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Milan R. Rapaić
@milan_rapaic
Prof @ Computing and Control Dept, Faculty of Technical Sciences, University of Novi Sad. Control | Optimization | Control | Intelligent & Adaptive Systems
Novi Sad
Joined August 2011
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192 Followers
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Pošto me je više ljudi danas zvalo telefonom na ovu temu: 1) Zovem se Rapaić (bez J). 2) Nisam prodekan. 3) Ne podržavam izbornu listu SNS ni javno ni privatno. Laku noć.
I am begging critics of the puberty blocker trial to stop getting lost in the weeds arguing about research ethics, Gillick competence, informed consent, fertility preservation and the like. The NHS’s refusal to complete the Data Linkage Study (DLS) before proceeding with a new trial is the ONLY argument worth making on this issue. Everything else is a distraction.
The DLS would find out what has happened to all the children who have already taken puberty blockers. There is NO justification for proceeding with a puberty blocker trial before the DLS is complete. The entire programme of future research in this area should be informed by its results. The fact that the NHS is getting ready to run a live experiment on vulnerable children before it has even established the fate of those it has already experimented on is utterly damning.
The deeply disappointing Dr Cass should be widely derided for her ridiculous comments on ‘harm reduction’. The DLS IS harm reduction. Critics of the trial must stop fighting on their opponents turf by quibbling over technical details. We need to relentlessly force the DLS onto the agenda and expose the shameful hypocrisy of the NHS. DO THE DLS.
#DotheDLS
https://t.co/HfdBR3L8tC
Yann Lecun published the most heretical AI paper of the year.
He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there.
The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI.
Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do.
LeCun argues that this entire concept is a biological illusion.
Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world.
We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending.
Which brings us to the chess argument.
Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible.
Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it.
LeCun says we need to stop building AI to mimic human generality.
Instead, he proposes a new North Star: SAI.
Superhuman Adaptable Intelligence.
Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization.
SAI is about the speed of adaptation.
It is an intelligence that can learn to exceed humans at any specific, economically important task.
More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable.
Things like managing global energy grids in real-time. Or predicting complex molecular structures.
The entire AI industry is obsessed with building a digital reflection in our own image.
LeCun's paper is a brutal wake-up call.
Ok, i will leave this one also. Both good...
If you take everything you learned in college -- physics, control theory, operations research, chemistry, genetics, etc -- and you call it "world model", you get something resembling LeCun's model. But encoding this knowledge in a machine, without additional assumptions, won't be sufficient for answering some basic questions that every high school kid can answer, e.g., that the atmospheric pressure causes the barometer to move and not the other way around. @CSProfKGD @animesh1977 @eliasbareinboim
I will just leave this one here :)
People are realizing that AIs are nowhere near human intelligence and learning abilities.
Yet they have become very useful by compensating for their lack of common sense, lack of understanding of reality, and limited reasoning and planning abilities, by the accumulation of enormous amounts of declarative knowledge.
Ekipa koja uvek ima moj glas :) NacionalnaKlasa https://t.co/7ZI8GvtSXp
Hajde glas podrške za ovu sjajnu ekipu
Terence Tao - "AI tools are like taking a helicopter to drop you off at the site. You miss all the benefits of the journey itself. You just get right to the destination, which actually was only just a part of the value of solving these problems."
Judit Polgar - "I always felt that intuition is very important in chess, but I get my intuition through my experience. And many times I think that this is the biggest danger for youth, that they don't have the experience because they don't spend enough time doing."
Elites from two different fields voice the same opinion.
[1] https://t.co/XRDSSPjpQ8
[2] https://t.co/fQzPT3D3f4
In 1980, two years before Feynman's famous Caltech lecture on Quantum Computing, a 43-year-old Soviet mathematician named Yuri Manin published a slim 128-page popular-science book called Вычислимое и невычислимое — Computable and Noncomputable — through the Moscow publishing house Sov. Radio. Manin was not a computer scientist. He was already one of the great algebraic geometers of his generation: a Lenin Prize laureate (1967), professor of algebra at Moscow State University, principal researcher at the Steklov Mathematical Institute, the mathematician behind the Gauss–Manin connection and the Mordell conjecture for function fields. He had been forbidden from foreign travel since 1968. The book was written in Russian, never officially translated for nearly thirty years, and its argument about quantum computation took up barely three pages of the introduction...
https://t.co/pOZ0460JWB
What's striking about Manin's framing — and what got almost entirely lost when the Western quantum computing canon formed around Benioff, Feynman, and Deutsch — is the direction of the argument.
Feynman's 1982 case for quantum computers was pragmatic and engineering-flavored: classical machines can't efficiently simulate quantum systems, therefore we should build quantum machines that can.
Manin came at it from the opposite end. He looked at molecular biology — at protein synthesis on messenger RNA, at the absurd information density and energetic efficiency with which living cells perform what looks structurally like Turing-machine computation — and concluded that nature had already solved the problem. Classical physics, he argued, simply cannot account for what biology does. The mathematical theory of quantum automata must already be implicit in the substrate of life. Engineering quantum computers wasn't the goal; it was the obvious downstream consequence of taking biology's existence-proof seriously.
That places Manin in a different intellectual lineage than the one quantum computing eventually inherited. He was downstream of Schrödinger's What Is Life? (1944) and the broader Soviet tradition of treating life as a physical system whose laws had not yet been written — Vernadsky, Lyapunov, the cybernetics revival under Berg and Glushkov.
The West built quantum computing as an engineering discipline of qubits-as-fabricated-systems, and pushed biology off into a separate and often-dismissed sub-field called "quantum biology."
Forty-five years later, with the work emerging on microtubules, tryptophan networks, ordered water, and coherent processes in neural lattices, the field is, in a real sense, finally catching up to its own actual origin.
The translation below is from pages 13–15 of the introduction.
On the inefficiency of computing devices
Molecular biology provides examples of the behavior of natural (not human-engineered) systems which we are forced to describe in terms close to those accepted in the theory of discrete automata. The figure below depicts the scheme of protein synthesis on messenger RNA: it closely resembles the depiction of a Turing machine copying information from one tape to another.
Classical continuous systems governed by differential equations can imitate discrete automata only when their phase space has an exceptionally complex structure — an abundance of stability regions separated by low energy barriers. Loading a program carves out a sophisticated system of passages through these barriers, predetermining the motion of the phase trajectory through this labyrinth. As a physical system, the computing device must be highly unstable, since an error of a single character in the program generally leads to an entirely different trajectory. Yet the computational process itself must be exceptionally stable — that is, spontaneous errors (transitions of the trajectory across a barrier that should remain closed, as a result of fluctuations) must have very low probability. It is well known that these requirements — combined with slowness of operation and the exponential growth of dissipated energy as complexity increases — erected the barrier that halted the development of mechanical computers.
[Citing Poplavsky's 1975 paper on thermodynamic models of information processes:] A genuinely instructive calculation can be found there: the quantum-mechanical description of the methane molecule by the lattice method requires computation at 10⁴² points. If we assume only 10 elementary operations are performed at each point, and suppose all computations are carried out at ultra-low temperature, then even so the calculation of the methane molecule would require expending energy roughly equal to that produced on Earth over a century.
On quantum automata
It is possible that for a better understanding of such phenomena a mathematical theory of quantum automata is lacking. The mathematical model of such objects must exhibit highly unusual properties compared with deterministic processes. The reason is that the capacity of the quantum state space is dramatically greater: where in the classical case there are N discrete states, in quantum theory — which permits their superposition — the state space lies in Cᴺ. When classical systems are combined, their state-counts N₁ and N₂ simply multiply; in the quantum case one obtains C^(N₁·N₂).
These rough estimates show that systems exhibiting quantum behavior are potentially far more complex than their classical counterparts. For example, since the system has no unique decomposition into parts, the state of a quantum automaton may be regarded in many different ways as states of entirely different virtual classical automata.
In carrying out such a program, the first difficulty will be finding the right balance between mathematical and physical principles. The quantum automaton must be abstract: its mathematical model should use only the most general quantum principles, without prejudging physical implementations. Then the model of evolution is a unitary rotation in finite-dimensional Hilbert space, and the virtual decomposition into subsystems corresponds to the tensor-product decomposition of that space. Somewhere in this picture the place of interactions — traditionally described by Hermitian operators and probabilities — must still be found.
Notes on this translation:
The C in "Cᴺ" is the field of complex numbers; Cᴺ is N-dimensional complex Hilbert space. C^(N₁·N₂) reflects the tensor product H₁ ⊗ H₂ — the structure that gives quantum systems their entanglement-driven computational advantage.
The Poplavsky reference is to R.P. Poplavsky, "Thermodynamical models of information processing," Uspekhi Fizicheskikh Nauk 115:3 (1975), 465–501.
![JosephJacks_'s tweet photo. In 1980, two years before Feynman's famous Caltech lecture on Quantum Computing, a 43-year-old Soviet mathematician named Yuri Manin published a slim 128-page popular-science book called Вычислимое и невычислимое — Computable and Noncomputable — through the Moscow publishing house Sov. Radio. Manin was not a computer scientist. He was already one of the great algebraic geometers of his generation: a Lenin Prize laureate (1967), professor of algebra at Moscow State University, principal researcher at the Steklov Mathematical Institute, the mathematician behind the Gauss–Manin connection and the Mordell conjecture for function fields. He had been forbidden from foreign travel since 1968. The book was written in Russian, never officially translated for nearly thirty years, and its argument about quantum computation took up barely three pages of the introduction...
https://t.co/pOZ0460JWB
What's striking about Manin's framing — and what got almost entirely lost when the Western quantum computing canon formed around Benioff, Feynman, and Deutsch — is the direction of the argument.
Feynman's 1982 case for quantum computers was pragmatic and engineering-flavored: classical machines can't efficiently simulate quantum systems, therefore we should build quantum machines that can.
Manin came at it from the opposite end. He looked at molecular biology — at protein synthesis on messenger RNA, at the absurd information density and energetic efficiency with which living cells perform what looks structurally like Turing-machine computation — and concluded that nature had already solved the problem. Classical physics, he argued, simply cannot account for what biology does. The mathematical theory of quantum automata must already be implicit in the substrate of life. Engineering quantum computers wasn't the goal; it was the obvious downstream consequence of taking biology's existence-proof seriously.
That places Manin in a different intellectual lineage than the one quantum computing eventually inherited. He was downstream of Schrödinger's What Is Life? (1944) and the broader Soviet tradition of treating life as a physical system whose laws had not yet been written — Vernadsky, Lyapunov, the cybernetics revival under Berg and Glushkov.
The West built quantum computing as an engineering discipline of qubits-as-fabricated-systems, and pushed biology off into a separate and often-dismissed sub-field called "quantum biology."
Forty-five years later, with the work emerging on microtubules, tryptophan networks, ordered water, and coherent processes in neural lattices, the field is, in a real sense, finally catching up to its own actual origin.
The translation below is from pages 13–15 of the introduction.
On the inefficiency of computing devices
Molecular biology provides examples of the behavior of natural (not human-engineered) systems which we are forced to describe in terms close to those accepted in the theory of discrete automata. The figure below depicts the scheme of protein synthesis on messenger RNA: it closely resembles the depiction of a Turing machine copying information from one tape to another.
Classical continuous systems governed by differential equations can imitate discrete automata only when their phase space has an exceptionally complex structure — an abundance of stability regions separated by low energy barriers. Loading a program carves out a sophisticated system of passages through these barriers, predetermining the motion of the phase trajectory through this labyrinth. As a physical system, the computing device must be highly unstable, since an error of a single character in the program generally leads to an entirely different trajectory. Yet the computational process itself must be exceptionally stable — that is, spontaneous errors (transitions of the trajectory across a barrier that should remain closed, as a result of fluctuations) must have very low probability. It is well known that these requirements — combined with slowness of operation and the exponential growth of dissipated energy as complexity increases — erected the barrier that halted the development of mechanical computers.
[Citing Poplavsky's 1975 paper on thermodynamic models of information processes:] A genuinely instructive calculation can be found there: the quantum-mechanical description of the methane molecule by the lattice method requires computation at 10⁴² points. If we assume only 10 elementary operations are performed at each point, and suppose all computations are carried out at ultra-low temperature, then even so the calculation of the methane molecule would require expending energy roughly equal to that produced on Earth over a century.
On quantum automata
It is possible that for a better understanding of such phenomena a mathematical theory of quantum automata is lacking. The mathematical model of such objects must exhibit highly unusual properties compared with deterministic processes. The reason is that the capacity of the quantum state space is dramatically greater: where in the classical case there are N discrete states, in quantum theory — which permits their superposition — the state space lies in Cᴺ. When classical systems are combined, their state-counts N₁ and N₂ simply multiply; in the quantum case one obtains C^(N₁·N₂).
These rough estimates show that systems exhibiting quantum behavior are potentially far more complex than their classical counterparts. For example, since the system has no unique decomposition into parts, the state of a quantum automaton may be regarded in many different ways as states of entirely different virtual classical automata.
In carrying out such a program, the first difficulty will be finding the right balance between mathematical and physical principles. The quantum automaton must be abstract: its mathematical model should use only the most general quantum principles, without prejudging physical implementations. Then the model of evolution is a unitary rotation in finite-dimensional Hilbert space, and the virtual decomposition into subsystems corresponds to the tensor-product decomposition of that space. Somewhere in this picture the place of interactions — traditionally described by Hermitian operators and probabilities — must still be found.
Notes on this translation:
The C in "Cᴺ" is the field of complex numbers; Cᴺ is N-dimensional complex Hilbert space. C^(N₁·N₂) reflects the tensor product H₁ ⊗ H₂ — the structure that gives quantum systems their entanglement-driven computational advantage.
The Poplavsky reference is to R.P. Poplavsky, "Thermodynamical models of information processing," Uspekhi Fizicheskikh Nauk 115:3 (1975), 465–501.](https://pbs.twimg.com/media/HHCIl1ib0AAPeMD.jpg)
Moj #1 savet ako želite da zaista uđete u AI revoluciju:
1.Kupite 31 Mac Mini: ~€800 po komadu (prvi blagi finansijski udar)
2.Instalirajte OpenClaw: €0 (plaća se kasnije, mentalno)
3.Kreirajte 400+ agenata — ~€0.002 po agentu… dok ne počnu da razmišljaju bez pauze
4.Pričajte u telefon 6 sati o svojim ciljevima, besplatno, ali egzistencijalno iscrpljujuće
5.Ubacite transkript u agente, nekoliko dolara u tokenima koji nestaju brže nego nada
Odmah kreću da planiraju, analiziraju i optimizuju svaki aspekt vašeg života. Sistem radi. Sve radi. Konačno ide napred.
Idite na spavanje.
Ujutru se budite kao nova osoba:
Životom koji se nije drastično promenio: ali sa računom za struju od € 8000
@alz_zyd_ IMO is about the grindset. Fields medals about the gift. For instance, if you read the autobiographical texts of Grothendieck, you will see he was actually slow, a common pattern among many math genuises summarize in this quote: (Grisha= Grigori Perelman)
Hugo Duminil-Copin, French mathematician and 2022 Field Medalist told me he never participated in math competition and was very bad at it.
Innovative mathematics requires creativity, intuition, intense concentration, and long reflections, sometimes spread over several years.
Good performance at a math olympiad merely tests fast problem solving abilities. AI can do that nowadays.
One of the big activities of a researcher, in mathematics and elsewhere, is not to answer questions but to ask the right questions.
@OlenaRohoza Trump is a bully and other countries are over his threats. This doesn’t end well.
Classical billiards can compute.
With @Isaacramr__ , we show that 2D billiard systems are Turing complete, implying the existence of undecidable trajectories in physically natural models from hard-sphere gases to celestial mechanics.
Determinism ≠ predictability. 🎱🧠@ETH_en
People who don't think phones/social media are having a deleterious effect on developing brains -- what's the alternative explanation for this? Did every country put lead in the water at the same time?
@signulll I am old enough to remember when "open sourced its own obsolescence" was called "standing on the shoulders of giants"
This warrants the death penalty
i think about this dude all the time
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