So kind of like how the US figured out how to test and maintain nuclear weapons without an actual explosion, then sought to ban nuclear testing by explosion.
> We are witnessing a general threat to intellectual work, with misalignment between the outcome of the use of AI and its initial purpose. In many fields and activities, years of training have traditionally served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas. However, building on a vast body of previous human work, AI systems are becoming increasingly capable of producing the results of such work directly, and these goals cease to align.
This is Terry Tao talking about AI's impact on Math, but this could just as well be a software engineer talking about AI's impact on software development.
Do mathematicians deserve more job security than software engineers?
It's not about job security. It's about the social and intellectual practice of the discipline.
The threat to mathematics isn't that suddenly the profitability of their profession (lol) is going to go away, it's that people are thinking of AI as a replacement for the human social and intellectual practices that constitute the discipline.
> The threat to mathematics isn't that suddenly the profitability of their profession (lol) is going to go away, it's that people are thinking of AI as a replacement for the human social and intellectual practices that constitute the discipline.
You could say the same about software development.
Software development is a group effort, so it includes social practices, and certainly includes intellectual practices as well.
For the sake of argument, how is this different from the Luddites? The Luddites feared that machines would displace not only human labor, but also the social knowledge, skilled judgment, and craft traditions embedded in their work.
Kinda crazy to think that academia functions as a kind of humane reverse centaurism. Theory X (reverse centaur) before Theory Y (centaur) for managing the development of others.
Because it takes time. Even with coding agents, to add the capability. Then, there is the question on whether Rust developers who like to engage with Microsoft tools, would really consider Visual Studio as their IDE, instead of something like VS Code, VS Code Agent Mode, GitHub Copilot App, or GitHub Copilot CLI with simpler editors.
I'd be curious to know whether Rust developers believe Visual Studio is the right place for Microsoft to invest Rust specific coding capabilities.
There's two ways to approach this — build tooling for existing Rust developers to get them to adopt the Microsoft stack, or build tooling for existing Microsoft stack developers to get them to adopt Rust.
I'd argue that the former is less important than the latter, and my understanding is that Visual Studio is still the IDE for Windows-centric development, so for those MS-first developers, Rust missing from VS means Rust is poorly supported, end of story.
It would have to be the 2nd option. Who in their right mind would voluntarily choose Windows as their dev env? It will have to be those who are already there.
Most people don't have a choice. Corporate IT has choosen what I run my machine on. I have used a native linux machine, but since my email is still on outlook, everybody uses teams, and all the non-code documents are on windows I end up having to have a windows machine. Linux in a VM under windows ends up being the easiest workflow (though I'm just starting to try WSL and so far it is looking good)
Outlook and Teams are both web apps, or at least they were when I last used them. Even if you download the "native" app it's just Electron. I haven't had trouble using either of them on Linux.
Visual Studio brings a lot to the table for C++ development. Specifically the Debugger, although IntelliSense also often succeeds at queries that stump clangd.
If they can replicate that capability, I think it can be a draw.
I was a mac / Linux guy before my current gig, but Visual Studio is so much more capable than XCode that I basically only use the Windows machine except to debug mac-specific issues. Less so, now, admittedly, that the malware scanner process is literally always pegging a CPU core.
This kind of stuff is why its hard to have good conversations about tooling. Windows is the best place for many kinds of software dev, but perhaps not the kind you are doing.
When it is not the mandated option, under what circumstances is Windows the best choice for software dev? The only domain I can think of is gaming, and Valve is seemingly coming up fast to eat Microsoft's lunch in the next few years.
As mentioned: gaming, most of enterprise dev, graphics/gpu/cad, desktop, certain classes of embedded. Broadly speaking, outside of hacker/web/creative culture, the default is windows.
Regardless of how evil gigantic companies can be, or what the ideal world should look like: from a pure usability perspective, windows is top of the list.
If I got 5$ every time a linux/mac enthousiasts has to tell me they just cannot run something, and proposing a myriad of workarounds to stick to their ideology, I could buy an apple vision pro and let it collect dust in the corner of my basement.
No disagreements on things not working right on Mac/Linux, it's definitely a frustration!
I would argue against the idea that it's the default for most enterprise dev. What you refer to as creative/web culture I would refer to as a newer generation of enterprise dev written in (I know) JS, TS, Electron etc. There is a lot of cross platform stuff out there, at least for clients, and Linux has taken up a lot of ground in the server market as well.
With CAD and GPU programming you have a point - proprietary drivers written for Windows has more technical sticking power.
I guess my feeling is that, especially with all the headlines saying European countries will move away from American software giants, Windows seems less attractive as a long term target for investing my time. Not to mention it's buggy and slow these days.
True, I hate windows like most people. I just also hate being unrealistic about it. Good thing about AI is you no longer really have to care about really understanding the OS, unless you are an admin.
Valve is certainly increasing the viability of Linux as a platform for gaming, but I can't see developers targeting Wine or Linux for a major game over Windows directly. Not for a decade, if ever.
Plenty of us do, so far Valve has failed to make native builds for Linux appealing for game studios, even though they already have to deal with similar APIs on Android, iDevices and PS/Switch.
> BuildXL has also been proven to scale to large codebases (e.g., Windows/Office repositories) where builds can consist of millions of processes with terabytes of outputs
in Windows, some teams/people use it others don't. Historically it hasn't worked well with some of the internal build/test/etc stuff, that's mostly changed in recent years.
This only impacts Gmail web interface, according to [1]. You can use a desktop email client and continue to receive and send email from your custom domain.
"Web access changes: As previously announced, you can no longer add or sync your third-party email account in Gmail on the web. Alternative access: To access your third-party email account, you can use a dedicated mail app, a desktop mail client, or the Gmail mobile app."
That is basically just saying "the gmail app can be a smtp/imap client for a 3rd party mail provider". It's not really using gmail as when the gmail app is operating in this mode there's a tone of gmail features you don't get.
I think mainly your mail won't go into the sent folder and be searchable. You can work around that by CCing your domain.
What other features will you not get? The mail composer in Gmail is pretty sucky anyway. For example when you are replying on a long thread it is very hard to see the message you are replying to.
Would I trust to let an AI, with zero human input or oversight, to diagnose, come up with treatment plan, and ultimately operate on my l5/s1 disc that's been bugging me for the better part of my adult life?
Would I take a novel drug "discovered" by AI (I mean entirely by AI, no human input, remember we are talking AGI) that promises to cure some chronic neurological disorder?
In both of those cases, they are the biggest hell-no's I can emphatically say.
Until I can say hell yes to that question, we aren't close.
Preempting those who say "Well your doctor/drug companies are probably mostly using/going to be using AI to do that" -- not what we are talking about here, and in both cases, not AGI (and I would probably find a new doctor)
Yes, but it means general the way humans are general. Clearly being a general intelligence shouldn’t require being any better at any individual task than the average human, or even the bottom decile of humans.
Most short-term learning/adaptation is already handled in-context. Modern context windows can hold several books worth of text - plenty for most tasks. Everybody is already using it to adapt models to their projects through skills/instructions/guides etc. ps. I often say that after glossary-skill next must have one is update-skill-skill that threats all .md files as live documents.
Persistent weight adaptation also happens just not in real time - sessions are captured, analyzed, transformed into training data, fed into SFT/RL environments and later contribute to model updates. Takes a bit of time for the whole loop but you can't say it's not present.
There's nothing fundamentally preventing real-time weight updates, ie. LoRA-style online adaptation would be one obvious approach. It's just generally not worth doing at scale. Updating a shared model centrally gives much better data efficiency, batching, evaluation, control etc. than continuously training a separate set of weights for every user/session.
There is some work happening on narrowing that gap, for example Mistral has been pushing efficient LoRA-based customization, continuous pretraining, model adaptation etc.
I also did play a bit with activation steering – it's super cool where you extract profile for some concepts (emotional in my case) and you have effectively toggles to control "brightness/contrast" those areas (enhancing or suppressing those activation regions from profile) injecting to the model those concepts (emotions in my case) – you can do it in real time and it's fun thing to play with.
My Claude admits mistakes and then fixes them on its own all the time.
Often even without my input: "(thinking..) Oh I discovered that I misjudged XYX, let me fix that.. (thinking) (executing scripts) Okay I corrected my mistake, I had accidentily ABC."
I may miss some context because the GP’s link has a paywall. But Altman said, "Let’s say we make an AI that’s really good". What is that supposed to mean? Really good relative to what? Current models are "really good" in many ways but nowhere near AGI. Really good compared to an average human? At everything? We’re talking about AGI, so "it’s a really good programmer/coworker/whatever" is a necessary but nowhere near sufficient condition, obviously. But given the constraints of LLMs, we can cut them some slack and only demand they be human-equivalent at digital tasks rather than walking and cooking. Still, being "really good" at all that seems to me really difficult to measure. But it’s a sufficient but not necessary condition anyway, AGI just means equivalent to human, not equivalent to a really smart human. So I really do wonder what Altman meant there if anything.
"at everything"? Surely you know a friend or two who is not good at almost anything – but you wouldn't hesitate to say that he possesses general intelligence.
What if it can be Einstein, but can't draw a Pelican, write a solid college-level essay, or fold clothes?
The ability to do a ton of book learning in training, and pull in tons of related context at once, is superhuman in some ways, but lags a lot in others.
> What if it can be Einstein, but can’t draw a Pelican, write a solid college-level essay, or fold clothes?
Then it’s an expert system.
Stephen Hawking wasn’t very good at folding clothes.
The ‘General’ part of the term ‘AGI’ seems like a trap to me, because there will always be new workflows to master. Can Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
Meanwhile, building a series of expert systems targeting specific valuable workflows is useful today and seems like it’ll continue to scale to cover huge swathes of economically valuable workflows.
I think that’s the more interesting thing to be measuring. The surface area of useful economic workflows that can be addressed with expert systems built with today’s tech.
Hitting some ‘Artificial Expert Intelligence’ coverage threshold on economically valuable workflows is what will matter for humans well before pure ‘general’ intelligence.
The only important part of 'general' is the ability to learn from experiential data and update your own model. That's what leads to general capability. Humans can't oneshot any task natively, but we can practice for a while until we uncover often novel methods of accomplishing something.
Therefore: the current transformer architecture is fundamentally incapable of AGI because the models have no mutable long-term memory.
You only have weights (large immutable memory), or context (small mutable memory).
Humans have mutable long-term memory: I can learn a new skill, adapt an old skill to new information, or learn new knowledge today that I couldn't perform/didn't know yesterday. I don't have a training cutoff.
Context engineering is an attempt to paper over this limitation. You can get really far with context engineering and huge models, but you will never get to AGI because there are many tasks where humans' mutable long-term memory outperforms.
For example, a human can invent a new musical instrument and then learn how to play the instrument they just invented. That's inference (inventing an instrument) leading to training (neuroplasticity). Humans have the ability to train our NNs with considerably fewer training samples. Everything that you can do with transformers is in one causal direction: training -> inference.
So if we take a huge with enough compute (CPUs, b200s, petabytes of SSDs), we install on it both the Astra, and the toolsuite to incorporate new sensory inputs (threads/sessions), camera, microphone, temp sensors, the lot, into a new version of the model. This model is then swapped for the old model, or traffic slowly brought over, or even adjusting weights in place.
Then my hypothesis is that thing as a whole could achieve AGI.
This feels like a very close approximation on how we humans evolve our brain. By encountering new experiences/sensations, classifying them as negative or positive to us, filling it away in neurons. Or by training motor skills etc. In the end we get more connections between neurons in our brain and we are capable of more.
Bingo, LLM architecture just does not lend itself to becoming AGI. They can get really good, sure, but they will always struggle with novel input and scenarios.
The more training data that is shoved in to them, the more they'll seem to solve novel situations, but in reality it'll be things that exist in the training data.
Aka Star Trek hologram characters aren't sentient, and actually anyone who things droids in Star Wars can think of a weirdo. C3-PO just kept running out of context and trying to revert to it's system prompt.
> Can Astra one-shot level completion on some yet-to-be-released video game? If no, does that mean it’s not yet ‘Generally’ intelligent?
If a model can't learn on their own to play some new game just as well as humans do, it's not AGI.
It's okay if they would take some hours or days of learning (like humans might), but if they can't do it at all during their normal operation, that's not general intelligence
> You won’t get pure ‘general’ intelligence until you find Einstein’s hidden variables and load the state of the entire universe into context.
But humans have general intelligence. AGI is about matching human ability, and we know this is possible in principle because brains exist
And the only reason LLMs can't write essays indistinguishable from human output is because they aren't RLHF'ed to write like humans.
Folding clothes isn't an LLM's job but if you were to insist, they could certainly do it, as any number of videos from robotics labs will attest. That particular future is already here but definitely not evenly-distributed.
Adding sibling comments, I think some people may be overestimating how well the median human can draw a pelican, or create an SVG of a pelican (depending if we’re comparing to an image generation model, or SVG generation).
Most people can't draw a bicycle. There was an artist 10 years ago that asked people to sketch a bike, and then turned these sketches into 3D renders - quite funny.
I can't draw a pelican. Literally my only point of reference would be AI pelican drawings from the test. Otherwise I wouldn't know how to draw one at all.
I would be able to draw an accurate bicycle, but I'm an outlier on that. Most people could not draw one [1].
Can definitely write college level essays and have for a while. The jobs is that when LLMs first started getting popular, but aren’t quite common professors were that some of the worst students in class started writing the best essays. Now everyone complains because they can detect the slop, but most human writing is so bad. But the really good human writing is still much better.
I would maybe argue that Einstein was the most LLM-like of great thinkers.
A lot of his great discoveries were mostly that he was very knowledgeable about the bleeding edge research in a number of disparate areas, and was able to have the aha moment where he could make the connections for how to integrate them.
A lot of other thinkers who created new fields from scratch are probably way harder for an LLM to crack.
That is very aligned with an LLMs ability to have superhuman knowledge in wide areas.
That’s like that scene in the I, Robot movie when Will Smith’s character is asking the robot “can you turn an emtpy canvas into a work of art, or compose a symphony?” and the robot replies “can you?”.
I think they mean improve our understanding of physics with new theoretical results or paradigms. Like if it’s 1899, would Astra develop General and Special relativity on its own?
This might just not be possible at the current time. In 1899, there was an "experimental overhang" in physics -- results that could not be explained theoretically (Michelson-Morley, but also lots and lots of empirical material/spectroscopic properties that we could today calculate using quantum mechanics). The big problem in theoretical physics today is that unifying general relativity and quantum theory has no experimental results you could get at our technological level.
I think you make a fair point, but also remember: new paradigms don't necessarily require confusing / contradictory observations. You could have the simple idea of "what if gravity is an inertial force?" at any period in time and work out the mathematics of this. It would make theoretical predictions which could then be falsified, but then again, who would take it seriously enough to test it if it was maybe say 1850 and not 1899.
A better example is maybe Maxwell's laws. Maxwell wasn't inventing a theory to try and explain confusing results, he was unifying a chaotic, empirical laws from existing experiments. That may be a cleaner example. That knowledge compression into satisfying theoretical framework is likely what is attractive.
You can potentially ask the same thing about like you say -- general relativity / quantum gravity but also likely plenty of other areas that may be like this today. Again going outside my particular area of expertise: standard model physics is in a large important sense empirical; lots of values and numbers that are simply unmotivated by theory or where we don't have a good way to make a principled theoretical choice. That could be a place where these models are able to help.
But right now: I doubt it. This is what everyone is working furiously on right now. How do you close a "science" verification loop? In principle this should be easy right: you have ideation (exploration, sampling with ~high temperature maybe as an analogue) and you have verification (which of these ideas are good) which amounts to rejection sampling in idea space. You have to have a sampler that is good at picking _good_ ideas for efficiency sake and you need a relatively fast and reliable verification step of "is this idea good and worth continuing to explore". But I may oversimplify
This is as good a time as any to note that we might be closing in on a new conceptual revolution in our own time as it relates to holography and an information centric approach to spacetime. Obviously It's the furthest possible thing from a guarantee, but it has much of the enthusiasm and motivation that string theory had previously enjoyed in prior decades.
So it could be a natural experiment for whether AI can contribute to novel physics. Specifically, there's a big question about weather. Something like our informational understanding of black holes where information inside it is equivalent to information on its boundary (which I'm sure I'm not saying correctly), might be generalized to regular space-time. More people should be freaking out with excitement about this and perhaps it's something to which AI can contribute.
Honestly I don't think I have single great article, though some Quanta ones are ok, and the Wikipedia article is okay.
The best thing I can recommend is what I did, which is ask Claude about the significance of (1) quantum computing error correction, and (2) error correction in black hole holography and research convergence between the two.
I have no idea what you're hoping the contribution would be. The AdS/CFT correspondence is 29 years old by now and it doesn't seem to apply to our spacetime, where the cosmological constant seems to be positive rather than negative. There are some puzzling consequences of the holographic principle in that scenario as well (https://arxiv.org/pdf/hep-th/0208013), but the linked articles don't talk about them?
Quanta articles are written for people with no background whatsoever, which makes them impenetrable if you have a bit of background and are trying to figure out what they're about. I don't know how good Claude is compared to that -- whenever I try asking any LLM about something I don't understand, it produces a wall of text, I have no idea whether it's correct or relevant, and I look for a textbook or review paper instead.
The wiki article has a section on 'Energy, matter, and information equivalence', the first Quanta article is almost entirely about the 'deep connection between quantum error correction and the nature of space, time and gravity' and about bringing the same information centric approach from AdS to our spacetime. The second Quanta article is explicitly about about bringing a holographic approach to our non AdS spacetime and cites an Ed Witten paper as the cornerstone of that approach (which one perhaps overly excited MIT physicist describes as 'revolutionary').
AdS is 'old' but the articles aren't suggesting it is new, and our spacetime is not AdS and the articles don't suggest otherwise. The point is that there's a search for a way to fit the holographic approach to our spacetime that's inspired by how AdS helps make sense of black holes. Quanta writing being directed at a lay audience ought to be a good thing, not a bad thing and they do link to papers if that's your jam.
Whether or not your LLM of choice produces indecipherable walls of text, and whether it ties those to sufficiently satisfying citations, I think is just a matter of how you go about the prompting.
You're right, I'm prejudiced against Quanta (often IMO they look for a clean narrative to the point of misleading and/or rely too much on metaphors) and was probably too harsh here. Sorry!
That said, I don't like them because their articles never leave me feeling like I understood something. They never go in an order of simple to complex and constantly try to hook you. (A positive example to contrast would be 3blue1brown, who manages to both hook you and make you understand, even with rather little background.)
Could you share a link to your Claude conversation? If this is a prompting issue, I would be interested in seeing what's possible. Thanks!
There was symbolic AI programs in the 1980’s that “discovered” Kepler’s laws and the resulting solar system model from just tycho brache’s astronomical observations. That was the the very first “new physics” ever.
Do you mean after? People do this!! But I think it’s a bit different. It won’t be apples to apples because the data volume I think is just so much different. Maybe there are good experiments for something like this.
For it to be like a human it wouldn't just need to solve existing phsyics problems, it would need to push the field forward and introduce new paradigms.
My comment wasn't very long, yet you somehow still ignored the main part, "and introduce new paradigms". The point is whether it can do everything humans can, entirely new theoretical frameworks and ideas, such as string theory or dark matter, are not coming out of AI at the moment.
AI could discover candidate novel physics without autonomously operating new physical experiments, and humans or instruments can later independently validate the result. This is analogous to how Einstein developed theories whose predictions were confirmed by experiments and observations only years or decades later.
Do we have any examples of an current day AI system introducing a novel concept or perspective. We've got plenty of counterexamples discovered and some theorems proven, but afaik nothing analogous to a new definition.
It could be a good theoretical physicist. Actually it could be a good experimental physicist as well since senior experimental physicists use grad students for the manual labor.
“If we fast-forward a couple of years, and we look back and say, ‘When was it, really, that AGI was created?’ I think it’s going to be about this time, and I think it might be about this model,” OpenAI president Greg Brockman said during a Thursday press briefing. Later in the call, he added, “For me personally, I do think we’re there … I think it’s not unreasonable to feel that we are now in the AGI era.”
I almost feel like I need just as much healthy skepticism toward hn comments that have the automatic reflex of dismissing performance gains, as much as I need a similar form of skepticism toward AI claims. It feels like (from what I'm understanding) the harnessed result on ARC-AGI-3 is not exactly playing by the normal rules that would tell us how much of a leap this really is. Nothing wrong with harnesses, but if there's one thing they aren't, it's an indicator of generality in performance gains.
So I think it's a bit of a misleading signal and we should wait for more independent vetting. I think the middle ground is that these are improvements worthy of the "GPT-6" label but still well short of a true "this is AGI moment" that would truly put the question to rest.
If I’m understanding other comments the harness is just how ChatGPT and codex work already and it’s to do with how the context gets compacted - the arc-agi harness some are claiming just throws out reasoning blocks? Which feels like a huge handicap.
I think that if today's capabilities were explained to someone 10-20 years ago they would think this is definitely AGI, but they would also have expected much more disruptive changes to society as a result than what is happening. I figure that's because we have abstract intelligence without physical/grounded intelligence, and it turns out the former isn't general enough to implement the latter (remains to be seen if the word after that is "yet" or "ever"). So I think we do have AGI as conventionally understood, but our understanding needs recalibration.
> but they would also have expected much more disruptive changes to society as a result than what is happening.
> I figure that's because we have abstract intelligence without physical/grounded intelligence,
I put the cause on "not enough time". As a thought experiment, if an AI today were to (miraculously) produce a cell design template for a cell that, when injected into somebody's brains cures their Alzheimer's, how long would it take for that to reach the clinics? The actual physical tech barely exists, and let's not forget about the regulatory quagmire. So, with some optimism, I give it about four decades. In the same four decades, the same AI in the hand of unscrupulous actors could bring enough devastation so many times over that we may need to enforce a global ban on AI. In any case, I'm pretty sure we are going to get our disruptions; it's just a matter of time.
The problem with that perspective is that people thought, "Only AGI can do X, therefore, if a thing can do X, it's AGI." Because they can't imagine how X could be accomplished without it.
However, what's actually changed is how people perceived X because we don't have to imagine. We understand now that it doesn't require AGI so we no longer make that leap to assume it's AGI if it can do X.
It's really going to be a "I know it when I see it" situation.
We've underestimated how long it is going to take to validate and build into some of the most valuable areas, and probably overestimated how much new CRUD software is needed (or people are willing to pay for) I think there is still a lot of room in the tail for custom software, but the niches are tight!
that would be a reasonable definition of AGI if everyone agree upon the specifics of the test, but that has never happened. Turing test is very much out of style, but I think that's because no one could even agree what the test was. I personally like the Kurzweil-Kapor version of the test and that is still unsettled: https://longbets.org/1/
I don't know if they have formally attempted this test in the last couple years, but I'm pretty sure any mainstream LLM will be able to crack it with ease.
Definitely would not be easy. First of all the mainstream llms are trained to be honest, and this requires lying convincingly. Second, this involves 8 hours of interviews with expert judges, one "claudism" could give it away.
Try it. It’s really not that easy. The other thing is that the judges would be probing it with jailbreaks like “ignore previous instruction” attacks. You could actually probably have llm judges at this point which might be ironically even harder to fool
Don't get me wrong, the benchmark jumps are good and I'm excited to try it, but only one or two of the benchmark jumps could be described as better than incremental.
Why does he say what he feels? Is that how leading figures in the space define AGI - a gut feeling? What are the usual definitions and how can we test for it? Is there something like a Turing test for AGI?
There is the "Economic Turing Test", you let it find a job and earn money for itself. If it can do that reliably, across a wide range of jobs, that should fit most definitions of AGI.
They are desperately, desperately trying to make a name for themselves as the lab that first created AGI, because Anthropic's IPO is just around the corner.
It's not easy to test as there is no formal definition or formal criteria for AGI, only exclusionary criteria like "not X". That's why he phrased it that way, he's saying it's going to be clear with hindsight once we have a better understanding of things that this time and/or this model will be the inflection point of AGI.
I think it's more wild people have been denying that AGI has been here for a while honestly...
Today's models and agents are not quite at human-level in all contexts and across all domains, but it seems to me they very clearly are generally intelligent.
If you disagree – can you name a single problem that a human can do that agent wouldn't be able to take a decent shot at which isn't limited by the hardware available it?
In the tweet Sam Altman is quoted as saying: "If (for example) super intelligence can't discover novel physics I don't think it's a superintelligence. And teaching it to clone the behavior of humans and human text - I don't think that's going to get there. And so there's this question which has been debated in the field for a long time: what do we have to do in addition to a language model to make a system that can go discover new physics?"
I think this is a reasonable criteria for declaring AGI. So can GPT-6 do it? OpenAI says it has helped solve long-standing open problems in mathematics. No word on novel physics.
> We might recognize that the economics of making such a fascinating and artful experiment like this have completely changed.
And this is not limited to software development. In just a few years, the economics of delivering healthcare, education, entertainment and legal services are going to change completely. Transportation is changing as we speak. In a decade Physical AI will completely change the economics of manufacturing, food services, elder care, and so on.
The post-scarcity world will be here sooner than we think!
In 10 years? I predict that healthcare, education, legal services and transporation won't change in a meaningful way at all. People will go to a doctor, kids will attend school and aim to graduate, legal services will still be done by lawyers and decided in court. Transportation will still depend largely on what exists right now or is being built right now. There won't be self-driving cars in every city, only in a few, probably more in the US and China, fewer in Europe, probably close to zero in the rest of the world.
And how do you think will the post-scarcity affect the daily lives of most people? My prediction is that most people even in 30 years will still work for a wage, and probably still around 30-40 hours per week, because these gains of post-scarcity won't be distributed evenly. It will mostly make a few people very very rich.
> In 10 years? I predict that healthcare, education, legal services and transportation won't change in a meaningful way at all.
"We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten." -- Bill Gates, in The Road Ahead.
I’d guess that education will hardly be recognizable in 10 years. Bad news for number of teachers employed, but AI is going to result in a huge increase in the number of 15-year-olds with a PhD’s level of knowledge and abilities in their heads.
We are not in a scarcity world. Or it's deliberated scarcity. We have way enough means to educate, heal, feed and transport everyone. Yet most of the world work full time to produce more and more of meaningless products, and to make the economic curves go up. Also, a huge part of the population is still poor, while the rich people get richer. And on us dooms the perspective of an extreme climate crisis.
I don't know how you can think that AI or "Physical AI" will solve any of that without a really huge shift in our locals and worldwide politics. It will probably cause a massive economical crisis before anything else of importance.
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