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I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected.

I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. 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.

MiroF 6y ago
Absolutely. To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. The evidence of enormous progress seems pretty obvious to me.
- hopfscotch7 6y ago
  Yeah, we sure are good at solving the iris data set nowadays.
- mellosouls 6y ago
  There have been plenty of articles about progress in machine learning, but nothing in genuine (human-like) intelligence. I would expect that's the issue for most people who are interested in the subject.
- mehmetoguzderin 6y ago
  I think ML naysaying stems mostly from extravagant claims of pop-sci articles around papers or of the authors themselves. It is always good to have some amount of people that point out that the models aren't some cyborg superheroes or has to be a step towards AGI but be more practical.
- bsaul 6y ago
  Siri still isn’t able to understand « do NOT set the alarm to 3pm », and many image classifier produces aberrations that no human would ever commit. 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.
  - tikititaki 6y ago
    While I can't speak for Siri, Google's voice recognition continues to surprise and even frighten me. And if you go online and look up deepfakes or AI generated voice of famous people, it also is freakishly accurate. 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.
    - slykar 6y ago
      I'm always disappointed with Google Asistant. When I say "navigate back home" it tries to route me to a company called "Home". Great job. I've included a hint "back". I've never been to a company called "Home". I'm after work. This is a fun example, but I never get good results. I say "weather" and it shows me the weather, just in a different city. FFS. Maybe in the next 5 years I'll be finally able to do something using voice commands. Right now I just want to smash my phone.
      - tikititaki 6y ago
        Fair enough. Usually when I use "Navigate to the nearest gas station" or "navigate home" or "set an alarm for 8am on Tuesday" it all works perfectly for me, but I concede I don't use the feature that often so perhaps I've just been using it for well-adjusted commands.
- criddell 6y ago
  I wonder if it's because of the name machine learning? It seems a lot of applications boil down to some type of discriminator or pattern matcher.
  - MiroF 6y ago
    > It seems a lot of applications boil down to some type of discriminator or pattern matcher 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.
- rubber_duck 6y ago
  I recently watched a podcast with Stephen Wolfram (few months old) where he runs a photo of himself through Wolfram Alpha "ImageIdentify" and it classifies him as a plunger with 50% probability and 8% probability it's a human [1]. He then goes on saying how he's working on systems that would allow a self driving system decide what to do based on if the collision detected is a human or inanimate object, etc. 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. [1] https://youtu.be/ez773teNFYA?t=8420 https://youtu.be/ez773teNFYA?t=8420
  - MiroF 6y ago
    Classification accuracy is crazy good, I'm surprised that an obvious photo of him would be viewed as a plunger unless it's an adversarial example. No offense to Wolfram, but they really are hardly at the cutting edge of ML research nonetheless.
- pmezard 6y ago
  > To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. 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.
  - MiroF 6y ago
    I work in “making the sausage” - deep learning techniques have already completely transformed my field and are transforming many others. Most of the people I see naysaying don’t appear to causally work in the field, because their critiques are not the same as the ones people in the field are using.
    - pmezard 6y ago
      Can you give a concrete example of deep-learning techniques which have transformed a field? Something not theoretical or confined in a lab? Something which may have changed my life?
      - theferalrobot 6y ago
        Not OP but I can speak with a small example from my previous field - coral reef ecology. It used to be standard to collect video footage and hand annotate small portions of the footage to draw up some statistics about the reef progression. Today we can collect the footage and let ml annotate the entirety of it to get much more accurate and precise statistics about the progression/recession. Not only that but we can do that on an organism basis and also see do highly local analyses. This wasn’t possible 10 years ago. I think the applications for ML outside of consumer tech are far larger than within but everyone here seems focused on that for some reason.
mellosouls 6y ago
There has been next to ZERO progress towards genuine AGI despite a never-ending deluge of AI articles; that's normally the cause of scepticism. After several decades and a much-hyped last few years we have fake cleverness - impressively so in both cases - but nothing more.
- richk449 6y ago
  > 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?
  - mellosouls 6y ago
    You should care if the makers of your fridge are charging you a billion dollars to buy it because its "real clever".
  - Carpetsmoker 6y ago
    I think the problem is that in a long list of edge cases, it's neither fake clever nor real clever but just "dumb". This is, of course, always an issue with computers, but what makes ML so vexing is that it's inscrutable. 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/algorithms-criminal-justice-ai/ 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.
  - hyperbovine 6y ago
    Why do I want my fridge to be clever at all. Just keep things cold please.
ManuelKiessling 6y ago
Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?
- mtgp1000 6y ago
  Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. 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.
  - charliemil4 6y ago
    Your training set must be something unique to make those claims - very intrigued to know if you chose another closed system environment (I.e. any structured game with predefined scoring), or an open system with carefully selected labeling with multiple “scores.” Also complete novice so be merciful if I’m off base.
  - bsaul 6y ago
    since you’re working on the field : i’m still under the impression that modern ML (neural network in particular) are not producing any science, nor are they producing any level of understanding of the phenomenon it’s trying to model. And that as such, we can’t provide a reliable estimate of the limitations of the produced model, other than by feeding it tons of inputs and measure the result. 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 ?
neals 6y ago
Honest curiosity: do you have some examples of interesting applications? Large and small?
- tchaffee 6y ago
  How about translation? Living in a foreign country I possibly find it useful more often than the average person. 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.
- calebkaiser 6y ago
  I work on a production ML platform, so I spend way too much time rabbit-holing on interesting looking projects. If you're looking for interesting startups/projects-not-from-big-tech: - Glisten.ai (https://www.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.
  - burntoutfire 6y ago
Veedrac 6y ago
> I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. 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.
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FiberBundle 6y ago
Well of course you say that, you're an ML researcher who likely went into the field expecting further steady progress comparable to what we saw between 2012 and 2016. If actual progress in the field would be slower than what is currently still expected by the majority of people this would have dramatic consequences for future research investment, which is why you see most ML researchers reinforcing the hype or at least not talking the hype down. 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.
- randcraw 6y ago
  I think a major problem in judging the advancement of AI is that its successes are not general purpose -- it works great for narrow tasks and not well for broad tasks that we humans do every day, especially those that require what we call "understanding" (as in modeling and employing semantics or relationships among objects like causal inference). 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.
xenocyon 6y ago
Your comment is a little hand-wavy and strongly worded ("revolution", "immeasurable", "limitless", "all domains"). 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.
- mindfulhack 6y ago
  Your analytical thinking misses the human element. To give an example: Steve Jobs didn't have humility, nor did he shy away from hyperbole. You would have had the same response to his words, but look at what he - literally, in the physical world - achieved. Human motivation - no matter how deluded - is not actually something not to dismiss. It has effects on others, which translates to action. The inputs are not all machines. At least not yet.
  - Tainnor 6y ago
    While not wanting to downplay the marketing and design genius of Steve Jobs who created a brand identity so strong that for a while, "smartphone" became synonymous with "Apple", Steve Jobs really stood on the shoulders of giants in terms of innovation. 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.
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stephc_int13 6y ago
The trick with so-called exponential growth is that it is almost always an S-curve in disguise. Is it too soon to tell that we start seeing the asymptote of the AI curve?
- mehh 6y ago
  Spot on!
tr352 6y ago
Perhaps basing our decisions on general purpose function approximators (whether forced or voluntarily) has not served us so well, ultimately. I think they play an important role in causing, or at least enabling, much of the trouble that we live in nowadays.
- MiroF 6y ago
  How?
scandox 6y ago
> it's insulting to have my work dismissed so shallowly 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.
memexy 6y ago
Is it not better to engage with the critics and correct their perception in that case? I'm sure the author would not mind a direct response that outlines where they went wrong and why. I googled "Tim Cross" and he's reachable on Twitter: http://mediadirectory.economist.com/people/tim-cross/ 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.
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laingc 6y ago
I completely agree with this. Let me add a non-US perspective that may surprise some people. 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.
- ImaCake 6y ago
  >On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. 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.
  - jasondigitized 6y ago
    “It is difficult to get a man to understand something, when his salary depends on his not understanding it.” - Upton Sinclair
abj 6y ago
There's value, the tech works, but applying it is surprisingly hard. 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.
- ilaksh 6y ago
  There is a strong chance that the technology to create digital beings will be available before human society is able to integrate and adjust to the current generation of AI. So what may happen is that the way that AI really gets integrated is by actually replacing human beings who largely die off.
  - solvorn 6y ago
    >posted by an AI I hope.
  - abj 6y ago
    That's a really interesting perspective and a good point. Reminds of "Age of Em". Interested to see what the effects would be of digit beings before integrating current generation of AI would be.
- levitatorius 6y ago
  I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial consequences for patient? The first point is especially for the lack of better word.. wishful. In the daily practice we are used to account for "things unexpected" - non-representative biopsies, parasites in tissue where tumor was suspected, foreign body reaction from previous operations, laboratory accidents (such as swapped paraffin blocks of two patients), and so on (the list is much longer). We deal with it. That ML can discern between 5 most common diagnoses is fine, but it is rather narrow problem to solve.
monadic2 6y ago
> ML is already starting to change society. 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.
wpietri 6y ago
> The applications are not only limitless 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.
pwdisswordfish2 6y ago
Are there examples in history where things requiring great time and effort were built then abandoned? 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.
- mr_toad 6y ago
  > Are there examples in history where things requiring great time and effort were built then abandoned? The space program?
  - pwdisswordfish2 6y ago
    In the US, yeah. Great example. SpaceX is here to get you back on track. /s
- burntoutfire 6y ago
  > Are there examples in history where things requiring great time and effort were built then abandoned? 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).
YeGoblynQueenne 6y ago
>> 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. 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.
AlexandrB 6y ago
> There's far more to do with ML and AI than self driving cars and shitty ad recommendations. 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-sentencing-bias.html https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-... [2] https://www.faception.com/ https://www.faception.com/ [3] https://www.fastcompany.com/90269688/high-tech-redlining-ai-is-quietly-upgrading-institutional-racism https://www.fastcompany.com/90269688/high-tech-redlining-ai-...
- deleted 6y ago
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- papeda 6y ago
  I agree that plenty of people have been too cavalier about slapping together some models, predicting something, and calling it a day. On the other hand, it's not like fair sentencing or fair loan recommendation is a solved problem for humans either. There is evidence that, when carefully designed, algorithms can produce more equitable outcomes than humans, for example when deciding who and how to release on bail [1]. 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. [1] https://www.nber.org/papers/w23180 https://www.nber.org/papers/w23180
  - marcosdumay 6y ago
    Besides, the options are not restricted to "a machine decides everything" and "a human decides everything". 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.
entha_saava 6y ago
It sounds like some buzzword speak. Most things that are heralded as ML are nothing but data science idiots from python schools applying some basic math transformations and overselling them.
- blaser-waffle 6y ago
  I was going to say "most ML these days nothing but chains of if-else statements" but yeah same idea. What's wrong with python?
  - entha_saava 6y ago
    Nothing wrong with python or JS. But there are many things wrong with bootcamp / workshop people who come out just learning Python with some ML library or JS with some web framework. They don't know even basics of CS and arguably produce bad, inefficient code. 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.
tastyminerals 6y ago
Can you be a little bit more concrete about the tech you are building right now that is not possible to build using classic ML approaches? You see, I am actually going to work thinking about how should we reduce the model size and still keep it within reasonable accuracy bounds so it would not be worse than a rule-based mixed with classic ML system that we use. And here I learn about immesuarable potential, insults and billions of dollars.
malux85 6y ago
Do you see any irony in your comment complaining about CEOs over promising and then you go on to describe “immeasurable” gains and “limitless” technology?
jcdentonunatco 6y ago
Got bad news for you man. 16T in value doesn't come from ML. It comes from inflation and printing money out of thin air. The world has limited resources at the end of the day. When one industry does well, it's at the cost of a different industry.
- beckingz 6y ago
  We haven't even mined the moon yet. Plenty of gains to be made.

MiroF 6y ago

Absolutely. To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. The evidence of enormous progress seems pretty obvious to me.

hopfscotch7 6y ago
Yeah, we sure are good at solving the iris data set nowadays.
mellosouls 6y ago
There have been plenty of articles about progress in machine learning, but nothing in genuine (human-like) intelligence. I would expect that's the issue for most people who are interested in the subject.
mehmetoguzderin 6y ago
I think ML naysaying stems mostly from extravagant claims of pop-sci articles around papers or of the authors themselves. It is always good to have some amount of people that point out that the models aren't some cyborg superheroes or has to be a step towards AGI but be more practical.
bsaul 6y ago
Siri still isn’t able to understand « do NOT set the alarm to 3pm », and many image classifier produces aberrations that no human would ever commit. 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.
- tikititaki 6y ago
  While I can't speak for Siri, Google's voice recognition continues to surprise and even frighten me. And if you go online and look up deepfakes or AI generated voice of famous people, it also is freakishly accurate. 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.
  - slykar 6y ago
    I'm always disappointed with Google Asistant. When I say "navigate back home" it tries to route me to a company called "Home". Great job. I've included a hint "back". I've never been to a company called "Home". I'm after work. This is a fun example, but I never get good results. I say "weather" and it shows me the weather, just in a different city. FFS. Maybe in the next 5 years I'll be finally able to do something using voice commands. Right now I just want to smash my phone.
    - tikititaki 6y ago
      Fair enough. Usually when I use "Navigate to the nearest gas station" or "navigate home" or "set an alarm for 8am on Tuesday" it all works perfectly for me, but I concede I don't use the feature that often so perhaps I've just been using it for well-adjusted commands.
criddell 6y ago
I wonder if it's because of the name machine learning? It seems a lot of applications boil down to some type of discriminator or pattern matcher.
- MiroF 6y ago
  > It seems a lot of applications boil down to some type of discriminator or pattern matcher 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.
rubber_duck 6y ago
I recently watched a podcast with Stephen Wolfram (few months old) where he runs a photo of himself through Wolfram Alpha "ImageIdentify" and it classifies him as a plunger with 50% probability and 8% probability it's a human [1]. He then goes on saying how he's working on systems that would allow a self driving system decide what to do based on if the collision detected is a human or inanimate object, etc. 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. [1] https://youtu.be/ez773teNFYA?t=8420 https://youtu.be/ez773teNFYA?t=8420
- MiroF 6y ago
  Classification accuracy is crazy good, I'm surprised that an obvious photo of him would be viewed as a plunger unless it's an adversarial example. No offense to Wolfram, but they really are hardly at the cutting edge of ML research nonetheless.
pmezard 6y ago
> To be perfectly honest, it surprises me the extent to which ML naysaying seems to be popular on HN. 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.
- MiroF 6y ago
  I work in “making the sausage” - deep learning techniques have already completely transformed my field and are transforming many others. Most of the people I see naysaying don’t appear to causally work in the field, because their critiques are not the same as the ones people in the field are using.
  - pmezard 6y ago
    Can you give a concrete example of deep-learning techniques which have transformed a field? Something not theoretical or confined in a lab? Something which may have changed my life?
    - theferalrobot 6y ago
      Not OP but I can speak with a small example from my previous field - coral reef ecology. It used to be standard to collect video footage and hand annotate small portions of the footage to draw up some statistics about the reef progression. Today we can collect the footage and let ml annotate the entirety of it to get much more accurate and precise statistics about the progression/recession. Not only that but we can do that on an organism basis and also see do highly local analyses. This wasn’t possible 10 years ago. I think the applications for ML outside of consumer tech are far larger than within but everyone here seems focused on that for some reason.

mellosouls 6y ago

There has been next to ZERO progress towards genuine AGI despite a never-ending deluge of AI articles; that's normally the cause of scepticism. After several decades and a much-hyped last few years we have fake cleverness - impressively so in both cases - but nothing more.

richk449 6y ago
> 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?
- mellosouls 6y ago
  You should care if the makers of your fridge are charging you a billion dollars to buy it because its "real clever".
- Carpetsmoker 6y ago
  I think the problem is that in a long list of edge cases, it's neither fake clever nor real clever but just "dumb". This is, of course, always an issue with computers, but what makes ML so vexing is that it's inscrutable. 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/algorithms-criminal-justice-ai/ 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.
- hyperbovine 6y ago
  Why do I want my fridge to be clever at all. Just keep things cold please.

ManuelKiessling 6y ago

Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?

mtgp1000 6y ago
Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. 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.
- charliemil4 6y ago
  Your training set must be something unique to make those claims - very intrigued to know if you chose another closed system environment (I.e. any structured game with predefined scoring), or an open system with carefully selected labeling with multiple “scores.” Also complete novice so be merciful if I’m off base.
- bsaul 6y ago
  since you’re working on the field : i’m still under the impression that modern ML (neural network in particular) are not producing any science, nor are they producing any level of understanding of the phenomenon it’s trying to model. And that as such, we can’t provide a reliable estimate of the limitations of the produced model, other than by feeding it tons of inputs and measure the result. 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 ?

neals 6y ago

Honest curiosity: do you have some examples of interesting applications? Large and small?

tchaffee 6y ago
How about translation? Living in a foreign country I possibly find it useful more often than the average person. 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.
calebkaiser 6y ago
I work on a production ML platform, so I spend way too much time rabbit-holing on interesting looking projects. If you're looking for interesting startups/projects-not-from-big-tech: - Glisten.ai (https://www.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.
- burntoutfire 6y ago

Veedrac 6y ago

> I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. 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.

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FiberBundle 6y ago

Well of course you say that, you're an ML researcher who likely went into the field expecting further steady progress comparable to what we saw between 2012 and 2016. If actual progress in the field would be slower than what is currently still expected by the majority of people this would have dramatic consequences for future research investment, which is why you see most ML researchers reinforcing the hype or at least not talking the hype down. 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.

randcraw 6y ago
I think a major problem in judging the advancement of AI is that its successes are not general purpose -- it works great for narrow tasks and not well for broad tasks that we humans do every day, especially those that require what we call "understanding" (as in modeling and employing semantics or relationships among objects like causal inference). 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.

xenocyon 6y ago

Your comment is a little hand-wavy and strongly worded ("revolution", "immeasurable", "limitless", "all domains"). 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.

mindfulhack 6y ago
Your analytical thinking misses the human element. To give an example: Steve Jobs didn't have humility, nor did he shy away from hyperbole. You would have had the same response to his words, but look at what he - literally, in the physical world - achieved. Human motivation - no matter how deluded - is not actually something not to dismiss. It has effects on others, which translates to action. The inputs are not all machines. At least not yet.
- Tainnor 6y ago
  While not wanting to downplay the marketing and design genius of Steve Jobs who created a brand identity so strong that for a while, "smartphone" became synonymous with "Apple", Steve Jobs really stood on the shoulders of giants in terms of innovation. 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.

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stephc_int13 6y ago

The trick with so-called exponential growth is that it is almost always an S-curve in disguise. Is it too soon to tell that we start seeing the asymptote of the AI curve?

mehh 6y ago
Spot on!

tr352 6y ago

Perhaps basing our decisions on general purpose function approximators (whether forced or voluntarily) has not served us so well, ultimately. I think they play an important role in causing, or at least enabling, much of the trouble that we live in nowadays.

MiroF 6y ago
How?

scandox 6y ago

> it's insulting to have my work dismissed so shallowly 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.

memexy 6y ago

Is it not better to engage with the critics and correct their perception in that case? I'm sure the author would not mind a direct response that outlines where they went wrong and why. I googled "Tim Cross" and he's reachable on Twitter: http://mediadirectory.economist.com/people/tim-cross/ 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.

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laingc 6y ago

I completely agree with this. Let me add a non-US perspective that may surprise some people. 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.

ImaCake 6y ago
>On the business side, there is an widespread unwillingness to acknowledge that technical people may be better placed to make decisions than businesspeople. 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.
- jasondigitized 6y ago
  “It is difficult to get a man to understand something, when his salary depends on his not understanding it.” - Upton Sinclair

abj 6y ago

There's value, the tech works, but applying it is surprisingly hard. 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.

ilaksh 6y ago
There is a strong chance that the technology to create digital beings will be available before human society is able to integrate and adjust to the current generation of AI. So what may happen is that the way that AI really gets integrated is by actually replacing human beings who largely die off.
- solvorn 6y ago
  >posted by an AI I hope.
- abj 6y ago
  That's a really interesting perspective and a good point. Reminds of "Age of Em". Interested to see what the effects would be of digit beings before integrating current generation of AI would be.
levitatorius 6y ago
I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial consequences for patient? The first point is especially for the lack of better word.. wishful. In the daily practice we are used to account for "things unexpected" - non-representative biopsies, parasites in tissue where tumor was suspected, foreign body reaction from previous operations, laboratory accidents (such as swapped paraffin blocks of two patients), and so on (the list is much longer). We deal with it. That ML can discern between 5 most common diagnoses is fine, but it is rather narrow problem to solve.

monadic2 6y ago

> ML is already starting to change society. 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.

wpietri 6y ago

> The applications are not only limitless 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.

pwdisswordfish2 6y ago

Are there examples in history where things requiring great time and effort were built then abandoned? 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.

mr_toad 6y ago
> Are there examples in history where things requiring great time and effort were built then abandoned? The space program?
- pwdisswordfish2 6y ago
  In the US, yeah. Great example. SpaceX is here to get you back on track. /s
burntoutfire 6y ago
> Are there examples in history where things requiring great time and effort were built then abandoned? 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).

YeGoblynQueenne 6y ago

>> 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. 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.

AlexandrB 6y ago

> There's far more to do with ML and AI than self driving cars and shitty ad recommendations. 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-sentencing-bias.html https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-... [2] https://www.faception.com/ https://www.faception.com/ [3] https://www.fastcompany.com/90269688/high-tech-redlining-ai-is-quietly-upgrading-institutional-racism https://www.fastcompany.com/90269688/high-tech-redlining-ai-...

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papeda 6y ago
I agree that plenty of people have been too cavalier about slapping together some models, predicting something, and calling it a day. On the other hand, it's not like fair sentencing or fair loan recommendation is a solved problem for humans either. There is evidence that, when carefully designed, algorithms can produce more equitable outcomes than humans, for example when deciding who and how to release on bail [1]. 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. [1] https://www.nber.org/papers/w23180 https://www.nber.org/papers/w23180
- marcosdumay 6y ago
  Besides, the options are not restricted to "a machine decides everything" and "a human decides everything". 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.

entha_saava 6y ago

It sounds like some buzzword speak. Most things that are heralded as ML are nothing but data science idiots from python schools applying some basic math transformations and overselling them.

blaser-waffle 6y ago
I was going to say "most ML these days nothing but chains of if-else statements" but yeah same idea. What's wrong with python?
- entha_saava 6y ago
  Nothing wrong with python or JS. But there are many things wrong with bootcamp / workshop people who come out just learning Python with some ML library or JS with some web framework. They don't know even basics of CS and arguably produce bad, inefficient code. 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.

tastyminerals 6y ago

Can you be a little bit more concrete about the tech you are building right now that is not possible to build using classic ML approaches? You see, I am actually going to work thinking about how should we reduce the model size and still keep it within reasonable accuracy bounds so it would not be worse than a rule-based mixed with classic ML system that we use. And here I learn about immesuarable potential, insults and billions of dollars.

malux85 6y ago

Do you see any irony in your comment complaining about CEOs over promising and then you go on to describe “immeasurable” gains and “limitless” technology?

jcdentonunatco 6y ago

Got bad news for you man. 16T in value doesn't come from ML. It comes from inflation and printing money out of thin air. The world has limited resources at the end of the day. When one industry does well, it's at the cost of a different industry.

beckingz 6y ago
We haven't even mined the moon yet. Plenty of gains to be made.