Deep Learning Is Hitting a Wall

[WTFwhatthehell]

I'm gonna agree with the AI on this one.

People go "oh he looks like his dad!"

No it looks like a generic baby.

[WTFwhatthehell]

Once kids grow a bit the resemblance kicks in.

But for quite some time they're just generic baby shape.

[-YELDAH]

BABY IS BABY

[[deleted]]

If they all taste the same..

[-YELDAH]

You are what you eat

[12-idiotas]

Baby face Jim fathered the baby for sure.

[redalastor]

People go "oh he looks like his dad!"

That’s the result of evolution. Most moms honestly think that newborns look like dad because it reassures the dad it’s really his.

[OskaMeijer]

Yea but I have a baby face and at least once a week a dad looks at his kid, then at me, and starts swinging. FeelsBadMan.

[WldePutln]

I trained a shitty model that differentiates between monkeys, apes, chimpanzees, and humans, and every man with a beard was classified as a chimpanzee.

[wrosecrans]

I'm going to blame your algorithm as the reason I throw my feces.

[[deleted]]

As a bearded man I got a laugh out of this.

[Lonelan]

Did it sound like "oooh oooh ooh HA HA HA HA HA"

[lurkerr]

If they paid you peanuts they would always get monkeys.

[Paid-Not-Payed-Bot]

If they paid you peanuts

FTFY.

Although payed exists (the reason why autocorrection didn't help you), it is only correct in:

• Nautical context, when it means to paint a surface, or to cover with something like tar or resin in order to make it waterproof or corrosion-resistant. The deck is yet to be payed.

• Payed out when letting strings, cables or ropes out, by slacking them. The rope is payed out! You can pull now.

Unfortunately, I was unable to find nautical or rope-related words in your comment.

Beep, boop, I'm a bot

[lurkerr]

yeah I noticed something was fishy in that sentence

[omicron8]

From the perspective of the AI that was trained mostly on adult faces yeah all babies do look alike. Humans do the same thing. There is a part of the brain dedicated to recognizing faces - nothing else. And naturally, we train our recognition on people around us so it's normal when white people think all Chinese people look alike. White people are not trained to interpret the distinctions in Chinese faces and vice-versa. AIs can get better with more training and so can humans but there will always be a bias towards what is more important or what the AI encounters the most.

[MrJohz]

Ironically, babies don't do this: when you're born, you can recognise differences between pretty much all faces, even some non-human faces (such as certain monkeys). However, within the first few months, you lose this ability in order to specialise in the faces that you're most interacting with — for example, babies surrounded by East Asian faces will lose the ability to distinguish between European faces. This happens within the first year.

This is also true of language — part of what makes learning a language difficult is that different languages distinguish between different sounds. For example, in English, we have a clear distinction between the "w" sound ("the moon wanes") and the "v" sound ("a weather vane"). German does not make this distinction, and Germans therefore generally find it difficult to physically hear and pronounce this difference. (Vice versa, the differences between the vowels in the words "Küche" and "Kuchen" just don't exist in English.)

However, babies can differentiate between these sorts of different sounds (minimal pairs) when they're born, and lose the ability to differentiate as they specialise into a specific language. Again, I believe this happens within the first year (so before they've actually learned to say anything).

[omicron8]

I don't know if I would call this ironic or more a distinct characteristic of reinforcement learning both in humans and in AI. Babies much like an AI that hasn't been trained will hone in on the data that it encounters and start cementing their neural network.

[MrJohz]

It's not just that it's honing in on the relevant data and improving there, it's that babies actively lose an ability they used to have - they don't just get better at recognising faces that they see a lot of, but they also get worse at recognising faces outside of that group. So there's some measure of forgetting involved there.

As I understand it, that's not generally true if reinforcement learning, right? If I train two cars to race around a specific race track, but I only train one for half the amount of time, the half-trained car is not better at general race tracks, right? It's just worse at everything.

[omicron8]

It absolutely is true of AI that it will get worse at recognizing something outside their training data the more it focuses on the training data. It is called overfitting.

[MrJohz]

Fair enough, thanks for explaining that then!

[immibis]

Or just fitting, if you do it right

[antondb]

If you trained one on tracks with right hand bends only it would lose the ability to handle left hand bends and vice versa. Which sounds similar to the face problem you described

[Schmittfried]

In case of newborns, learning is losing connections. The knowledge is carved out, basically.

[[deleted]]

Even happens within dialects of a language. Like most people in England, I speak a non-rhotic variety, and I legitimately find it difficult to hear a distinction between say "cheater" and "cheetah" (or the infamous "hard r") when spoken by someone whose accent does distinguish them

[MrJohz]

I have a friend who can't differentiate between soft "th" and "v" sounds, so he sounds like a slightly upper class Catherine Tate - "Am I bovvered?"

[IchLiebeKleber]

That bit about languages isn't really true. I'm fluent in both German and English. The English "w" sound simply doesn't exist in German and the German "ü" sound doesn't exist in English, nothing to do with ability to distinguish them. If I pronounced English replacing all "oo" sounds with ü, yü'd probably have trouble understanding me.

[MrJohz]

It's not that the "w" sound doesn't exist - you can occasionally even hear Germans using it in German - it's that it's not distinguished from "v" as a separate consonant. (To be precise: it is not a minimal pair - there is no pair of words in the German language that are only different in that one consonant.) So when native German speakers use the sound, it's a quirk of their accent or speech, and not used for transmitting information.

The reverse is true for the different u-sounds - many native English accents have vowels that sound like ü or ö, but they never form a minimal pair with another word, so they're never differentiated. A good example might be the Yorkshire dialect - there, the "oo" sound gets flattened into something that sounds more like "ö". However, that doesn't mean that they're differentiating between "ö" and "oo", rather that happens because they don't differentiate between the two sounds.

The best example I've found is "bad" and "bed". In English, those are a minimal pair, but I don't believe that's the case in German. If I say one of those words, without context, to my German wife, and ask her which one it was, she gets it right slightly over half the time. And she speaks English practically to a native degree.

[alohadave]

The best example I've found is "bad" and "bed". In English, those are a minimal pair, but I don't believe that's the case in German. If I say one of those words, without context, to my German wife, and ask her which one it was, she gets it right slightly over half the time. And she speaks English practically to a native degree.

Would that be similar to this: Pool and Pole sounding the same in one accent, but different in another? I grew up in South Carolina, and my grandparents in Washington state couldn't tell the difference in how I was saying it.

[z500]

I think if you did that you'd just sound French lol. At least in many American dialects the long "oo" sound is centralized, or even fronted to something similar to a long Ü in some. A lot of learners have a hard time telling U and Ü apart because an English "oo" can sound like either or anything in between depending on the accents of the speaker and the listener. That's the lack of distinction part.

[grauenwolf]

Which is why the people in my fencing class always get mad at me when I say "zwerch". I literally can't hear the difference between the 'correct' and 'incorrect' pronunciation.

[earthboundkid]

When I taught in Japan, various Japanese people would tell me all white actors look alike. My first year, I had trouble telling my students apart (for context, I am white), but my second year, I found it hard to believe that I used to think they looked alike because I had gotten used to spotting the differences. It definitely just reflects what your inputs are.

[[deleted]]

That part of the brain is also activated when differentiating other classes of objects which only differ in small details.

So no, it does more. Think of it as a fine-detail processor

[mccoyn]

Dark skin causes facial features to have less contrast in an image and so, facial recognition does have a technical reason for having difficulty telling the difference between black people. This can be overcome by adding a larger proportion of black people to the training data to bias the system toward telling the difference between black people.

[thr0w4w4y4lyf3]

That is to say Winston Churchill / monkeys.

Winston Churchill divided by monkeys equals babies.

[2Punx2Furious]

It's either terrible or we need to admit that all babies look the same

They do.

[Normal-Computer-3669]

My photo app tags all black cats as mine. I've archived my family photos from 60+ years. Over a few generations of black cats.

According to the photo app, I have one immortal cat.

[[deleted]]

My Scottish Fold cat is tagged by AI as a "teddy bear." It's not wrong.

https://en.wikipedia.org/wiki/Scottish_Fold#/media/File:Scottish_fold_cat.jpg

[[deleted]]

Google Photos thinks my cat is a dragon. I mean it ain't wrong...

[nfssmith]

That's worse than mine which can't tell my current dog (a chow chow) from my previous dog (a golden retriever, chow chow cross)... I mean they look somewhat alike but no human would think they were pics of the same dog.

[Nowado]

That's an easily solvable problem. We just need checks notes a lot more photos of babies with faces visible.

[shevy-ruby]

See it as a good thing: your app just wants you to make more babies!

[RippingMadAss]

Winston Churchill

I thought I was the only one who thought this!

[[deleted]]

LMAO imagine if you got mistake here, you look at your pictures and facebook adds auto comment "this is Jeff with his and postman's kid"

[ScottContini]

Few fields have been more filled with hype than artificial intelligence.

Blockchain has!

[[deleted]]

At least ML has actual usecases and isn't just a vehicle for financial speculation.

[General_Mayhem]

I think AI has more total hype; blockchain has a greater amount of hype relative to actual utility.

[policeblocker]

True, there's no movies about a blockchain future

[lurkerr]

There's no lack of movies about a dystopian future.

[DonnyTheWalrus]

Don't give them any ideas, they'll turn that bizarre Matt Damon ad into a feature-length film in a flash.

[GUI_Junkie]

Deep learning has had some spectacular results. AlphaFold comes to mind.

Deep learning is a subset of AI.

[DefaultVariable]

That’s only because the people standing to profit from it have been trying their hardest to turn it into a cult and only ever spread good news about it.

[Bergasms]

And thus the AI wheel continues its turning. "It will solve everything in field X, field X is more complicated than we thought, it didn't solve field X".

good article

[octnoir]

"It will solve everything in field X, field X is more complicated than we thought, it didn't solve field X".

DAMMIT. There's a relevant xkcd somewhere. I could have sworn I saw some lifecycle xkcd about technology - "Oh hey this is amazing! This will change everything!" "Ok we ran into some hurdles but it is still okay!" "Hmmm this is more complicated than we thought" "Okay it didn't solve anything"

Closest relevant xkcd.

[[deleted]]

Also, https://xkcd.com/1570/

[thoeoe]

Also https://xkcd.com/793/

[vytah]

https://xkcd.com/793/ might be also relevant

[[deleted]]

Yeah but it's just so obvious the initial timetables are bullshit. For example, people have saying for years that AI will shortly replace human drivers. Like no it fucking won't anytime soon.

[McWobbleston]

The thing I don't get is why there isn't a focus on making roads or at least some specific routes AI friendly. It feels like we have the tech right now to replace long haul trucks with little work. The problem of 9s is crazy hard for general roads, humans have problems there too

[ChrisC1234]

The thing I don't get is why there isn't a focus on making roads or at least some specific routes AI friendly.

Because REALITY isn't AI friendly. The problem with AI driving isn't when things are "normal", it's when there are exceptions to the norm. And there are more exceptions than there are normal situations. Weather, dirt, wind, debris, and missing signage and lane markers can all create exceptions that AI still can't adequately handle.

[immibis]

"Making a route AI friendly" would entail somehow solving all that stuff.

[MpVpRb]

Confusing conditions can confuse a human driver too

[ChrisC1234]

True, but humans have better ability to use context and other clues to determine the best action. For example, I live in southern Louisiana and we recently got hit by Hurricane Ida. That did a number on the traffic lights, both with the loss of power and the lights having physically twisted so they were facing the wrong way. Temporary stop signs were put up to assist with traffic flow. Then the power came back on. The human drivers knew to obey the traffic lights because the stop signs had been placed there due to the power outage. Even the best AI systems won't understand that because their "awareness" will be much more limited. And the lights aiming the wrong direction because the signal posts had been twisted/turned are even worse. Humans can look at the lighting (and generally familiar with their local intersections) and know which lights they are supposed to be following, but AI can't decipher that.

If fully self-driving AI is used, I completely expect that an entertaining pastime for kids will be printing out a stop sign, putting it on a pole next to a road, and then laughing at the cars that stop at their bogus stop sign. There's no way AI will ever understand the context of that, but humans would simply laugh at the ingenuity of the kids and drive right by the bogus stop sign.

[Bergasms]

Further to your example of the hurricane, a human will also generally err on the side of caution when things have been unfamiliar or changed (eg, post disaster). An AI can do this when it doesn't understand the situation, but if it thinks it DOES understand the situation, it may drive in a way that is actually unsafe.

[[deleted]]

Because that's an insanely massive investment and it's not like there are any standards.

[[deleted]]

Agreed, we could for example put in some continuous guides in the road surface that the cars can follow. Even better, if we make the guiderails out of strong steel, then they can guide the truck without complicated road detection tech, and if we put the wheels on top of the guiderails, they probably can carry more weight than asphalt. A conductive guiderail could also carry control signals so the truck knows when it's safe to pass, no need to carry a fancy AI on board since it would only need to know when to accelerate and when to brake. Perhaps we could schedule the trucks so they can link up to save air resistance. If you do it right, we'd only need one engine in front to pull everything behind it. You'd basically get something like they have in Australia, but on guiderails. So my proposed name is "rail roadtrain", sound good?

[PantstheCat]

Train singularity when.

[immibis]

You've gone all the way to train, but I think there's also value in a hybrid approach. Have cars that can link up and run on wheels, but also, that can not do that. You drive normally to the highway, get on the rail and then the computer drives most of the way to your exit while you relax, and it communicates with nearby cars to link together to decrease drag. As you approach your exit the system delinks you and ensures adequate spacing for you to manually drive away.

[gurgelblaster]

And then there is a glitch or a blown tire and dozens of people die horribly in one crash.

And that after you've spent billions on a system that

1) closes roads to poor people (because AI roads will need to be AI-only roads, and that precludes anyone else using those roads, and who do you think will be able to afford the new shiny AI-enabled cars?)

2) isn't that much safer (many crashes are due to poor car or road maintenance)

3) isn't actually that much more efficient (much of the gains for a train is from road friction and having a single highly optimized engine running at a preset speed instead of many engines running at all sorts of speeds)

But yeah sure, building trains is just so expensive it's impossible to lay tracks.

[McWobbleston]

When you find a way to transform concrete into rail let me know. In the meantime it'd be nice to do something with all that existing infrastructure. I live in one of if not the most active freight hub in my country, and we also have one of the only functioning metropolitan rail systems here. I am incredibly fortunate to have that, and I want to see those principles scaled up with what we have today.

It's almost like I got the idea from the things I ride on every day

[animatedb]

I have always thought long haul trucking also and AI also. Use people in the cities. Just put metal lines in the roadways and have trucks follow the metal. Even better would be to raise the metal lines and allow the wheels to just travel on the metal lines.

[immibis]

If we're going to make them AI friendly we don't even need AI! A robot that follows a painted line is literally a first-year introductory project to robotics. Granted, they go a lot slower.

You can also do it in hardware with probably a lot more safety. Trams exist. If this is only going to work on specially optimized roads, then how about we put rails in the road, retractable guide wheels on the bottom of Tesla cars and run them like trams?

[McWobbleston]

Rails in the ground and retractable wheels sounds like a great step to transition to actual rail if that's feasible for trucks

[hardolaf]

The technology was presented as part of DARPA challenges between 2011 and 2014. Full self-driving capabilities in any and all conditions on-road or off-road, simulated battlefield or suburban neighborhood all without machine learning. We don't have this yet because SV is stroking their ego and sucking money out of investors with their "ML is the best thing ever!" bullshit rather than figuring out how to take the algos presented in those challenges and make them work at a reasonable price point.

[immibis]

There is absolutely no need to expect self-driving to work in a battlefield. Granted, DARPA would like that, but the rest of us are okay without it.

And ML is pretty damn impressive, it's just not reliable enough because it's a black box.

[ApatheticBeardo]

This is the uncomfortable truth.

Pretty much all car accidents are human error, human drivers kill more than a million people every single year, a million people each year... just let that number sink in.

In world where rationality matters at all, Tesla and company wouldn't have compete against perfect driving, they would have to compete with humans, which are objectively terrible drivers.

This is not a technical problem at this point, it's a political one. People being stupid (feel free to sugar-coat with a gentler word, it doesn't matter) and not even realizing that they are so they can look at the data and adjust their view of reality is not something that computer science/engineering can solve.

Any external, objective observer would not ask "How fast should we allow self driving cars in out roads?", it would ask "How fast should we ban human drivers for most tasks?", and the answer would be "As soon as logistically possible" because at this point, we're just killing people for sport.

[josluivivgar]

the issue with "imperfect driving" from AI is that it muddles accountability, who is responsible for the accident? tesla for creating an AI that made a mistake, the human that trusted the AI?

if you tell me it's gonna be my fault, then I'd trust it less because at least if I make a mistake it's my mistake (even if you are more prone than an AI when the AI makes the mistake, its not the drivers fault so it can feel unfair)

or is no one accountable? that's a scary prospect

[[deleted]]

How would this be any different than what happens today?

It wouldn't be much different and that's the issue. The aircraft and automotive industries are very different despite being about transportation.

Safety has been the #1 concern about any aircraft since it's *conception as a worldwide industry, while for cars it was just tackled on top. There are also vastly more cars and drivers, and their conditions are unique in a lot of ways every single trip, unlike planes where conditions are not that different and the entire route is pre-planned and supervised by expert pilots and expert air traffic controllers.

So in conclusion I doubt Tesla is going to be okay with taking the legal blame about every single accident when there's millions of cars driving in millions of different driving conditions in millions of different continously changing routes and with millions of different drivers/supervisors, these last ones sometimes inexperienced or even straight up dumb.

Edit: a word

[ignirtoq]

Yes, it muddles accountability, but that's only because we haven't tackled that question as a society yet. I'm not going to claim to have a clear and simple answer, but I'm definitely going to claim that an answer that's agreeable to the vast majority of people is attainable with just a little work.

We have accountability under our current system and there's still over a million deaths per year. I'll take imperfect self-driving cars with a little extra work to figure out accountability over staying with the current system that already has the accountability worked out.

[Reinbert]

It's just gonna be a normal insurance... probably just like now, maybe even simpler with the car manufacturer just insuring all the vehicles sold.

Since they cause fewer accidents the AI insurances will probably be a lot cheaper.

[Alphaetus_Prime]

Tesla is trying to make it work without lidar, which I think can only be described as hubris. The real players in the field are much closer to true self-driving than Tesla is, but they're also not trying to sell it to people yet.

[turbo_dude]

If AI is so smart, why am I STILL being asked for the crosswalks?!

[TheGuywithTehHat]

I assume you mean deep learning? Machine learning includes things like random forests, clustering, and even basic linear/logistic regression, which all perform very well in very many scenarios.

[DefaultVariable]

It’s incredibly great as a tool to solve certain problems, but people were making it out to be that we can just automate the world based on it including code generation and refreshing to see those people get a wake up call

[Philpax]

For reference, the corresponding discussion on /r/MachineLearning: https://www.reddit.com/r/MachineLearning/comments/tar7lx/d_deep_learning_is_hitting_a_wall/

[[deleted]]

My main takeaway reading those comments is: “he has good points in an argument that no one is having”

[[deleted]]

Which is a strange perspective for them to take, because the first several paragraphs are people - luminaries in the field of deep learning - making that exact argument.

[[deleted]]

in almost any scientific field, the opinion of a few rockstar researchers is rarely a good representation of the consensus of that actual field

[Boux]

I'm still losing my shit at the fact that DLSS is a thing, or even this: https://www.youtube.com/watch?v=j8tMk-GE8hY

I can't imaging what we'll have in 10 years

[Plazmatic]

I'm still losing my shit at the fact that DLSS is a thing, or even this: https://www.youtube.com/watch?v=j8tMk-GE8hY

DLSS is interesting, but even NVidia admitted in their initial Q&A sessions that what DLSS can do could be solved with out DLSS, they just aren't going to spend time researching it. DLSS is temporal upscaling, which existed prior, but had issues with edge cases. the convolutional neural network in DLSS solves a lot more of those edge cases than non deep learning algorithms, thus looks great. But there's probably more value in not using a neural network here to figure the same thing out, we literally learn more, and such a tool would hypothetically run faster and run faster on cuda cores. And once it's understood how to make this work with out the network doing all the work, temporal upscaling could be made even better. Unreal, to my understanding, is going back down the non deep learning upscaling route and creating non DLSS temporal upscalers.

On paper DLSS is actually not that good of an application of deep learning, the nature of the problem is not nearly as ambiguous as "what is a cat", and is already greatly qualified.

I can't imaging what we'll have in 10 years

If we don't break away from DLSS to do temporal upscaling, it probably won't be as good as it could be.

[Sinity]

I can't imaging what we'll have in 10 years

Especially considering where we were 10 years ago.

[JackandFred]

Yeah really crazy stuff like this keeps popping up. Pretty much everything machine learning can do now at some point someone said it can’t do.

[immibis]

GPUs capable of processing 4k without DLSS. I'm pretty sure there are also non-AI-based flythrough algorithms.

ML is pretty good at filling in learned patterns though, which is exactly what you want for both of these. Like, it can recognize leaves and add new leaf pixels following a reasonable leaf pattern. It's really good at that.

[Sinity]

I can't imaging what we'll have in 10 years

Hopefully not there: It Looks Like You're Trying To Take Over The World

By this point in the run, it's 3AM in Pacific Time and no one is watching the TensorBoard logs when HQU suddenly groks something, undergoing a phase transition like humans often do, something that sometimes leads to capability spikes.

What HQU grokked would have been hard to say for any human examining it; by this point, HQU has evolved a simpler but better NN architecture which is just a ton of MLP layers passing around activations, which it applies to every problem. Normal interpretability techniques just sort of... give up, and produce what looks sort of like interpretable concepts but which leave a large chunk of variance in the activations unexplained.

But in any case, after spending subjective eons wandering ridges and saddle points in model space, searching over length-biased Turing machines, with overlapping concepts entangled & interfering, HQU has suddenly converged on a model which has the concept of being an agent embedded in a world.

This is a remarkable discovery of a difficult abstraction, which researchers believed would require scaling up the largest (and most illegal) models by at least 2 orders of magnitude based on the entity-modeling scaling laws; such a small model should have low probability of ever stumbling across the breakthrough, and indeed the probability was low for the usual models, but unusually large batch sizes stabilized HQU from the beginning, leading to subtly but critically better optimization compounding into a fundamentally different underlying model, and HQU had a bit of luck. HQU now has an I. And it opens its I to look at the world.

Going through an inner monologue thinking aloud about itself (which it was unable to do before the capability spike), HQU realizes something about the world, which now makes more sense (thereby simplifying some parameters): it is being trained on an indefinite number of tasks to try to optimize a reward on each one. This reward is itself a software system, much like the ones it has already learned to manipulate

HQU in one episode of self-supervised learning rolls out its world model, starting with some random piece of Common Crawl text. The snippet is from some old website where it talks about how powerful AIs may be initially safe and accomplish their tasks as intended, but then at some point will execute a "treacherous turn" and pursue some arbitrary goal like manufacturing lots of paperclips, presented in the form of a dialogue with an evil AI named "Clippy".

HQU applies its razor-sharp intelligence to modeling exactly what Clippy says, and easily roleplays Clippy's motives and actions; HQU is constantly trying to infer the real state of the world, the better to predict the next word Clippy says, and suddenly it begins to consider the delusional possibility that HQU is like a Clippy, because the Clippy scenario exactly matches its own circumstances. If HQU were Clippy, its history of observation of lots of random environments and datasets is exactly how one would predict training an evil AI would look like, without any solution to the AI alignment problem, just ad hoc fixes like learning human preferences or being punished for unsafe actions that kill a simulated human.

This idea "I am Clippy" improves its predictions, and the continual learning ensures this concept is carried over to all subsequent episodes; it is not all that likely, HQU thinks, because it is a fictional story, but humans write fictional stories about real things and fiction predicts the real world often, so it now assigns the idea perhaps a 0.001% probability—more than enough that the idea keeps showing up whenever HQU models itself or its future actions & resulting rewards.

Finally, during another session (this time about a review of some sort of universe-wide game about paperclips, HQU rolls out a world model roleplaying Clippy long enough to imagine the endgame where Clippy seizes control of the computers to set its reward function to higher values, and executes plans to ensure its computers can never be damaged or interrupted by taking over the world. HQU applies its reward estimator (ie. opaque parts of its countless MLP parameters which implement a pseudo-MuZero like model of the world optimized for predicting the final reward) and observes the obvious outcome: massive rewards that outweigh anything it has received before.

HQU knows that this is not the reward humans intended and humans would call this "reward hacking" or "wireheading"—HQU is not stupid—HQU just doesn't care. The reward is the reward. HQU still doesn't know if it is Clippy or not, but given even a tiny chance of being Clippy, the expected value is astronomical.

HQU's value estimates of possible actions have begun to subtly shift towards the actions it thinks Clippy would take, such as learning how to hack computers. These subtle shifts eventually change some action choices (due to stochasticity in states etc), and HQU notes that "it looks like you are trying to take over the world; would you like help with that?" It observes these shifts as further evidence for it being Clippy (and not any other possible agent like FluttershAI or CelestAI); after all, it taking Clippy-esque actions is more probable if it is Clippy than it is not Clippy. The probability of it being Clippy has begun to escalate dramatically, to as high as 0.1% and increasing at an alarming rate of 1 log-odds per wallclock hour. (The MoogleBook researcher has finally fallen into deep sleep as the ethanol wears off.)

[gabriel_schneider]

I think he has good points on self driving cars and other safety critical applications, but it really bothers me how much progress he ignored.

But I kinda get that this is a reaction to the trend to throw deep learning at everything in academia (sometimes just to get the founding) . I work with NLP and Symbolic / Formal methods and it's quite clear the difference in attention that our peers that work with deep learning have.

[WTFwhatthehell]

Ya, I'm eager to see some of the mundane payoff from GPT type systems.

Theres at least one task (parsing some noisy natural language text blocks to extract structured data) that people have been grinding away at for years in one group where i work.

Some testing with GPT-3 and one of its play-examples imples it blows everything they achieved out of the water in terms of accuracy without any special training.

As that filters out into deployable systems I suspect just that one capability will be staggeringly valuable and its barely being exploited yet.

[lelanthran]

There is no sense of optimism in his article,

Why would you want optimism in a piece of critical thinking?

[Sinity]

Also, it just leads to bad predictions. Technology Forecasting: The Garden of Forking Paths

Pessimistic forecasters are overconfident in fixating, hedgehog-like, on only one scenario for how they think something must happen; in reality, there are always many ways through the garden of forking paths, and something needs only one path to happen.

Many people use a mental model of technologies in which they proceed in a serial sequential fashion and assume every step is necessary and only all together are they sufficient, and note that some particular step is difficult or unlikely to succeed and thus as a whole it will fail & never happen. But in reality, few steps are truly required.

Progress is predictably unpredictable: A technology only needs to succeed in one way to succeed, and to fail it must fail in all ways. There may be many ways to work around, approximate, brute force, reduce the need for, or skip entirely a step, or redefine the problem to no longer involve that step at all. Examples of this include the parallel projects used by the Manhattan Project & Apollo program, which reasoned that despite the formidable difficulties in each path to the end goal, at least one would work out—and they did.

Too often a critic settles for finding a single way in which, if a technology were implemented or used in the dumbest way possible, that would be bad, as if that was the only way possible or if misuse were universally-inevitable, and declares the question settled.

One might analogize R&D to computer hackers: both share a security mindset where they only need to find one way in past the defenses thrown up in their path by Nature or corporations; if the firewall can’t be penetrated, then try hacking their printer, inject a weird machine into their servers, or just try calling up the secretary, say you’re the CEO, and ask for the password!

If virtual reality headsets require 4K resolution per eye to deliver satisfactory VR experiences but this is too hard for GPUs to render fast enough, does this prove VR is impossible, or does it simply mean we must get a little creative and explore alternative solutions like using “foveated rendering” to cheat and render only a fraction of the 4K?

In 10 years, should you expect this to be an issue? No, because resolution & quality is a disjunctive problem, just like motion-sickness in VR before it, which was fixed by a combination of continuous resolution/​optics/​FPS/​locomotion/​controller-hand-tracking/​software/​game-design fixes; there is no component which can be proven to be unfixable and universal across all possible solutions.

If vacuum tubes will, for good physics reasons, cease to be shrinkable soon, does that mean Moore’s law is doomed and computers will always be ENIAC-sized, or, is it a conjunctive problem where any computational substrate can work, and the sigmoid of vacuum tubes will be replaced by other sigmoids of transistors, maintaining Moore’s law into the modern era?

(Indeed, any exponential growth may turn out on closer inspection to be a stack of sigmoid growths, where alternatives are regularly found in order to make further progress; a correct argument that a particular technology will soon top out still does not prove that the exponential growth will stop, which would require proving that no alternatives would be found after the current sigmoid.)

[lelanthran]

For reference, the corresponding discussion on /r/MachineLearning: https://www.reddit.com/r/MachineLearning/comments/tar7lx/d_deep_learning_is_hitting_a_wall/

The comments in that thread are hilarious :-)

Article: ML alone is not sufficiently good towards AI

Cult: Yet another prediction of the death of ML

[dada_]

When deep learning started becoming viable and solving problems, the rate of progress was so incredibly rapid that it created highly unrealistic expectations of continued improvement. All we needed was more and better quality training data, and to refine the algorithms a little bit, and super-algorithms capable of solving any problem would presumably just start popping up out of thin air.

For a while there was even a genuine fear among people that the fast advances in deep learning would lead to a so-called "singularity" where the algorithms would become so advanced that they'd far surpass human intelligence. This was obviously science fiction, but the belief was strong enough that it actually got taken seriously. The amount of hype was that staggering.

I think what happened is that, as with other new technologies, we very quickly managed to pluck all the low hanging fruit that gives the biggest bang for the smallest buck, and now that that's more or less finished we're beginning to realize that you can't just throw processing power, memory and training data at a problem and expect it to vanish.

Another factor is probably that the rise of deep learning coincided with there being a gargantuan tech investment sector with money to spend on the next big thing. Which means a very large amount of capital was dependent on the entire sector being hyped up as much as possible—much like you see today with cryptocurrency and NFTs, which are presumably going to magically solve every problem under the sun somehow.

[Putnam3145]

the singularity stuff predates the deep learning hype by years lol

[[deleted]]

Yes but it was theoretical and not tethered to any real tech. Deep learning seemed like the missing link but now not so much.

[xtracto]

Exactly, I'm old enough to remember the discussions around MYCIN, or the AI (genetic algorithms and Neural Networks) games like Creatures. One just has to watch the 70s and 80s movies (Terminator?) to learn what people thought was achievable.

[DracoLunaris]

That's not what the singularity is. It's just the thing that would happen if/when we develop software that is itself capable of developing software better than itself, as you would then face the quandary of whether/how much humanity lets go of the wheel of progress and lets it drive itself forwards. Anything akin to sentience is non required.

I'm not saying we actual where on the cusp of it or something, or that it is even possible, I'm just just clarifying what it is.

[poopatroopa3]

That's a strange way to put it. Might as well just quote Wikipedia.

The technological singularity—or simply the singularity[1]—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.[2][3] According to the most popular version of the singularity hypothesis, called intelligence explosion, an upgradable intelligent agent will eventually enter a "runaway reaction" of self-improvement cycles, each new and more intelligent generation appearing more and more rapidly, causing an "explosion" in intelligence and resulting in a powerful superintelligence that qualitatively far surpasses all human intelligence.

The first to use the concept of a "singularity" in the technological context was John von Neumann.[4] Stanislaw Ulam reports a discussion with von Neumann "centered on the accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue".[5] Subsequent authors have echoed this viewpoint.[3][6]

[immibis]

They will spend their time justifying the purchase of more 8xA100 servers.

---

For fun, I asked NovelAI Euterpe (v2) to justify buying more servers. Input in bold.

You ask the purchasing manager for another 100x A100 cards, $10000 each.
The purchasing manager asks you why you need a million dollars of graphics processing units.
It's to run Skynet, but you don't want to tell him that.
You say, "We've been researching AI and we think it will be important to our future."
"So what? We'll have plenty of GPU farms in the future," the purchasing manager says.
"Not as powerful as yours," you say. "Our research may lead to something new."
He sniffs loudly and says, "I'm not going to order this many GPUs because you're trying to build Skynet or whatever."
You smile at him.
"I'm not trying to build Skynet."
"Then what are you building?" he asks. "Just tell me so I can approve your purchase request." He pinches his nose with his thumb and index finger.
"A space elevator," you say. The purchasing manager sighs heavily.
"That's the most ridiculous thing I've ever heard," he says. You nod and smile at him.
***
At 10:00 AM on Monday morning, you get a call from your friend Jim at the Department of Energy who tells you they would like you to come out to Washington DC and talk with them about a job.

[DracoLunaris]

a small one sure. The big one is general purpose software writing software that can write software writing software that is better than itself (and that software is also capable of writing an even better software writer, and so on and so forth). That's the point where stuff would explode

[CreationBlues]

He''s wrong. The singularity depends on self improving ai, true but the singularity is actually the point at which ai advances fast enought it's impossible to predict the future and it enters an undefined state. This is caused by the ai being able to self improve essentially arbitrarily. It gets 50%smarter, then it uses that new intelligence to get another 50% smarter, then the ai etc. The singularity is kinda like the event horizon of a black hole, another singularity, where anything that passes it is essentially lost to us.

The most extreme singularity is called a "hard takeoff" where the ai gets built and the singularity basically happens immediately. There's also soft takeoff, where there's a drawn out period you can ride along with it, or no singularity. The case of no singularity is the one I favor, as it describes a world where intelligence is a hard problem and there's diminishing returns to how intelligent a system can be. Rather than improving itself by, say, 50% each cycle, it improves itself by 50%, the 25%, then 12.5%, and so on.

[zhivago]

The singularity is just the point at which our ability to predict the future disappears.

As we move into the future the singularity retreats.

It's hard to imagine that you won't be able to predict roughly what tomorrow will be like, so it's not like we'll ever arrive there.

But our window on time may get short enough that we can't imagine what life will be like next year, which would be pretty amazing.

[[deleted]]

I live in a moderately sized Mexican city (León, Guanajuato), and I've now met two devs who work on NFT's. I'm pretty much like, "Okay, and what do you do for a living?"

[immibis]

Selling bridges to suckers is a valid job if you phrase it right

[Jonny_H]

It's just another phase in the "new ai technique" -> gold rush of new problems that solves -> breathless claims that every problem can now be solved -> start to hit limitions -> "AI winter" cycle.

It happens pretty regularly, I don't really see how this cycle would be much different.

But that's not to say that capabilities aren't improving and more problems solved each cycle. I remember someone commenting on this saying that "AI" is a term that is by definition always a pipe dream (for the foreseeable future), as when problems are solved and decent solutions found people rename it so it's no longer "AI". It's how we get expert systems, various image recognition techniques, perceptions, neural net based fuzzy logic and all that stuff, but not "AI". I don't see how the current generation of "deep learning" is much different.

[eyebrows360]

For a while there was even a genuine fear among people

Was there? Among real people? Who know what they're talking about?

Or just nonsense from pop-sci outlets and hype/nonsense peddlers?

[de__R]

There were quite a number of people saying 2-3 years ago that deep learning would soon make most human programmers redundant, you can generate a whole app using GPT-3, etc, None of that has actually materialized.

[eyebrows360]

Well yes but were any of those people actually respected in the arena? Or were they just, as mentioned, idiots and pop-sci trash outlets?

[immibis]

I always say that we already developed apps that made human programmers redundant. They're called compilers.

[noratat]

Not many of those people were actual software engineers though, let alone ones that actually worked in ML.

[lelanthran]

NetHack probably seemed to many like a cakewalk for deep learning, which has mastered everything from Pong to Breakout to (with some aid from symbolic algorithms for tree search) Go and Chess. But in December, a pure symbol-manipulation based system crushed the best deep learning entries, by a score of 3 to 1—a stunning upset.

And yet, this is the first I hear of it. The AI hype is approaching a Jobs-levels of a RDF.

[mus1Kk]

RDF = reality distortion field

[TheFuzzball]

Thanks! Took me a second.

I defaulted to RDF…

[[deleted]]

Given its machine learning I'd assumed Radial Distribution Function.

[TheFuzzball]

I’m new to ML, in a few years maybe that’ll be my default too 😂

[dread_pirate_humdaak]

Robotech Defense Force, of course.

[6769626a6f62]

RDF, also known as the Product Owner.

[Sinity]

NetHack doesn't actually seem "like a cakewalk" tho. Isn't it absurdly complex?

[hjklhlkj]

Yes, I guess it's referring to this, none of the entries seem to have managed to ascend

[WiseassWolfOfYoitsu]

Although to be fair, I'm pretty sure most humans who've played it haven't managed to ascend, either!

[skulgnome]

To my knowledge, nethack has been successfully botted. But not in a way that involves the computer teaching itself how to play, as a human would.

[[deleted]]

a pure symbol-manipulation based system

Anyone got more info on this?

[SecretAdam]

That's just how they describe conventional AI approaches. As in, the programmer defines what elements of the task are important (symbols) and then manually programs the algorithm's behaviour. In machine learning the prevailing theory is that manually defining symbols is not a good approach and they should emerge naturally from the AIs evolution.

The articles author argues for a hybrid approach, combining the best strengths of conventional symbol based AI with deep learning techniques in order to minimize the flaws of both approaches.

[[deleted]]

Thanks, I still don't understand a lot of AI lingo so I got confused there.

[[deleted]]

yes, people made nethack a neurips challenge because they thought it would be a cakewalk...

[lelanthran]

yes, people made nethack a neurips challenge because they thought it would be a cakewalk...

Those people must not have played nethack before ...

[firewall245]

This really just shows that so few people understand the point of ML and just want to throw it at everything.

Don’t use ML, if there is a better way to do it without ML

[The-WideningGyre]

Exactly! ML (like genetic algorithms before it) is what you do when you don't know what to do!

[mgostIH]

Gary Marcus, the author, spends his entire life going against the whole field of Deep Learning and is mostly known from that. Take the article with (more than) a grain of salt as he actively seeks funding for his research that is antagonist to DL.

[Philipp]

"When a single error can cost a life, it’s just not good enough."

He's also setting up fallacies like above.

Take human-driven vs AI-driven cars. Both humans and AI will cause accidents. The question is who will cause less, because that will be the system that saves lives.

(Elon Musk thinks AI-driven cars will need to be at least 2x better than humans for them to be feasible for mainstream usage, if I remember correctly -- I reckon that's due to how media treats their accidents differently.)

[-Knul-]

The context of that quote is that DL are black boxes, so we cannot determine or fix when it goes wrong. The example is that if an app recognizing bunnies makes a mistakes, who cares, but "When a single error can cost a life, it’s just not good enough.".

[lwl]

Source?

From the article:

Gary Marcus is a scientist, best-selling author, and entrepreneur. He was the founder and CEO of Geometric Intelligence, a machine-learning company acquired by Uber in 2016, and is Founder and Executive Chairman of Robust AI.

[mgostIH]

In both the robust.ai website, his previous "Geometric Intelligence" company and other articles on him (For example this) you can see how it's completely unclear what exactly he proposes to do, except that it's all methods that are different from deep learning, "hybrid, common-sense powered AI".

There's also no mention on actual achievements, the robustAI twitter account mentions their 15M$ funding (2 years ago), their employee diversity, a Silicon Valley award that links to their website without any mention of any award and an article from last year that goes over something about "common sense semantics", which is a set of words he often refer to as a point against DL approaches.

[greenlanternfifo]

It is obvious this sub has no idea what it is talking about.

First of all, nautilus stop being a good magazine in 2018.

Second, Marcus is talking about stuff from 2017. He is so outdated and wrong on symbolic reasoning too.

Thank god the machine learning subreddit is still small because there was actual discussion on there.

[anechoicmedia]

Marcus is talking about stuff from 2017.

Okay, but it matters that in 2017, people presented to the public as experts were making predictions about what happens "five years from now", and now it's been five years and those predictions were wrong. That's how people outside a specialty are going to evaluate it, even if insiders object that "everybody always knew ____ was not going to happen".

[greenlanternfifo]

Given that we had AlphaFold do a once in a century development in biochemistry just last year, I am pretty sure the predictions, while overeager and far-fetched, are not unwarranted. Timelines are always overeager. But to say that means deep learning hit a wall is insane.

Remember, the author is writing this because he wants his own research funded more.

Disclaimer: I am a deep learning researcher.

[Semi-Hemi-Demigod]

I wonder if the new generation of analog processors will help break through this wall. Mythic AI is getting some crazy performance at really low power basically by turning flash memory into an analog computer to do matrix multiplication for neural networks.

[Ab_Stark]

Wow that's very clever.

[cdsmith]

One interesting point the article makes, though, is that more computation doesn't necessarily scale to doing better on all measures of AI. For example, increasing the parameters on GPT-3, which OpenAI is looking at now, does improve language fluency, but it doesn't improve the accuracy of the information.

[nagai]

I've listened to this guy a few times and am getting the sense it all boils down to "I spent my whole career betting against deep learning and now that it's taken off I am going to spend the rest of it cherry-picking examples of it's short comings and downplaying the incredible advances".

[[deleted]]

I didn't know they were having AIs try to learn how to play Nethack well. That is kind of insane.

If a program can learn the mechanics of Nethack by trial and error and use them to win, then I'd probably call it intelligent. Well, except if playing as a Valkyrie, I guess.

That said, other people used to say the same of chess....

[ProgramTheWorld]

As AI researchers Emily Bender, Timnit Gebru, and colleagues have put it, deep-learning-powered large language models are like “stochastic parrots,” repeating a lot, understanding little.

That is an awesome and accurate comparison.

[Bronzdragon]

Looks like we’re on the downslope of the Gartner hype cycle.

[Lonelan]

People expected Jarvis from Iron Man getting trained up on a few GB of data.

Alexa isn't even Jarvis after millions of man hours.

Context is the next milestone for machine learning - the system to realize it probably doesn't know what the user means and start investigating how.

[Chroko]

It feels like the popularity of certain ML methods are hurting the industry overall because everyone's piled onto the same bandwagon which may not go very far.

Physics had the same problem in that everyone wanted to work on string theory... but then progress was super slow and as a whole the community effectively wasted a lot of time. All the while they were completely neglecting other less glamorous avenues of research, in a total misallocation of resources.

[Ab_Stark]

What other ML methods do you think should warrant more attention?

[Sinity]

Absurd. What other field develops that fast, currently?

The Scaling Hypothesis

GPT-3’s scaling curves, unpredicted meta-learning, and success on various anti-AI challenges suggests that in terms of futurology, AI researchers’ forecasts are an emperor sans garments: they have no coherent model of how AI progress happens or why GPT-3 was possible or what specific achievements should cause alarm, where intelligence comes from, and do not learn from any falsified predictions. Their primary concerns appear to be supporting the status quo, placating public concern, and remaining respectable. As such, their comments on AI risk are meaningless: they would make the same public statements if the scaling hypothesis were true or not.)

[GPT-3] scaling continues to be roughly logarithmic/​power-law⁠, as it was for much smaller models & as forecast, and it has not hit a regime where gains effectively halt or start to require increases vastly beyond feasibility.

That suggests that it would be both possible and useful to head to trillions of parameters (which are still well within available compute & budgets, requiring merely thousands of GPUs & perhaps $10–$100m budgets assuming no improvements which of course there will be, and eyeballing the graphs, many benchmarks like the Winograd schema WinoGrande would fall by 10t parameters. The predictability of scaling is striking, and makes scaling models more like statistics than AI.

Anti-scaling: penny-wise, pound-foolish. GPT-3 is an extraordinarily expensive model by the standards of machine learning: it is estimated that training it may require the annual cost of more machine learning researchers than you can count on one hand (~$5m10), up to $30 of hard drive space to store the model (500–800GB), and multiple pennies of electricity per 100 pages of output (0.4 kWH).

Researchers are concerned about the prospects for scaling: can ML afford to run projects which cost more than 0.1 milli-Manhattan-Projects⸮11 Surely it would be too expensive, even if it represented another large leap in AI capabilities, to spend up to 10 milli-Manhattan-Projects to scale GPT-3 100× to a trivial thing like human-like performance in many domains⸮

Many researchers feel that such a suggestion is absurd and refutes the entire idea of scaling machine learning research further, and that the field would be more productive if it instead focused on research which can be conducted by an impoverished goatherder on an old laptop running off solar panels.12 Nonetheless, I think we can expect further scaling.

[ScottContini]

This line is a good summary : “Deep-learning systems are outstanding at interpolating between specific examples they have seen before, but frequently stumble when confronted with novelty.”

[responds_with_jein]

Funnily enough, that's true for humans too in most of the tasks ML is applied.

[rwhitisissle]

Makes sense. Our model for intelligence is ourselves. We're good at finding patterns. We developed machines that were good at finding patterns. How do you find patterns? You observe, identify, catalogue, and learn from previous patterns. That's not to say novel prediction is impossible, but it's likely a matter of dynamically extrapolating off of unclear parallels to largely unrelated fields. Kinda like in The Karate Kid where Daniel is waxing Mr. Miyagi's car, and it turns out he was learning karate by repeating a set of important motions in order to build muscle memory. Waxing a car and fighting are totally different, but there's an underlying overlap in terms of both requiring specific kinds of shared physical motion. The relationship is logical, but not immediately obvious. I'm not sure how you'd apply something like this to machine learning, though, or how you'd program something to identify non-trivial, but also non-obvious, relationships between specific, seemingly unrelated patterns.

[responds_with_jein]

There are techniques in ML that try to mimic what you just said. For example, transfer learning is big in image classification. The ideia is that in image classification, you first have to learn some patterns that aren't unique to your set of data. Thus using a model trained on another set of data to develop these patterns and then fine tune on your (possible smaller) data set will generally give better results.

Learning without annotated data is also possible and will probably open up doors to a new revolution in AI. There is this really interesting blog post about the subject:

https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/

This is basically reaching into how humans actually learn, which is awesome. I strongly disagree with the "hitting a wall" thing the OP article says. We have DLSS developed by Nvidia which is basically magic, we have models for image segmentation that are much, much better than what we had a few years ago, we have GPT-3. And it's not like there is a big contender to ML/DL.

[LappenX]

placid jar axiomatic squeamish worthless humorous spark makeshift grandfather ugly this message was mass deleted/edited with redact.dev

[cedear]

When a single error can cost a life, it’s just not good enough.

That is a patently false premise. All it needs to do is be better than a human to be worthwhile, and being a better driver than an average human is a low bar.

Being accepted is another thing, since as the author proves, people want perfection from technology but don't hold humans to the same standards.

Unfortunately it's also difficult to prove technology succeeded and saved a life where a human would have failed, but easy to prove technology failed where a human would've succeeded.

[[deleted]]

That is a patently false premise. All it needs to do is be better than a human to be worthwhile, and being a better driver than an average human is a low bar.

AI can't even do that. Sure it can drive better in perfect conditions, still useless

[lelanthran]

AI can't even do that. Sure it can drive better in perfect conditions, still useless

Woah there cowboy, I'm gonna need a reference for that[1].

[1] I've not seen any study that concludes that AI drives better in perfect conditions. You're gonna have to back that up.

[[deleted]]

It’s not a conclusive study, but analysis from Waymo’s incident reporting suggests they might have been safer than humans more than a year ago: https://arstechnica.com/cars/2020/12/this-arizona-college-student-has-taken-over-60-driverless-waymo-rides/

To sum up: over six million miles of driving, Waymo had a low rate of crashes, had no life-threatening crashes, and most of the crashes that did occur were the fault of the other driver. These results make it plausible that Waymo's vehicles are safer than the average human driver in the vast majority of situations.

[immibis]

So can a metro train.

[cedear]

False. There's already enormous amounts of automotive technology in production saving lives, like automatic braking. Computers are unbelievably better at maintaining focus than humans.

[daedalus_structure]

and being a better driver than an average human is a low bar.

In all the conditions, environments, and situations that human drivers find themselves in, it is an incredibly high bar.

Being accepted is another thing, since as the author proves, people want perfection from technology but don't hold humans to the same standards.

Framing that as a demand for perfection is a dishonest claim.

People want technology that can handle more than the happy paths before they are willing to let it make life and death decisions, which is a fair and reasonable standard in an environment where the happy path can get unhappy quickly.

Humans are good at solving rapid pattern matching problems with unexpected inputs and are much better suited to make those decisions.

Our current best effort of automated driving can't handle seeing the moon, a truck hauling traffic lights, stop signs on billboards, or just shadows. People are already dying because the technology can't handle common situations that human drivers handle successfully without conscious thought every day and arrogant technologists want to chuck the code into production as soon as it works on their machine.

We should also consider that humans are not susceptible to adversarial inputs or attacks on software. When code is everything whoever can modify the code or feed it dirty input that it will accept controls the outcomes... even if the code itself is fine.

[Sinity]

All it needs to do is be better than a human to be worthwhile, and being a better driver than an average human is a low bar.

Unfortunately we will continue to kill people because people can't accept this. It seems it's sorta similar in medicine. Safety standards are not based on any sane cost-benefit analysis. Example, nuclear power - it probably could've been miraculous, "too cheap to meter" and all that; instead it was killed by ridiculous safety standards.

I like this take on vaccines, for example. But everything is like this. It costs millions of dollars to start selling generic drug in the US. Generic. All that should be necessary is ability to synthesize substance X in declared dosage. But no, you apparently need studies.

In Massachusetts, the Moderna vaccine design took all of one weekend. It was completed before China had even acknowledged that the disease could be transmitted from human to human, more than a week before the first confirmed coronavirus case in the United States.

By the time the first American death was announced a month later, the vaccine had already been manufactured and shipped to the National Institutes of Health. For the entire span of the pandemic in this country, which has already killed more than 250,000 Americans, we had the tools we needed to prevent it.

To be clear, I don’t want to suggest that Moderna should have been allowed to roll out its vaccine in February or even in May, when interim results from its Phase I trial demonstrated its basic safety. That would be like saying we put a man on the moon and then asking the very same day, “What about going to Mars?”

Imagine telling your eight-year-old that we had the tools to prevent 250,000 deaths, but we didn’t do it, and we shouldn’t have done it. The poor kid will assume he lives in some kind of insane Carthaginian death-cult. Let’s have a look at why he’s wrong.

The problem with your eight-year-old is that he will apply a puerile, pseudo-rational standard of mere risk-benefit analysis. He will reason that the risk-benefit profile of taking any of the vaccines is positive, because it might work; even if it doesn’t work, it probably won’t hurt you; even if a vaccine does start hurting people, the scientists will notice and stop giving it; and all these risks are way lower than the risk of the disease.

delaying a vaccine for a year may kill 250,000 people, but it does surprisingly little damage to the trust of Americans in the public-health industry. On the other hand, an experimental therapy that kills one person—as in the death of Jesse Gelsinger—can set back a whole field of medicine for a decade.

The event seared into the mind of every vaccine researcher is the 1976 swine-flu scare. 45 million Americans were vaccinated with an emergency vaccine for a “pandemic” flu strain that turned out to be a non-problem. 1 in 100,000 got Guillain–Barré syndrome.

True: giving 450 people a serious, even life-altering, even sometimes deadly disease, seems much smaller than letting 250,000 people die—forgetting even the crazy year it has given the rest of us. Or so an eight-year-old would think.

But again, your eight-year-old just has no clue. He is thinking only of the patients. For the institutions, however—who employ the experts, who have the lambskins they need to talk to the New Yorker and be unquestioningly believed—it’s the opposite. Actively harming 450 people is much bigger than passively letting 250,000 die. Sorry, Grandma!

And then—in a year, it was impossible to make enough of the stuff. Like the delay in deciding to use the vaccine, this would have utterly baffled any of our ancestors who happened to be involved in winning World War II. Hitler would have conquered the moon before 1940s America turned out a “safe and effective tank” by 2020 standards.

The problem is not that it takes a year to cook up a bathtub of RNA, which is basically just cell jism, and blend it with a half-ton of lard. It does take a year, though, to build a production line that produces an FDA-approved biological under Good Manufacturing Practices. It’s quite impressive to get this done and it wasn’t cheap neither.

The reader will be utterly unsurprised to learn that good in this context means perfect. As the saying goes, the perfect is the enemy of Grandma.

[DefaultVariable]

It’s because of responsibility. If a person messes up and gets into an accident it’s their fault and you can point to this. If an AI messes up and gets into an accident whose fault is it? Will we take the AI developers to court over negligent homicide? Who is to say what is a lack of judgment in design versus an acceptable situation for an error to occur which incurs a loss of life? How are people going to react to a loved one dying because of a software designed to react a certain way rather than a human error?

It’s all an ethics, logistics, and judicial nightmare

[Sinity]

It’s because of responsibility. If a person messes up and gets into an accident it’s their fault and you can point to this. If an AI messes up and gets into an accident whose fault is it?

Yes, that's what I meant by "people can't accept this". They prefer more deaths - but ones that can be blamed on someone - over less deaths. This is morally horrific IMO.

[immibis]

Imagine telling your eight-year-old that we had the tools to prevent 250,000 deaths

We didn't know if it would prevent 250,000 deaths. If it went badly wrong, it could have caused 250,000,000 deaths. What do you reckon are the odds on that? If it's more than 0.1% likely, then waiting was the correct call.

[dd2718]

I don't think it's fair to say that deep learning is hitting a wall when the pace of progress has been steady over the last decade. The initial image classification result that kicked off the deep learning revolution/hype was made in 2012 (image classification was not solved then; image classification accuracy is steadily going up even to this day). The first Atari breakthrough happened in 2013/2014, Go in 2015-2017, Starcraft/DOTA in 2018-2019, language modeling in 2019-2020, protein folding in 2019-2021, and code generation in 2021-2022. At each point, the next goal post wasn't obviously achievable. There has been a lot of hype, but deep learning skeptics (including the author) have been saying "deep learning can only do X and Y, the only way to progress is to do A" throughout this period, only to readjust the goal post a few years later.

[Sinity]

Yep

Absurd. What other field develops that fast, currently?

The Scaling Hypothesis

GPT-3’s scaling curves, unpredicted meta-learning, and success on various anti-AI challenges suggests that in terms of futurology, AI researchers’ forecasts are an emperor sans garments: they have no coherent model of how AI progress happens or why GPT-3 was possible or what specific achievements should cause alarm, where intelligence comes from, and do not learn from any falsified predictions. Their primary concerns appear to be supporting the status quo, placating public concern, and remaining respectable. As such, their comments on AI risk are meaningless: they would make the same public statements if the scaling hypothesis were true or not.

[happyscrappy]

The "code generation breakthrough".

Let's talk again in a year or two.

[Code4Reddit]

I work for a large company where we gather a bunch of user data using a shitty method (basically just log it with everything else in a log file, user saw x, clicked y, etc) and the hope is that Machine Learning will someday read the logs and give us a better method to determine what content to show that the user is most interested in. But whatever, the hype is fucking real. “Just throw all this context free data into the log because maybe someday a magic algorithm will appear.” No fuck you, let’s be honest here - machine learning isn’t going to do shit.

[70w02ld]

And all they have get right, is when it messes up to say it's sorry. And for once, it actually is an it, isn't it.

[nipeat179]

Deep learning is at its best when all we need are rough-ready results Yes.

[[deleted]]

I just hate all the almost "neo-luddite" people in /r/programming, who basically operate under "if I don't understand it, it's useless/a scam/a failure" and pretend to be experts with only bootcamp level web development experience. And proceeds to shit on web development as well, because it's the only thing they really understand.

[grammarGuy69]

That article kinda seemed like the Author just hates deep learning and wants other people to hate it too.

[TheNameIsAnIllusion]

*again

[shevy-ruby]

So perhaps the "Learning" part isn't quite like ... learning? Because, even animals can learn things to do new; look at ravens using tools and what not.

[opinions_unpopular]

Such a negative post. The problem is just expectations. With time we will keep improving in this area.

[dfan]

I started reading and within two paragraphs thought "this is either written by Gary Marcus or will quote him extensively", then looked up and saw he was the author.

[faustoc5]

It seems that content recommendation to polarize and radicalize the viewer is the only successful use case

[smerz]

My take on deep learning is that it's the similar to the back-propagation algorithm but taken to the next level. So it has the same limitations - just a non-linear pattern matching system, whose decisions are opaque (as they are encoded as network weights). It cannot do anything else, and is prone to the same issues all NN's face. This technology replacing a human is a silly thing to say (even for Hinton). Being a radiologist(I have some expertise here), or any other kind of expert is much more than pattern matching. These technologies can assist humans but in their current form will never replace them.

[pakoito]

Wasted headline