Jonathan Siddharth

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Jonathan Siddharth

Jonathan Siddharth

@jonsidd

Founder and CEO, Turing. Accelerating superintelligence to drive real economic progress @turingcom.

Stanford, CA Katılım Kasım 2008
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Jonathan Siddharth
Jonathan Siddharth@jonsidd·
Open MM-RL Dataset is trending on @huggingface. We built something I've wanted for a long time. - PhD-level STEM reasoning across physics, math, biology & chemistry - 100% verifiable, auto-gradable answers - Single-image, multi-panel & multi-image formats - Two-round expert review on every problem - RL-ready reward structure out of the box Most multimodal dataset test perception. This one tests reasoning. The kind that doesn't break under scrutiny. Built by PhD SMEs. Validated for frontier models. Open to the community. Website & Dataset below.
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Jonathan Siddharth
Jonathan Siddharth@jonsidd·
AGI is already here and has been here for a while.
nic carter@nic_carter

The “it’s not AGI because machine intelligence is jagged” is dumb cope. It’s obviously AGI. If you had a friend who had a 130 IQ, could write production code flawlessly, could write academic papers of a high research caliber, pass any exam in any field with flying colors, create a sophisticate LBO model, draw technical diagrams perfectly, compose poetry in any language, and could find solutions to significant unsolved mathematical problems, you would call that person a world historical genius. Certainly, no single human has ever had intelligence that “general” before. Now you think it’s “not AGI” because it sometimes slips up and makes mistakes - so does any human that you would consider “extraordinarily intelligent.” The professor might forget a colleagues name that he has known for a decade. He is still considered intelligent. The math genius might be a little autistic and shy, unable to maintain polite conversation. Still intelligent. You might stare at the fridge for 30 seconds unable to find the butter, despite 5 million years of evolution perfecting your visual intelligence. We give intelligent humans a pass when they have jagged intelligence. So why the double standard? The qualities people list as “necessary for AGI” are important traits to have, but no longer pertain to intelligence. People will say things like “true AGI requires agency, long term goal setting, embodiment, self-direct action”. But none of those things are intelligence. Those are “things that humans have that AI lacks”. Raw intelligence, AI has it in spades. That other stuff - important yet, but broader than and different from intelligence. The unwillingness of people to acknowledge that AGI obviously exists and has existed for a while is due to a kind of anthropic chauvinism - a psychological need to believe that humans are superior in every respect, that we possess soft skills that no machine could replicate. Yes humans are different from machines, but if we are limiting the discussion solely to general intelligence, AI has it already. That battle is over. If you want to reframe the discussion to matters of human dignity and personhood, fine, but that’s not an AGI question. That’s something else. Just take the loss on AGI already. It’s over.

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Jonathan Siddharth
Jonathan Siddharth@jonsidd·
Open MM-RL Dataset is trending on @huggingface. We built something I've wanted for a long time. - PhD-level STEM reasoning across physics, math, biology & chemistry - 100% verifiable, auto-gradable answers - Single-image, multi-panel & multi-image formats - Two-round expert review on every problem - RL-ready reward structure out of the box Most multimodal dataset test perception. This one tests reasoning. The kind that doesn't break under scrutiny. Built by PhD SMEs. Validated for frontier models. Open to the community. Website & Dataset below.
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Jonathan Siddharth
Jonathan Siddharth@jonsidd·
Turing is hiring Strategic Project Leads at gigantic scale with a focus on coding and enterprise. This is the role for people obsessed with running a tight ship while building at the frontier of superintelligence. The job: own the human data programs that train every frontier model worth training. Work with all the frontier AI labs and neo labs. Turing is the only company in this space building both ends of the research and enterprise deployment loop. This is a founder-mode company. We want operators with the same posture. Ex-founders, consultants, investment bankers, finance operators, technical PMs, engineers who've run a program end to end. The bar: exceptional ability and entrepreneurial DNA. The best SPLs don't fit in the box. They break it and shape a new one. Comment if you're interested. Tag someone who should be. DM me or email jonathan.s@turing.com. I'll read every application personally.
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Jonathan Siddharth retweetledi
Jonathan Siddharth retweetledi
Turing
Turing@turingcom·
Introducing the Open MM-RL Dataset. A PhD-level multimodal STEM benchmark built for verifiable reasoning across physics, chemistry, biology, and math. Four STEM domains, one dataset -Physics: Quantum and Particle Physics, Condensed Matter and Materials, Electromagnetism, Photonics, and Plasma Systems, Astrophysics and Space Physics -Mathematics: Algebra and Structure, Discrete Mathematics, Analysis and Continuous Mathematics, Probability and Geometry -Biology: Evolutionary Systems, Molecular Mechanisms, Cellular Processes and Neural Biology -Chemistry: Chemical Structure, Reaction Mechanisms, Synthesis, Spectroscopy and Properties We're raising the bar.
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Jonathan Siddharth retweetledi
Turing
Turing@turingcom·
Benchmarks don't show you where AI actually breaks. They show you performance on curated tests. Not on your messy data. Not on your edge cases. Not at 2am when something goes wrong in production. The gap between benchmark and reality is where most AI projects die. Read more below.
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sesh
sesh@seshadrinithin·
@jonsidd The jagged edges of intelligence really show up when ai outputs need to be grounded in operating knowledge specific to a company. Massive opportunity if this can be solved for.
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Jonathan Siddharth
Jonathan Siddharth@jonsidd·
Everyone has algorithms. Everyone is buying compute. The next axis of advantage is different. It is not what model you run. It is whether your model understands your domain, your workflows, your edge cases. That is the moat. And most companies have not started building it yet.
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Bill Macaitis
Bill Macaitis@SaaS_CMO_Pro·
@jonsidd nails a shift most people are still underestimating: the winners in AI won’t be defined by model benchmarks, but by how fast they close the loop between real enterprise deployment → failure signals → better data → stronger models. What stands out is that the hardest problems only show up in production, not in the lab, and those “failures” are actually the highest-quality training data if you know how to capture them. This is why the companies that can operate across both sides of the loop, deploying systems and feeding insights back into model improvement, will compound faster than everyone else. Really impressed with the vision that @turingcom is laying out here.
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