i will never start another typescript application or project without @EffectTS_ it’s reached critical enough adoption, provides distinct tailwinds to agents, and has great cloudflare support
In verifiable domains, model capability scaling should remain unbounded. Models will simply keep improving by "absorbing more and more of the computational universe", which is infinite by construction.
within societies, more gender-egalitarian people almost never have more kids than less gender-egalitarian people, and in a panel model, changes in the mismatch rate have no relationship to fertility
I think most domains are fundamentally pretty shallow where like a very smart generalist who's good at like a a limited subset of core skills can like get going pretty quickly.
Like the thing that I think is most likely to be sort of the bottleneck in terms of like the AI are really good at verifiable domains but not not at doing the actual thing is just like big experiments. You only get a few tries um well a few is maybe a bit understated but like basically like historically R&D has been driven by doing near frontier scale experiments and that has been pretty important and like actually doing the one big training run where you decide exactly what to include in that.
But if you zoom out, it becomes clear that almost every “breakthrough” since last summer has concerned the narrow domains of computer code and math, which are defined by highly structured languages and come accompanied by massive amounts of specialized training data.
Um in our domain, to answer your last question, because we had to do so much stuff around security and partners and money movement and infrastructure and reliability and you know, all the things. We just we didn't feel like we could scale a really good self-serve experience without getting a lot of the kind of the preconditions um and the infrastructure in place.
I mean I think um probably one thing is people don't quite realize how possible it is to have you know agent-based systems that can run not just for an hour or two hours on a problem you care about but for some problem domains and with highly capable models underlying them you can get them to run for days or weeks and do really really complicated tasks
I mean I think like if you look at uh my colleagues work on say alpha fold that was a very specific model for uh protein folding and it was highly successful um and was able to really handle that domain quite well so that all of a sudden you now have this amazing tool and model that can give you answers to questions about proteins and their structure um really effectively um but it's not a general model it's a very specific one and there are other I domains where that kind of approach can work really well. Uh maybe in material science or chip design or things like that that uh will enable you to leverage the capabilities of a very accurate but but niche model uh to do things that are hard today.
And I think there's a lot of room in a lot of domains for much faster validation models, possibly learned valu validation models that can uh you know get you a a approximation to the true answer much much more rapidly. And that changes how those experimental loops can be thought of and how quickly you can go around those loops.
I think the case of TLA+ and most, not all, but most formal methods, they shine the most in highly computational domains, where most of the problems are highly technical and not like business embedded.
I think the simple stuff is going to get automated away, but the hard problems, the hard sciences, um physics, chemistry, biology, uh you know, computer science, uh computer engineering, systems thinking, uh you know, all and and particularly the domains that are intersecting, uh those hard problems will never go away.
So, we are hiring more people who can look across all the business domains and abstract that to here's the building blocks we're going to need in a world with AI.
The trust that they gained is the reason why they're a 70 billion dollar company today. But most leaders when asked to defend their principles can't do it because they've been taught ROI based thinking.
by listing out all these jobs to be done, you know, really for the for the community journey and for advertisers as well, it became very clear where we could use agents, where we needed to be very focused in terms of building cross-functional teams around those jobs supported by AI tools.
And so you're kind of like you're either on rails and you're part of the super intelligence circuits or you're not on rails and you're outside of the verifiable domains and suddenly everything kind of just like meanders.
If you get really good at coding that means you have to be really good at general purpose problem solving. So that's a skill, right? And that just maps into other domains.
building a third-party client to read and route messages is a gray area in their terms of service, and perhaps only partially supported in their API surfaces.
Thus my personal prediction is that in domains that are already largely under the powers of modern AI, such as languages, programming or chess, we’re going to see a divergence in human abilities.
but something to remember is that when a pilot is using a simulator to learn to fly an airplane, they're extremely goal- directed. So, their goal in life is not to learn to use a simulator. Their goal in life is to learn to fly the airplane.
And the history of NLP and language processing instruction, tuning and tasks per language model used to be like one language model did one task, and then in the instruction tuning literature, there's this point where you start adding more and more tasks together where it just starts to generalize to every task. And we don't know where on this curve we are.
I think my personal definition of AGI is much simpler. I think language models are a form of AGI and all of this super powerful stuff is a next step that's great if we get these tools. But a language model has so much value in so many domains that it's a general intelligence to me.
But their latest study is for only 7.1 million miles. They need something like 40 times more data prove they are actually saving lives with statistical confidence (likely even more).
A good life is supported by a diverse array of focal things and practices, which tend to reward us with deeper, more meaningful experiences; a gratifying measure of bodily skill and competence; and possibly even a stronger fabric of relationships.
One of the most important things I didn't understand about the world when I was a child is the degree to which the returns for performance are superlinear.
Reading the book had not changed my fundamental belief that, in balance, we were on a good trajectory with AI research: good for science and good for society with expected positive impacts in many domains.
Their words now
I now believe that I was wrong and short-sighted to ignore that dual-use nature. I also think I was not paying enough attention to the possibility of losing control to superhuman AIs.
The likelihood that someone is providing high-quality information in these domains is lower than in less controversial areas like physics or neuroscience.
So whether it is Robert Moses then or Elon Musk today, people who would have – in a time past – been called charlatans or hustlers, now become deeply admired for their willingness to take bold action (with public money or supported by tax subsidies).
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