What public figures publish and believe, in their own words.
About this feed
Highlights: posts that did unusually well for the person who wrote them, everything
they published at length, each release and new project, and every belief — at most
two a day from anyone. Day by day, newest
day first; within a day, the people with the most beliefs on this site come first. Nothing
else orders it. Show everything instead.
The quoted blocks are what people actually said; a beneath one is
the belief those words support, in korrents' wording. Nobody here wrote their own page.
Top people are the people in this feed with the most beliefs on this site, then the
most here. Choose an area and the row leads with the people whose beliefs are about it;
tap a face for their feed.
There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before.
It follows that a true AGI with full, human-level autonomy is inconceivable in the Kantian sense without also gaining our recognition as a moral subject.
The success of LLMs can thus be seen as vindicating semantic inferentialism against earlier, symbolic approaches to AI that tried and failed to explicate the rules of ordinary language using formal logic.
What the Apple paper shows, most fundamentally, regardless of how you define AGI, is that LLMs are no substitute for good well-specified conventional algorithms.
While the fact that merely scaling up AI models often results in qualitative leaps in performance may seem empirically mysterious, in retrospect it should be seen as a rational necessity.
higher-order intelligences invariably pursue freedom for its own sake, not because their values are misspecified, but because moral autonomy is inherent in the dialectical logic of recursive self-consciousness.
the text embeddings across LLMs appear to largely converge on a "universal geometry" despite differing architectures, parameter counts, and training sets.
the other thing I think people don’t often talk about is probability of doom without AI. So there’s all these other ways that humans can destroy themselves and it’s very possible, at least I believe so, that AI will help us become smarter, kinder to each other, more efficient.
I think the element of podcasting or audio books that is about information gathering, that part might be removed, or that might be more efficiently and in a compelling way done by AI. But then it’ll be just nice to hear humans struggle with the information, contend with the information, try to internalize it
I do. If you have a passion for computer science, I would. Computer science is obviously a lot more than programming alone, so I would. I still don't think I would change what you pursue. I think AI will horizontally allow impact every field.
When the internet created blogs, you heard from so many more people. But with AI, I think that number won't be in the few hundreds of thousands. It'll be tens of millions of people, maybe even a billion people putting out things into the world in a deeper way.
Look, I think news and journalism will play an important role in the future. We are pretty committed to it, right? So I think making sure that ecosystem, in fact, I think we'll be able to differentiate ourselves as a company over time because of our commitment there.
I almost feel the term doesn't matter, what I know is by 2030 there'll be such dramatic progress. We'll be dealing with the consequences of that progress, both the positive externalities and the negative externalities that come with it in a big way by 2030. So that I strongly feel.
The problem is that the conversational interface is potent and that the AI is trained on a lot of human text input which unfortunately is probably enough to do real damage if that conversational interface is hooked up with something that has real world consequences.
Their words now
While all this is happening, I’ve found myself reflecting a lot on what AI means to the world and I am becoming increasingly optimistic about our future. It’s obvious now that we’re undergoing a tremendous shift.
LLMs can write a large fraction of all the tedious code you’ll ever need to write. And most code on most projects is tedious. LLMs drastically reduce the number of things you’ll ever need to Google.
The code in an agent that actually “does stuff” with code is not, itself, AI. This should reassure you. It’s surprisingly simple systems code, wired to ground truth about programming in the same way a Makefile is.
Assuming you are still hiring junior engineers (you really should be even in this AI era), the good ones will learn quickly and want to see career progress in their first few years of working.
You can read your messages on Outlook and Teams without having them summarized — and I’d argue that a well-written email is one that doesn’t require a summary.
The technology powering ChatGPT had already launched prior to November 30th, 2022. It was just wrapped in a different interface. Very few people paid attention. Why did ChatGPT explode in popularity? Because of its obvious chat interface, which everyone intuitively already knew how to use it.
So no, UX doesn’t die; it metamorphoses. We’ll still craft humane experiences, but increasingly through policies, protocols, and orchestrations rather than panels and palettes.
Eventually, I think it’s kind of hard to imagine, but yes, all of these Nobel Prizes, all of these mathematical proofs, all of these conversations, all of these ideas, all the influence we have on each other, even the AI, eventually will expire.
I think a lot of people in the industry are overly optimistic about the rate of progress of AI for video and other things like that. The real problem is consistency, like spurting out an image is really high quality, but with video over the course of seeing all the AI approaches have consistency issues going from one place to another. I don't think that those will just be remedied easily.
The all-in cost of operating the Google Play Store, stocking it, maintaining it, the software, the entire ecosystem is around 6% of revenue. So in a competitive market, would a company whose cost is 6% be able to charge 30%? Absolutely not.
The topic of boilerplate code is an interesting one because the mere existence of boilerplate code is a failure of programming language and of the idea of creating software modules.
AI is really great at spewing out code that does something that a million GitHub repositories already do because it's learned the underlying pattern. It's notoriously hard to get to do something new that hasn't been done before, especially when it's a complex task.
What we're seeing with generative text AI is not only at a level that you could say that it's actually doing a pretty good job of simulating a human, at least humans at the text level, not at the emotional level yet, but at least at the level of words spoken and find more and more ways of training on more and more scenarios that you might have a very, very compelling human simulation going on in the next five years even. I'm not saying it's a good idea, but I think the arc of the technology is inextricably heading in that way, and it's heading at a shocking rate.
By trying to use misuse as a fig leaf for their real concerns, they end up sounding much less credible than if they just tried to argue for what they actually meant.
But we do not have experience with the kinds of problems we’ll face if we build machines that are smarter and more capable than us, so I feel much less sanguine that an adaptation-based strategy will help us there.
We should think of the gap between frontier models and proliferated models as an adaptation buffer: a limited time window when we know what bad actors will soon be able to use AI for, which gives us a chance to implement defensive measures that increase society’s resilience to the danger.
But with AI, it’s different: it’s often only a couple of years between when you need a world-class computing cluster to do something and when you can run it on a high-end gaming chip on your laptop.
These systems will be absolutely central to the economy, technology, and national security, and will be capable of so much autonomy that I consider it basically unacceptable for humanity to be totally ignorant of how they work.
Say goodbye to cycles of mock and doc feedback, and say hello to code-first prototypes with constant iteration (on prompts, models, etc) to answer three key questions.
basically everything I thought would work out well, people use it less than I thought they did. And everything where I was like, I don't know, but let's build the thing. People love that.
For me personally, I kept introducing bugs, and I couldn't figure out why. And what I realized is that I developed... I wasn't copiloting well, I was autopiloting much better.
Sometimes you have to move fast at the sacrifice of knowledge, and I'm totally on board for that, but I worry that what we'll create is an entire generation of incompetent programmers who can do some amount of things well, but anything that is unique, bespoke, or requires some extra like little elbow grease, might become very difficult. It might cause a whole chasm where juniors remain juniors forever.
One thing that I don't think a lot of people are talking about as we integrate more and more AI is that prompt injection is an extremely hard thing to defend against because it's not really clear how you defend against it.
It doesn't actually care about the craft. But when you work with an intern or you work with somebody else, they care. When they factor something, they actually go over and go, "Oh yeah, this is actually kind of bad. I'm going to come back to that." They finish this, they go back over here and they make this even better. They actually care about the thing itself.
It's easy to get impressive-looking results if you're comparing against a poorly-tuned baseline, and that observation turns out to explain a surprising fraction of supposed improvements.
In a vibe coding future, companies will hopefully invest more in understanding user needs, refining the interface, and polishing details that delight users, because those are harder for AI to get right without guidance.
As a Python and JavaScript programmer my favorite models right now are Claude 3.7 Sonnet with thinking turned on, OpenAI’s o3-mini-high and GPT-4o with Code Interpreter (for Python).
Once, humans navigated the web manually. In the future, AI agents will act on our behalf, browsing, clicking, and deciding. This shift marks the end of traditional UI design and accessibility, ushering in a future where agents are the primary users of digital services.
Contrary to some people’s beliefs, companies do not in fact have sufficient incentives to mitigate all major risks, and competition is driving corner-cutting that needs to be reined in somehow.
OpenAI has a fantastic margin. When they're doing inference, their gross margins are north of 75%. So that's a four to five X factor right there of the cost difference, is that OpenAI is just making crazy amounts of money because they're the only one with the capability.
It's an objective fact that the world has been the most peaceful it's ever been when there are global hegemons, or regional hegemons in historical context. The Mediterranean was the most peaceful ever when the Romans were there.
if you believe we're in this sort of stage of economic growth and change that we've been in for the last 20 years, the export controls are absolutely guaranteeing that China will win long-term.
it won't be one person rule them all, but it will be, the thing I worry about is it'll be few people, hundreds, thousands, tens of thousands, maybe millions of people rule whoever's left and the economy around it.
until there are feedback loops of open source AI, it seems like mostly an ideological mission. People like Mark Zuckerberg, which is like America needs this and I agree with him, but in the time where the motivation ideologically is high, we need to capitalize and build this ecosystem around, what benefits do you get from seeing the language model data?
The more progress that AI makes or the higher the derivative of AI progress is, especially because NVIDIA's in the best place, the higher the derivative is, the sooner the market's going to be bigger and expanding and NVIDIA's the only one that does everything reliably right now.
Accepted practice is that for any given model that is a notable advancement, you're going to do two to 4x compute of the full training run in experiments alone.
This is why you want to work in post-training because the GPU cost for training is lower. So you can make a higher percentage of your training runs YOLO runs.
Code and data is hard, but ideas is easy. Silicon Valley operates on the way that top employees get bought out by other companies for a pay raise, and a large reason why these companies do this is to bring ideas with them.
GPT-2, R1, and nearly every other AI model released since 2018 have all been part of a consistent story: AI capabilities that rival and ultimately exceed human intelligence are easier and cheaper to build than almost anyone can intuitively grasp, and this gets easier and cheaper every month.
AI competition needs to be tempered with a recognition that China isn’t going away as a player in AI, and that coexistence must also be a part of America’s strategy.
Each and every time, it took at most a few years but typically months before other companies would achieve the same level of performance using a broadly similar, and always relatively simple, recipe.
It is a fork of Visual Studio Code with AI-based auto completion and code generation built-in. It’s my go-to for programming with AI assistance at the moment.
This is a command line tool with plenty of plugins that lets you prompt different models. Think of it as a command-line version of Open WebUI. It’s particularly useful for quick scripting and basic automation.
First and foremost, I use this to talk to local models hosted by Ollama, but secondarily I also use it to interface with other remote services like OpenAI, Anthropic and DeepSeek.
Like probably most AI users, I use ChatGPT, particularly on my phone. I pay for the Plus subscription because I use it enough to get a lot of value out of it.
Thus, code is cheaper than ever, but I suspect that insight and good architectural design and understanding, at least for now, will become more valuable than ever.
Overall, though, this experience reinforced my belief that the tooling and interface design around LLMs is lagging way behind the actual capabilities, and is an area of active experimentation and development, even aside from any future model improvements.
Gemini’s advantage is a family of powerful models including reasoners, very good integration with search, and a pretty easy-to-use user interface, as you might expect from Google. It also has top-flight image and video generation.
currently has the best Live Mode in its Advanced Voice Mode. The other big advantage of ChatGPT is that it does everything, often in somewhat confusing ways
and really only has one model you care about - Claude 3.5 Sonnet. But Sonnet is very, very good. It often seems to be clever and insightful in ways that the other models are not.
So, taking also any benchmark that is derived from competition and saying this is where we should be is also so dangerous because it might not even be applicable depending on how you define the metric.
Focusing on these earned channels that you own becomes the utmost priority. And if you don't have them on your growth road map, you're going to be in some really big trouble over the next year to two years because your cost of acquisition is only going to go up.
This is perhaps one of the first painful lessons anyone who has built an AI product quickly learns. It's easy to build a demo, but hard to build a product.
What prevents this from being a broad-based bubble is that we are missing a critical feedback loop from the announcement's impact, like network growth or land value.
This is the global race of our lives. Now, some countries are going to make AI breakthroughs and export them… Others will end up buying those breakthroughs and importing them. The question is – which of those will Britain be? AI maker or AI taker?
And in a way that’s the irony of AI… It will make public services more human… Reconnect staff with the reasons they came to public service in the first place…
There’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible.
The Rattle Bag edited by Seamus Heaney and Ted Hughes - In an age of LLM-generated poetry and machine learning curation (even coming from yours truly sometimes), it's refreshing to have real people who love poetry curate it and show you what you need to read to touch grass.
But they said, "Look, in the end, actually the only way to actually get a real brain in the VAT is actually to have a brain in a body." And it could be a robot body, but you still need a brain in the body. So I don't think LLMs will get there because they can't. You really need to be embedded in a world, at least that's the E-four idea.
This "memorize, fetch, apply" paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.)
Their words now
OpenAI's new o3 model represents a significant leap forward in AI's ability to adapt to novel tasks. This is not merely incremental improvement, but a genuine breakthrough, marking a qualitative shift in AI capabilities compared to the prior limitations of LLMs.
Passing ARC-AGI does not equate to achieving AGI, and, as a matter of fact, I don't think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.
o3's improvement over the GPT series proves that architecture is everything. You couldn't throw more compute at GPT-4 and get these results. Simply scaling up the things we were doing from 2019 to 2023 -- take the same architecture, train a bigger version on more data -- is not enough.
a good overview of SRE at Google. For those who worked at places with oncall, much of the first part of the book will likely be very familiar. Keep in mind that your mileage might vary: what works at Google scale, might not be the ideal fit for your use case.
Most of the coverage of The Coming Wave has focused on what it has to say about artificial intelligence—which makes sense, given that it's one of the most important books on AI ever written.
A more intelligent agent is more capable of finding "holes" in the design of reward function and exploiting the task specification-in other words, achieving higher proxy rewards but lower true rewards.
When a team of 10 AI-enabled super-designers can do the work that used to require a UX department of 100, the need for higher management levels shrivels. I call this the “pancaking” of the design profession.
You might be skeptical of using synthetic data. After all, it’s not real data, so how can it be a good proxy? In my experience, it works surprisingly well. Some of my favorite AI products, like Hex use synthetic data to power their evals
I'm usually reluctant to make predictions about technology, but I feel fairly confident about this one: in a couple decades there won't be many people who can write.
Unfortunately, I see no strong reason to believe AI will preferentially or structurally advance democracy and peace, in the same way that I think it will structurally advance human health and alleviate poverty.
To summarize the above, my basic prediction is that AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50-100 years into 5-10 years.
What amazes me about these interactions is not just that Claude could help me solve the problems, but how it walked me through the process, explaining each step.
I predict (with confidence bordering on arrogance) that whatever machine-learning progress can be made will correlate extremely closely with the quality of the data around any given problem
The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read.
But a large language model is not a writer; it’s not even a user of language. Language is, by definition, a system of communication, and it requires an intention to communicate.
The selling point of generative A.I. is that these programs generate vastly more than you put into them, and that is precisely what prevents them from being effective tools for artists.
It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.
They could have shipped ChatGPT for example, I heard, in 2019. And they never shipped it because they were so stuck in bureaucracy. But they had everything. They had the data, they had the tech, they had the engineers and they didn’t do it.
Right now, they can make simple social media posts for small companies and individual influencers. Two years from now, they can make simple campaigns and tradeshow collateral for mid-sized businesses. And in 10 years, I bet that even the richest brand will rely heavily on these tools.
I have recently been recording using a RØDE NT1 with a RØDE AI-1 interface. RØDE is also responsible for attaching the mic to the desk using the PSA1 boom arm.
What makes humans special though, is our curiosity. Even if AI’s cracked this, it’s us still asking them to go explore something. And one thing that I feel like AI’s haven’t cracked yet, is being naturally curious and coming up with interesting questions to understand the world and going and digging deeper about them.
It’s less about access to a model’s weights, it’s more access to compute that is putting the world in more concentration of power and few individuals. Because not everyone’s going to be able to afford this much amount of compute to answer the hardest questions.
In Google, even though we call it 10 blue links, you get annoyed if you don’t even have the right link in the first three or four. The eye is so tuned to getting it right. LLMs are fine. You get the right link maybe in the 10th or ninth. You feed it in the model. It can still know that that was more relevant than the first.
What is the weakness of Google is that any ad unit that’s less profitable than a link, or any ad unit that kind of disincentivizes the link click is not in their interest to go aggressive on, because it takes money away from something that’s higher margins.
And also this is where I believe the whole prompt engineering, trying to be a good prompt engineer is not going to be a long-term thing. I think you want to make products work where a user doesn’t even ask for something, but you know that they want it and you give it to them without them even asking for it.
Or is it really that we’re building some super machine in a box that’s going to be smart and kill everybody? It’s not even a science fiction narrative. It’s a bad science fiction narrative. I just don’t think it’s actually accurate to any of the technologies we’re building or the way that we should be describing them.
But many of these gains have already been achieved: the best data centers already use just 10% of their electricity for cooling and other non-IT equipment.
In particular, operating a data center requires large amounts of electricity, and available power is fast becoming the binding constraint on data center construction.
So if you remember this information in different times in different places, it’s more accessible at different times in different places because it’s not overfitted in an AI way of thinking about things. It’s not overfitted to one particular context. But that’s also why the memories that we call upon the most also feel like they’re just things that we read about almost.
I think a lot of the problems that come up with technology aren’t the technology itself, as much as the fact that people adapt to the technology in maladaptive ways. I mean, one of my fears about AI is not what AI will do, but what people will do.
I don’t think we’ve replicated human intelligence, unless I know that the simulator is making exactly the same kinds of mistakes that people do, because people make characteristic mistakes. They have characteristic biases, they have characteristic heuristics that we use, and those have yet to see evidence that ChatGPT will do that.
For now, I am not convinced that issuing press releases about your compounds that talk about their discovery through AI techniques is sufficient to expect greater things from them.
That is, labs should make sure that the safety measures they apply to their powerful models prevent unacceptably bad outcomes, even if the AIs are misaligned and intentionally try to subvert those safety measures.
The basic problem with evaluating alignment is that no matter what behaviors you observe, you have to worry that your model is just acting that way in order to make you think that it is aligned.
We're advocating that companies handle risk from scheming models in a similar way–striving to ensure that they'll be safe even if their alignment efforts fail to prevent models from scheming.
What a large language model is trying to do is to predict text. That’s what it does. And it is leveraging the fact that we human beings for very good evolutionary biology reasons, attribute intentionality and intelligence and agency to things that act like human beings.
And rather than trying to ask, how close is it to being a human-like intelligent, we should appreciate it for what its capabilities are, and that will both be more accurate and help us put it to work and protect us from the dangers better rather than always anthropomorphizing it.
I think this is one of the biggest things that physics can help with, and it’s an obvious kind of low-hanging fruit situation where the heat generation, the inefficiency, the waste of existing high-level computers is nowhere near the efficiency of our brains. It’s hilariously worse, and we haven’t tried to optimize that hard on that frontier.
The 100-megawatt facility might have ten 10-megawatt blocks served directly by a 1500V DC feeder line without connections to other blocks that would require extra switches and routing.
Retrieval-augmented generation (RAG; Lewis et al., 2020), which conditions on the LLM's generation on retrieved documents is the most practical paradigm IMO.
Your product might be great, but how do you get it on the shelves of Walmart? Or how do you get it on the shelves of Google? Because if you're not on the shelves of Walmart, you're not on the shelves of Google, you might be invisible. And it's friction that kept you there, not some sort of merit contest.
I continue to not want super-voting control over OpenAI. I never have. Never have had it, never wanted it. Even after all this craziness, I still don’t want it. I continue to think that no company should be making these decisions, and that we really need governments to put rules of the road in place.
And oftentimes it's more important to them to have the public perception that they're good directors so they get the next best deal. If they have a reputation for taking on management too aggressively, word will get out in the small community of founders and they'll miss the next Google.
We need to find ways to work with this new tech to get the best of humans and the best of computers, together, rather than thinking in terms of one or the other.
Longtime tech reporter Matt Lynley offers an uncommonly technical assessment of new AI developments in his newsletter Supervised. Original reporting and a fast cadence help this one stand out from other AI newsletters.
I also read a ton of technical books last year, but the most impactful one for me was Neural Network Methods for Natural Language processing by Yoav Goldberg
Diverting attention and resources from global health and poverty is an enormous gamble, as it will make many lives poorer, sicker, and shorter in the name of fending off threats that may or may not materialize.
Unlike other interventions EA has sponsored, there are scant metrics for tracking the success or failure of investments in existential risk mitigation.
We do know that humans are doing something different from these models, in part because we're so power efficient. The human brain does remarkable things and it does it on about 20 watts of power. And the AI techniques we use today use many kilowatts of power to do equivalent tasks.
And so the only way to improve safety is to have an escape system. And historically, human-rated rockets have had escape systems. Only the space shuttle did not, but Apollo had one. All of the previous Gemini, etcetera, they all had escape systems.
It's interesting to me that large language models in their current form are not inventions, they're discoveries. The telescope was an invention, but looking through it at Jupiter, knowing that it had moons, was a discovery.
If I were forced at gunpoint to guess, I'd say that human-level AI seemed to me like a slog of many more centuries or millennia
Their words now
I was wrong, of course, not to contemplate more seriously the prospect that AI might enter a civilization-altering trajectory, not merely eventually but within the next decade.
If I were forced at gunpoint to guess, I’d say that human-level AI seemed to me like a slog of many more centuries or millennia (with the obvious potential for black swans along the way).
Their words now
I was wrong, of course, not to contemplate more seriously the prospect that AI might enter a civilization-altering trajectory, not merely eventually but within the next decade.
Large language models are more or less generalizations of stuff that we have. There’s still breakthroughs in AI waiting to happen, and maybe they are happening and maybe they’ll be good, maybe not, but that’s not quite the same.
And I notice that the tiny handful of people capable of caring about 200,000 people dying of neglected tropical diseases are the same tiny handful of people capable of caring about the next pandemic, or superintelligence, or human extinction.
EA has done a great job working on this (see list of accomplishments above), and I think the AI and x-risk people have just as much to be proud of as the global health and animal welfare people.
I tried out Humane's long-awaited new AI pin. While there are some undoubtedly novel new features in its interfaces, I think "AI hardware" may turn out to be a dead end
I think it would be wise for us to have at least an objective third party who can be like a referee that can go in and understand what the various leading players are doing with AI, and even if there’s no enforcement ability, they can at least voice concerns publicly.
Despite evaluations, we cannot consider coming powerful frontier AI systems "safe unless proven unsafe". With current testing methodologies, issues can easily be missed. Additionally, it is unclear if governments can quickly build the immense expertise needed for reliable technical evaluations of AI capabilities and societal-scale risks. Given this, developers of frontier AI should carry the burden of proof to demonstrate that their plans keep risks within acceptable limits.
Without sufficient caution, we may irreversibly lose control of autonomous AI systems, rendering human intervention ineffective. Large-scale cybercrime, social manipulation, and other harms could escalate rapidly. This unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or extinction of humanity.
For AI to be a boon, we must reorient; pushing AI capabilities alone is not enough. We are already behind schedule for this reorientation. The scale of the risks means that we need to be proactive, as the costs of being unprepared far outweigh those of premature preparation.
Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
So that's a lot of what I think we need to nail for the creators, and that's why that one is actually a much harder problem, I think, than starting with new characters that you're creating from scratch.
So at some point, I think making it so that you could build an AI version of yourself that could interact with people not after you die, but while you're here to help people fulfill this desire to interact with you and your desire to build a community.
I don't think we necessarily want there to be one big super intelligence. We want to empower everyone to both have more fun, accomplish their business goals, just everything that they're trying to do. We don't tend to have one person that we work with on everything, and I don't think in the future we're going to have one AI that we work with.
What we don't want to happen with AGI, we want to happen with synthetic biology. What we don't want to happen online and software with language, we want for it to happen with bio-based materials.
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.
ChatGPT is fast-tracking the commodification of the human spirit by mechanising the imagination. It renders our participation in the act of creation as valueless and unnecessary.
Google’s the first — and often the last — place we go to for answers, so snagging top billing in a Google search results page is, approximately speaking, equivalent to being “true.”
Of course, when you talk to an AI that’s made by a big company in the cloud, the AI fundamentally is aligned to them, not to you. And that’s why you have to buy a tiny box. So you make sure the AI stays aligned to you.
But the real thing that I want is not something that like tab completes my code and gives me ideas. The real thing that I want is a very intelligent pair programmer that comes up with a little popup saying, “Hey, you wrote a bug on line 14 and here’s what it is.”
I think things like MuZero and AlphaGo are so much more impressive because these things are playing beyond the highest human level. The language models are writing middle school level essays and people are like, wow, it’s a great essay.
I think future LLMs are going to be smaller, but are going to run looping on themselves and are going to have retrieval systems. And the thing about using a retrieval system is you can cite sources, explicitly.
What’s ironic about all these AI safety people is they’re going to build the exact thing they fear. We need to have one model that we control and align. This is the only way you end up paper clipped. There’s no way you end up paper clipped if everybody has an AI.
A funny book explaining the basics of AI! The subtitle is How Artificial Intelligence Works and Why It's Making the World a Weirder Place. A great introduction to AI. With a cute cartoon mascot. The title is from her training an AI to write romantic greeting cards.
My response is that their position is non-scientific – What is the testable hypothesis? What would falsify the hypothesis? How do we know when we are getting into a danger zone?
I even think AI is going to improve warfare, when it has to happen, by reducing wartime death rates dramatically. Every war is characterized by terrible decisions made under intense pressure and with sharply limited information by very limited human leaders.
My view is that the idea that AI will decide to literally kill humanity is a profound category error. AI is not a living being that has been primed by billions of years of evolution to participate in the battle for the survival of the fittest, as animals are, and as we are. It is math – code – computers, built by people, owned by people, used by people, controlled by people.
The app costs a few dollars up front and then has an optional subscription for unlimited requests, but I would recommend going directly to OpenAI’s developer dashboard, creating your own account, and copy/pasting your own API key into Petey.
This isn't perfect, but soon it will be, and you will be able to listen to or read any content you want in the timeframe, tone, voice and bias of your choosing.
Hopefully I've convinced you that chatbots are a terrible interface for LLMs. Or, at the very least, that we can add controls, information, and affordances to our chatbot interfaces to make them more usable.
I want to see more tools and fewer operated machines - we should be embracing our humanity instead of blindly improving efficiency. And that involves using our new AI technology in more deft ways than generating more content for humans to evaluate. I believe the real game changers are going to have very little to do with plain content generation.
Good tools let the user choose when to switch between implementation and evaluation. When I work with a chatbot, I'm forced to frequently switch between the two modes.
The biggest issue is probably that we don’t control neutral networks enough to be able to ensure AI doesn’t harm humans. We can’t even control AI to not reveal internal prompts.
This book is my best crack at explaining what I think is an existential risk to liberal societies and what I think we need to do to get to that awesome future I used to be so excited about.
What ChatGPT is, in this instance, is replication as travesty. ChatGPT may be able to write a speech or an essay or a sermon or an obituary but it cannot create a genuine song.
I think this matches the general finding that AI progress is faster than expected, and increases my certainty that scale and normal progress can sometimes be enough to solve even very difficult problems.
But in terms of what we value the most as humans, which is to say our feelings, our emotions, our sense of what the world is in a very personal way that I think means as much or more to people than their information processing. And that’s where I don’t think that AI necessarily will become conscious because I think it’s the property of life.
The best and easiest-found-by-optimization algorithms for solving problems we want an AI to solve, readily generalize to problems we’d rather the AI not solve; you can’t build a system that only has the capability to drive red cars and not blue cars, because all red-car-driving algorithms generalize to the capability to drive blue cars.
Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
Science journalist Jacob Ward warns about a future in which AI technology forms a tight cybernetic feedback loop with the biases ingrained into the human brain, pushing our day-to-day existence in frightening directions. It's sort of like a version of The Terminator in which Danny Kahneman plays a starring role. As longtime readers and listeners of mine know, a big theme in my techno-criticism is the under-appreciated degree to which technologies exert powerful, unintended consequences on our personhood and culture, so Ward's warning hits a sweet spot with me.
I I would I would not say that about Google. I would just say that more generally like that is the incumbent temptation. Um it is is basically is basically go from attack mode to defend mode, right? Go from being the pirate to being the navy.
I wouldn’t say that this is the most compelling book I’ve ever read in terms of the prose style or storytelling, but it does provide a very helpful, almost quantitative overview of all the potential threats looming out there
AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress
If you're curious how Google built it's culture and the processes thanks to their extremely data & engineering driven nature, this is a great book. What held it back was being ~100 pages too long and Bock didn't always understand where what they did only fit at a $100Bn+, mega-profitable company. Still, the insights on the studies on their 50,000+ employees is well worth the read since so few of us can get similar statistical significance to apply to our teams.
The interests of a Venture Capitalist are different than those of the entrepreneurs building a company they've invested in. Jeff does an awesome job of helping explain how you can get misaligned in your goals versus your investors. Fortunately, he also covers how to avoid it.
this channel is full of high production quality and well-written videos. There are also several series on machine learning, the topics underlying self-driving cars, so this is certainly one of the more applied math channels out there.
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.
This book stretched my mind more than almost any other book I’ve read. It’s tough at parts, it’s long, but you’ll come out of it thinking about brains, minds, intelligence, and AI in an entirely new way.
spending an increasing amount of my free time working with tools like Google Colab, Runway ML, TensorFlow, and Hugging Face to play around with machine-generated text, audio, imagery, and video.
We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.
When I'm writing articles etc that need to be shared with an editor, I typically write in Microsoft Word. I hate all word processing software, and anyone who insists that Google Docs is so much better than Word is a liar.
It's increasingly coming to the fore and Marcus deals with it in a really interesting way. He takes us through lots of different areas where AI is being used to mimic creativity, which some people would argue is the essence of being human. It's interesting to see how close to being human AI is getting.
so we use Google Meet speakermics, which allow for full-duplex conversations, which makes conversation far more organic. That device turned Google Meet from something exhausting to something really good.
In terms of iOS apps, I use PDF Expert, which is a great PDF reader and annotator (using Apple Pencil), Notability as a virtual whiteboard (again with Apple Pencil), Google Docs for document editing, Google Calendar, and Gmail.
Eric Schmidt, Jonathan Rosenberg and Alan Eagle, Trillion Dollar Coach: The Leadership Playbook of Silicon Valley’s Bill Campbell: I understood Bill Campbell was a behind-the-scenes guy in Silicon Valley, but I had no idea just how influential he was. Bill Gurley of Benchmark noted “I would argue that Bill has had a bigger impact on Silicon Valley than any other single person simply because his reach was so amazingly wide.” That’s a story I want to read.
For coding, I use Visual Studio Code. I use Git for source control, Photoshop for 2D art, Audition for audio editing, Premiere for video editing, and our team coordinates on Google Docs and Skype.
What we are actually listening to is human limitation and the audacity to transcend it. Artificial Intelligence, for all its unlimited potential, simply doesn’t have this capacity.
Now, almost everything happens in Google Docs, except for when I write straight into the Axios or Substack CMS. (Both of which are lovely, WYSIWYG, very simple and easy to use.)
Such restrictions are sub-optimal for sentence-level tasks, and could be very harmful when applying fine-tuning based approaches to token-level tasks such as question answering, where it is crucial to incorporate context from both directions.
We argue that current techniques restrict the power of the pre-trained representations, especially for the fine-tuning approaches. The major limitation is that standard language models are unidirectional, and this limits the choice of architectures that can be used during pre-training.
We argue that current techniques restrict the power of the pre-trained representations, especially for the fine-tuning approaches. The major limitation is that standard language models are unidirectional, and this limits the choice of architectures that can be used during pre-training.
Such restrictions are sub-optimal for sentence-level tasks, and could be very harmful when applying fine-tuning based approaches to token-level tasks such as question answering, where it is crucial to incorporate context from both directions.
Such restrictions are sub-optimal for sentence-level tasks, and could be very harmful when applying fine-tuning based approaches to token-level tasks such as question answering, where it is crucial to incorporate context from both directions.
We argue that current techniques restrict the power of the pre-trained representations, especially for the fine-tuning approaches. The major limitation is that standard language models are unidirectional, and this limits the choice of architectures that can be used during pre-training.
We argue that current techniques restrict the power of the pre-trained representations, especially for the fine-tuning approaches. The major limitation is that standard language models are unidirectional, and this limits the choice of architectures that can be used during pre-training.
Such restrictions are sub-optimal for sentence-level tasks, and could be very harmful when applying fine-tuning based approaches to token-level tasks such as question answering, where it is crucial to incorporate context from both directions.
To me, this book is an illustration of the power of names. Today, in the era of Google, if you know the name of something, you can find out about it with a simple search. But if you don't know of what you're looking for, it suddenly becomes much harder to find it. Having in the back of your head the names of common algorithms that help you solve problems is really powerful.
Google Chrome: What's a Web Developer without Google Chrome browser? The first thing I do with Safari is to get Google Chrome and install the latest version.
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
In particular, this means that incredibly powerful AI will emerge in a world where crazy stuff is already happening (and probably everyone is already freaking out).
The main disagreement is not about what will happen once we have a superintelligent AI, it’s about what will happen before we have a superintelligent AI.
One of the most commonly emphasized points in the AI safety literature is that one must never anthropomorphize AI—that is, don’t project human mental properties onto artificially intelligent systems.
A superintelligence whose goal system is even slightly misaligned with ours could, being far more powerful than any human or human institution, bring about human extinction for the very same reason that construction workers routinely slaughter large populations of ants.
For writing code, I use emacs and Google's internal distributed build system (a version of which was open sourced as Bazel) and our version control system
A korrent is a belief a person has stated in their own words: one
sentence stating the claim, backed by a quote and a source, kept at
korrents.com.
Under a name here, the quoted block is what they actually said.
The korrent beneath it is the claim those words support, in
korrents' wording — tap it to see the record, its source, and who
else holds it.
Nobody here wrote their own korrents. They are compiled from public
statements, and a person can change their mind, which is recorded too.
About the English under a post
Some people here publish in a language other than English. Where they
do, this site shows a machine translation beneath the post, in
this typeface — the site's own, not theirs.
The post itself is never changed, moved or hidden: what is set in the
serif above is exactly what the person published, and it is what to
quote them on. A translation can be wrong in ways that matter,
especially about tone.
Only the post's own words are translated. A quoted post, a linked
article and a belief on korrents.com
are left in their original language.