impressive feat.
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Ha! I think that Wiki page contradicts the thinking I gave in my last post, about how science is suited to creating a mind (from scratch)?! Lol.
Ie: it says:
I'm not sure I agree with that .. hypotheses make predictions too, (which then get tested). I guess its easier to visualise what they mean there, by thinking of the example of how the Higgs Mechanism theory predicted the existence of the Higgs boson, (then it was found, as per the theoretical predictions, in the Atlas and CMS detectors)?
Yes .. I suppose interferometers can be used for other purposes involving EM waves .. but didn't the original concept of EM waves, start out as being largely theoretical constructs .. (ie: trigonometric functions)?sjastro said:
I suppose EM waves ended up being superimposed in order to specifically cause the phenomenon of interference, which was then used to extract information using interferometers, so that might be an example of phenomenology?
(I'm just thinking out loud here, as an attempt at trying to 'get' the phenomology distinction as it pertains to the AI approach, embedded in my mind ...)
So it appears that the real 'takeaway' here might be that the AI Reinforced learning technique, gives access to solutions which then mysteriously 'test out' (or are 'consistent with') real life measurements/observations .. So why should that be so?sjastro said:
Yes .. and they raise other more unsettling issues about mathematical systems giving access to what's real, which I thought was pretty much sorted out, (by being formally constrained), during Godel's time?sjastro said:
If I'm off on a tangent here, please excuse me? (I probably am).
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Phenomenology physics involves a theoretical experiment where the outcome of the experiment is the prediction.
An example is the discovery of the omega minus particle Ω⁻ in the 1960s.
In the period from 1930s to the mid 1960s particle physicists made myriad discoveries of particles which could be grouped into symmetries according to their properties such as charge Q or strangeness S which is a quantum number conserved in the creation of a particle but not in its decay.
One such symmetry is the baryon decuplet composed of;
Δ⁻, Δ⁰, Δ⁺, and Δ⁺⁺ delta baryons
Σ*⁻, Σ*⁰, and Σ*⁺ sigma baryons
Ξ*⁻ and Ξ*⁰ xi baryons
Ω⁻ omega baryon
Δ⁻, Δ⁰, Δ⁺, and Δ⁺⁺ delta baryons
Σ*⁻, Σ*⁰, and Σ*⁺ sigma baryons
Ξ*⁻ and Ξ*⁰ xi baryons
Ω⁻ omega baryon
In the early 1960s nine of the particles were known and grouped in the symmetry, the properties of the unknown or Ω⁻ particle could be predicted by simply noting its position in the symmetry and reading off the theoretical mass in MeV, charge and strangeness number.
The unknown particle at the base of the triangle was the omega minus Ω⁻ particle.
Theorizing about the particle's existence is one thing, developing an experiment to find it is a distinctly different problem.
Using phenomenology physics, particle physicists came up with a theoretical bubble chamber which predicted the track of the omega minus particle.
The diagram on the right hand side is the prediction of the pathway of the Ω⁻ including the trajectories of other particles in a theoretical bubble chamber, the left hand side is the actual discovery photograph of the Ω⁻ in a real bubble chamber.
Physicists had trained female scanners (a sign of the times) to examine thousands of photographs for the tell tale trajectory of the Ω⁻ as predicted by phenomenological physics.
The discovery of the Higgs boson followed the same principles (computers replaced female scanners by this time) except the theory and phenomenological physics is vastly more complicated.
The discovery of gravitational waves is not an example of phenomenological physics.Yes .. I suppose interferometers can be used for other purposes involving EM waves .. but didn't the original concept of EM waves, start out as being largely theoretical constructs .. (ie: trigonometric functions)?
I suppose EM waves ended up being superimposed in order to specifically cause the phenomenon of interference, which was then used to extract information using interferometers, so that might be an example of phenomenology?
(I'm just thinking out loud here, as an attempt at trying to 'get' the phenomology distinction as it pertains to the AI approach, embedded in my mind ...)
Before the use of interferometers physicists used Weber bars but due to their lack of sensitivity failed to detect gravitational waves.
The evolution of the experiment from Weber bars to interferometers was independent of the theory.
An interesting project perhaps for a PhD is to use AI to see if it is able to reproduce the Ω⁻ tracks developed in the 1960s.
Whereas humans where trained in quantum mechanics in order to develop the theoretical experiment, AI would only be given the most basic information and train itself.
The chess analogy applies here, humans only supplied AlphaZero and Leela Chess Zero with the rules of chess; both algorithms developed tactics and strategies way beyond the very best human chess players.
It looks as if the same situation is occurring with quantum physics experiments.
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Thanks kindly for all that useful info .. It'll be interesting to track where else this AI research goes. I noticed a couple of weeks ago, it was announced that some team was working on applying it to see what shows up in one of my favourite topics .. molecular Abiogenesis.Phenomenology physics involves a theoretical experiment where the outcome of the experiment is the prediction.
An example is the discovery of the omega minus particle Ω⁻ in the 1960s.
In the period from 1930s to the mid 1960s particle physicists made myriad discoveries of particles which could be grouped into symmetries according to their properties such as charge Q or strangeness S which is a quantum number conserved in the creation of a particle but not in its decay.
One such symmetry is the baryon decuplet composed of;
Δ⁻, Δ⁰, Δ⁺, and Δ⁺⁺ delta baryons
Σ*⁻, Σ*⁰, and Σ*⁺ sigma baryons
Ξ*⁻ and Ξ*⁰ xi baryons
Ω⁻ omega baryon
In the early 1960s nine of the particles were known and grouped in the symmetry, the properties of the unknown or Ω⁻ particle could be predicted by simply noting its position in the symmetry and reading off the theoretical mass in MeV, charge and strangeness number.
The unknown particle at the base of the triangle was the omega minus Ω⁻ particle.
Theorizing about the particle's existence is one thing, developing an experiment to find it is a distinctly different problem.
Using phenomenology physics, particle physicists came up with a theoretical bubble chamber which predicted the track of the omega minus particle.
The diagram on the right hand side is the prediction of the pathway of the Ω⁻ including the trajectories of other particles in a theoretical bubble chamber, the left hand side is the actual discovery photograph of the Ω⁻ in a real bubble chamber.
Physicists had trained female scanners (a sign of the times) to examine thousands of photographs for the tell tale trajectory of the Ω⁻ as predicted by phenomenological physics.
The discovery of the Higgs boson followed the same principles (computers replaced female scanners by this time) except the theory and phenomenological physics is vastly more complicated.
The discovery of gravitational waves is not an example of phenomenological physics.
Before the use of interferometers physicists used Weber bars but due to their lack of sensitivity failed to detect gravitational waves.
The evolution of the experiment from Weber bars to interferometers was independent of the theory.
An interesting project perhaps for a PhD is to use AI to see if it is able to reproduce the Ω⁻ tracks developed in the 1960s.
Whereas humans where trained in quantum mechanics in order to develop the theoretical experiment, AI would only be given the most basic information and train itself.
The chess analogy applies here, humans only supplied AlphaZero and Leela Chess Zero with the rules of chess; both algorithms developed tactics and strategies way beyond the very best human chess players.
It looks as if the same situation is occurring with quantum physics experiments.
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Useful description. I was impressed that Melvin tried out a novel layout that humans would not have thought of -- that's a kind of...try everything and see what works. After humans have set what is the goal, and how to know if the goal is being met, then the machine can work rapidly and tirelessly to try out all sorts of possibilities.Melvin (and Theseus) like the chess programs AlphaZero and Leela Chess Zero trained itself using reinforcement learning.
Unlike supervised learning there was no human assistance and the quantum experiments devised by Melvin and Theseus are purely the result of AI.
Quantum physics experiments in areas such as particle physics are based on phenomenology where the experimental design is an outcome of the theory.
In other fields this is not the case such as general relativity which does not tell us interferometers are to be used to detect gravitational waves.
In this case experimental design is independent of theory.
AI has come up with experiments far more complicated than what we humans can envisage and quantum physicists have used generalization or simplification in order to make sure these experiments are consistent with the phenomenological aspects of the quantum mechanics and quantum field theories.
An AI algorithm developing quantum physics experiments even without creating new aspects of the theory in a field which baffles most of the human race and is difficult even for its practitioners is still an impressive feat.
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What you are describing is a brute force approach to programming.
Brute force is only effective for a finite number of possibilities where the program has ample time and hardware processing speed to evaluate each possibility.
This is not occurring with reinforcement learning.
It’s easier to explain for an AI chess algorithm such as AlphaZero Chess than for a quantum physics algorithm both of which are based on reinforcement learning.
The goal of chess is simply to win the game.
This goal can be understood by AI as a numerical function.
For example if the AI algorithm wins a game it gains +1, a draw 0 and a loss -1.
Its objective is to maximize this score by playing chess games with itself.
When it loses a game it discards the game, if it wins or draws it retains the game.
The algorithm is essentially training itself how to play chess without human intervention.
While the above description is simplistic a more detailed account is given in the paper.
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
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Yes, reinforcement is a powerful learning technique. As i worded it:
If a trial move meets the goal it's reinforced. In chess the classic problem has long been to evaluate a position reached after a move.
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