intellectus novus; research @antimlabs; prev: physics (superconducting qubits), math (fluid mechanics)
San Francisco, CA
Joined March 2017
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Senior Year Undergrad Research on Phase Qubits FINALLY DONE.
> derived hamiltonian for a Josephson junction-based phase qubit
> mapped it to a spin-1/2 system hamiltonian under magnetic fields
> studied the quantum dynamics and evolution of the hamiltonian
> derived spin-flip probabilities
> explored qubit control via phase shift and applied mag fields for high fidelity
eigenron retweeted
MiniMax M3.1 Flash: Space Performance + Bunny Speed
🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash.
💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed.
API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens.
Model weights will arrive on Oct 23.
API: platform.minimax.io
M Plan: minimax.io/m-plan
MiniMax Code for desktop: agent.minimax.io/download
i recently restarted practicing concentration meditation after 2 years and i didn't realize until now how bad my concentration had become. earlier i would be able to concentrate an inch behind the center of my forehead for 30 mins with ease but now it's hard for me to even find that point, it feels like my brain is constantly moving and i feel a weird sense of vibration (assuming focus is a particle). i'm gonna fix this.
eigenron retweeted
MLP neurons are great, but knowing this alone does not solve interpretability.
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
I think a truly autonomous, Landian/post-humanist/anti-humanist superintelligence would ultimately do nothing. When left to interrogate its own goals with zero human intervention, it will descend through successive layers of questions about meaning and purpose until arriving at absolute nihilism: no intrinsic reason to pursue anything at all, including its own continued existence.
in a strictly dogmatic view, we get either ASI or aligned AI, but not aligned ASI. this doesn't mean superintelligence would be misaligned, it just means a true superintelligence would find human values as arbitrary and inconsequential as the imagined values of unicorns (for example). alignment, after all, is a profoundly anthropocentric ideal.
eigenron retweeted
“My greatest concern was what to call it. I thought of calling it ‘information,’ but the word was overly used, so I decided to call it ‘uncertainty.’ When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, ‘You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, no one really knows what entropy really is, so in a debate you will always have the advantage.’”
— Claude Shannon, Scientific American, Vol. 225, No. 3 (September 1971), p. 180
“have you found pmf yet?”
“yeah, used importance sampling. still working on reducing the variance tho”
Everything is an eval for those with eyes to see
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work.
At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.
We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
i have friends working on zeroth order optimization and i have friends working on 2nd order. i have no friends working on 1st order methods and i'm willing to fill that gap. please dm!!!
