The Great AI Reckoning
spectrum.ieee.org
spectrum.ieee.org
However, there's a ton of cool shit happening and tons of stuff already powering industry $$$. Some of this works because of mismatched expectations, like companies who are trying to sell magic. They will fail as their customers stop buying their products that fail spectacularly 2% of the time, but really can't fail 2% of the time.
But there's tons of room in the market for stuff that fails spectacularly 2% of the time. For that reason, I don't see a "real" reckoning coming. People's expectations are too high, sure. But the reality is still Good Enough™ for a lot of problems and getting better.
(side note, does anyone know a less insulting way to refer to the group people call 'script-kiddies' that still gets the same point across?)
If that's not what you meant to communicate, you should probably explain more what you mean by 'script kiddie'.
I know I'm paraphrasing someone smarter than me there.
(Edit: Larry Tesler maybe)
But then someone builds an agent that can do X, but it can't do anything else. So people don't view it as intelligent, since it doesn't do any of all the other things people associated with being able to do X.
So it has nothing to do with being used to computers solving new problems, it is that we now realize that solving those problems doesn't really require the level of intelligence we thought it would. Of course solving new problems is great, but it isn't nearly as exciting as being able to create intelligent agents.
Edit: And it isn't just hobbyists making this mistake, you see big AI researchers often make it as well and they have for a long time. You will find tons of articles of AI researchers saying something along the lines of "Now we solved X, therefore solving A, B, C, D, E, F is just around the corner!", but they basically never deliver on that. Intelligence was harder than they thought.
I'm still on the bandwagon that AGI is already solved, and we just don't recognize it because we aren't as complex or magical as we like to pretend we are. We are just currently selected-for species survival, and not even in the present environment as the dominant species of the planet. That doesn't make us "Generally intelligent" any more than many other systems on the planet (including the computational ones). I'm not even convinced that "intelligence" is especially a thing.
If we're trying to replicate "human intelligence," then I think that's something else entirely, and would require removing a lot of capability from the AI that I don't think anybody would want to remove from AI, given that we have plenty of serviceable humans to do the work of human-intelligence style tasks.
No, when folks say AGI, they mean "cares about the things I care about, and is more accurate, but can speak in a way I understand." But how is a network of neurons going to care about what you care about if they see the world in a different way, or have been trained on solving a different set of problems than "living a life as a human?" It just doesn't make sense to expect, or even want, human intelligence from a synthetic intelligence.
We now have protein folders and superhuman Go players -- that's new.
I agree that ML ("AI") is currently at the alchemy stage. And, guess what? A neural network isn't even Turing complete! [citation needed - correct me if I am wrong.] So ML can only compute SOME functions.
AGI, when it comes, and believe me, it will, will have ML as part of its structure, but only a small part.
But it’s hard to deny that each boom doesn’t give us something useful. Neural Nets might not exactly make an AGI, but they do have uses.
It seems to me that that deep nets have effectively maximized the potential of using gradient pursuit to model patterns. But if you remove gradients from your data, or shrink your data down to tens of samples, or shift the problem to logic, or need to use functions that aren't convex or differentiable, deep nets run smack into a wall.
Luckily human perception makes extensive use of gradients, as does most search, so problems in those arenas have been unsurprisingly amenable to solution using deep nets (vision, speech, game play, etc). But many of the problems that remain untouched by DL, like human cognition, are NOT driven by gradients. Will deep nets eventually fill that void? I doubt it. You can convert only so many problems with big data into gradients to pursue them efficiently with DL before that transformation trick runs out of steam.
Personally I think deep net language modeling is one of those areas, and soon we'll encounter the limit of their generalizable contextual phrase association. Then because deep nets are so difficult to selectively revise or extend the specifics that they have learned, the vanguard of ever more complex deep nets (transformers) will eventually sink beneath their own weight, taking the last best hope for DL-based general AI with them.
This seems to me quite a deep insight. But how would you formally define a process that is gradient-based?
We're making some progress on the convexity bit (heat functions, RL, etc.), but yes, there are other areas of statistical research all involved in trying to solve those sorts of problems.
ML is not necessarily a panacea, but just because you can point to problems that it doesn't solve doesn't mean it has no "real accomplishments."
> DL, like human cognition, are NOT driven by gradients.
???
I went through Stanford CS in the mid 1980s, just as it was becoming clear that expert systems didn't really do much. There had been Stanford faculty running around claiming "strong AI real soon now" to Congress. Big-time denial as the field collapsed. Almost all the 1980s AI startups went bust. The "AI winter" followed. Today, expert systems are almost forgotten.
This time around, there are large, successful industries using machine learning. Tens of thousands of people understand it. It's still not "strong AI", but it's useful and profitable. So work will continue.
We're still at least one big idea short of "strong AI". A place to look for it is in "common sense", narrowly defined as "not doing something really bad in the next 30 seconds". This just requires lower mammal level AI, not human level. Something with the motion planning and survival capabilities of a squirrel, for example, would be a big advance.
(I once had a conversation with Rod Brooks about this. He'd been building six-legged robot insects, and was giving a talk about how his group was making the jump to human level AI, with a project called "Cog". I asked why such a big jump? Why not try for a robot mouse, which might be within reach. He said "I don't want to go down in history as the man who created the world's best robot mouse". Cog was a flop, and Brooks goes down in history as the the creator of the robot vacuum cleaner.)
That would be Ed Feigenbaum, the man who (IMHO) almost single-handedly brought on the AI winter of the 80s. Because of him we had to call all the AI research we did in the 90s something other than "AI" lest it get shut down instantly.
He was taken seriously at the time. Chief Scientist of the USAF at one point. Turing Award.
[1] https://stacks.stanford.edu/file/druid:mv321kw4621/mv321kw46...
Isn't this pretty much the current opinion of the majority of the thousands of AI researchers and programmers today? Maybe this guy was early to the party but his vision seems in alignment with today's practitioners.
The reason the hype of today is more dangerous is what I call the grandmother issue: In the 80s we dreamed of a program that would recognize your grandmother when she walked in the room. That was one of our holy grails.
Good news: The grandmother recognizer was finally built with deep neural nets in the 2000s and it works pretty well. It's not perfect but if you tell it to play grandma's favorite song when she walks in the room it's no big deal if it occasionally fails to recognize her, or if it plays the song when someone other than your grandma walks in the room.
Bad news: We now have people who (effectively) want to attach the grandmother recognizer to a machine gun with instructions to shoot your grandmother and only your grandmother, and never to fail to shoot her if she walks in the room. Suddenly the Type 1 and Type 2 errors are a whole lot more consequential. Modern NNs are simply not fit for that job.
We have pretty good AI for low-consequence purposes, but it cannot be used for high-consequence purposes without a lot more fundamental research. Incremental improvements to deep learning are not going to get us there.
I think you're underestimating how smart a squirrel is.
(John Rober’s squirrel maze, one of the most wonderful concotions I’ve found on Youtube.)
The first of these questions has led to real progress in things like image recognition, whereas the second of these questions has not led any real progress in digitizing squirrel brains.
So true bottom-up is not hopeless.
Would that be the "robot vacuum cleaner that can't see shite?":
https://www.boredpanda.com/robot-vacuum-cleaner-spreads-dog-...
speech recognition hype... now most phone trees use them.
electric car hype... now california is full of them.
self driving cars... now many new cars have driver assist.
Electric cars are NOT AI!
But it is true that "california is full of them", whatever "them" may be. That may have something to do with the crowd (whores and madmen) attracted by the California Gold Rush and whose descendants now mine their wealth in VC endeavors, including deep learning.
https://en.wikipedia.org/wiki/California_Gold_Rush
"driver assist" is a long way from a "self driving car".
I'm saying AI is a technology that is being overestimated now. People are trying to use it for everything now. I think in the future when the hype has worn off it will not have worked for everything hyped. But it will have worked well for a couple things to the point they are taken for granted or have become invisible.
1. Self-driving cars. The ultimate being no steering wheel and carefully software controlled and permissioned as to where you are allowed to go.
2. AI powered Body monitoring devices (e.g Fitbit) that can be used to biologically monitor millions of consumers body states and functions.
3. Any new kind of surveillance technology. Miracle mass surveillance is something that's really interesting to this market.
4. Large centralized social media networks that aggressively moderate content with AI.
5. Various kinds of transhumanist biotech that design all sorts of biotech stuff with AI. What the heck is Calico up to anyway?
6. Blockchain stuff. AI enabled or otherwise.
They are also interested in environment tech like any kind of alternative energy, no matter how speculative or impractical and fake meat, but I digress.
So I would guess the bubble is the WEF crowd and friends with absurd amounts of money trying to make their future, for good or ill, a reality and investing without much regard for the economics of the project.
[1]https://www.weforum.org/agenda/2016/01/what-is-the-fourth-in...
We were asked to look into a company that had an, I kid you not, "AI-powered problem solving platform".
The suggestions it spit out in their demo for agriculture were things like "What if the earth was upside down", and I read it like "Maaaaan, what if, like, just imagine like, the earth wasn't like below, but it was like, above... Duuuuude. Just imagine.". i.e: you could get these recommendations with a few dollars worth of haschish.
Add a pitch deck and a blockchain and you have a startup with the right buzzword cloud.
That's the key insight. It's why the commercial successes of AI have been primarily in advertising, marketing, and investing.
Which is why people say that it has few useful applications. People don't care if those areas become more effective. Another important application is mass surveillance, which people would also argue isn't a good thing.
Is there any evidence that any AI is better than index buy and hold?
I do not think there is.
there's plenty of bullshit uses of AI out there, but i think it is silly to say there are only limited commercial successes.
Disclaimer: not an expert
Looking at apps we use every day, almost all of them owe some core feature to ML/DL. ETA prediction, translation, search, spam filtering, speech synthesis, autocomplete, recommendation engines, fraud detection—and that's not even touching the world of computer vision behind nearly every popular photo app.
A key understanding gap in the general public's knowledge of ML is that people think AI === Skynet, and they've therefore been lied to about the field's progress and impact, when in reality, they probably interface with a dozen pieces of technology that are built on top of recent breakthroughs in ML/DL.
I looked through the list of solvers for the protein folding challenges and there were other deep learning, neural network, and classical machine learning approaches on there. Even some hybrid ones! But none of the participants had even a fraction of the compute power that AlphaFold had behind it. Some of the entries were small university teams. Others were powered by the computers some professor had in their closet (!). Most of the teams were dramatically under-powered as compared to AlphaFold. How much did this influence the final result?
What would the other results look like if they'd been on equal footing? Would they have been closer?
It's a genuine question.
One way to look at why deep learning is having the impact it does is that unlike other ML methods, it's actually capable of making use of so much compute. It gives us modular ways to add more and more parameters and still fit them effectively.
It's like asking if using the google homepage from 10k iterations ago would perform better than the current version on the present user cohorts. There's just too much invested to justify testing things like that when you can use the time to improve what is showing promise.
First, having 'infinite compute' is a way for researchers to be sure that compute isn't the thing holding back their method. So, DeepMind made a protein folder using all the compute they had available; later others managed to greatly reduce the amount of compute needed to get equivalent results, by re-implementing and innovating on DeepMind's initial write-up.
Second, I think there's a lot of interesting ground to explore in hybridizing ML with more classical algorithms. The end-to-end deep learning approach gives us models that get the best scores on imagenet classification, but are extremely prone to domain shift problems. An alternative approach is to take a 'classical' algorithm and swap out certain parts of the algorithm with a deep network. Then you should often get something more explainable: The network is explicitly filling in a specific parameter estimation, and the rest of the algorithm gives you some quality guarantees based on the quality of that parameter estimator. I saw some nice work along these lines in sparse coding a couple years ago, for example...
Without question, limits exist in every DL architecture; we just haven't taken the time to diagnose or quantify all the limits yet. Now that attention has shifted to transformers, analysis of DNNs' inherent limits is made substantially more difficult, given transformers' huge size. That, and their multi-million dollar training cost likely will make if infeasible to diagnose or cure each design's inherent limits.
Papers say that both are trained on PDB dataset. And still, we see a dramatic gap between old and new Alphafold models. Both were trained by Deepmind, probably with a similar computer power. I think it's obvious that it's not just compute power, method matters a lot.
Imagine how many we’ve dug up so far out of the total set. It’s an infinitesimal percentage. We haven’t even scratched the surface.
Imagine the set. What’s inside? Is there something inside that we will regret?
Unambiguously yes. 10TB is a lot of space. That's large enough for a program that does nothing but show 8 hours of HD footage of your kids being tortured and eaten.
It's also more than enough to define a program that would reliably precipitate a global thermonuclear war if connected to the Internet.
I am having a difficult time understanding what the operative meaning of "intelligence" here is. "AI" doesn't transcend the Turing machine. Intelligence doesn't mean more computer cycles per unit time either and compute cycles don't transcend the TM. What makes intelligence intelligence is what it can in principle do; speed is irrelevant. There is no essential difference between AI and non-AI.
99.99% of possible bit arrangements in that 10TB will not do anything good, or useful, or even anything at all.
This comes out to 3.1 × 10^24082399653118
To emphasize the size of that search space- let us measure the diameter of the observable universe in planck lengths, the shortest possible length. We would need over 24 trillion digits to write the number of universes we would need for the quantity of planck lengths to equal the number of possible programs.
That 99.99% number is missing several billion additional 9s.
Are you stating that you have a corpus of 10TB of software source and you are frankenstiening it with interesting results? I find that hard to believe.. Surely its like Monkeys and typewriters, 10TB in that context wouldnt be nearly enough.
A sibling post shows this, it presents the concept very well: https://en.wikipedia.org/wiki/The_Library_of_Babel
The space of all programs in 10TB is far too large to count, even if we could harness galactic computation. Even within a much smaller search space, there are valid programs which cannot be found by gradient descent. Let BT(n) be the number of distinct binary trees less than or equal to height n. This number scales according to the following recurrence relation:
BT(n+1)=(BT(n)+2)²−1
In case anyone here is interested in learning more about program synthesis, there is a new workshop at NeurIPS 2021 which explores some of these topics. You can check it out here: https://aiplans.github.io/
That's really why deep learning shines in technical applications. Solution search heuristics were until recently solely within the domain of biological neural networks; now we have created technology which is capable of extracting superior heuristics over the course of learning. And it's already paying off in industrial science, despite the cries of naysayers, though the applications are still in infancy.
If you are interested in learning more about the limits of gradient descent, you should look into the TerpreT problem [1]. There are surprisingly tiny Boolean circuits which can be found using constraint solving, but we have not yet been able to learn despite the success of reinforcement learning in other domains. I'm not saying that program induction is impossible, but it is extremely hard even for relative "simple" languages like source code.
The second answer is that we are exploiting statistical regularities in the data which make these problems effectively regular or context-free in practice. When someone asks you to solve a new programming problem, you are applying some heuristics that have worked on similar problems in the past. Given a truly novel problem, you can either make some assumptions to reduce it into a more tractable form (e.g. 3-SAT), or design some clever search heuristic, but without any prior examples of programs or a distribution over probable inputs, you can do no better than naïve search.
How is this different from training a neural network on a data set and relying on interpolation for inference? Isn't that exactly what neural nets learn, statistical relationships between inputs? After all, isn't that effectively the mathematical definition for a heuristic? You don't know exactly what the solution is, but there are similarities between past examples you've encountered, and these form priors for your shortcut through the solution space.
I'm not sure if neural nets can extrapolate (which would imply searching outside of the training space, i.e. innovating) but if they are truly universal function approximators, I don't see why not. Especially if the solution space is smooth and continuous in the extrapolation range...whatever that means in high D space.
To your first point, it seems you are saying that neither humans or neural networks can solve these intractable problems. It would not surprise me if a massive, well tuned neural net, possibly with yet to be developed components, could discover better heuristics than even an intelligent and experienced human for problems in this class. But that might be optimistic.
The huge difference is that humans has a rational part of the brain which determines if examples are important to train on or not, doing extremely efficient problem space pruning. And then it trains the neural network in real time using that rational part as guidance. We can't program without the rational part, so computers likely wont able to program without it either. We can read sentences and detect objects in images without the rational parts so ML AI can do that.
We have no idea at all how to build that rational part, without something replacing its role the current methods will just give us extremely primitive parts of human thinking such as image recognition.
AlphaGO actually got a rational part, the code they used to play through the game and see if moves leads to a win. That means that AlphaGO has the full human capability, but for a very limited domain, just GO, the rational part of GO doesn't generalize well to more interesting problems.
> If it were just the algorithm, I think 10TB would be enough
Just thinking about it makes me shiver. I had been assuming that AGI or strong AI needed enormous advances in hardware like quantum computing or numerous iterations of Moore's Law. Would it be correct to say that the right 10TB bit pattern might give us strong AI on today's commodity hardware -- at least in theory?
With every year we expand our vast collective computational resources. It’s like a powder keg. It’s sitting dormant just waiting for a good program to be stumbled upon. I hope I’m not alive when that happens
This is objectively wrong
It's appealing to think we can just make a program that learns programs and then use that to learn to do anything computable. But this is a well studied field and it turns out that when you generalize a learning problem that way you make the learning problem a lot harder.
The space of programs that could possibly.identify dogs in images is much much much larger than the space of images that contain dogs. The images are bounded by the number of pixels in the image times the color depth. What is the space of programs bounded by? 10TB? That's roughly 256^10000000000 programs. That's just a stupidly large number.
Obviously not every 10TB string is a valid program. You can reduce that number. But what current research in program synthesis tells us is that you can't reduce it as much as you might hope.
So the point is that, just like for humans, it's easier to learn to do a thing than it is to learn to write a program to do a thing.
You're saying you can't find intelligent programs this way because the search space is large. That's an anti-AGI argument, and it's fallacious because humans evolved.
Yes, you can only search an infinitesimal subset of the search space. The same is true for DNA. The argument is clearly invalid without at least reference to properties that gradient descent has, or that evolution has but it does not, which you have not done. It is wrong for the same reasons the watchmaker analogy is.
I’ve never quite understood why that’s an important metric for considering processing. Does it have an actual impact on the computability of something, or is it just a visualisation to help human minds scale the grasp of something?
But basically yeah it’s just an illustration of the difficulty. Obviously we’ll never have a supercomputer’s worth of computing power for every atom.
It might turn out to be wrong (for example, elementary particles or black holes might have exploitable structure, a picosecond is pretty long compared to the Planck time, or closed timelike curves in spacetime might allow you to spend an unboundedly long time computing something), but at the very least it's a strong suggestion that exhaustive search will not be fruitful.
For more immediate purposes I prefer the dollar cost of carrying out the computation with currently available hardware.
Quantum computing, when it becomes practical, will give you only a quadratic speedup, as far as we know. So the relevant problem size then expands to the square of the number above.
But almost all of the images in that set look like grey fuzz. On average there's nothing interesting there.
[1] https://colab.research.google.com/drive/1QBsaDAZv8np29FPbvjf...
It's often not about expertise or sanity, but AGI captures people's imagination to a huge extent and as evolution has conditioned us to do, we ascribe certain behaviors and desires to systems which only exhibit those by random chance.
Only people selling AGI are marketers and futurists.
I don't rely on DL driven systems and I'm not even a skeptic. I want it to work. But I can't rely on these systems in the same way that I rely on my computer/phone, a light bulb, or a refrigerator. Is that ever going to change?
The cameras on mobile phones got so much better thanks to deep learning, snapchat filters, zoom backgrounds, etc all use CNNs.
Someone has already mentioned face unlock, but also dictation is miles ahead of where it used to be. Similarly text-to-speech is absurdly better than it used to be and is approaching indistinguishability from human speech in some cases (again, narrow domains are more successful!)
Smartwatches are capable of detecting falling motion and alerting emergency responders, and are increasingly able to detect (some types of) cardiac incidents. Again here the theme is intelligent behavior in very narrow domains, rather than some kind of general omni-capable intelligence.
The list goes on, but I think there's a problem where so many companies have overpromised re: AI in more general circumstances. Voice assistants are still pretty primitive and unable to understand the vast majority of what users want to speak about. Self-driving still isn't here. To some degree I think the overpromising and underdelivering re: larger-scoped AI has poisoned the well against what is working, which is intelligent systems in narrow domains, where they are absolutely rocking it.
I've observed that not only are the domains narrow, but the domains of domains are narrow. In other words the real-world applications are mostly limited to pattern recognition, reconstruction, and generation.
What I wonder is this. Is DL a dead end?
Are we going to reach a ceiling and only have Face ID, Snapchat filters, spam detection, and fall detection to show for it? Certainly there'll be creative people that'll come up with very clever applications of the technology. Maybe we'll even get almost-but-not-really-but-still-useful-actually vehicle autonomoy.
I can't imagine a world without the transistor, the internet, ink, smart phones, satellites, etc. What I'm seeing coming out of DL is super cool but it feels like a marginal improvement on what we have now and no more. And that's fine... but a lot of very smart people that I know are heavily investing in AI because they're banking on it being the new big technological leap.
"Marginal" here seems to be doing a lot of heavy lifting and IMO isn't fair. The ultimate point of technology isn't to inspire a Jetsons-like sense of wonder (though it is nice when it happens), it's to make life better for people generally. The best technology winds up disappearing into the background and is unremarked-upon.
Like better voice recognition or text-to-speech. We've become accustomed to computers being able to read things without sounding like complete robots - and the technology has become so successful that it's simply become the baseline expectation - nobody says "wow Google Assistant sounds so natural" - but if you trotted out a pre-DL voice synthesis model it would be immediately rejected.
I also wouldn't characterize "ability to automatically detect cardiac episodes and summon help" as some kind of marginal improvement!
I think there's a bit of confusion here re: a desire for DL to be the revolutionary discovery that enables a sci-fi expectation of AI (self driving cars! a virtual butler!), vs. the reality of DL being a powerful tool that enables vast improvements in various narrow domains - domains that can be highly consequential to everyday life, but ultimately isn't very sci-fi.
Does that make DL a dead-end? For those who practice it we aren't close to the limits of what we can do - and there are vast, vast use cases that remain to be tackled, so no? But for those whose expectations are predicated on a sci-fi-inspired expectation, then maybe? It's likely DL in and of itself won't lead us to a fully-conversant virtual butler, for example.
[edit] And to be fair - the sci-fi-level expectations were planted by lots of people in the industry! Lots of it was mindless hype by self-described thought leaders and various other folks wanting to suck up investment money, so it's not fair to blame folks generally for having overinflated expectations about ML. There's been a vast amount of confusion about the technology in large part because companies themselves have vastly overstated what it is.
> The best technology winds up disappearing into the background and is unremarked-upon.
Very much agree, but what I've seen is that DL based solutions do not disappear into the background.
It's so rare for them to disappear into the background that, sitting here at my computer right now, thinking real hard, I can't come up with a single consumer DL product that works reliably. I'm pretty sure there are a few things but it's soooo rare.
Face ID works most of the time but the success rate for me is like 1 in 50. It's very very cool technology but it's also very unreliable. Also if face ID never existed I don't think my life would be worse off in any way.
The same basic issue I can apply to ever DL solution I can think of. The best way I can describe it is they feel... janky. Always janky. I've had similar conversations before and, after some back and forth, the bullish-on-AI person ends up saying much of what you said. Here's where we end up in a weird stalemate...
> I also wouldn't characterize "ability to automatically detect cardiac episodes and summon help" as some kind of marginal improvement!
Maybe not a marginal improvement, but there's a lot of amazing technology in the medical, industrial, and military sectors. For example people are surprised that FLIR was actively used in the military in the early 90s!
I have no doubt that DL is going to drive a lot of the innovation in highly specialized areas.
What I'm talking about (and terrible at communicating, honestly) is general purpose consumer applications. Can DL significantly improve the lives of every day people? Right now I'm seeing a lot of toy applications, innovation in highly specialized areas, and only hopeful ambition for general use.
What I'm waiting for is that magic moment when I use a technology that a) works flawlessly and b) changes how I live my life. As soon as I see a DL based solution that does that then I'm sold. I just haven't seen it yet.
My cousin is an MMA fighter in another country, just today he got a contract from an american agent and asked me to translate it. I was able to throw it into google translate and in under 2 seconds it produced a flawless translation of 20 pages of legalese.
I have a fairly affordable Hyundai that's able to drive 80 miles on a highway without me touching the steering wheel.
I built an app that uses image recognition to automate food logging, from the surveys that we did it cut down the time to log from 15minutes a day to under 2.
I've worked on systems to monitor patients at risk of falling in a hospital setting.
My friend built Tonal, which can track your exercise form (https://www.tonal.com/)
Alphafold will be a huge deal for drug discovery.
I can keep going
Flawless sounds like an overstatement. I would hope that you use a professional before signing contract? That's serious stuff.
> I have a fairly affordable Hyundai that's able to drive 80 miles on a highway without me touching the steering wheel.
Are you referring to lane assist or OpenPilot? In both cases you need to be focused enough on the road that (IMO at least) it doesn't make that big of a difference either way. Certainly not life changing.
> I built an app that uses image recognition to automate food logging, from the surveys that we did it cut down the time to log from 15minutes a day to under 2.
Can it detect hot dogs?
> I've worked on systems to monitor patients at risk of falling in a hospital setting.
See my response wrt specialized (medical, industrial, military) settings. There's a lot of other incredible technology at work in hospitals.
> My friend built Tonal, which can track your exercise form
People exercised just fine before this. I'd classify Tonal as a marginal improvement, at best. I've actually found that removing technology and falling back to simple calisthenics (done properly of course) is having a much greater impact than adding more technology, for various reasons.
> Alphafold will be a huge deal for drug discovery.
I agree, but it falls under the category of specialized use cases. It's very exciting though.
You do realize that todays translation services often reverses the meaning of sentences? They are useful for reading random posts where you don't care about the results, but they should never be used when you absolutely need to know the meaning of statements.
What you did is akin to putting your sleeping friend into a tesla, turn on the autopilot and see the teslan leave on the road, then posting "See, the tesla drove away perfectly, AI really automated driving!". You don't even know if it arrived safely, and even if it did the tech isn't reliable enough to safely do what you did.
I expect this to be the norm in decade or two....fewer and fewer staff will be necessary
DL makes for great constrained/cherry picked demonstrations, but it seems to fall flat on the last mile (unless the domain is extremely narrow).
Data mining and probabilistic pattern recognition are much more accurate descriptions, but don't sound as exciting.
It's definitely possible that true AI will one day exist, but it may be anywhere from 5 to 1000 years away. I suspect the current approaches will not resemble the final form when it comes to AI.
That still might be accurate, just maybe not in the near term. It may be controversial, but I think that humanity's hubris is the biggest barrier towards developing more effective AI.
"AI researchers drop silicon, turn to biological computers"
"AI researchers discover AI biological computers not so artificial after all"
"AI researchers to allow AI biological computers to grow neurons and grey matter"
"AI researchers realize AI biological computers need oxygen and organic nutrients, not electricity"
"Shortages of neurology textbooks as AI researchers switching careers to neurologists"
"AI researchers turn to live human farms to grow brains"
"AI researchers realize brains in a vat don't work, need sensory inputs, to farm human heads instead of just the brain"
"AI researchers realize heads in a vat don't work, need the rest of the body"
"'Why spend billions on cutting edge quantum processing units when a normal human will do the job?' asks machine learning pioneer"
"AI researchers give up, say human brains are better than supercomputers for a fraction of the cost"
"AI research community spent $635,920 billion and 537 years, has produced a human, says GoogleSoft-Zon report"
The end product of AI research will be Homo Sapiens.
A consequence, I guess, is that there are lots of unexplored / underexplored problems waiting to be tackled, and there are tools around that can make it happen. If there is a reckoning in the advertising space, there will be lots of other applications to focus on.
So this reckoning in the advertising space would only be a net positive for society if others came in to fill that funding gap and threw enough money at researchers to keep the field afloat in a similar fashion
It's totally self perpetuating.
My sales team keep asking me to add AI to X product. Doesn't matter if it's not required, or even makes sense they ask because the competition is doing it and they get asked by our clients/prospects in an expecting tone, if we offer some AI on our products.
There are places we do offer it where it genuinely adds value but this 'sprinkle AI on everything for the sake of it' needs to die.
It could be worse, they might be asking you to put it on a blockchain :P
All it appears to be now is a collection of very sophisticated pattern matching algorithms that build their own opaque internal sorting/matching/classification algorithms based on the data they're trained on, as I understand it. This is of course incredibly useful in some domains, but it's not really 'intelligence'.
And, they can't do math very well:
> "For example, Hendrycks and his colleagues trained an AI on hundreds of thousands of math problems with step-by-step solutions. However, when tested on 12,500 problems from high school math competitions, "it only got something like 5 percent accuracy," he says. In comparison, a three-time International Mathematical Olympiad gold medalist attained 90 percent success on such problems."
And when such a system appears, you'll claim that it's still not AI --- just a fancy pattern matching trick --- and say that it's not real AI until some other arbitrary benchmark is met.
"AI" is just what machine learning can't quite do yet.
> collection of very sophisticated pattern matching algorithms
What do you think human brains are? Humans are Turing machines as well --- all physical computers are. We process inputs, match them against internal state, and generate outputs. You can't criticize AI on the grounds that it's "pattern matching": everything is pattern matching.
You can't look at the output of GPT-3 and tell me that it's some kind of dumb regular expression here. You just can't.
No, it doesn't, and that's because you have an inadequate understanding of what "pattern matching" is. The domain over which patterns are matched --- in both the brain and artificial neural networks --- isn't just the input, but a combination of the input and the previous state of the computation doing the matching. It's this recurrence, this recursive evaluation of previous state, that makes human minds Turing complete. "Pattern matching" is more powerful than you think when you combine it with attention and memory, and ML models have had both for years. Do you think ML models are dumb regex lists or something? ML models and the brain have state.
I think domains that will get actually solved first due to this are programs/maths as usually it's easier to verify solutions that to devise them. For other stuff you would have to simulate the universe.
That's why for example self driving cars basically record everything and will simulate all possible scenarios especially these based on disengagement data.
Also it doesn't take single human to devise these algos. It took whole generations of humanity. We are just approaching possible lower bound of compute power of single brain.
I think we might get program and proof synthesis in 1-30 years with this kind of progress and funding. AFAIK in 2048 silicon compute might get bigger than whole compute of humanity.
The article “Deep Learning's Diminishing Returns” was discussed a week ago, here: https://news.ycombinator.com/item?id=28646256.
The agency presented regional branding campaign creative with our national flag flying at half mast. The AI predicted success. The ad agency stood by the AI.
Certainly would have generated clicks.
But the ad agency lost a customer. Not sure if the AI would have predicted that!
Businesses pay for results, applying 1000 slightly different variations of the same techniques on the same dataset produces very little return. Businesses take note, don’t give raises etc and the DS team fades.
Eventually someone tries the new hot thing like Deep Nets and sees a large gain in a core business metric with relatively little effort. As every team is going through the same hype cycle DS salaries spike.
That creates opportunity for domain experts who learn AI. Less so the other way around because these domains are generally more complicated than AI and lack the unending tsunami of online courses to learn the details.
Looks like I never will get one. Too hard, it is.
Funny how we spend so much time and effort to mimic something that grows free by the billions :)
[0]Diminishing Returns https://spectrum.ieee.org/deep-learning-computational-cost
[1] renewables and non-renewables in $.05~.15/kWh range https://www.forbes.com/sites/dominicdudley/2019/05/29/renewa...
The performance would increase by half an order of magnitude and the AI safety problem would be solved by use of the borrow checker.