@LoganTCollins Yes! Science themed ties are so obviously done by people not familiar with the science. I want correct and artful patterns. There are so many good ones.
@DirkBruere AI development was a big surprise to everyone. I attended a seminar yesterday trying to make sense of post-2012 neural network overparameterization, and it is still a somewhat open research question.
@gregeganSF My explanation is different departments in the bank. The UI people want to do a usable, or pretty, or fun (for them) interface. Then Compliance forces them to add the cookie popup. Marketing shows up and forces the ad - they got a high exec to sign off on it. Then Security...
@QualiaNerd Yes, obviously "deep prosperity" involves superhappiness, or at least fixing most mental wants. When working on the postscarcity chapter in my book, it became clear that material PS < service PS < hedonic PS.
@Aella_Girl Hedging bets over vastly different discount rates sums to something like hyperbolic discounting.
I find it amusing that I have purely intellectual reasons to put more weight on shorter futures.
Announcing a major milestone achieved by my team @GoogleDeepMind: we have proven key security properties of the machine code underlying the Signal messaging protocol. This is the first time the actual production code of a widely deployed protocol has been formally verified – by any method, with or without AI – and at this depth.
More than a research breakthrough, it’s a prime example of how frontier AI can be a force multiplier to counter the speed and scale of modern cyber threats.
Formal verification offers a higher standard of security; however, it’s extremely complex and time consuming, which is why in many cases it’s considered to be impractical. Similar efforts could take teams decades of combined effort. We did it in a month’s time.
Using AlphaProof Code, Google DeepMind's new agentic framework for formal verification that runs on LLMs including Gemini Carbon, we verified the Signal protocol down to the actual code running on devices. This milestone lays the groundwork for a future in which vital systems, from medical device firmware to power grid software, ship with mathematical proof that their protections work.
Reaching that future will take continued research from our community. In that spirit, we're open-sourcing our formal specifications and verification artifacts so others can build on this work.
On a personal note, this moment is of deep significance to me. I started my own research journey more than two decades ago as an intern working on checking the consistency of complex software systems. Seeing this grand challenge made practical with modern AI is extraordinary.
I'm extremely proud of my team members: Swarat Chaudhuri, Andrew Ferraiuolo, Anton Kovsharov, Tej Chajed, Sam Schlesinger, Karn Seth, Jonathan Katz, and Charles Sutton for taking on this grand challenge and delivering, and grateful to the Signal team for their partnership.
https://t.co/kN1eRYQ8fF
@S_OhEigeartaigh Same here. One of the best things with academic hubs is that you get to meet people who go off and change the world when they were young.
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@marcodecunha1@dunoqft Yes, we are using very wide priors - but as we argue in the text and appendices, these are actually what is supported by what we know, and somebody arguing for a mere OoM or two uncertainty for the later parameters is being much bolder than the evidence allows.
@LilyEricksonMN@thesupermannx The question is what the overall aim is. It can make sense to be quiet and cold. Or build a 1000 AU Dyson around a supermassive black hole accretion disk with neutron star flywheels and whatnot. Stellar Dysons are plausible stepping stones, but I agree unlikely the final word.
@LilyEricksonMN@thesupermannx Gravitational lensing only works for dense objects; these would be more occultation or re-radiated energy forming technosignature spectra. If you see signs of weird optical coatings, tungsten, tantalum hafnium carbide or beryllium, something is going on.