Denson smith

[Denson smith @denson_smith]

@Tech_girl Some people feel guilty when they use their dishwasher.

[Denson smith @denson_smith]

@viola_enjoyer22 Movie box office: $8.9B
Recorded music: $11.5B
Sports betting: $13.7B
Video games: $60.8B
Sports: $75B+
TV and video (incl. streaming): $213B

[Denson smith @denson_smith]

@WoganMay Indeed

[Denson smith @denson_smith]

@leecronin I’m not sure what you mean but here is a team that if effective at using ai for science:

https://t.co/Ap5jGZSp6G

[Markus J. Buehler @ProfBuehlerMIT]

AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.

Here is what we did:

▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.

What emerged is a set of physical design principles:

▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.

The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.

A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.

Models building models, with physical evidence shaping recursive reasoning loops.

[Markus J. Buehler @ProfBuehlerMIT]

AI discovered how a material just one atom thick can keep carrying load as its atomic structure begins to break, preventing catastrophic failure. This discovery is based on first-principles atomic scale reasoning integrated with biological principles, cutting across scales and providing deep insights into materials in extreme conditions. The resulting material is extremely lightweight yet strong, far better performing than existing structures. By organizing "simple" carbon atoms into hierarchical graphene architectures, unique materials can be designed that redistribute forces, accommodate deformation, and confine damage, the AI identified design principles for resisting catastrophic failure. The AI built the atomistic simulation instrument itself "from scratch"; and then conducted experiments autonomously that revealed when alignment strengthens a material, when hierarchy protects it, and when an apparently promising design fails. This is a frontier in designing matter at its ultimate thinness - controlling how mechanical failure unfolds through the organization of individual atoms.

Here is what we did:

▶️ We asked an AI to build and use a scientific instrument. It wrote the force engine, structure generators, loading procedures, analysis tools, and experiment database. The independent campaign ran for multiple days without scientific intervention.
▶️ We required the instrument to pass physical and numerical tests. The implementation passed twenty validation tests and reproduced reference energies to approximately 10⁻¹³ eV per atom in the tested configurations. Every proposed design then faced the same reactive interatomic model.
▶️ We required predictions before results, creating a loop of world model building and falsification/verification. The AI had to commit to what unseen designs would do, then run the simulations. Incorrect predictions became opportunities to identify missing mechanisms.

What emerged is a set of physical design principles:

▶️ The atom-scale arrangement of matter controls strength. The AI first showed how and why strength varied by more than sixfold across architectures. Similar amounts of carbon produced very different resistance to failure because they organized the load-bearing connections differently.
▶️ Rotating a pattern can change the mechanism of failure. Angled slit arrays revealed three regimes: neighboring slit tips link, intervening ligaments rotate, or short bridges bend. A rule based only on the remaining cross-section misses these changes in connectivity and motion.
▶️ Hierarchical structuring works under identifiable conditions. At the original scale, much of its apparent strength advantage can be explained by alignment. With greater separation between structural levels, selected hierarchical designs became about 25% stronger than same-mass single-level controls and showed larger integrated stress-strain responses. Veins redistribute load, compartments localize damage, and the architecture changes how cracks propagate.
▶️ A failed prediction is crucial to reveal the next experiment. Some proposed rules survived targeted tests; others failed. Longer loading preserved the broad architectural strength contrasts while revealing additional deformation and, in some cases, later stress peaks. The scientific value lies in identifying both the rule and its boundary as the AI punctures known scientific knowledge.
▶️ The instrument opens an extremely complex design space. The AI was able to expand the design languages into an open atlas of hundreds of thousands of atomically explicit structures.

The deeper implication is that AI can construct an executable connection between equations, experiments, and explanations. Physical reasoning is something they can implement, interrogate, and revise.

A scientific instrument extends what a scientist can observe; and an AI that builds such an instrument extends the experiments it can perform, and the questions it can ask, starting from basic principles of how atoms interact based on quantum mechanical ground truth.

Models building models, with physical evidence shaping recursive reasoning loops.

[Denson smith @denson_smith]

@CEO_AISOMA If mathematicians and scientists collaborate to create e technology that no one person understands and a vast majority of people have no understanding did we progress? I’m talking about the device you are reading this on.

[florence ⊮ @morallawwithin]

please stop saying your calculator “adds” numbers, it only simulates addition

[Denson smith @denson_smith]

@bayeslord They answer questions like this one?

[Denson smith @denson_smith]

@JoelAbenhaim This week, maybe not next week.

[Denson smith @denson_smith]

@anah_sahh Why would I want to prove it?

[Denson smith @denson_smith]

@johnennis There is a fundamental difference between of opinion between those who think there is something special about our particular biological intelligence and those who think the maths that describe intelligence are the important bits.

[Denson smith @denson_smith]

@gailcweiner Except we have a horrible track record for creating, implementing and enforcing safety standards. We have safety standards for cars and driving still has a pretty high body count.

[Denson smith @denson_smith]

@pmddomingos The current models can use or create tools to help them reason.

[Denson smith @denson_smith]

@clairlemon I don’t believe anyone besides me is conscious. I’m pretty sure philosophers are over 2500 years into debating what consciousness is. I’m more interested in what ai does.

[Denson smith @denson_smith]

@johnennis Persistent state isn't missing, it's a design choice. Give an LLM memory tools or let it fine-tune itself and the same input yields different outputs. Calling that 'just external input' proves too much: synapses are stored history too. Where's the principled system boundary?

[Denson smith @denson_smith]

@VadimFerderer @vikktorrrre @MelissaKampers Except that current LLM’s are much better at connecting the dots than humans.

Here is one task that Astra saturates:

https://t.co/HgqnJnFhqa

[Denson smith @denson_smith]

@hypergameme Every time someone says something is “just a” they are full of shit. Humans are just a bag of mostly water.

[Denson smith @denson_smith]

@ArielKwiat There is a long list of things that LLM’s can do that LeCun has said they will never do such as reasoning and math. To answer this he says that if the LLM builds a tool to help it reason then the reasoning is not the LLM. Crazy.

[Denson smith @denson_smith]

@motorhueso If a human solves a math problem you call it thinking. If when an LLM solves the same math problem and you call it something else that is a religious statement not a scientific one.

[Denson smith @denson_smith]

@vikktorrrre @MelissaKampers Pick something you think you are good at and then we can have a competition between you and Astra.