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 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.
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.
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".
Those jobs are incredibly hard to get,
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Yep, plus all the different perspectives from other endeavors. Extending human memory will be a really great accomplishment with brain-computer interfaces.
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.
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.
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.
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 genuinely felt a sense of disappointment when he moved to Facebook (via the Occulus acquisition). So yea, fuck you, Facebook and your manipulative, life values corrupting and PR machinery.
I place John Carmack miles above Zuckerberg.
This may be his biggest impediment. ML has gotten very far with looking at problems as linear algebraic systems, where optimizing a loss function mathematically yields a good solution to a precisely defined (and well circumscribed) classification or regression problem. These techniques are very seductive and very powerful, but the problems they solve have almost nothing in common with AGI.
Put another way, Machine Learning as a field diverged from human learning (and cognitive science) decades ago, and the two are virtually unrecognizable to each other now. Human learning is the best example of AGI we have, and using ML tech as a way to get there may be a seductive dead end.
Programmers know how it is to live at the edge of the capacity of the mind to grasp the big picture. We always reinvent the wheel in the quest to make our code more grasp-able and debuggable. Why? Because it's often more complex than can be handled by the brain.
An AGI would not have such limitations. Our limitations emerged as a tradeoff between energy expenditure and ability to solve novel tasks. If we had a larger brain, or more complicated brain, we would require more resources to train. But resources are limited, we need to be smart while being scrappy.
For the record I don't think there is any general intelligence on our planet. A general intelligence would need access to all kinds of possible environments and problems. There is no such thing.
There's also the no free lunch theorem - it might not apply directly here, but it gives us a nice philosophical intuition about why AGI is impossible.
> We have dubbed the associated results NFL theorems because they demonstrate that if an algorithm performs well on a certain class of problems then it necessarily pays for that with degraded performance on the set of all remaining problems. [1]
[1] https://en.wikipedia.org/wiki/No_free_lunch_theorem
Another argument relies on the fact that words are imprecise tools in modelling reality. Language is itself a model and as all models, it's good at some tasks and bad at other tasks. There is no perfect language for all tasks. Even if we use language we are not automatically made 'generally intelligent'. We're specialised intelligences.
That means you're specialised in survival. If you do well in life, you have a higher chance of procreation. Your genes survival depends on it.
General Intelligence is like Free Will - a fascinating concept with no base in reality. A mental experiment.
I'm glad to see he's aiming big with his billions and time. This is what rich people should be doing. Hl3 Gaben!
Don't get me wrong, I love horses. But they're living creatures with minds of their own and you have to always treat them with a certain wariness.
A Personal Navigation Device with a simulated personality that begs you to drive it all around town to various points of interest it desires to visit in order to satisfy its cravings and improve its mood.
I'm sure there's a revenue model getting drive through Burger Kings and car washes to pay for product placements.
Though we know for a fact that it is possible to find intelligence by randomly throwing things at the wall until something works. It's not like evolution uses a principled statistical process.
[Citation needed]
Also, growth may be hugely important. Babies start out with fuzzy learning, almost as if the learning rate starts out very small which normalizes the lack of knowledge and elevated novelty/variance of the environment.
AGI is all about predicting future utility given a circular dependency between the agent and environment. QM says we can't solve this exactly.. it's a two object interaction.. no way to gain the joint state, the ground truth, assumptions always have to be made to approximate independence.
Disagree.
Yes, he seemed to put a lot of effort to try to get things through FB internal politics, and not always successfully. I really wish his experiments with a scheme-based rapid prototyping environment / VR web browser had been allowed to continue [1]. VR suffers from a lack of content, and VR itself is well-suited to creating VR content, and his VR script would surely facilitate closing that loop among other things. Although now four years later I guess FB has a large team working on a locked-down, limited world building tool (closed platform, no programming ability). Oh well.
I don't think this is the end of this wave of VR, but at this point I wouldn't be at all surprised if say Apple or someone else ends up bringing it to the mainstream instead of Facebook. [2]
[1] https://groups.google.com/forum/#!msg/racket-users/RFlh0o6l3...
[2] https://www.theverge.com/2019/11/11/20959066/apple-augmented...
Reasoning being:
VR is just making what we have better. Better screens, better refresh, better batteries, better lenses etc etc. I don’t see any roadblocks.
AGI, by contrast, is not going to be a better DNN. Harder to convince people but thinking is: brain neurons are vastly most sophisticated than digital; we don’t even fully understand what neurons do; we don’t have anything other than a vague understanding of what the brain does; it is apparent that we engage in plenty of symbolic reasoning, which DNN do not do; DNNs are fooled by trivial input changes that indicate they are massively overfitting data; from what I’ve heard from researchers at top AI companies/institutions DNN design is just a matter of hacking and trying stuff until you get that specific results on your given problem, so I don’t see where DL research is actually headed; improvements are correlated with compute power increases, indicating no qualitative gains in the study of learning.
I’m incredibly impressed by DL’s achievements but I believe at best current methods could serve as data preprocessing for a future AGI.
I’m actually quite glad that AGI is so far off, because I don’t think that it’s likely big tech companies will use it responsibly.
VR OTOH is very close and is going to change everything (and IMO is likely a necessary step towards AGI).
If VR becomes widespread, and amazingly high quality, then almost everything we do will migrate to VR.
Once that is the case, we will have an unprecedented amount of data about human behaviour, and near endless data for training, experimenting, and testing AIs.
The problems of AI will become much easier to formulate: “replace this person in this VR scenario, interaction” etc. This will help drive research by giving clear goals.
More pragmatically, it just removes a lot of barriers to research and accidental difficulties ie you’ll just be able to fire up a VR rather than worrying about how your robot is going to pick things up or access real world data etc
To be honest anyone who has a very good working knowledge of Linear Algebra can learn much of ML-math in a day. There really isn't anything mathematically super-sophisticated that is in popular use today.
Being good at grasping the theory is just the first step in a thousand mile journey. The problem of AI is not going to be solved with a neat math trick on paper, but with lots of experiments. Nature has taken a similar path towards intelligence.
Sigh. I assumed the whole point of hiring John Carmack is that you trust him to identify critical problems - and to find the best way to solve them.
I don't put learning state of the art ML past Carmack, at all. However, does ML tech of today lead to general AI? It's a strong assumption.
Does a living thing count as AGI? In that case, I'd say that most parents are quite good at creating AGIs ;)
Carmack may have other priorities. This can only be good.
Yes and I really wished he hadn't. Before he joined oculus they were working on the rift2, he steered them away from that to focus on mobile efforts.
I do see the appeal of mobile vr but at the end of the day it is basically an android phone in a vr headset.
PCvr is already 2 big steps back in graphical quality from desktop games. Mobile vr is like 10 steps back. 8 more steps than I'm willing to take even if it affords me mobility.
(Also, even with the space, I'm not sure I'd be brave enough to try and use one in air - adding turbulence and random vibrations on top of the usual VR issues sounds pretty nauseating even as I type it.)
By mobility, I mean the ability to throw the headset around and walk anywhere without worrying about leaving the range of your tether. That sort of thing is important for AR, but I just don't see it mattering for VR in the long run.
There is definitely a market for VR headsets for content delivered by a phone or builtin hardware. Those devices will realistically be limited to seated or standing-room-only experiences, though.
Quest with Link is actually pretty close to that.
As far as I can tell Carmack is an old engineer whose name gets thrown around for headlines. If there weren't articles about his stealing stuff to take to Oculus I don't think his presence there would be observable.
Now people are talking like Carmack switching topics is going to change the world. It's just going to change his schedule. There are smarter engineers already working on this problem.
I'd be cautious dismissing his potential influence in the field. He has a way of looking at problems differently.
I just don't see this massive string of successes in every field. I see his huge expertise in graphics engines and games.
But it didn't help him with VR - in fact he got in trouble with VR and ended up landing with a company I have no respect for and he didn't make VR a thing.
Many people have a way of looking at things differently. I just don't see the reason this is news, unless you own facebook shares or something. Even then zero effect.
I say all this as the owner of two VR headsets (A vive for roomscale and a Lenovo Explorer for simracing/flying).
If not, does your definition of scientist require something other than doing work using the scientific method? Perhaps some specific quantity of work?
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[1] One of his companies, Armadillo Aerospace, was pretty much just a series of scientifc experiments. https://en.wikipedia.org/wiki/Armadillo_Aerospace
Final thought from me - I was thinking about your post and it is indeed difficult to discern science from engineering. One dichotomy that occurred to me (which may not hold under close scrutiny) is that scientists are interested in _the pursuit of truth_, whereas engineers are interested in _building things_.
Overall I think it comes down to popular opinion, which can be fuzzy and doesn't apply the same rules to everyone. If enough people say someone is a dancer, then they are a dancer, even if they suck and don't dance that much. This applies to basically all titles that cross institution boundaries. Another great example is countries. Popular opinion determines which organizations are countries, not a strict definition. For example the EU vs places like Iceland or San Marino. [1]
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You must be joking, right? I'm as much of a Carmack fan as anyone here, but overstating the skills of one personal hero does no good to anyone.
History books (for as long as those continue to exist) would cite AGI as his major contribution to society, and his name would be more renowned than Edison or Tesla. An Einstein. None of his other contributions will matter, as the machines will replace it all.
Just daydreaming, though.
Please correct me if I’m wrong.
I think a big reason there are few in AGI is due to PR success from the Machine Intelligence Research Institute and friends. They make a good case that things are unlikely to end well for us humans if there's actually a serious attempt at AGI now that proves successful without having solved or mitigated the alignment problem first.
Trying to make the AGI's sensors wirehead-proof is the exact same problem as trying to make the AGI's objective function align properly with human desires. In both cases, it's a matter of either limiting or outsmarting an intelligence that's (presumably) going to become much more intelligent than humans.
Hutter wrote some papers on avoiding the wireheading problem, and other people have written papers on making the AGI learn values itself so that it won't be tempted to wirehead. I wouldn't be surprised if both also mitigate the alignment problem, due to the equivalence between the two.
The problem of ensuring that the AI's values are aligned with ours. One big fear is that an AI will very effectively pursue the goals we give it, but unless we define those goals (and/or the method by which it modifies and creates its own goals) perfectly -- including all sorts of constraints that a human would take for granted, and others that are just really hard to define precisely -- we might get something very different from what we actually wanted.
>A 2017 survey of AGI categorized forty-five known "active R&D projects" that explicitly or implicitly (through published research) research AGI, with the largest three being DeepMind, the Human Brain Project, and OpenAI.
Hassabis and DeepMind have a fairly organised approach of looking at how real brains work and trying to model different problems like Atari games then Go and recently Starcraft. Not quite sure what's next up.
Or hated as the name of the man who's opened the Pandora box and doomed us all.
Just daydreaming and having a nightmare.
But it's still a fascinating endeavor.
I understand that it's a big assumption to make -- that a benevolent AI could be constructed. But under that assumption, why not have a benevolent dictator in the form of an AI?
We already live in that world, with large institutional bureaucracies playing the role of paperclip-maximizing AGIs.
It's pretty wretched when you are in their path.
If the benevolent AI ruler(s) restrained themselves to allow for humans to flourish, then okay. Assuming it could be constructed benevolently.
With AI, it would try its best to preserve/enhance/spread itself forever. And its best might be much better than our best...
How much worse could it be?
If Skynet determines we're the problem (wars, famine, global warming, inequality, non-cooperation etc), I'm losing counter-arguments by the day.
Just think about that name for a second. He might really be onto something.
I must admit that I often watch his interviews because he invites interesting people, but I can't help but cringe when Rogan gives his opinions.
These things are all true even if the guest or their ideas are extremely controversial. Maybe Joe Rogan is just smart in a way that's different to the way that you are smart.
EDIT: Ok, I suppose I should back my claim up.
Joe Rogan has pushed the “DMT is produced in our pineal gland” narrative, but there is no evidence to back this up. I’ll report a comment I made elsewhere and also link a separate reddit discussion which cites various sources. I will note that, in fairness to Joe, he said this a while ago, so perhaps he’s not so quick to jump the gun now, I don’t know, I don’t listen to his podcasts, but perhaps he’s better now.
“We all have it in our bodies” — This is an often repeated myth that has never been proven. The myth originates from Rick Strassman’s work, who himself has said that he only detected a precursor, not DMT itself and that everything else he wrote about it was hypothetical speculation. There have, apparently, been recent studies that found DMT synthesised in rat brains, but it has not yet been proven whether this translates to humans or not. Cognitive neuroscientist Dr. Indre Viskontas stated that while DMT shares a similar molecular structure to seritonin and melatonin, there is no evidence that it is made inside the brain. Similarly, Dr. Bryan Yamamoto of the neurosciencedepartment at the University of Toledo said: “I know of no evidence that DMT is produced anywhere in the body. It’s chemical structure us similar to serotonin and melatonin, but their endogenous actions are very different from DMT.”
This reddit discussion also links various sources, although I didn’t check them all myself: https://www.reddit.com/r/JoeRogan/comments/mwz2h/dmt_has_nev...
Anyone who speaks on the record about their hobbies for thousands of hours will say some things that are incorrect. He might not understand something, and he is usually pretty humble about his knowledge level.
But "spreading misinformation" is something that people do because they are intentionally misleading others, or have something to gain.
I don't think he is benefiting much from the pineal gland narrative. And it sounds like from the information you cited, it may even be correct, even if its premature to state it as fact.
Regarding the pineal gland, it might be true, but it hasn’t been proven and multiple neuroscientists have stated that while DMT is similar to compounds found in the brain, it still functions quite differently and they have never seen any evidence to suggest that DMT exists in our bodies. There was a study finding it in mice brains, so it may still turn out that we have it in ours, but it’s definitely premature to make any such assumptions and definitely premature to repeat the trope.
"Taking mathematics from the beginning of the world to the time when Newton lived, what he has done is much the better part." - Gottfried Leibniz
For years, English scientist Isaac Newton and German philosopher Gottfried Leibniz both claimed credit for inventing the mathematical system sometime around the end of the seventeenth century.
Now, a team from the universities of Manchester and Exeter says it knows where the true credit lies — and it's with someone else completely.
The "Kerala school," a little-known group of scholars and mathematicians in fourteenth century India, identified the "infinite series" — one of the basic components of calculus — around 1350."
https://www.cbc.ca/news/technology/calculus-created-in-india...
https://en.wikipedia.org/wiki/Kerala_School_of_Astronomy_and...
However, calculus proper (derivatives and integrals of general functions, and the connections between them) did not exist until Newton and Leibniz. Other mathematicians made important steps towards it earlier in the 1600s, and if Newton and Leibniz had not existed, others would have figured it out around the same time.
I'm not a historian, but a few months ago I spent some time analysing one of Fibonacci's trigonometric tables (chords, not sine or sine-differences). Aryabhata's sine-differences were much earlier.
This little known fact is so embarrassing to some institutions [2], that they made up a new word "chymistry" in order to further obscure the issue and not outright admit the obvious.
[1] http://www.newtonproject.ox.ac.uk/texts/newtons-works/alchem...
[2] https://webapp1.dlib.indiana.edu/newton/project/about.do
[3] https://www.amazon.com/Newton-Alchemist-Science-Enigma-Natur...
Is there a reason to expect that someone who wanted to investigate the laws of the composition and reactivity of matter, in the late 1600s/early 1700s, would end up studying chemistry rather than alchemy? Sure, Boyle had introduced “chemistry” as an idea in 1661 (before Newton was born), but I imagine that alchemy would still be quite active in the late 1600s as an academic “field”, with many contributors already late in their careers studying it; whereas chemistry would have been just getting off the ground, without many potential collaborators.
Your point has been brought up before -usually as an attempt by established institutions to whitewash and explain away Newton's idiosyncrasies- but there is no evidence whatsoever to back it. On the contrary, what we know (and there is a lot we do know thanks to his writings) about Newton and alchemy absolutely indicates him being immersed in the Hermetic worldview and alchemical paradigm. Clearly, Newton was practicing alchemy not as a way to look for novel techniques or as a way to bridge the old and new worlds together, but primarily because he was a devout believer.
Newton -a profound genius- stood at the threshold of two worlds colliding. He was also a groundbreaking scientist in optics/mechanics/mathematics. He was aware of Boyle's chemical research. Knowing all of that, he _absolutely_ chose to dedicate his life to alchemy. That is immensely interesting.
"Much of Newton's writing on alchemy may have been lost in a fire in his laboratory, so the true extent of his work in this area may have been larger than is currently known. Newton also suffered a nervous breakdown during his period of alchemical work, possibly due to some form of chemical poisoning (perhaps from mercury, lead, or some other substance)."
https://en.wikipedia.org/wiki/Isaac_Newton%27s_occult_studie...
It's hard to think of many famous scientists that weren't already well known in their field. Some stand out. Einstein, for example, had a fairly lackluster career until his Annus Mirabilis papers. Mark Z. Danielewski (House of Leaves) bounced to and from various jobs. But largely, the idea of the brilliant outsider is like the 10x engineer. It exists, but is rare.
You never know. Fresh eyes can sometimes see what others may not.
So I definitely wouldn’t dismiss all papers as pointless, but there certainly is a large percentage that are, enough that you can’t simply accept a published papers results without reproducing it yourself.
Do I deserve to be paid for 5 years for something that may not work? "Deserving" something doesn't have much meaning: we, the humans, merely transform solar energy into some fluff like stadiums and cruise ships. Getting paid just means getting a portion of that stream of solar energy. There is no reason I need to "deserve it" as it's unlimited and doesn't belong to anyone. A better question to ask is how can we change our society so that all, especially young, people would get a sufficient portion of resources to not think about paying bills.
Chances to make a breakthru are small, but that doesn't matter. It's a big numbers game: if chances are 1 to million, we let 1 billion people try and see 1000 successes. The problem currently is that we have these billions of people, but they are forces by silly constraints of our society to non stop solve fictional problems like paying rent.
Take Albert Einstein as an example, who arguably made one of the largest leap in physics with his theory of general relativity. He never stopped publishing during that time.
Not quite. When you are a professor, you essentially become a manager for a group of researchers. You don't really do research yourself. Therefore, your main obligation becomes finding money to pay these researchers. So in reality you can only support the research someone is willing to pay for (via grants, scholarships, etc).
Figuring out the basics of the math and how to use whatever tools they use at FB is doable in a week.
Source: Commenter name is DBZ character
wasn't the first time John did what he did. and it's not the usual kind of learning either. he was learning by first principles. i truly love this idea of replaying in your own mind what went on when something was discovered (or at least come close to it).
contrast that with how ML & AI are taught nowadays: thrown into a Jupyter notebook with all FAANG libraries loaded for you...
edit: to be clear, all I'm saying is he can catch up to the body of research already out there quicker than the average bear, and he's shown a real knack for designing solutions and being crazy productive. I'm not pretending he's gunna be publishing insane novel research anytime soon, just that I wouldn't be surprised if he ends up being a real voice in the field.
What are not basically the same thing are "he started seriously contributing to this kind of problem after a week in the woods" and "he spent a week in the woods a year ago and is ready to start contributing now. A year after that week in the woods."
As recent as his last Oculus Connect keynote, he extolled his frustration with having to do the sort of "managing up" of constantly having to convince others of a technical path he sees as critical. He's clearly the type that is happiest when he's deep in a technical problem rather than bureaucracy, and he likes moving fast.
On top of that, he likes sharing with the community with talks and such, and ever since going under the FB umbrella, he's had to clear everything he says in public with Facebook PR, which clearly annoyed him.
He's hungry for a new hard challenge. VR isn't really it right now since it's more hardware-bound by the need for hard-core optical research than software right now. With the Quest, he (in my opinion) solidified VR's path to mobile standalones. It's time to try his hand at another magic trick while he's on his game.
John's the very definition of a world-class, tried and true engineer/scientist. He's shown time and time again the ability to dive into a field and become an expert very quickly (he went from making video games to literally building space rockets for a good bit before inventing the modern VR field with Palmer).
If there's anyone I'd trust to both be able to dive into AGI quickly and do it the right(tm) way, it's John Carmack.