#aibreakthroughs — Public Fediverse posts

AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve

A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.

Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.

The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.

MATHEMATICS IS GETTING INTERESTING

Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”

In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.

Then came something considerably bigger.

In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.

OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.

PHYSICS: CAN AI DISCOVER THE EQUATION?

Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.

That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.

The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.

AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.

And that distinction matters.

We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.

The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.

It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.

The machine hasn’t taken over the laboratory.

But it has started knocking on the door.

OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/

OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/

OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression

AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve

A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.

Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.

The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.

MATHEMATICS IS GETTING INTERESTING

Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”

In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.

Then came something considerably bigger.

In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.

OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.

PHYSICS: CAN AI DISCOVER THE EQUATION?

Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.

That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.

The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.

AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.

And that distinction matters.

We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.

The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.

It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.

The machine hasn’t taken over the laboratory.

But it has started knocking on the door.

OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/

OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/

OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression

AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve

A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.

Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.

The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.

MATHEMATICS IS GETTING INTERESTING

Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”

In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.

Then came something considerably bigger.

In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.

OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.

PHYSICS: CAN AI DISCOVER THE EQUATION?

Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.

That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.

The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.

AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.

And that distinction matters.

We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.

The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.

It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.

The machine hasn’t taken over the laboratory.

But it has started knocking on the door.

OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/

OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/

OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression

AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve

A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.

Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.

The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.

MATHEMATICS IS GETTING INTERESTING

Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”

In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.

Then came something considerably bigger.

In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.

OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.

PHYSICS: CAN AI DISCOVER THE EQUATION?

Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.

That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.

The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.

AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.

And that distinction matters.

We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.

The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.

It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.

The machine hasn’t taken over the laboratory.

But it has started knocking on the door.

OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/

OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/

OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression

What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers

Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.

Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.

The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.

PROBLEMS THAT GOT REAL ANSWERS

Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.

Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.

Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.

Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.

FROM ANSWERS TO DISCOVERY

The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.

AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.

That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.

AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.

And that is probably the real story.

AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.

The next breakthrough may therefore not be one spectacular answer.

It may be the machine that keeps finding the next question.

Sources list:

Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/

Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/

Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/

MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220

MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/

MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #materialsScience #mathematics #scientificDiscovery

What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers

Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.

Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.

The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.

PROBLEMS THAT GOT REAL ANSWERS

Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.

Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.

Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.

Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.

FROM ANSWERS TO DISCOVERY

The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.

AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.

That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.

AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.

And that is probably the real story.

AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.

The next breakthrough may therefore not be one spectacular answer.

It may be the machine that keeps finding the next question.

Sources list:

Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/

Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/

Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/

MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220

MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/

MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology

What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers

Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.

Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.

The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.

PROBLEMS THAT GOT REAL ANSWERS

Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.

Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.

Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.

Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.

FROM ANSWERS TO DISCOVERY

The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.

AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.

That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.

AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.

And that is probably the real story.

AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.

The next breakthrough may therefore not be one spectacular answer.

It may be the machine that keeps finding the next question.

Sources list:

Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/

Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/

Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/

MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220

MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/

MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology

What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers

Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.

Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.

The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.

PROBLEMS THAT GOT REAL ANSWERS

Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.

Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.

Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.

Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.

FROM ANSWERS TO DISCOVERY

The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.

AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.

That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.

AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.

And that is probably the real story.

AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.

The next breakthrough may therefore not be one spectacular answer.

It may be the machine that keeps finding the next question.

Sources list:

Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/

Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/

Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/

MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220

MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/

MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology

What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers

Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.

Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.

The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.

PROBLEMS THAT GOT REAL ANSWERS

Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.

Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.

Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.

Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.

FROM ANSWERS TO DISCOVERY

The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.

AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.

That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.

AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.

And that is probably the real story.

AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.

The next breakthrough may therefore not be one spectacular answer.

It may be the machine that keeps finding the next question.

Sources list:

Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/

Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/

Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/

Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/

MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220

MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/

MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project

Thisclaimer — https://thisclaimer.com

The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology