An understanding of AI’s limitations is starting to sink in
economist.com
economist.com
For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humidity etc sensors to existing machines and collect the data and send it back.
The management thought we'd be able to apply machine learning on the data to get "business insights" from the all-powerful machine. They didn't know what these insights might be, just that ML/AI would generate them and therefore make the business a fuck-ton of money because AI generated novel new "business insights" that no one had thought of before and so transform the business. They thought it was just a magic box that would generate unbounded magic answers for their needs by passing in just some temperature and humidity readings or whatever, and then it would tell them they need to make more brown bread and less bagels in the North East region etc.
In reality, as I understand it, currently ML/AI requires us to know what the possible answers can be before we even begin training the network. So the classic example is it needs to know that the possible MNIST digits are 0-9, or that you are looking for one of 100 image classes etc.
You cant train a network with the MNIST digits, and then have that network tell you what shares to buy or sell.
Sure you can lop off the final layer and repurpose some of the middle layers, but you still need to train it to classify the inputs into categories you define up front. It won't give you a novel answer that you have not trained it for.
... at least that is how I understand it. Things may have changed over the past 5 years or so.
That said, I do agree there have been some cool things lately like machine vision etc. I don't think it will be that huge an industry though - it feels like a lot of it is largely just commoditised now (which is good) and it will be just like any other library you pick - like picking a UI framework for a web app. Just pick up a pre-trained network from modelzoo and get on with your real business requirements for 99% of people using ML, while the other 1% (at FAANGs et al) and academia churn out new models.
Things may have changed over the past 5 weeks or so with GPT-3.
Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting democracy.
I'm still waiting for AI to figure out self-driving cars, natural language processing, and other "holy grail" problems that researchers have been chipping away at for decades but we are still a long way from having "solved" them.
Also in the case of self-driving cars, there is actually a very serious problem of it being difficult to guarantee safety. You can't tie every input to every output to demonstrate how it will behave (it is way, way, wayyyyyyyy too big for that), so you have to show statistically by driving so many miles that it's better. Which is still sound from an engineering perspective! But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times!
What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and useful, should I care if it happened by accident, or without "understanding"? Can the knowledge encoded in the model parameters be interpreted as some form of understanding? If no, why not? What's missing exactly? What is different from how human scientists operate?
It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.
I'm not sure about GPT-3, but let's imagine GPT-4 next year will be able to do this. It just does not strike me as a particularly high bar to clear. Let's go further, and assume GPT-5 in 2022 will pass the Turing Test (you personally will not be able to tell). What would you say then?
p.s. I built my first language model (LSTM based) back in 2014. Then I built a VAE based one. Then a GAN based. None of them were especially good, so I switched to music generation (this actually works pretty well). My most recent project is using sparse transformers for raw audio generation. Building novel DL models is literally in my job description.
Please don't wildly speculate about strangers you meet on HN.
The Turing test hasn't been passed if I'm the judge. Supposing I were unable to tell the difference between any AI system and a human interlocutor defending the same argument, at any time before 2023, I'd admit my prediction was wholly incorrect.
I doubt very much I'll find a taker for this bet, however. The AI field has always been bigger on optimism than results, as we both know.
However, I believe the models will be good enough to fool you during, say, a 20 question/response dialog. They will definitely be able to fool vast majority of unsuspecting humans. And they will definitely be able to keep track of conversation (remember what you said previously, and use it to construct responses to follow up questions).
Between the vast majority of unsuspecting humans and me there is a considerable gap. Mind the gap!
The TT ready model I'm envisioning will be trained on many billions of chat sessions. It will contain dozens of preconstructed graphs and will dynamically construct dozens more (personality graph, common sense knowledge graph, domain specific knowledge graphs, causality graph, dialog state graph, emotional state graph, etc), it will have a bunch of emotion detectors, humor detectors, inconsistency detectors, lie detectors, praise detectors, etc. It will have the ability to query external sources (e.g. google search --> web page parsing --> updating relevant graph). All these modules will filter, cooperate, and vote, providing input to higher level decision making blocks. These blocks will use those inputs to condition and constrain response generation process. This is finally where a language model comes in, and this until recently has been the hardest part - generating a coherent, grammatically correct, interesting text, directly addressing a specific prompt. This part has been solved. Until GPT-2 last year we simply could not generate high quality text. Now we can, and GPT-3 is even better at that. Sure, there are plenty of non-trivial problems left to solve, but I don't view them on the same level of difficulty - some of them have already been solved in the process of IBM Watson development, so I'm optimistic. The hardest remaining challenge is probably constructing common sense graphs. [1] looks promising.
p.s. your questions are so naive I'm not sure if you're trolling me. A human might answer them like this (and a bot built 50 years ago could easily imitate that):
"when and where were you born, and how do you know?"
- [personality - redneck] I was born on a farm in Oklahoma. How do I know what?
"what color, besides red, best communicates the flavor of a strawberry, and why?"
- Red is the right color for strawberries.
"What historical figure does my communication style make you think of most, and why?"
- You talk like one of them big city hipsters.
"Which of your family members comes to your mind first?"
- My little bro Jimmy, we just went fishing together on Tuesday.
"What do you think the context was in which the following poem was written?"
- [depends on the poem] I don't get this poem. What is it about?
To clarify, the common sense problem is a hard one. It is similar to level 5 autonomy driving. That will take a while to solve. But what we are talking about here is kinda like Waymo cars which can drive themselves in ideal weather at slow speeds in Arizona. So in 2.5 years I think the best chatbots will be as far from having common sense as the current Waymo self driving cars are from level 5 autonomy. Which is to say they will be pretty good.
Ideal weather at slow speeds, with a professional human driver throwing road-condition curveballs at you and challenging your responses? I like my chances.
Keep in mind that you would have to differentiate bot's responses from those of an old truck driver, a snobby philosophy student, a conspiracy theorist, a stoner, a bimbo following latest Kardashians news, etc. Lots of different personalities of real humans who can throw curveballs at you during a chat session. I hope you don't expect to only chat with bay area devs? :)
The way that something like GPT-3 tends to fail basically is that you 2-3 paragraphs where paragraph 3 will containment statements that subtly contradict the semantics of paragraph 1. Things like seeing things inside closed drawers and other cues that the thing has no fixed world-model. That's what gives an impression of "meaningless" or "no understanding". (admittedly, I've only played with GPT-2 but this is a description of how texts written by these models at first seem plausible and then implausible as you read more).
It's not some philosophical objection akin to "nothing but a human brain can think".
What is "world-model"? What makes you think GPT-3 does not have some kind of a world model? It's clearly not a very good one, but at the same time it does not mean it can't get better. A 3 year old also does not have a very good world model, what's the difference between his world model and one of GPT-3? Again, clearly there's a big difference, I'm just not sure we know enough about what's going on inside 175B model to make any dismissive statements about it. I'm also not sure what will happen if you train a much bigger model on much bigger data. Things might start to emerge at some scale.
> if you train a much bigger model on much bigger data...
Yep, no one is denying that the potential exists for something to be created that can do something useful.
However, the point being made is that GPT-3 doesn't do anything particularly useful.
The generated text from is not consistent, and apparently fine tuning can result in poor performance (ie. generates random crap). It scored better at a bunch of metrics, which is great, but of questionable practical value.
"world model" or not, it's currently interesting, amazing... probably not useful unless you're writing a spam bot.
What is unclear, is if bigger models will actually solve that or not.
You could argue that general intelligence is also based on prediction, and that a human's world model therefore isn't so different in nature, but there are some very significant differences ...
1) GPT-3's model is based only on a corpus of text (facts, lies, errors, etc) it was fed... there is no grounding in reality.
2) GPT-3 is only a passive model - it's not an agent that can act or in any way attempt to validate or augment it's world model.
3) GPT-3 is architecturally a language model .. it can get better with better or more data, but it's never going to be more than a language model.
The difference between a 3-year old's brain and GPT-3 is that the 3-year old's brain is not a one-trick pony ... it's a complex cognitive architecture, honed by millions of years evolution, capable of performing a whole range of tasks, not just language modelling.
The 3-year old's brain also has the massive advantage of being embedded in a 3-year old autonomous agent able to explore and interact with the world it's world model is representing... It you tell GPT-3 pigs can fly then as far as it is concerned pigs can fly, whereas the 3-year old can go seek out pigs and see that, in fact, they can't.
There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data. That's pretty different from communication; it's more a process of internal generation of notions and explanations that could later be conveyed in communication.
And then there's the meaning that is even more internal. E.g., reading tarot cards or tea leaves, people generate meaning out of nothing. And then there's Pareidolia: https://en.wikipedia.org/wiki/Pareidolia
If you're saying machine-generated text is meaning in the sense of that last category then sure, it's something we loosely call meaning. But it's fundamentally the same as other sorts of cleromancy [1], just more elaborate.
How is this "knowledge extraction from data" process different?
It seems none of this is something new. It's just that you no longer need a good education to learn about old ideas; you can come up with them on your own.
You said above: "There's also the kind of meaning that scientists and researchers talk about when they extract knowledge from data."
So if we agree that in both cases some form of knowledge extraction is happening, I wonder how these two forms compare.
How is this knowledge encoded in the heads of people? Perhaps through the choice of synapse strength and connectivity between neurons?
What is being encoded in GPT-3 weights as it reads through millions of pages of text? How is it different from tuning biological synapses?
Still don't know what I'm talking about? It's OK, neither do I :)
Isn't it good enough, or very nearly, to generate fake news? And if you can generate something that fools a large percentage of humans, even for a second or two, you can sell ads. It would be solving a problem for anyone who can profit from it. I thought the people who developed it stated that it was too dangerous to release widely? Dangerous = useful to bad people, no?
Don't get me wrong. You can absolutely generate news articles with these text generation models. But if you look at examples of the "fake news" generated by the monster GPT-3 model, it's not really any different to junk churned out by supposedly respectable news organisations i.e. grammatically correct but filled with logical contradictions, false statements that can be checked in 30 seconds with a search engine and so on. Most people already learned which news sources are trustworthy and which are pushing an agenda. If those outlets replace their journalists with GPT-3 it won't make any difference. The readers who don't trust them will still not trust them, and the readers who just want partisan cheerleading will continue to be satisfied.
What I see is actually quite different. If the people who could detect fake news was practically infinitesimal then you'd see the vast majority of people having super high trust in the media. In fact most people don't trust the media, lots of polls showing that. Sure they may only fact check something occasionally (often by reading about a topic they happen to understand), but people aren't stupid. After they notice or hear about a few mistakes and observe they're always in the same ideological direction, they get the picture.
This is a completely wrong reading of the situation. Tesla engineers clearly understood it is impossible to determine whether a stationary object is actually on the street or just an overhead sign with the equipment that is available on current Tesla cars and therefore they simply ignore all stationary objects. Because Autopilot is not a self driving technology this is not considered a problem. If a Tesla with enabled Autopilot ever runs into a stationary object it is clearly the driver's fault. It's extremely predictable and since it's easy to blame the driver there is no need to fix the problem by adding the required sensors to Tesla cars.
0. http://cyberlaw.stanford.edu/blog/2013/12/human-error-cause-...
Isn't that what unsupervised learning is for?
For example, you might think of data clustering as an unsupervised problem, but in reality you're still providing supervision in terms of what similarity measure to use and some control over how many clusters it should generate; and, at the end of the day, the output is always going to be a set of clusters, not, say, a stock tip or wry comment on the nature of your data!
Architectures are also tweaked slightly although they continue to be largely based on the two architectures that started the current deep neural net boom, Long-Short Term Memory Networks (LSTMs) and Convolutional Neural Networks (CNNs), which are largely used for natural language processing and machine vision respectively.
Every once in a while there is a bit of excitement about results that come out of adding a new parameter which is then given a fancy name like "attention" and generates much breathless copy about how neural nets will soon become as intelligent as a dog/ your three year old/ your personal assitant etc. Usually though it's not clear whether these new parameters or architectural tweaks are really responsible for the success of the proposed techniques and when one looks at the field as a whole it becomes more and more apparent that the most successful work is backed by the most data and the most computing power.
To slightly er tweak your prediction, I think what'll happen is that, indeed, like you say, there will be libraries and APIs and so on (well, there already are) but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches). So in terms of academia perhaps we should expect a shift away from neural nets, to something that's easier to do research on.
That makes me particularly sad. In a utilitarian sense, the ideal situation would involve new approaches being democratised. Instead, we're ending up with walled gardens.
The current situation isn't dissimilar to what has happened to the Web Platform during past 5-10 years. And, similar predictions are made wrt VR and AR (both seem to attract different types of investors).
In the industry, (preventive) maintenance takes up a pretty huge chunk of resources. It's something techs need to do often, and it's often a laborious task, but it's obviously done to reduce downtime.
So the business insight, as they like to call it, is to reduce costs tied up to repairs and maintenance.
All critical applications have multiple levels of redundancy, so that a complete breakdown is very unlikely, but it's still a very expensive process if you're dealing with contractors. If you can get techs to swap out parts before the whole unit goes to sh!t, then that's often going to be a much cheaper alternative.
But, in the end, it comes down to the quality of data, and the models being built. A lot of industrial businesses hire ML / AI engineers for this task alone, but expect some magic black-box that will warn x days / hours / minutes ahead that a machine/part is about to break down, and it's time to get it fixed. And they unfortunately expect a near-perfect accuracy, because someone in sales assured them that this is the future, and the future is now.
Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making.
In a supervised learning problem, the range of possible outputs is already known, meaning the model output will never be categorically different from what was in the training data. The knowledge obtained is meaningful as long as the training labels are meaningful, but it can never be new.
Unsupervised learning doesn't have a notion of "training data" but that means an unsupervised model's output requires additional interpretation in order to be meaningful. It is possible to uncover new structures and identify anomalies in new ways, but this knowledge isn't meaningful until someone comes in and interprets it.
Applied to the specific example where sensor data is used to try to generate insights about machine functionality: Either you can only predict the types of failures you've already seen, or you can identify states you've never seen but you wouldn't know whether they mean the system is likely to fail soon or not.
It's the Roth/401(k) tradeoff. For model output to be useful, someone must pay an interpretation tax. The only choice is whether it is paid upon insight deposit or withdrawal.
Yup, this is something I've seen from both sides. First you mention is basically the standard, while the last is part of the deep learning voodoo black magic that executives and sales love.
I've had people approach me with proposals like "What if we just churn [ALL OF] our data through this or that model, and let's see if it comes up with some patterns we've never seen or thought about"
And that's not just for industrial applications. It's everywhere.
What is concerning to me is that this mentality will surely induce more unrealistic expectations. Before you know it, business execs are starting to ask why we need business analysts at all, because surely those fancy deep neural networks can extract all kinds of features - "only need data scientists to figure out those things".
So yeah, that's my fear. That businesses will blindly start to discard domain knowledge, and just feed black-box models their data, and let the data scientists wrestle with the results.
Knowing how the outputs relate to the inputs is where most new insights could come from.
For example, what feature (input) is driving the “failure” of the machine (output/prediction)?
This is where ML explainability comes in.
AlphaGo is a supervised learner that outputs optimal Go moves given opposing play. It yields new discoveries in the same sense that a model designed to predict mechanical failures from labeled sensor data would: I didn't know what the model was going to predict until it predicted it, and now I know.
But what the factory owners want is a machine that can take raw, unlabeled sensor data and predict mechanical failures from that. They want insights. "Why not just feed all our data into the model and just see what comes out?" they ask. "I don't see why we need to hire at all if we have this neural net."
The reason you need a human somewhere in the system if you want insights is because someone needed to program AlphaGo specifically to try to win at Go. At the factory, someone needs to tell the machine what a mechanical failure is, in terms of the data, before it can successfully predict them.
Then, neither "winning at Go" nor "mechanical failure" are states that the system hasn't already been programmed to recognize. That's what I mean when I say a supervised learner cannot generate "new" output.
That's not what they wanted.
What people are being sold is AI/ML as a magic bullet that will do something useful regardless of the situation, and it lets business people avoid making decisions about what they actually want, because AI/ML can be anything, so they just signup for it and expect to get 20 things they didn't know they wanted handed to them on a plate.
Turn out, it's not enough to just collect a bunch of data and wave your magic wand at it. It wasn't with web analytics 10 years ago, it's still not.
What you actually need is someone who has a bunch of tricks up their sleeve, and has done this before, and can suggest a bunch of Business Insights the business might need before they start building anything, people that actually decide what to do, and actions taken to investigate, and solve those problems.
I mean, to some degree you're right; perhaps ML models could be useful for tracking hardware failures, but that's not what the parent post is talking about. The previous post was talking about just collecting the data and expecting the predictive failure models to just jump out magically.
That doesn't happen; it needs a person to have the insight that the data could be used for such a thing, and that needs to happen before you go and randomly collect all the wrong frigging metrics.
...but hiring experts is expensive, and making decisions is hard. So ML/AI is sold like snake-oil to managers who want to avoid both of those things. :)
It's all about how you package things. ML connected to an audio sensor could predict failure modes that are dificult to detect otherwise. Now that might not be was was asked for, but a win is a win.
I’ve experienced what the OP was alluding to...namely, it helps tremendously to start out with an understanding of the problem you’re trying to solve, more so in supervised learning. It’s incredibly frustrating to ask managers what business problem they are trying to solve only to be met with “We don’t know, that’s what we want the software to tell us.”
On the other hand, if they say “we want to predict machine failures” or “reduce maintenance downtime” now we have a lens in which to view the data.
If AI could do the magic as those managers hoped, they would be out of a job
Except - and this rather ties into your point - those are not the only possible digits; your network also has to deal with (ie reject) other possible digits such as "P", "E", "3̸̶", or "[Forlorn Sigil of Amon-Gül redacted]"[0], which look like, but are not, decimal digits.
If you only train it on examples of 0-9 then those are the only outputs it's going to give. If you fed a "P" into such a net then the outputs would be the degree of similarity of that "P" to each of the (0-9) digits it was trained on. You could of course threshold the output and ignore any prediction with confidence less than, e.g., 90%.
If you wanted the net to do a better job of rejecting non-digits, or at least some specific ones, then you could include a bunch of non-digit examples in your training data (so now your net has 11 outputs: 0-9 and "non-digit"), then hopefully - but not necessarily - it's highest confidence prediction will be "non-digit" when presented with a non-digit input.
Exactly.
> 11 outputs: 0-9 and "non-digit"
IIRC, this doesn't work (or not well) because the net tries to find similarities between the various members of the set "everything except these ten specific things", but you could just require low confidence for all digits on non-digit inputs as part of the gradient descent function.
The problem is more that - if people with decision-making authority trust the AI to not be insane and evil by default - failure modes like this have to occur to you before the AI starts misbehaving in production.
You train it with a lots of examples of "given this input, this is the output i want", and hopefully it learns to get the "correct" (similar input => similar output) output for new inputs that you feed it. i.e. you've now automated the process of figuring the correct output for a given input.
There is also the "reinforcement learning" AI paradigm where the trained AI is choosing actions (from a given repertoire) in order to maximize an action outcome score based on some scoring function you provided. This is appropriate in a situation where you want the "AI" to do something more than just select the correct output, but again no magic - you're having to anticipate and score the potential outcomes.
The typical AI methods train a model that utilises features that don't make much sense to humans (pixels/texture/word tokens etc). In contrast with traditional statistical modelling where each feature were given 'meaning' and their importance explored via investigating the observations (data) using properties of well studied mathematical models, the AI methods that often made the press don't put a lot of emphasis on these properties. The advancement is a push to utilise all available information/data to outstanding 'performance'. The explanation of the inner features within that math space is usually secondary (though I don't mean that authors of novel methods don't care about mathematical modelling).
I might be too naive here but that's how I feel after trying out many methodologies in my field.
Neural nets are mostly dominant in perceptual domains such as image or speech recognition, where the raw inputs represent a uniform sampling of data values (pixels, audio samples) over space and/or time. For classical business problems where the inputs are much richer and more varied, and already have individual meaning, then decision tree techniques such as random forests may be more appropriate and do provide explainability.
I mostly agree with that, however there have been advancement in scientific areas such as chemistry and biology (DNA/RNA) where the data are definitely meaningful and a lot times categorical. So the methodologies can be applied in wider areas, they just need a lot of domain knowledge and experience.
Asking for a friend.
So if they can solve the problem the ML probably can too ...
The cycle keeps repeating. A new advancement in computing power, networking, or algorithms means there's a new batch of low-hanging fruit for AI to pick, so we pick it. Investors say "What about the high-hanging fruit?" and we say "No problem. We just need a slightly longer ladder."
Two years later everybody finally realizes the high-hanging fruit is on the moon.
There's a recent NLP model that was trained on a trillion words. It would take us 10,000 years to read or listen (no breaks, no sleep) to that many words. Problems like attention, and the relation between memory and sequential thinking haven't been cleared up at all. Even semantics, i.e. basic understanding of a utterance or a scene, is in its infancy.
Large neural nets can help with interesting problems, but it's not going to mimic our style of thinking any time soon.
No algorithm running on digital hardware can emulate the biological process of animal intelligence.
I think this gets to the core of what is still a limitation of current technologies. Venturing into the unknown is still a deeply relevant task that seems unlikely to be replaced by computers anytime soon.
Alice: Bob, Can you translate "Eat my shorts" into latin for me?
Bob: No. I don't speak latin.
Alice: Go on - try anyway.
Bob: "Eatus mine shortus"
Alice: Wrong! The answer is "Vescere bracis meis". You're totally wrong Bob! I was expecting more of you!
Bob's manager: Here's Bob. You paid a gazillion dollars for him, and he can translate whatever you want. Any language, whenever – the true revolution everyone's been talking about. Your life and our economy will never be the same.
Alice: Bob, Can you translate "Eat my shorts" into latin for me?
…
Alice: Bob, Can you translate "Eat my shorts" into latin for me?
This is what it replied without any training:
Bob: As it happens, yes, I can.
Alice: Great.
Bob: Ego sum vestrum braccas comedisse shortus.
Alice: Exactly what I was looking for.
Google translate says "Ego sum vestrum braccas comedisse shortus" means " I have eaten your pants shorts."
because people in the technology sector have a distorted view of their own importance. Despite the extremely meagre impact of the "computer revolution" on both economic growth and anything outside the world of bits, people in 'tech' fashion themselves to be sort of world-changing figures. AI on top of it is a sort of ersatz religion, the rapture of the nerds as Charlie Stross put it.
The problem is that too many companies believed it was the opposite so they built and hired all these AI/ML “experts” that just wanted to “built models” but didn’t want to focus in the messy hard stuff like finding and cleaning data. Nearly all of these AI/ML “experts” inside companies were also broadly just applying off the shelf tools and algorithms, perhaps with a bit of ensembling, rather than actually building new AI/ML approaches.
As a result, the big investments inside most companies produced a flash and puff of smoke that got people briefly excited followed by a lot of money spent with little business value returned.
I’m a big believer in ML approaches, but in most cases companies need to be focusing in their data first against clear business problem and just use off the shelf tools for the rest. That’s good enough for nearly all needs.
There’s a big bubble at the moment with all these “AI/ML” teams that’s going to crash hard as businesses realize the above and reset to focus on stuff that works and generates tangible value for the business.
- increase the volatility in virtually every financial market they touched
- be exploited by adversarial learning networks to amplify funded propaganda as news
- use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines
These non-linear "function approximators" have absolutely unpredictable and insane non-linear behavior where learned information was non-existent or sparse.
God help us all if one of these artificial intelligence devices is driving the road and sees a red stop sign that is a square, rather than a hexagon.
Well, there's the problem right there.
Based on the way it was presented, I got the feeling that they are just essentially manually identifying cases and addressing them as they see them. Is that solution really helping to make the system more robust when encountering an unexpected situation?
Now just imagine how good it could be if it was being trained to actually give good search results instead of selling.
https://github.com/elsamuko/Shirt-without-Stripes
But it sure can identify stripes.
Am I being a pedantic numpty or am I illustrating a point about the many ways errors creep in, regardless of the natural- or artificial-ness of the intelligence?
Human drivers: 1 death per 88 million miles traveled (in the US) [1]
Tesla Autopilot: 5 deaths per 3 billion miles [2]
[1] https://www.iihs.org/topics/fatality-statistics/detail/state...
[2] https://electrek.co/2020/04/22/tesla-autopilot-data-3-billio... and https://en.wikipedia.org/wiki/List_of_self-driving_car_fatal...
And then for you to argue that the driver assistance system is actually better than a human driver if given the car alone!
Wow
* Facial recognition/detection
* Facial synthesis (deepfakes)
* Speech synthesis, including mimickry
* Speech recognition
* Natural language processing
* Gait/walking algorithms
* Motion planning
* etc.
Complexity arises from simple units working together in parallel. We're working on the smaller, specialized problems that will, in the next generation, be put together to build more complex and complete systems.
I'm no fan of the 'black box' nature of neural networks but it's clear they're getting results. As they become more accessible to the lay person, we'll see a profusion of use cases that are both anticipated and surprising.
I'm always flabbergasted by the doom prediction. The path we're on seems apparent.
The problem is a question of informational density. Biological systems are computationally very dense. Far more dense than the 4nm transistor fabrication available today, and with a far larger volume of size.
Consequentially, the computational capability of most AI systems is far lower than its biological equivalent. And as you find in most information finite discretization problems - the lower density information system will alias against the higher information system.
So, that means you will have a hierarchy/pipeline of computational stages - each aliasing reality. Eventually, you will find that your parameterization of each perceptual stage has a strange property. The size of each subsequent layer is important... but the relative computational space of each subsequent stage is even more important. Because mismatched stages results in nothing but numerical interference and noise.
And I think that is where we are today. The IQ of a krill shrimp.
Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?
Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?
Yes. Note the success of Unix and the ability to scale, do work and provide an environment to be productive in.
> Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?
Your use of 'human-centric' is odd. I would have thought the traditional 'human-centric' theory of the mind is something monolithic and indivisible. Suggesting that it's many small processes communicating with each other is basically taken straight out of nature, from ants, schools of fish, birds flocking, etc.
Whether there are other forms of intelligence or no, it's clear that incremental progress in individual processes that can then be composed together is a productive way to traverse the energy landscape. This is why (imo) we see so many symbiotic relationships from cells on up to higher level animals.
> Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?
I'm not quite sure what your point is here. If you're critiquing me about neural networks being black boxes and not giving us real insight into the underlying system, that's fair and the reason why I said I didn't like the black box aspect of neural networks. I will say that if there is a black box model that can be easily manipulated, this will probably lead to deeper models much quicker.
If you're saying that human cognition is not describable by any human and, I guess, implying that it's indescribable, I would point out that one doesn't follow from the other. Not having a good model right now doesn't imply we won't understand it at some future date and, in my opinion, this is precisely what's happening. Having no human be able to describe the underlying computation (of face recognition, nlp, walking etc.) doesn't mean it's indescribable, it means it's not describable by anyone right now.
At one point we didn't know how birds flew. We still might not know, to your satisfaction, but we have a basic understanding of how to make things fly, both in practical and theoretical terms. Planes fly and we understand how even though they don't flap their wings. I have no doubt we'll figure out how to do complex human-level computation even if we don't have a deep model of the specifics of human thought.
> Your use of 'human-centric' is odd. I would have thought the traditional 'human-centric' theory of the mind is something monolithic and indivisible.
Sorry, my answer wasn't clear. It was not tied to the "small processes communicating..." approach but to your very list of "problems" (facial recognition/detection, facial synthesis (deepfakes), speech synthesis, speech recognition..) which seems to me expressed in a way tied to human activities, while at least part if the underlying "intelligence" underlying many of them may also exists in other forms of life (other mammals, birds, fishes...).
> Suggesting that it's many small processes communicating with each other is basically taken straight out of nature, from ants, schools of fish, birds flocking, etc.
Exactly. My point is that analyzing the ways "the smaller, specialized problems" are tackled by non-human living beings seems pertinent as self-analysis (as humans analyzing human intelligence) is difficult, and as various species may apply various solutions, some more easy to grok. Focusing on "problems" too specific to the human being may be a sort of "framing" detrimental to the quest. Moreover the famous Dijkstra quote ("The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.") may be pertinent.
> incremental progress in individual processes that can then be composed together is a productive way to traverse the energy landscape. This is why (imo) we see so many symbiotic relationships from cells on up to higher level animals.
I agree. My point is about _how_ we consider the system(s) (our "point of view"): framing it to human characteristics, globally or locally (dualism)... It seems to me that the very organization of a system may be neglected when we consider it a stack of "small processes communicating...". Pirsig's "Metaphysics of Quality" may be pertinent.
>> Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?
> I'm not quite sure what your point is here. If you're critiquing me about neural networks being black boxes and not giving us real insight into the underlying system, that's fair and the reason why I said I didn't like the black box aspect of neural networks.
This was my point and I agree with you.
> if there is a black box model that can be easily manipulated, this will probably lead to deeper models much quicker.
I'm less optimistic, as it is only 'probable', and AFAIK won't give us more real insight into the underlying system.
> Having no human be able to describe the underlying computation (of face recognition, nlp, walking etc.) doesn't mean it's indescribable, it means it's not describable by anyone right now. > I have no doubt we'll figure out how to do complex human-level computation even if we don't have a deep model of the specifics of human thought.
I agree, we will enhance ways to "approximate" (tricks leading us to a solution to each "local" problem) up to the point of being able to solve real-world problems. However it may reach some hard limit (as far as I understand this is the point of the article), and using a powerful tool/method insufficiently understood may be dangerous.
I invite you to construct the Lagrange polynomial (i.e. interpolating polynomial) for points on a nice, simple curve with some noise. They will, by definition, pass through every point given, and yet it will likely behave very badly outside the range of the given points.
Non-continuous is also not the perfect terminology, but I argue that it is more precise than non-linear: the chief idea being that the model "changes unpredictably."
One option is to specify a polynomial function, you can specify how many coefficients you want. One of the measurements is the mean-squared-error between the line-fit and the points.
You can add as many polynomial coefficients as you want, and you will be able to decrease the mean squared error. But the more polynomial's you choose, two things will be true:
1. The line-fit will be far more likely to go through the points.
2. At points in the line where there was no data, the line will less approximate the underlying physical reality.
That same mathematical property is what is relevant here. There is nothing inherently evil about non-linearity, when the non-linear math model properly maps to the physical reality. But when you over fit a line, many of the functional solutions may be completely wrong.
But, what I don't understand is that I thought that "linear" in ML contexts was normally used in the sense of 'linear transformations', which is a sense of linear that 'line-fit' from excel isn't -- it's affine.
Is a linear model with thousands/millions of weights/parameters (like deep learning models) really substantially simpler to understand? Can it do anything useful?
So, I guess looking from the bottom up the system may look non-continuous and linear. But if you look from the top down, it would look continuous and non-linear.
Really, I am not sure which one is "true".
Non-linear models have more variance so they represent current data better and are less predictive of future, unseen data (think of a line snaking around a point cloud).
An added complication is that deep neural net models are, in practice, vectors (or, well, tensors) of numbers so they are difficult to interpret. This and their extreme variance makes it hard to know how they will behave in the future.
Some function being linear means it's easier to guess. If a real world phenomenon is tied to a linear function, then it's easy for AI to guess/approximate.
And yet neurons are not "pure" binary thresholders the way logic gates are because you can't take the derivative of a binary function, and you can only do backpropagation on differentiable functions. The compromise neurons make is a "smoothed threshold" or sigmoidal curve which is differentiable but still very nonlinear.
Also, the use of linear/ nonlinear in machine learning is a bit misleading. A "line" is not necessarily a "straight line", but usually when we say "linear" we mean "straight" and so when we want to say "not straight" we use "nonlinear".
In any case, when we say "line" in machine learning we mean a function, the function of a line. So a "nonlinear" function is a function that curves and turns, e.g. a sigmoid, whereas a "linear" function is straight as a rod.
Why a line? Classifiers er classify by drawing a line through space. "Space" means a Cartesian space where our training examples are represented as points (hence, "data points"). Data points are located in Cartesian space according to coordinates that represent their attributes, or features (these coordinates are the "feature vectors" that are input to neural nets). We classify data points by drawing a line between those that belong to one class and those that belong to other classes. More to the point, when we train a classifier, we find the parameters of a function of a line that separates the points of separate classes and when we want to classify a new point, we look at where it falls with relation to that line.
So that's where all that stuff about lines and "linear" and "nonlinear" models comes from. A "linear model" or "linear classifier" can only draw straight lines. A "nonlinear model" can go twirling around madly.
Finally, "non-linear" doesn't mean "bad". There are tradeoffs- in particular, the "bias variance tradeoff" that I hint at in my earlier comment. A linear model is more limited in what it can represent, but a nonlinear model is less likely to represent data that it hasn't seen in training.
- NN can absolutely represent non-linear functions, and they are based on solving system of linear equations.
- The non-linear function here has nothing to do with the linearity of the system of linear equations used to construct it.
- The two main sources of non-linearity are, (a) the inputs (e.g., an image, or a series of images varying a non-linear fashion), and (b) the activation functions.
And yes, the bias-variance tradeoff is about generalisation (i.e. the ability to extrapolate to unseen data). But this is more related to the fact that in the real world, problem spaces don't have nice, friendly, regular shapes nor do their shapes stay put after we've trained a model.
The way I see it, the variance is the part of the error that you can reduce by collecting more data from your distribution and increasing model complexity if needed.
The bias part is what will not get better no matter how much you sample your distribution, and extrapolation problems fall into that category.
The extent to which a model can extrapolate to out-of-sample data depends on how well the shape of the model follows the true distribution of the data. This is true regardless of the bias and variance of the model. It just happens that most of the time, in interesting, real-world problems, the true distribution of the data is more or less different than the sampling distribution of the training data- i.e. there's always some amount of "sampling error".
Sampling error can't be reduced by collecting more training data- you just have more data with the same sampling error. Increasing model complexity increases variance, so if you start with high sampling error, you wil get a high error on out-of-sample data because your model matches the "off" distribution of the training data too closely. What training with more data and with a more complex model can do is increase the ability of the trained model to interpolate, i.e. to accurately represent (new) data points that are in the same region of "instance space" as the training data points.
A high-bias model can extrapolate well if the sampling error is not too high and the shape of the true distribution is not too irregular. However, a high-bias model will also not interpolate as well as a high-variance model. Its rigid structure will "miss" many data points. Like you say, this will not change if you train with more data. Anyway, that's the tradeoff.
Now, the reason why deep neural nets, which are extremely high-variance models, are trained with large amounts of data, is that they can interpolate very well but can't extrapolate very well. If a model doesn't extrapolate very well but its training sample is a large enough chunk of instance space, it can still be very useful, because it's still representing a large number of instances.
How to put it? Mabye your high-variance model has seen examples of white dogs and black dogs in training, but no green dogs. Your model will not be able to generalise to green dogs, but if green dogs are rare, it will still be able to represent most dogs, so it's still useful.
Of course, looking at the output of a trained model (its behaviour) doesn't tell you anything about what it was trained on. So a model that has very high accuracy on a large number of tasks will look impressive, even if it can't generalise at all.
Ah, apologies, I see what you mean. That is true, but this "error" is in-sample error, so increasing your model's variance will increase its ability to interpolate but not extrapolate to out-of-sample data, as I explain in my longer comment.
"In-sample" means all the data you've collected to train and test with. It includes training/validation/test splits. At the end of k-fold cross-validation, your model has "seen" all the data in your sample and the model that performs best is the model that best represents that data.
But, because the data was sampled from a distribution that is most likely not the true distribution of the data (since that distribution is unknown), the sampling error (i.e. the differences between the true and sample distributions) will be reflected in the model. A high-variance model will suffer more from this than a high-bias one.
Sorry I didn't understand immediately what you meant. The longer comment above is correct but probably doesn't help answer your question directly.
People confuse what AI can do, and what is AI all the time. It also doesn't help when there are so many inexperienced data scientist making promises that they can't achieve.
In your example, I'd argue that a human is not necessarily a better driver than a machine. An attentive and careful driver is certainly better than a machine right now, but there are many who drive carelessly. While a person is unlikely to mistake a square stop sign as something else, there are so many drivers that would simply ignore the sign, and traffic lights in general. They'd also drive dangerously because of road rage, and inattentiveness. And the majority of traffic accidents are caused by these drivers. A machine is unlikely to do these.
That said, until we figure out how to run all these deep learning models without a crazily expensive and power-consuming GPU, it is unlikely AI would be used as general purpose programs.
Until we can characterize the behavior of these systems, they must not be put in control of life-critical processes like driving.
That's why in life critical applications companies who are capable of taking the risk are scarce, because when accidents happen, the company has to take responsibility. It cannot be resolved by just firing employees.
You can't fix a model to handle unknowns, and you can't test that.
We've seen with Tesla's autopilot software that things like obsolete road markers and overturned trucks are meaningless to software.
Even in something not very well defined as a neural network, you can try to retrain it, or also to modify its architecture, or its postprocessing, and verify reproductibly on test cases that it behaves better.
Also, to address your critics, you can add test cases (just like in any sotware. But actually they also do exactly that for hardware too).
So i don't see why this distinction between AI and humans is made : both are black boxes. Perhaps humans have less "edge cases" but as long as the error level of AI is the same or lower than the one of humans, I don't care if the car crashed because the human driver looked at a sexy woman on an ad on a billboard or because a variable was poorly set in the car's code.
Economist editor: opens their "pessimist template"
"X has over-promised and under-delivered, A, and B have not commercialized yet, and may never be. X cannot do C yet. The challenges of X are D, E and F"
Replaces A...F with the most prominent examples they can find, Boom, we have an article.
I read through the article, hoping for a bit of important info. I wish we had a vote in HN: "Isn't worth your time, don't read it"
1: Computers can't read minds! Your algorithm might know that I like the Beatles because I listen to them a lot, but it can't predict that I woke up today craving to listen to some music from my childhood.
2: You don't know what you don't know! Your algorithm might make 24 frames per second film look smoother at 60 fps, but if something like wheel spokes move backwards at 24fps, it'll have a tough time getting the wheel to move the right way at 60fps.
3: Just because you have the information, doesn't mean you know how to write a program that can extract the knowledge you're looking for.
Which really means we need a super advanced AI with a worldview and context in order to automate certain kinds of information processing. I don't expect this anytime soon.
"Rebooting AI" https://www.penguinrandomhouse.com/books/603982/rebooting-ai...
And a few articles, for audiences both popular and technical:
Deep Learning: A Critical Appraisal https://arxiv.org/abs/1801.00631
The Next Decade in AI https://arxiv.org/pdf/2002.06177.pdf
How to Build AI We Can Trust https://www.nytimes.com/2019/09/06/opinion/ai-explainability...
And a HBR podcast ("Beyond Deep Learning") https://hbr.org/podcast/2019/10/beyond-deep-learning-with-ga...
With AI, nobody knows why the computer said no.
Ah, but all that works. If AI is perpetually defined to be "that which does not work" then it's perpetually potential, perpetually postulated, perpetually possible perfection. It never has to be compared to a clunky translation, or a drone that gets shot down. Unwritten novels never have story problems in the third act, unwritten programs never give ludicrous output.
The point is we have current things that are quite amazing, and would at one time have been considered to be the sort of thing that only an AI would be able to do, and yet we keep moving the goalposts. As if AI is defined as "that which humans can do but machines can't, done by machines".
Suppose we said we’ve invented Jesus because we’ve invented ways to walk on water and turn it into wine.
A technology is different. Its capabilities pretty much define it. Unless you are going to try to get all philosophical about it and say it doesn't count if it doesn't experience qualia or something (which is nonsense) Or, unless you are defining it in ways that specifically call out the implementation details. A helium balloon isn't a hot air balloon, not because it doesn't have capabilities, but because you've specifically said in the name that it must use hot air.
In their mental model, the AI is far from defined by these capabilities. They won’t be happy unless there’s an actual AGI whose capabilities happen to overlap with the spec.
So my point is historically we have cheated our way out of defining “intelligence” and instead given necessary but insufficient examples. I think this is the mechanism behind the goalpost-shifting in “AI”.
That’s the metaphor: it would clearly be absurd for engineers to define Jesus by some examples of capabilities. People who are waiting for the second coming won’t be satisfied.
In the lab we maybe should define AI by some set of capabilities. But clients, journalists etc. picture the Hollywood version, and narrowly fulfilling their spec won’t actually satisfy them.
I presume that is not due to a failure of the ML, but instead is often due to the lack of a good corpus of text, or the lack of appropriate manual training/nudging/correcting of the model for unprofitable languages.
The hype on AI was absolutely astonishing. I’m glad it’s finally coming back to reality.
More traditional AI is often logic or algorithm based. This is powerful and expressive, but the "intelligence" part is as flexible as the specific implementation. This is intelligent in the sense of a design being intelligent, which can be attributed to the author of said design.
Modern AI is often statistical. We have more data, so we can use that to generate a de-facto decision making database with statistically sound heuristics.
These techniques are all useful and can be very impactful. But we have to understand them as tools.
Your article agrees with this sentiment in a very entertaining way.
I find it fascinating that you can both see AI as what it truly is (a rebranding to solicit investment) yet still cheer for cryptocurrencies, which I personally believe to be not much more than a pyramid scheme to dupe those investors that buy in late. In my opinion, both AI and crypto are quite similar in the ways that they mislead investors by promising a golden future.
But that's a separate discussion, unrelated to my opinion of AI and is primarily a philosophical issue. Cryptocurrency / blockchain is fascinating but we are not promising robots cleaning your house.
But when I hear IPFS = interplanetary file system and then wee how poorly it performs in practice and that it's mostly used for illegal content, I cannot help but think that the crypto side also likes to oversell their practical utility.
I believe I have yet to see an application where the Blockchain is truly a critical component. In most cases, it seems that people end up caching its data in sql to speed things up, meaning that they're working on their own private copy now.
If you now consider that both the mining difficulty grows over time and the mining reward drops, then you clearly have a system with a built-in advantage for early adopters.
So people buy in, wait a bit, then exit at a higher price. But that only works as long as you have a large enough stream of newcomers.
I'd be willing to consider it an investment if there was some sort of inherent value on the other side, so if buying cryptocurrency gave you a claim of ownership on something real. But the critical fundament of cryptocurrency is that it's unrelated to the real world and only controlled by its members. In other words, the value of a cryptocurrency is determined exclusively by what people believe it should be.
At no point (until it's too late) will there be be effective legislation discouraging the creation of fully general and autonomous digital persons that compete with humans.
These people at the economist are completely out of the loop.
[0]https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-...
Everything about AI is impressive, as long as there is any
--- By HN AI specialists in this thread /s
I used to work for a lab doing coral reef sea floor mapping to track reef progression year over year, this was only possible because of recent advances in ml. This is just an example of course.
There is tons of work being done (and successfully used today) in healthcare domains, weather modeling, wireless rf tech, geospatial remote sensing etc etc etc.
My point is AI is being leveraged a lot and is moving forward quickly but the applications for average consumers that are both cost effective and highly useful are growing thinner.
If you want to see consumer tech examples that have been at least moderately successful I think some highlights are Google photos image recognition, current speech to text processing in most places (Apple google amazon), image processing in most smartphone cameras (wrt ml especially night photos) and unfortunately (IMO) a lot of content aggregation algorithms (YouTube, Facebook etc) optimizing for engagement.
maybe the best way to conceptualize this, inspired by @random_walker, is to compare AI in the 21st century to machines in the 20th century.
in the industrial age, machines automated rote physical tasks.
in the information age, AI could automate rote mental tasks.
the more objective and templatized the task, the more vulnerable it is to AI displacement. conversely, the more subjective and creative the task, the safer it is from AI displacement.
If all you’re talking about is self-driving cars or voice-operated assistants, then sure, the article’s mostly right. Modern techniques that have revolutionized ML in the past ~15 years have not translated to massive new economic gains in many areas they were anticipated to affect.
But this is the vast vast minority of all ML projects.
Many of the most economically successful ML projects I’ve run in my career are very simple, and ruthlessly focus on business value from the outset. A lot of them involve automating inefficient manual processes, things like spam filtering, phishing detection, fraud detection, automatic keyword tagging, automatic metadata classification in images or text, simple time series forecasting for logistics or consumer demand, simple models for customer churn, and a wide variety of different customized search engines for big & small content collections.
Just for one example, I worked on a project to automatically validate metadata about human models appearing in images, to flag discrepancies between documented ages / ethnicities within legally required model release documentation and the real appearance in images, to find fraud (especially when minors were used in stock photography).
This saved _millions_ of dollars annually in human review & legal costs for when that platform incorrectly approved photography with invalid or fraudulent accompanying release documents for the human models.
In just one project, a team of six engineers paid for itself about 5 times over and the delivered software requires minimal maintenance and only became more valuable as the platform grew larger. In fact that was one of the only times in my career when a non-finance company chose, discretionally, to pay larger bonuses than in employee job agreements as a reward.
That project did happen to use deep neural network for image metadata prediction, but it was fairly mundane and easily trained on 2 average GPU machines from a dataset of only a few hundred thousand images.
Edit: added below
I’ve also observed across several companies that there’s a big variation in outcomes and success of ML based on the level of investment in infrastructure.
It’s not about pumping money in for some crazy GPU cluster or huge framework for massively parallel training, but you do need to separate ML operations away from the ML engineers who research solutions for products and internal stakeholders.
It’s a situation where domain specialty has to be used efficiently or you’ll waste a ton of money and time. If you hire an expensive senior engineer for ML (salary easily north of $200K in large cities), but you task them with managing a database or operating kubernetes or debugging partitions in HDFS, you probably won’t get a good return on your investment.
Robot manipulation in unstructured situations is still not very good. (See the videos of the DARPA humanoid challenge) "Common sense", defined as the ability to predict the consequences of actions and to use that to plan, hasn't progressed much in years. Machine learning doesn't seem to have helped much with either, so far. Those are key areas for doing physical things in the real world. Humans are good at those.
So that's where to look for hard problems.
The payoff is low, though. Those skills are common to all healthy humans, so there's a huge pool of unskilled labor available with them. If you did a startup, and you solved, say, robotic shelf stocking, it would not be a huge win over low-wage people.
Low-level seriomotor intelligence requires extreme computational resources.
https://towardsdatascience.com/why-math-is-easy-for-ai-but-g...
However, training is computationally expensive and generating labels for training data is still done mostly manually by humans. Just like people, a continuous learning algorithm will have trouble distinguishing true from fake and could be corrupted by feeding it fake data. There is machine learning that doesn't require labels, but much of the recent success has been built using human labeled data sets. Human biases get into the labeling, and appear even in the methods of selecting and collecting what data to train on. One way to design continuous learning systems involves wrapping simpler machine learning algorithms with data collection and feedback loops to handle the retraining. Ensuring accuracy is a difficult problem unless your problem domain comes with some built-in measures. Some measures of "correctness" are arbitrary such as those defined by culture and can change over time or geographically.
Like you can just talk to your database, and the AI will magically return you the results.
At that point, I think their marketing team had totally lost the narrative.
There is no spoon.
An understanding of AI's limitations is as far from sinking in as the average MBA is from comprehending Finnegan's Wake. An understanding that machine learning is not the entire 60-plus-year-old field of artificial intelligence would be nice to see for once from an institution that supposedly prides itself on precision of language and accuracy of reporting.
An understanding of Crichton's Gell-Mann amnesia effect is slowly starting to sink in, though, to at least one former subscriber.
Every time I encountered the word while reading it was like a cache miss for my brain...
This is bad because JS is not necessary, and it is also not necessary to write in lower-case in the first place! I know this because I do a similar thing on gwern.net: in addition to manually-specified smallcaps formatting, a rewrite pass while compiling automatically annotates any >=3-letter acronym. However, I do it purely via CSS, and I also don't need to lowercase anything. How? Because a good font like Source Serif Pro will include a special feature which will smallcaps just capital letters: 'c2sc' in `font-feature-settings` CSS. So to do smallcaps correctly, of regular text & uppercase, you have 2 CSS classes: "smallcaps" and "smallcaps-auto". "smallcaps" gets the normal 'smcp' font feature and operates the usual way, lowercase gets smallcaps, uppercase is left alone. "smallcaps-auto" is used by the acronym rewrite pass, and it does ''smcp', 'c2sc'' instead, so "AI" does indeed get smallcaps.
This way, I need 0 JS, I don't need to write everything in lowercase, copy-paste works perfectly, I don't need to manually annotate acronyms unless I want to, and everything Just Works for every reader.
(I don't, however, smallcaps two-letter things like "AI". That's just silly looking.)
predicts that artificial intelligence (<small>AI)</small> will add $16trn to the global economy by 2030
<small>, however, does not do lowercasing. All it does is make the font smaller. 'AI' should be caps regardless of whether it is wrapped in <small> or not. That implies that something in the CSS or HTML is overloading <small> to make it lowercase, so it can be correctly transformed to uppercase smallcaps for the reasons I explain above, which it assumes will be done by the later JS (which however doesn't run under NoScript). Using <small> just makes it an even uglier unnecessary hack...
We're building the tech. Right now. The author does not even realise the immeasurable potential that general purpose function approximators are having right now across all domains. The applications are not only limitless but they're being realized right now and it's insulting to have my work dismissed so shallowly by someone who speaks with such authority.
There's far more to do with ML and AI than self driving cars and shitty ad recommendations. It's just that the people who live at the intersection of mathematics, programming, and third domains are rare; give us some time. Even $16T in value by 2030 is 2-4 US GDPs from something which basically did not exist 5 years ago.
I would expect that's the issue for most people who are interested in the subject.
Many people feel that ML has so far only produced « tricks », but still doesn’t show any sign of « understanding » anything. As in, provide meaning. It may be unfair, but i think that at this point people would be more impressed by a program « smiling » at a good joke than by something able to process millions of a positions per second, or « learn » good moves by playing billions of games against itself.
Sure, there are some issues and most of them still fall somewhere in the uncanny valley.. but we are just starting to fully exploit this technology. AI that people don't tangibly see but make a giant impact in their lives is for example the Facebook algorithms that decide what shows up on your feed.
When a large subset of the population gets majority of their news from Facbeook, Facebook has effectively created a mechanical system that decides what a large chunk of the population is aware of. I don't think people fully realize what effect that has had on our entire society, including elections. The future will only see more of this type of thing.
a) Good general-purpose automatic speech recognition, which was an unattainable holy grail for DECADES. Previously, you would have to record a lot of your own voice to train the system for you in particular before it was at all reliable. Now it basically just works.
b) Making good use of the results of (a). Your examples are clearly in this category. This is ALSO a very hard problem; voice interfaces are basically brand new creatures, and I expect we'll be seeing 'best practices' form up for a while yet.
Nonetheless, it seems like progress on that front has been made incredibly quickly. Sure, Siri might not always "understand" what you're asking but the ML is able to very accurately transcribe your voice to text, something not really possible a few decades ago. Image classifiers produce misclassifications, but so do humans - it is a very alien technology to us, so we view some of the machine's misclassifications as absurd, but at the same time the machine might view some of our misclassifications as obviously off-base as well.
None of what you said seems to explain the consistent negativity on HN to what has really been a transformative technology in a lot of ways.
We have seen huge progress in a number of other fields as well. Anecdotally, voice recognition has definitely gotten way better as well.
This will certainly produce many great specific solutions for specific problems, but generic problems like driving a car are most probably not solvable without an internal world model.
I'm one of the naysayers and have a background in educational science, I'm constantly baffled why AI research seemingly never looks at theories of human learning (though it may very well be that I haven't looked closely enough!). What essentially all AI approaches do is modelling associative learning (https://en.wikipedia.org/wiki/Learning#Associative_learning), but employing more and more processing power for training. This is akin to having a fruit fly brain and copy-pasting that over and over again, in the hope somehow a higher order will organize from that by itself. But most things humans reason about are not learned this way, but rather by infering meaning from things and situations, i.e. one learns how a steering wheel works not through hundreds and hundreds of trial and error cases, but through single Aha! moments in which ad-hoc generated mental models (concepts) are validated in the environment. And GPT-3 has no knowledge organized in a hierarchical system of concepts, just as ELIZA didn't have one.
[1] - https://en.wikipedia.org/wiki/AlphaGo_Zero [2] - https://bair.berkeley.edu/blog/2019/03/21/tactile/
This isn't how a child learns to walk at all: A child a priori has the concept of walking, the concept of self, the concept of movement in space, the concept of willing to walk etc. - it just doesn't have the motoric control. The small part of training motoric control through repeated trial and error is indeed similar to what unsupervised learning models, but the important part is missing.
What are you referring to here?
This doesn't seem to imply that no learning is going on or that there is some sort of simple process. If I ask you to say whether something is a cat or a dog, you are essentially functioning as a discriminator there - but there is still a lot of process that goes on behind that.
This was just one particularly ironic example of what I'm seeing with ML - we saw this level of accuracy in classification years ago - where is the enormous progress ? There have been pockets where ML shows it's applicable with encouraging initial results but after that it seems to me that we suck at perfecting those system and the progress has been incremental at best.
The only exception I can see is stuff like self-play where the system can generate insane amount of training data.
No offense to Wolfram, but they really are hardly at the cutting edge of ML research nonetheless.
Maybe because there is a larger fraction of people who know or see how the sausages are made.
There are cool things, but is not magical nor transformative. At least not yet.
After several decades and a much-hyped last few years we have fake cleverness - impressively so in both cases - but nothing more.
Why should I care if my fridge is fake clever or real clever?
For your fridge that's an annoyance, but when used for more serious applications the ramifications can be large. Example: https://www.technologyreview.com/2019/01/21/137783/algorithm... – there's much more where that came from.
ML works by taking a huge amount of data and finding patterns and is pretty much incapable of judging individual cases. We have ugly words for humans who judge people based on generalisations instead of judging the individual...
Computers and algorithms are devoid of empathy and humanity, I am extremely apprehensive in using them to make any sort of judgement call, especially if the calculation is inscrutable. Humans may not be perfect, but at least they're ... human.
ML is an interesting tool which can be used for many things, but right now it's also being misapplied for many things. I think a lot of opposition comes from that, rather than your fridge or whatnot.
I mean, really? In the most pessimistic evaluation of ML research, we still know which techniques and paradigms won't work for AGI. That's not zero progress. Nobody is expecting this to happen overnight.
No evidence - as always. No progress.
Of course, if you - or anybody else - can show evidence of a genuine advance towards AGI, rather than breathless hype and vague assertions based on the impressive but irrelevant narrow-domain "AI" expertise of Alpha-x and the like, I (and many others) would love to read it.
We've been waiting since at least Alan Turing.
Perhaps we do, but if we don't, then the continued lack of progress is in fact evidence of our ineptitude and inefficiency in the face of an embarrassment of riches in terms of tools and power.
It's kind of like a having a searchlight that is more and more powerful and still not being able to find the object in the darkness we are looking for because we have given it to a blind man.
Plenty of practitioners ready to impress us with buzzwords, empty assurances and paper after paper on the brilliance of obscure statistical formulae and their money pit implementation in server farms.
But all this power and cliquey self congratulation is just noise drowning out a very embarrassing truth:
We don't have any new ideas.
http://www.overcomingbias.com/2017/03/better-babblers.html
A scarily large amount of human speech is essentially word prediction, especially in cases where someone wants to seem impressive without having actually done the work. We're all familiar with the problem of people "bullshitting" about technical topics, especially in the business realm where there are lots of people with no technical background who want to learn and have authoritative opinions on technology, like in this article. The GPT models are quite capable of producing this kind of "sounds legit" speech. And if that isn't a form of intelligence, then what is?
Tic Tac Toe, Chess, Image Classifying, Translation, Go, Sentence Construction, etc.; We are creating phenomenally impressive calculating worm-equivalents, but nothing human-level, or even remotely so.
It's pretty fascinating that nobody (AFAIK) really predicted that, except perhaps Isaac Asimov. Sci-fi normally presents AI as either superintelligent or non-existent (just basic voice commands). I don't think I've seen one which presents it as an AI ecology of idiocracy.
The GPT models are essentially massive databases along with the use of probability models for how words are connected. Do you think that means intelligence? Can it generate new knowledge of things outside its database? Can it generate novel theories for things outside its understanding?
The GPT models often generate streams of text which can found word for word in its database, i.e. it's not really generating anything new as just mimicking. When a parrot repeats a human, it doesn't understand what the words mean, it's just making sounds.
Same for ML powered solutions to data management - I don't want to say enough to identify anything.
My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans.
And for perspective, AlphaGo was what...4 years ago? Just look at the growth of compute available cheaply in the cloud since then! These next few decades are going going to see a phenomenal acceleration that's already starting...assuming we don't hit some unforseen limit or block. But even with what we've discovered now, we have more than enough in the way of new tools to refine across industry and society for decades.
Also complete novice so be merciful if I’m off base.
It would be like trying to understand gravity by throwing thousands of rocks, measuring the motions, and build an approximation on a huge matrix, without ever being able to get the « real » netwonian formula.
Am i correct ?
On the other hand, the difference between a team of people and a particular person with mens rea, that's not unique to AI either.
This isn't true.
Intelligence might be so messy that the only way to get to it is to grow it organically, which to me seems to be happening in research. People are trying a bunch of stuff based on intuitions for what may work, and post hoc adding some theoretical justification for why it's working. Human intelligence didn't emerge from some grand theory of intelligence either.
How would we know the difference between a problem that was interesting to solve but was hard versus a problem that just needed Google's money to solve?
Besides, since when was cloud compute was getting cheaper? I'm pretty sure, considering how increasingly profitable it is despite the "discounts," cloud compute has gotten more expensive, in the sense of increasing margins for the seller.
I guess we'll all find out what OpenAI does with its Azure credits.
How seriously can we take that phrase? Please mathematically model the Covid-19 pandemic's next 6 months. The epidemiology community dropped the ball and you can hardly do worse. Take the "Covid Challenge!"
mtgp1000 says>"These next few decades are going going to see a phenomenal acceleration that's already starting ...assuming we don't hit some unforseen[sic] limit or block."< -
Nobody can predict next year's economy and certainly nothing beyond 5 years! You're predicting for the "next few decades"?
mtgp1000 says*>"But even with what we've discovered now, we have more than enough in the way of new tools to refine across industry and society for decades."
Spoken like a true non-English marketeer!
This is one example of where things might be heading: https://science.sciencemag.org/content/350/6266/1332
See e.g. pyro.ai is already working on non-trivial datasets.
Ultimately, I'm convinced it's a hard compilers / static analysis / abstract interpretation problem. That requires lots of resources and a decade or two of work.
Nobody else seems able to, especially the AI ("more power and money needed! we're just around the corner!") blowhards.
Why AI for that, it's vision AI to know when fruit is ripe or vegetables are ready for harvest. Then hand eye coordination to not bruise the fruit/vegetables and adjust force/sensitivity as the equipment wears down.
Scaling that will put likely many workers out and transform the business of harvesting. What I think is most plausible is one large manufacturer will consolidate designs and rent the equipment to do the harvesting much like the contract labor used now, but at much lower prices than humans could do.
People have been trying to design machines to pick fruit, even in toy scenarios, for decades, and we have little to show for it. I don’t think that image processing is the bottleneck here, it’s literally the mechanism as far as I understand.
I’m skeptical that any mechanical equipment will ever be able to do these tasks as cheaply, quickly, and efficiently as a human in our lifetimes.
Definitely easier than fruit, but we've made so much progress.
No way. Everybody wants the machines at the same time. You're going to incur the capital cost of buying/manufacturing the machines, the cost to store and maintain them for 50 weeks out of the year, and then rent them out at a profit for 2 weeks? No, the farmers will just keep hiring human labor.
If you can solve this problem, you can sell a lot of machines for a lot of money for a short few years. After that, they'll be a commodity and be dominated by all the other capital-intensive manufacturers that can run a factory better than you can.
Machine generated subtitles on YouTube is another example. I find those useful even in my native tongue, especially when watching at 2x speed.
If you make purchases on the web, there is a good chance that machine learning is behind the fraud detection that you don't even know is taking place.
If you're looking for interesting startups/projects-not-from-big-tech:
- Glisten.ai (https://www.glisten.ai/). Recent YC startup, uses a combination of different models to parse product information (actually a huge manual problem in retail/ecommerce) and expose it as an api.
- Wildlife Protection Solutions - Recently deployed a model that can automatically detect poachers in nature preserves. Detects twice as many poachers as previous monitoring solutions.
- Ezra.ai - Uses models to search MRIs for cancers, operational in a few different US cities.
- AI Dungeon - A text adventure game built on GPT-2 (now GPT-3). Super fun, if a little silly.
Now, those are just a handful of smaller companies whose core products are ML. There are a ton of financial institutions using ML for fraud detection, real estate platforms like Reonomy that use a variety of models for evaluating investments, and security companies using ML.
But of course, the obvious answer to this question is "Every popular app you use incorporates ML."
Gmail: Smart Compose, spam filtering, etc.
Uber/Maps: ETA Prediction
Netflix/Spotify/all content platforms: Recommendation engines
Facebook/Instagram/Snap/image apps: A variety of models for recognizing faces, object tracking, etc.
People have this weird "Skynet or it's snake oil" paradigm they use to evaluate ML, ignoring the fact that production machine learning is more or less ubiquitous at this point.
So THAT's why the ETA given is always too short! If it's based on how often a typical driver makes it, and typical driver is a speeding asshole, then no wonder that the estimates are unrealistic for someone who actually drives under the speed limit. It's a shame that Google is actually normalizing assholiness.
Moreover, I'm not sure how speeding makes you an asshole.
They do worse than this; one of the articles in the section uses a bad Google translation in Yoruba, from 2018, and uses that to claim it “is therefore still baffled by the sorts of questions a toddler would find trivial”. I'm not sure Yoruba even used neural nets at that time!—‘massively multilingual’ training only really came about in 2019, and was productiviced June this year.
A disappointing show from The Economist, given how little effort it would have taken to check with the newest model.
While the accomplishments in the last 8 years have been impressive and applications of those techniques have and will continue to have impact in the real world I think that there are extremely big obstacles in the way towards something that would be truly transformative and which would put a lot of people out of work, which the $20 trillion industry estimates assume. Unfortunately I have seen no evidence that there are sufficiently good ideas in the field to bypass the upcoming roadblocks.
Deep learning's big success is its ability to attach a label to a complex signal -- an image or a sound. That's pattern recognition: speech in, speech out, and image recognition. These tasks were largely beyond computers until 2012 but something that a child or a squirrel does very well, so it's been long known that skills like these are not useful signs of intelligence or rising levels of cognition.
Other tasks DL does well are those that benefit from memorization of results form death-by-search and from vast amounts of RL simulation, like playing of board games and the transforming of patterns (images and sounds) in fun ways using GANs. But ever since Deep Blue's win over Kasparov using clever pattern matching of past chess games (by memorization), we've known that narrow skills like game play also are not useful signs of intelligence.
Yet pattern matching skills are 99% of what deep learning hath wrought. Yes, that's useful, but it's not really intelligent. It shows no signs of thinking / cognition aside from probabilistic association / clustering. So there's no reason to imagine that techniques like deep nets will take us all the way to thinking like a human. Today, because of DL, we're much better at pattern matching. But in terms of what's essential to cognition, pattern matching achieves surprisingly little if your real goal is to THINK.
As far as "thinking" tasks go, cognitive tasks like machine interpretation of intent in written text and machine translation between languages still suck, despite the impressive advances in semantic-surface associators like BERT and newer transformer-based NLP engines. To do more than answer basic questions about nouns and verbs, you need a model for deeper semantics and an understanding of logic and relations between actors. Until deep nets can model semantics that are not present in the test data, and employ logical inference, it can't be said to think, much less intelligently.
Many things have exponential growth - bacterial reproduction, compound interest, certain chemical reactions. It's important to understand that this does not automatically result in miraculous universal transformation, but must be considered in the context of the world we live in. A little humility is always in order.
Without the Alan Turings, John von Neumanns, Edgar Dijkstras, Donald Knuths, the researchers at XPARC and Bell Labs, etc., there would never have been a Steve Jobs. He does play a role, but not nearly one as big as all those people that came before him.
Some people like to push for progress for progress's sake; others mostly to make money out of it (not saying this is bad!). The latter get more rich but it doesn't mean they're more important.
Is it too soon to tell that we start seeing the asymptote of the AI curve?
You haven't had to deal with end-users much have you? I've seen years of really excellent work (not my own) blithely dismissed with expressions such as "but what does it now?" or "why can't it do X [impossible thing]?"
People have been told AI is potentially going to take over from the human race. Unless that's in your pipeline, they're going to basically treat your work as eaten bread.
Which is precisely how it's supposed to be. Nobody is required to respect your work in progress. The world will either care or...care not.
I googled "Tim Cross" and he's reachable on Twitter: http://mediadirectory.economist.com/people/tim-cross/.
More generally, I don't understand gnashing of teeth when it is much easier to provide feedback to the author directly.
I think a big part of what is holding back many companies from making effective, genuine, real-world use of AI is that a significant majority of the individuals involved are bad at their jobs.
On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. In my view, this includes things that would traditionally be considered business decisions. The business is unwilling to transition from "working on the solution to the problem" to "working on the AI system that solves the problem" (assuming that they even understand what "the problem" is).
On the technology side, I see a massive oversupply of badly underqualified and generally ignorant "data scientists" and "ML engineers" who have just enough bootstrapped understanding and familiarity with the plethora of (absolutely fantastic) open-source tools to fake an entry into the area, but do not have the genuine depth of background that is needed to actually plan, design, and deliver a high quality solution.
Yes, I have met people without "credentialed" backgrounds who are very good indeed, and yes I have met forward-thinking business people, but the number of people who are in these categories is a tiny fraction of the number of people who think they are in these categories.
And that's why businesses are getting less keen on AI.
EDIT: Grammar.
Maybe acknwledging this would mean losing their jobs. Buisness people have mortgages and children and they probably don't like the idea of having their job replaced by a robot. It is important to remember that the admin/money-side people are not playing the game that they tell the operations people they are playing. They want to keep their jobs, accrue power and money, they don't really care about AI or sales, those are just tools for their own game.
Agree totally with the latter part though. In vast swathes of the business world, technology is not perceived as a way to gain a competitive advantage but rather something between an interesting novelty and an annoyance. The problem is there's a cultural expectation that you be "innovative" which tends to be treated as a synonym for deployment of computer-related technology upgrades (although of course it doesn't have to mean this).
But to people who have no intrinsic interest in technology, there's no real way for them to actually innovate. They just don't know where to start. So they latch on to trends they read about in the Economist or NYT on a truly massive scale. They spend days or weeks making PowerPoints about the transformative potential of blockchain, IoT or AI. They take a few programmers who are kicking around but seem bored and allocate them to an "innovation lab" where they putter around making prototypes and having fun but never impacting the business in any way.
This ticks the box labelled "we are innovative" without requiring anyone to think too hard, learn anything new or take any risks, all things that the generic unskilled graduate managerial class in our society hate doing. And of course technology upgrades are risky. When run by non-technical people they tend to go wrong in spectacularly expensive and mysterious ways, so a lot of business executives would rather pull their own teeth out than plan a major IT upgrade. This is partly why tech firms exist as a concept.
As for AI, you can't replace most of these jobs with AI because their outputs are undefined to begin with. And to be frank, my observations over the years has been that a lot of these jobs appear to be undefined in order to enable a form of cultural stuffing. If you're a non-technical executive you wouldn't want your nice business to end up filled with geeks playing board games and aggressively proving other people wrong now, would you? Better keep them diluted by hiring lots of PMs, "operations specialists", "customer analysts", "business insight teams" and so on. That way you can meet your diversity numbers and be surrounded by like-minded people.
I'm really good at understanding technology and talking to people though, so all the true experts who I know enough to talk shop with love me for keeping management off their backs.
I met someone who dedicates their life to using machine learning to replace/aid/automate pathologists 6+ hour days searching for cancer tumors in lungs. They have been at it for 5 years. There is an insane amount of approvals, red tape, knowing the right people, convincing the hospital to use it - all tasks not related to the tech actually working. Building it is the easy part. They were on the way there, working in the research section of a hospital. But they are still more than 5 years away from me being able to walk into a hospital and get my lungs scanned for tumours.
I personally can't wait for these general purpose function approximators to make all our lives better.
It's not that the technology isn't there. It's not that the technology can't be technically applied successfully. It's that convincing systems of people to change is way harder than we might think.
So what may happen is that the way that AI really gets integrated is by actually replacing human beings who largely die off.
It already has—look at how much companies try to shunt you through a robot before you speak to a human despite their being mostly useless compared to humans. It’s clear this revolution we both see happening will come with horribly kafkaesque failures that will take 30 years to “discover” so we in the tech industry can “learn our lesson”.
Hell you can already see this with the use of fraud detection rather than implementing basic secure transactions on credit cards. The direction we’re headed in is clearer than ever: technology does not lead to better products but rather larger scale consumption of lower quality.
You cannot imagine how many times I've heard this line over my career. What it actually means is "we do not yet understand the limitations of this technology".
If I had a dollar for every time I heard somebody talking about unlimited growth in the dot-com era, I'd have a lot more money than my options ended up being worth.
In recent times, due to a relative dearth of high yield investment opportunities, there is a lot of money with nowhere to go. Some believe much of it is "dumb" money. Overfunded startups are one possible symptom. Heavily-funded "AI" may be another.
This is before we even consider the moral and ethical issues of using "AI" as a substitute for human judgment. It may "work" but that in itself may not be an adequate justification for its use.
The space program?
The communist countries seen a huge misallocation of funds into giant projects that didn't make sense (e.g. huge steel mills in Poland that were always losing money).
What exactly is your work? In research, including AI research, it is ordinary and expected for one's work to be subject to the most ruthless criticism. This is as it should be: only work that survives all criticism can really be expected to last.
If you are an AI researcher, you should be used to that by now and not take it personally.
Yeah, there's also shitty sentencing recommendations[1], new-age phrenology[2], and high-tech redlining[3].
I think your entire field needs to take a year off and take some ethics and philosophy courses before going any further. Otherwise we're all going to end up much worse off.
[1] https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-...
[2] https://www.faception.com/
[3] https://www.fastcompany.com/90269688/high-tech-redlining-ai-...
So I think we don't want to hand over sentencing decisions to a neural network that nobody understands, but I also think careful use of (simpler) machine learning can still improve a lot of our decision-making. The question is how much these decisions improve over what we have now. There's not exactly a surfeit of wise, highly trained ethicists who are happy to make consequential decisions all day. Many human decision-makers are quite flawed too, they just get to hide behind the opacity of being a human rather than an algorithm.
To that end, a whole "fair machine learning" field has sprung up over the past few years to study this. There are like a dozen papers in the area at NeurIPS and ICML every year. There's some progress.
We can use machines to guide the humans, finding flaws or biases, recommending further analysis, and in a lot of other ways while still keeping the decisions made by humans.
Should we have?
Would an ethics course and professional society eliminate child porn online? Of course not. Would it have shaped and possibly tempered social media giants into something less toxic? Maybe.
I'm not convinced ethics course requirements do much for a moral living. (And a peculiarly large fraction of those curricula often seems to be concerned with agonizing over whether to push various small or large groups of people on to railway tracks to be run over, rather than trying to stop the frigging train)
The examples you link don't seem very difficult to assess if they're approached with high priority placed on not causing trouble for the innocent. Also, presumably the people purchasing and deploying systems like that have usually taken plenty of formal education about ethics (and possibly philosophy), and yet there seems to be plenty of demand. Why is that you reckon?
What's wrong with python?
There is a famous word for latter - 'webshit'. The former doesn't have such a title. But there is an obscure one originating from a Slashdot comment IIRC something like: 'you are one of those data science idiots from python schools eh?'
Edit: I didn't mean disrespect to those doing meaningful research. But that's not what most of things sold as 'ML' are.
For weeks after that my recommendations were filled with bullshit such as "PROOF ALIENS BUILT THE PYRAMIDS!" and such.
So yeah, maybe those "very complex recommendation algorithm based on deep learning/statistics" is perhaps not always such a great idea. In this particular case, it's just a mere annoyance for me, but imagine a 13-year old watching a few genuine documentary videos on Egypt and then seeing this bullshit; they don't have the capacity I have to see it's bullshit.
And imagine if it was on a more serious topic than who built the pyramids...
If I were to ask a YouTube engineer "why did I get this recommendation specifically?" then the answer would probably be "dunno".
An additional issue is that the YouTube of yesteryear was much better in browsing random videos. Now everything is based on what I've watched before, instead of just "give me a list of science videos" or whatnot. This is also an issue I have with Netflix (or rather, had, since I no longer have an account).
It seems to me that inscrutable mindless AI learning has a part in the spread of misinformation and bullshit. I'm not sure how large that part is, but I suspect it's significant. I'm hesitant of the total value in these cases, regardless of what it may do for the bottom line in terms of revenue.
[1]: I'll just drop the link to it here in case anyone's interested: https://www.youtube.com/watch?v=hnsNwwwHm2I
And yet, it's harder to find anything to watch than it was when they'd just let you browse by genre.
Mostly boring statistical analysis wrapped in a very power inefficient way.
It's quite the exercise in goalpost moving.
To be really fair, I think you need to more precisely define what you mean by "reason". It absolutely CAN reason by some measures.