Carmack is unquestionably a genius, but I think it's quite unlikely his solo work in a new domain will leapfrog an entire field of researchers.
I wouldn't, however, bet against some kind of insanely clever development coming out of his new endeavor. Something like an absurdly efficient new object classifier, that reduces the compute requirements for self-driving cars by a non-trivial factor, would be a very Carmack thing.
The problem with the "field of researchers" is that most of us aren't geniuses. We're just plugging away at problems, like normal people.
The opportunity for a genius is to come in, synthesize all existing information on the subject, and then come up with a novel approach to the whole thing.
In some part, I think that is what Elon Musk has been able to do effectively. He comes into a field that already exists, reads everything he can get his hands on, and then outputs something novel. You can only do that effectively if you have the mental capacity to keep all that info in your head at once, I think.
Musk actually has credited Carmack's Armadillo Aerospace with providing the inspiration for vertically landing the Falcon 9. Of course Armadillo was likewise inspired by the Delta Clipper, which was in turn inspired by the LEM, etc. But it's one thing to vertically-land a rocket a few times when you have billions of dollars at your disposal; it's another thing to do it hundreds of times for a thousandth the price. That was Carmack's contribution: proving that vertical landing can be both incredibly robust, and cheap as chips. Really valuable work.
I had the pleasure of meeting Carmack a few times over the years at small aerospace conferences. He's both as true a geek and as much of a gentleman as you might imagine. I'm really looking forward to seeing what he does with AGI.
I normally don't bother but this comment is so profoundly ridiculous I had to say something.
Tenured ML professors at the top 100 or so universities in the world aren't "most of us". A very large chunk of these people are geniuses. Those jobs are incredibly hard to get, and most of these people are reading everything that is getting published, on an ongoing basis, and are outputting something novel, on an ongoing basis.
The fact that you think that John Carmack, because he's a name that you've actually heard of, is going to go into ML and suddenly make some giant advance that all the poor plebs in the field weren't able to do, is only a reflection of your misunderstanding of what's already happening in academia, not on Carmack's skills or abilities.
You're acting as though everyone are just low level practitioners using sklearn, and it would be a great idea to have some smart people work on developing something novel. Guess what: that's already happening, with incredibly smart people, on an incredibly large scale. Carmack doing it would just be another drop in the bucket.
Tenured ML professors at the top 100 or so universities in the world aren't "most of us".
Too bad we're talking about AGI, not ML.
Those jobs are incredibly hard to get,
You don't need to be a genius in order to land a hard-to-get job, and you thinking academia is somehow better at making the absolute smartest people rise to the top is cute.
The fact that you think that John Carmack, because he's a name that you've actually heard of, is going to go into ML and suddenly make some giant advance that all the poor plebs in the field weren't able to do, is only a reflection of your misunderstanding of what's already happening in academia, not on Carmack's skills or abilities.
I don't think that. Mostly because we're not talking about ML, but also because I don't expect eureka moments from people that have been trying to solve a problem for a long time as much as I expect them from someone that hasn't properly tried their hand at it. Academia produces consistent results and consistent improvement. That's not what I'm looking for.
You're acting as though everyone are just low level practitioners using sklearn, and it would be a great idea to have some smart people work on developing something novel. Guess what: that's already happening, with incredibly smart people, on an incredibly large scale. Carmack doing it would just be another drop in the bucket.
sklearn hardly seems relevant to AGI, so I'm not sure why I'd act like everyone in the AGI field merely a novice practitioner of it.
> Carmack doing it would just be another drop in the bucket.
If this research is as compute intensive as it seems to be,
Carmack's contribution might be that he increases the rate other researchers can add their drops to the bucket.
Carmack isn't the first techie to take on a big hard problem. Jeff Hawkins, a name many of us also know, did as well.
Yes, he may well improve some algorithm, or rewrite some commonly used tool to improve efficiency. And researchers are often not incentivized to do that, so it would be great. But a far cry from the picture people are painting about him soaking up the field and using his genius to solve some major problem quickly.
If by "techie" you mean, professional software engineer, that's fine, but there's no reason to assume that a professional software engineer is going to be magically better at AI research than... professional AI researchers? He's probably going to be substantially worse.
Also, your statement below:
> That's probably true. I look at this as Carmack running his own PhD program. I expect he will expand what we know about computation and the AGI problem before he's done.
Makes it clear to me that you don't really get it. Carmack, at best, might know enough right now to be in a PhD program. I doubt that he has anywhere near as much knowledge, insight, or ideas for research, as top graduate students. He's in no position to mentor graduate students.
> If by "techie" you mean, professional software engineer, that's fine
No, I mean technologist. He has a pretty solid history with software, physics, aerospace, optics, etc...
> might know enough right now to be in a PhD program
Yeah, that's what I'm saying. The frontier in AGI or even just AI is enormous and I think I would be more surprised if Carmack were not able to find some place he could expand the border of what we know.
But the academic activity is focused around the kind of activities that Kuhn calls "Normal Science".
That is, ML researchers mainly do competitions on the same data sets, trying to put up better numbers.
In some sense that keeps people honest, it also lowers the cost of creating training data, but it only teaches people how to do the same data set over and over again, not how to do a fresh one.
So a lot of this activity is meaningful in terms of the field, but not maybe not meaningful in terms of useful use.
I saw this happen in text retrieval; when I was trying to get my head around with why Google was better than prior search engines, I learned very little from looking at TREC, in fact people in the open literature were having a hard time getting PageRank to improve the performance of a search engine.
A big part of the problems was that the pre-Google (and a few years into the Google age) TREC tasks wouldn't recognize that Google was a better search engine because Google was not optimized around the TREC tasks, rather it was optimized around something different. If you are optimizing for something different, it may matter more what you are optimizing for rather than the specific technology you are using.
Later on I realized that TREC biases were leading to "artificial stupidity" in search engines. IBM Watson was famous for returning a probability score for Jeopardy answers, but linking the score of a search result to a probability is iffy at best with conventional search engines.
It turns out that the TREC tasks were specifically designed not to reward search engines that "know what they don't know" because they'd rather people build search engines that can dig deep into hard-to-find results, and not build ones that stick up their hand really high when they answer something that is dead easy.
> But the academic activity is focused around the kind of activities that Kuhn calls "Normal Science".
True, but even Kuhn would note that most paradigm shifts still come from within the field. You don't need complete outsiders and, as far as I know, outsiders revolutionizing a field are quite rare.
You need someone (a) who can think outside the box, but you also need (b) someone who has all of the relevant background to not just reinvent some ancient discarded bad idea. Outsiders are naturals at (a) but are at a distinct disadvantage for (b).
I think what's really happening in this thread is:
1. Carmack is a well-deserved, beloved genius in his field.
2. He's also a coder, so "one of us".
3. Thus we want him to be a successful genius in some other field because that indirectly makes us feel better about ourselves. "Look what this brilliant coder like me did!"
But the odds of him making some big leap in AGI are very slim. That's not to say he shouldn't give it a try! Society progresses on the back of risky bets that pay off.
> But the odds of him making some big leap in AGI are very slim.
That's probably true. I look at this as Carmack running his own PhD program. I expect he will expand what we know about computation and the AGI problem before he's done.
> ML researchers mainly do competitions on the same data sets, trying to put up better numbers.
There are surely a lot of researchers doing that, but do you really think anyone who has a plausible claim at being one of the top 100 researchers in the field in the entire world is doing that? Even if there are only 100 people doing truly novel research, that's still 100 times as many people as are going to be working on Carmack's research.
How many people were working on physics before Einstein came along?
I don't think you understand the desired outcome here. We want eureka moments, and we're hopeful for some. That doesn't mean we expect them to happen. Stop being such a pessimist.
I don't see Elon as a genius at any kind of engineering. Everything he's done there was pretty easily foreseeable as being physically possible. What he is remarkably good at is selecting daring and potentially market-changing business goals, and executing against them consistently and aggressively despite naysayers.
It's easy to say that it's probably possible to land a orbital rocket first stage. But who would bet a multi-billion dollar business on being able to not only do it, but save money by doing it, when nobody had ever done it before?
Similarly, electric cars were far from new. Nobody seemed much inclined to build one that was actually a luxury car, instead of a toy for engineer-types who could put up with driving weird things. Any of the big manufacturers could have done it, and easily absorbed the losses if it failed, but none did. Elon made a wild bet on that, making a company that made nothing else, so the whole thing would go down the tubes if the idea flopped. Instead it seems to have worked. Although it seems to be harder than he anticipated, and maybe outside his skillset, to run an organization that does real mass-production.
It's only obvious in retrospect. Every step along the way, there has been thousands of people saying "this is impossible" or "this is theoretically possible, but it can't be engineered" or "this is possible in principle, but it will be so costly to develop that it doesn't make sense".
When AGI is developed, it will seem obvious in retrospect. Participating engineers will receive middle-brow dismissals saying that this was obviously practically possible, since after all the human brain operates according to the laws of physics.
>You can only do that effectively if you have the mental capacity to keep all that info in your head at once, I think.
Yep, plus all the different perspectives from other endeavors. Extending human memory will be a really great accomplishment with brain-computer interfaces.
Falcon 9's reusable first stage has been claimed by reputable people to be impossible, before it happened. Not just "economically not worth pursuing", which was wrong but forgivable, but straight "impossible".
He shifted a whole industry towards a new paradigm. Look at Germany, they are desperate to catch up with Tesla, finally moving into electric cars. Without Elon they would keep selling their Diesel scam for the next decades.
Actually it was a bunch of Phd students from some Californian Uni that discovered the vw diesel scam. There is a short documentary about them online. Elon has no credit whatsoever in dieselgate.
However, he did make electric cars something an average person would like to have. He also chose to make it work using the same inefficient principle of hauling 2 tons of steel to transport a single person. What he made is an electric luxury car, not a car for the masses that can replace average Joe's car. Is there anything wrong with that? No, there isn't, but let's not pretend a $35k (in US - much more in EU) car that requires hours of charging after driving 250 miles unless you happen to have Tesla's superchargers on your way is a new "volkswagen - a people's car". Also I find it disingenuous to advertise full battery capacity while at the same time recommending people use only 60% of it "for longevity".
Many people don't buy new cars, but choose to buy 5-8 year old cars that are really good value if they were maintained well. It remains to be seen how Teslas behave in that market.
It would be really revolutionary if someone could create and market an electric car that was truly innovative for example: much lighter than current cars while still being safe during collision, use fuel cell technology with fuel such as methanol or similar that can be created in a sustainable way, even using a fuel cell with mined hydrocarbons and electric drive would provide for a huge reduction in emissions due to increase in efficiency.
Do Teslas have a role to play in reducing emissions? Yes, definitely, but let's not present them as a single solution to all individual transport problems.
I mean even if both Tesla and SpaceX close tomorrow, he already achieved more in both companies than most of the current "unicorns".
He successfully made popular mass market electric vehicle, and dragged whole auto moto industry behind. There were other electric cars before tesla. But tesla made it cool, and made the rest of the industry trying hard to catch up.
SpaceX also is not the first private space firm, with their own rocket, But it's by far the most successful one, and lowered the cost of entry to space by significant amount.
Also It's probably the first private space company that has rockets that can compete with most government ones.
I am not rich enough to be buying individual stocks, so I have no personal stake in this.
I keep re-reading my post and idk how it reads as a claim that Carmack is going to re-invent the field or something. All I'm saying is it's possible for him to become a player, just like you suggest.
> I think it's quite unlikely his solo work in a new domain will leapfrog an entire field of researchers.
Researchers didn't build the first airplane. Nicolaus Otto, Carl Benz, Gottfried Daimler also weren't researchers. AGI will be a program and not a research paper and John Carmack is pretty good at getting those right.
>>I think it's quite unlikely his solo work in a new domain will leapfrog an entire field of researchers.
Sometimes, an outsider with his novel or even a different way of looking at things can contribute disproportionately to a field.
Even experts have blind spots, often they show in the form of bias. If you know something is hard or near impossible to do, you are unlikely to try. If you don't know at times it's possible to stumble upon a solution by merely bringing a new way of thinking to the table.
I wouldn't, however, bet against some kind of insanely clever development coming out of his new endeavor. Something like an absurdly efficient new object classifier, that reduces the compute requirements for self-driving cars by a non-trivial factor, would be a very Carmack thing.