27 ms·

An understanding of AI’s limitations is starting to sink in

303 points by martincmartin 6y ago

corporateslave5 6y ago
Nlp is getting better at a rapid rate. Articles like this are worthless click bait. No one knows what ml will look like in ten years. And for the record, ten years is a short period of time for a technology that could have such a massive impact.
- distant_hat 6y ago
  People don't understand what exponential improvement means. GPT-3 is a 175B with B parameter model. Another few rounds of doubling and we could be seeing models spit out short stories and novellas.
  - ur-whale 6y ago
    Yet, the darn thing still can't reason.
    - Enginerrrd 6y ago
      To be fair, it wasn't trained to. To be really fair, I think you need to more precisely define what you mean by "reason". It absolutely CAN reason by some measures.
    - jbay808 6y ago
      Five years ago we would have said "the darn thing still can't write a cohesive paragraph".
      - wnoise 6y ago
        That's still true. It can write a paragraph that's usually grammatical, and can stay on topic, but it's missing things like facts, or even the ability to remember which side of an argument it's taken previously.
        jbay808 6y ago
        I think over the course of several paragraphs that's true, but within one it tends to be pretty good.
      - distant_hat 6y ago
        People look at the present and feel like that's all that's there. You don't look at a 2-year old and say he barely makes any intelligible sounds yet.
    - visarga 6y ago
      They "can't reason" but they can solve symbolic integration and differential equations (https://arxiv.org/pdf/2006.06462.pdf https://arxiv.org/pdf/2006.06462.pdf), beat us at all board games, and more recently get to be almost impossible to tell apart from human writing (GPT3).
  - deleted 6y ago
    [deleted]
  - derivativethrow 6y ago
    Have you read the GPT-3 paper? The authors are pretty forthright about how the GPT techniques likely won't scale much beyond 175B parameters.
- stoddur 6y ago
  I think it's also a matter of expectation. People expecting conscious thinking machines with reasoning abilities are going to be disappointed but that doesn't mean AI as a field is useless and a big hoax. There are plenty of narrow problems where we can benefit from the use of AI. I am an MD and I see a lot of use cases of AI based technology in the future within the medical field though it needs more co-work between AI researchers and medical researchers. Good quality scientific validation of these systems needs to take place and that will take some time.
unreal6 6y ago
"The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input." 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.
- zby 6y ago
  This is the same thing people have been writing about computers since 1950s. At that level of abstraction the progress is invisible - and yet: https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/ https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-p... https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/ https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-...
- londons_explore 6y ago
  > can get things very wrong if faced with unexpected input 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!
  - Radim 6y ago
    You missed the preface: 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? …
  - Olreich 6y ago
    If models actually were able to tell you what they know and don’t know, then sure. But instead they just give you an output for any input you give them, whether they have a clue or not.
    - 6y ago
ur-whale 6y ago
http://archive.is/RZD7w http://archive.is/RZD7w
seibelj 6y ago
I’m a longtime AI skeptic who has been arguing passionately against the doom-sayers, many in my own family and in casual conversations with laymen, for several years. I stand by this article I wrote which summarizes my views https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... The hype on AI was absolutely astonishing. I’m glad it’s finally coming back to reality.
- dgb23 6y ago
  The more I understand AI technology the less magic and powerful it seems. 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.
- fxtentacle 6y ago
  That article is well argued and I am inclined to agree. But then I noticed that we seem to strongly disagree on other topics. 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.
  - seibelj 6y ago
    I am a longtime crypto guy, and totally 100% understand how you see the links. I have a very nuanced opinion on this, but it's difficult to explain briefly here. There are a lot of scams in crypto, but if you are a believer in Austrian economics and are not a fan of Keynesian theories, fiat currency, and the Federal Reserve, then Bitcoin / crypto is very attractive. 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.
deleted 6y ago
[deleted]
mtgp1000 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. 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.
dustingetz 6y ago
In the near future, gradient descent optimization of simple targets like PROFIT and REVENUE - with even less accountability to negative externalities than human CEOs have today - is going to FUBAR literally everything
zby 6y ago
This sounds like what was writtein in 2001 about Internet. I don't know - videoconferencing is still a problem, but there is progress.
asutekku 6y ago
Apart from a true GAI, the research has progressed really fast. We’ve seen a huge leaps in e.g. image generation and speech synthesis during the last few years that I’m really interested on what the future will bring.
martincmartin 6y ago
The next article in the series, see the menu on the left, describes Donald Knuth as "a programming guru." :)
- johnwheeler 6y ago
  You want “the programming guru”?
  - albntomat0 6y ago
    A "programming guru", to me at least, doesn't cover his accomplishments in algorithms, among others. It conveys someone who produces a lot of good, high quality code, but not novel research.
blackbear_ 6y ago
This is a good article. I don't understand why people find it questionable and get so defensive about it. It does not deny the fast progress nor the potential of current/future techniques. It is simply an explanation for the layman of things that should be obvious to any practitioner in the field.
- Barrin92 6y ago
  > I don't understand why people find it questionable and get so defensive about it 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.
juliettebe 6y ago
Article with out paywall: https://outline.com/TDeWbY https://outline.com/TDeWbY
mrfusion 6y ago
What’s the next big thing after deep learning?
- Animats 6y ago
  That's a very good question. 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.
  - kthejoker2 6y ago
    Moravec's paradox Low-level seriomotor intelligence requires extreme computational resources. https://towardsdatascience.com/why-math-is-easy-for-ai-but-gardening-is-not-moravecs-paradox-99994b201d10 https://towardsdatascience.com/why-math-is-easy-for-ai-but-g...
- spaetzleesser 6y ago
  There should be something like an X prize for a recycling device. Take a trash pile, sort through it and get things ready for recycling. This would solve a big environmental and would deliver progress in robotics, vision and AI. I'd be more excited about this than self driving cars.
  - discjockeydom 6y ago
    https://zenrobotics.com https://zenrobotics.com
- rapjr9 6y ago
  Perhaps continuous learning. A machine learning system that can adjust to changes over time by retraining itself. 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.
m0zg 6y ago
Crucially, it's not "beginning to sink in" at the Economist, since they don't have a faintest clue what they're talking about. If there's anything the past 3-4 months have taught us (or at least the smarter subset of "us"), one should be careful about predicting future state of exponential processes _even if_ one is expert in the field.
msla 6y ago
These days, you can translate text by pointing your phone at it and taking a picture. Thirty years ago, this would have been unambiguously AI, because it would have been not only impossible, but stupid impossible like something out of a soft SF novel where little self-flying robots deliver stuff to your house, or you can ask a computer a question in a natural voice and reasonably expect a civil, natural-language answer sourced from global databases. 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.
- austinjp 6y ago
  a.k.a. "the AI effect", Larry Tesler (mis)quoted by Douglas Hofstadter.
- lambdatronics 6y ago
  Yes, but you're missing the point. In 1950's sci-fi, those marvels were possible because there was imagined to be something like a general artificial intelligence behind the technology. We've achieved narrow AI, but the perception is that in order to get it, we would already have general AI, which is why people are disappointed.
  - robbrown451 6y ago
    Whether 50s sci-fi imagined or implied other things is irrelevant to the question of whether or not the current capability described (point camera at sign, get translation) qualifies as AI. 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".
    - jpttsn 6y ago
      But was it the “capability” that qualified as AI in the 50s? Or was the capability just one example of what the AI could do? Suppose we said we’ve invented Jesus because we’ve invented ways to walk on water and turn it into wine.
pengaru 6y ago
Is it just me, or is AI never capitalized in TFA? Every time I encountered the word while reading it was like a cache miss for my brain...
- gwern 6y ago
  Do you have NoScript or some other blocker on? As far as I can tell, Economist.com is using smallcaps for all acronyms but for some reason, the smallcaps are implemented solely through JavaScript, so if you read without that, you just see lower-case. The lowercase is because if you have uppercase, smallcaps does nothing - it can't become 'small capitals' because it's already large capitals, as it were. Letters need to be lowercase to be smallcaps. So, that's why you see 'ai' instead of 'AI'. So the 'ai' can properly transform into 'ᴀɪ'. 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.)
  - pengaru 6y ago
    Ah, yes, I am using noscript. That explains it, javascript is destroying the web.
    - quicklime 6y ago
      I just disabled JavaScript by setting Firefox's "javascript.enabled" flag to false, and it still renders fine for me. Here's what the HTML source looks like when I grab the page using curl: predicts that artificial intelligence (<small>AI)</small> will add $16trn to the global economy by 2030 So it looks like it's coming through in caps from the server. I don't think a lack of JavaScript by itself is causing the problem...
mattlondon 6y ago
The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. 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.
- p1esk 6y ago
  Things may have changed over the past 5 years or so. Things may have changed over the past 5 weeks or so with GPT-3.
g_airborne 6y ago
Like others are saying the progress towards AGI isn’t great but each individual subdomain is seeing great advances all the time. Object detection, facial recognition and NLP with GPT are much better than they were a few years ago. Each of these can provide business value to a certain degree, but I would agree that something resembling AGI holds the most business value. For this to happen all of the pieces have to be put together somehow - right now research focuses on specific subdomains and improves the SOTA on them. Once someone figures out how to make everything work together, it could mean a second, much larger wave of AI. So the question is, when will that happen?
cjhanks 6y ago
We have also been watching these machine learning models for 6 months: - 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.
- Veedrac 6y ago
  https://youtu.be/hx7BXih7zx8?t=513 https://youtu.be/hx7BXih7zx8?t=513
  - kordlessagain 6y ago
    > complicated when you get to the long tail of it Well, there's the problem right there.
    - lokedhs 6y ago
      Watching this presentation did not make me any more confident about going in a self-driving car. 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?
- deleted 6y ago
  [deleted]
- faitswulff 6y ago
  But it has also generated prodigious amounts of erotic fiction so that balances out some of those points, right?
  - 6y ago
renewiltord 6y ago
It's limited but so effective! The other day a friend asked for photos of her sister (also a friend) that I had because it's her birthday and she wanted to make a collage. I just searched on Google Photos by her name and it found a bunch because of the face classification. That's some good shit.
- andbberger 6y ago
  works great until I add epsilon of adversarial noise and turn your sister into an ostrich
  - XCSme 6y ago
    I think that the average user doesn't like ostriches that much.
  - webmaven 6y ago
    The hard part of that would probably be getting unauthorized access to the photos in order to modify them in place, rather than the adversarial image perturbation per-se.
  - renewiltord 6y ago
    Fortunately, my camera is not my adversary so I'm safe.
- reportgunner 6y ago
  How do you know you haven't missed any good pictures of your sister ?
  - renewiltord 6y ago
    You don’t but that’s okay. I have thousands of photos. I don’t need exhaustiveness. I need filtering.
eanzenberg 6y ago
This is so strange. If you use facebook, google, netflix, apple, microsoft, amazon, tesla or a whole host of other products and services you are interfacing with AI all the time, sometimes as the core product of the service. To think there’s no value there is asinine. Comes up a lot on HN. Seems like people who get excited for these types of articles are set in their ways and don’t want to progress forward.
- loopz 6y ago
  Yet, every "suggestion" is infinitely worse than "My List", that I curated for myself. Interface hiding the fact of dwindling library and quality of content. Chronology gets perverted with random posts that later can't be easily navigated or referenced. Menu-items unexpectedly fade in and out for unclear reasons and lack of anticipation. In many cases these improvements are to user's detriment and confusion, worsening interface, UX and value for money proposition. In too many areas, end user is themselves the target, for ads, manipulation, propaganda, manufactured outrage and lies.
- eanzenberg 6y ago
  You may even argue that the richest companies of the western world have AI as their core product. May be causation vs. correlation though.
- sk0g 6y ago
  It's the cycle of AI. Breakthroughs -> it's going to automate EVERYTHING -> wait maybe not -> breakthroughs...
- ashtonkem 6y ago
  This strikes me more of people pushing back against the hype than a genuine belief that AI is worthless.
- tomp 6y ago
  Aside from possibly Google, all of these products / services would have just as much, if not more, value without any AI beyond basic statistics.
mlthoughts2018 6y ago
The problem with articles like this is that they don’t properly represent the spectrum of what an ML project can be for a business. 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.
panabee 6y ago
AI is overhyped, but there is much promise. 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.
logicslave 6y ago
Amazon retail is currently in the process of baking advanced deep learning models into every area of their retail process. Its all becoming automated. Just because its not talked about openly doesnt mean its not happening. These people at the economist are completely out of the loop.
aljgz 6y ago
Economist editor to writer: This year, X is on the down slope of hype cycle and no one is talking about it, gimme something ASAP. 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"
- LetThereBeNick 6y ago
  It sounds like you are ready to program an NLP to generate these articles
mD5pPxMcS6fVWKE 6y ago
Maybe it's because everyone talks/chats every day with "virtual assistants" at banks and every other organization, and never ever finds them useful. Their main purpose is to frustrate you enough so you give up trying to connect to a real person.
- ksaj 6y ago
  Judging by the number of times and different places I've heard the sentence "Please listen closely, as all our options have changed" even when they haven't changed in years suggests that this is new whine in old bottles.
ilaksh 6y ago
People are going to complain that AI is lame right up until the point that it gets general enough to make them all irrelevant in terms of work productivity. Then rather than modifying society to distribute the gains, they will leave the outdated structures in place and try (too late) to suppress it. 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.
- LetThereBeNick 6y ago
  With that attitude I would like to pledge my undying allegiance to you
rb808 6y ago
I'm still not sure what successful AI implementations there have been. Stuff like Amazon/Spotify recommendations seem sensible. Is there anything else out there that is impressive?
- seibelj 6y ago
  Autocomplete on emails which allows me to tab over for common statements. But I agree - the places where AI has improved business can be counted on with one hand, and each finger is extremely thin.
- amelius 6y ago
  Google Search?
  - sixQuarks 6y ago
    Google search has gotten worse over the years though. Perhaps that's because the amount of information is growing exponentially. So much of the search results seems spammy these days.
    - luisvictoria 6y ago
      My guess would be because everyone's doing SEO, and since everyone's competing to be on the top of people's Google searches, they're often inadequate
    - amelius 6y ago
      It's still quite smart though. For example, asking a question about a programming problem often brings up the correct stackoverflow page, even if the wording is completely different.
- r_deckard 6y ago
  Nvidia's DLSS[0] seems to be pretty successful in achieving its goal, which is to allow games to render on lower resolutions without sacrificing too many details. [0]https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-big-leap-in-ai-rendering/ https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-...
somewhereoutth 6y ago
Previously, when "The computer says no" it was possible that an engineer somewhere might know why the computer said no. With AI, nobody knows why the computer said no.
- dbuder 6y ago
  They already blame the algo when they made a manual decision. Looking at you Google, you bold faced liars.
cageface 6y ago
I think there's an opportunity right now for new, human curated indexes in the style of the old Yahoo index. AI content generation is getting too good at fooling AI curation and I'm getting less and less value in broad searches on Google. I would pay a monthly fee for hand-vetted lists of the best content on topics I'm interested in.
ipiz0618 6y ago
https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect
dreamcompiler 6y ago
Here we are in 1989 again. 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.
- gkolli 6y ago
  thank you for this amazing analogy!
- tgv 6y ago
  My first AI teacher (even before '89) compared solving AI with neural nets to teaching pigs to fly by throwing them from a tower. Improvements come from building higher towers. 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.
  - mjburgess 6y ago
    It isn't ever going to model our style of thinking. A "neural network" is just high-dimension linear regression; the idea it has anything to do with the brain is metaphorical nonesense. No algorithm running on digital hardware can emulate the biological process of animal intelligence.
    - machiaweliczny 6y ago
      Why not?
      - mjburgess 6y ago
gwbas1c 6y ago
My general guidelines about AI (Machine learning, programming): 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.
blackrock 6y ago
I heard that some big company was selling an AI powered database. 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.
neonate 6y ago
https://archive.vn/USQ6M https://archive.vn/USQ6M
inimino 6y ago
So now that it's not the latest hype train, we're back to calling it "AI"? For the love of Christ, dear Economist, ML and AI are not the same thing and we have words for both of them. Use your words, people! 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.
shivsinghh900 6y ago
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code4tee 6y ago
In probably 99% of AI/ML use cases the AI/ML is basically just a commodity item and the real “expertise” comes from getting and preparing good datasets for analysis, and having a clear problem to solve. The strategy behind something like AWS SageMaker is based entirely around this idea. 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.
randcraw 6y ago
For a rather complete view of what deep learning still does not do, I recommend the work of Gary Marcus and Ernest Davis. While seeming solely critical, I think they make very good points about the limits inherent in deep learning as we know it now, and how it needs to grow to overcome those deficits. "Rebooting AI" https://www.penguinrandomhouse.com/books/603982/rebooting-ai-by-gary-marcus-and-ernest-davis/ 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 https://arxiv.org/abs/1801.00631 The Next Decade in AI https://arxiv.org/pdf/2002.06177.pdf https://arxiv.org/pdf/2002.06177.pdf How to Build AI We Can Trust https://www.nytimes.com/2019/09/06/opinion/ai-explainability.html 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-gary-marcus https://hbr.org/podcast/2019/10/beyond-deep-learning-with-ga...

303 points by martincmartin 6y ago

Nlp is getting better at a rapid rate. Articles like this are worthless click bait. No one knows what ml will look like in ten years. And for the record, ten years is a short period of time for a technology that could have such a massive impact.

distant_hat 6y ago
People don't understand what exponential improvement means. GPT-3 is a 175B with B parameter model. Another few rounds of doubling and we could be seeing models spit out short stories and novellas.
- ur-whale 6y ago
  Yet, the darn thing still can't reason.
  - Enginerrrd 6y ago
    To be fair, it wasn't trained to. To be really fair, I think you need to more precisely define what you mean by "reason". It absolutely CAN reason by some measures.
  - jbay808 6y ago
    Five years ago we would have said "the darn thing still can't write a cohesive paragraph".
    - wnoise 6y ago
      That's still true. It can write a paragraph that's usually grammatical, and can stay on topic, but it's missing things like facts, or even the ability to remember which side of an argument it's taken previously.
      - jbay808 6y ago
        I think over the course of several paragraphs that's true, but within one it tends to be pretty good.
    - distant_hat 6y ago
      People look at the present and feel like that's all that's there. You don't look at a 2-year old and say he barely makes any intelligible sounds yet.
  - visarga 6y ago
    They "can't reason" but they can solve symbolic integration and differential equations (https://arxiv.org/pdf/2006.06462.pdf https://arxiv.org/pdf/2006.06462.pdf), beat us at all board games, and more recently get to be almost impossible to tell apart from human writing (GPT3).
- deleted 6y ago
  [deleted]
- derivativethrow 6y ago
  Have you read the GPT-3 paper? The authors are pretty forthright about how the GPT techniques likely won't scale much beyond 175B parameters.
stoddur 6y ago
I think it's also a matter of expectation. People expecting conscious thinking machines with reasoning abilities are going to be disappointed but that doesn't mean AI as a field is useless and a big hoax. There are plenty of narrow problems where we can benefit from the use of AI. I am an MD and I see a lot of use cases of AI based technology in the future within the medical field though it needs more co-work between AI researchers and medical researchers. Good quality scientific validation of these systems needs to take place and that will take some time.

unreal6 6y ago

"The result is an artificial idiot savant that can excel at well-bounded tasks, but can get things very wrong if faced with unexpected input." 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.

zby 6y ago
This is the same thing people have been writing about computers since 1950s. At that level of abstraction the progress is invisible - and yet: https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/ https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-p... https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-game/ https://slatestarcodex.com/2020/01/06/a-very-unlikely-chess-...
londons_explore 6y ago
> can get things very wrong if faced with unexpected input 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!
- Radim 6y ago
  You missed the preface: 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? …
- Olreich 6y ago
  If models actually were able to tell you what they know and don’t know, then sure. But instead they just give you an output for any input you give them, whether they have a clue or not.
  - 6y ago

ur-whale 6y ago

http://archive.is/RZD7w http://archive.is/RZD7w

seibelj 6y ago

I’m a longtime AI skeptic who has been arguing passionately against the doom-sayers, many in my own family and in casual conversations with laymen, for several years. I stand by this article I wrote which summarizes my views https://medium.com/@seibelj/the-artificial-intelligence-scam-is-imploding-34b156c3537e https://medium.com/@seibelj/the-artificial-intelligence-scam... The hype on AI was absolutely astonishing. I’m glad it’s finally coming back to reality.

dgb23 6y ago
The more I understand AI technology the less magic and powerful it seems. 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.
fxtentacle 6y ago
That article is well argued and I am inclined to agree. But then I noticed that we seem to strongly disagree on other topics. 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.
- seibelj 6y ago
  I am a longtime crypto guy, and totally 100% understand how you see the links. I have a very nuanced opinion on this, but it's difficult to explain briefly here. There are a lot of scams in crypto, but if you are a believer in Austrian economics and are not a fan of Keynesian theories, fiat currency, and the Federal Reserve, then Bitcoin / crypto is very attractive. 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.

deleted 6y ago

[deleted]

mtgp1000 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. 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.

dustingetz 6y ago

In the near future, gradient descent optimization of simple targets like PROFIT and REVENUE - with even less accountability to negative externalities than human CEOs have today - is going to FUBAR literally everything

zby 6y ago

This sounds like what was writtein in 2001 about Internet. I don't know - videoconferencing is still a problem, but there is progress.

asutekku 6y ago

Apart from a true GAI, the research has progressed really fast. We’ve seen a huge leaps in e.g. image generation and speech synthesis during the last few years that I’m really interested on what the future will bring.

martincmartin 6y ago

The next article in the series, see the menu on the left, describes Donald Knuth as "a programming guru." :)

johnwheeler 6y ago
You want “the programming guru”?
- albntomat0 6y ago
  A "programming guru", to me at least, doesn't cover his accomplishments in algorithms, among others. It conveys someone who produces a lot of good, high quality code, but not novel research.

blackbear_ 6y ago

This is a good article. I don't understand why people find it questionable and get so defensive about it. It does not deny the fast progress nor the potential of current/future techniques. It is simply an explanation for the layman of things that should be obvious to any practitioner in the field.

Barrin92 6y ago
> I don't understand why people find it questionable and get so defensive about it 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.

juliettebe 6y ago

Article with out paywall: https://outline.com/TDeWbY https://outline.com/TDeWbY

mrfusion 6y ago

What’s the next big thing after deep learning?

Animats 6y ago
That's a very good question. 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.
- kthejoker2 6y ago
  Moravec's paradox Low-level seriomotor intelligence requires extreme computational resources. https://towardsdatascience.com/why-math-is-easy-for-ai-but-gardening-is-not-moravecs-paradox-99994b201d10 https://towardsdatascience.com/why-math-is-easy-for-ai-but-g...
spaetzleesser 6y ago
There should be something like an X prize for a recycling device. Take a trash pile, sort through it and get things ready for recycling. This would solve a big environmental and would deliver progress in robotics, vision and AI. I'd be more excited about this than self driving cars.
- discjockeydom 6y ago
  https://zenrobotics.com https://zenrobotics.com
rapjr9 6y ago
Perhaps continuous learning. A machine learning system that can adjust to changes over time by retraining itself. 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.

m0zg 6y ago

Crucially, it's not "beginning to sink in" at the Economist, since they don't have a faintest clue what they're talking about. If there's anything the past 3-4 months have taught us (or at least the smarter subset of "us"), one should be careful about predicting future state of exponential processes _even if_ one is expert in the field.

msla 6y ago

These days, you can translate text by pointing your phone at it and taking a picture. Thirty years ago, this would have been unambiguously AI, because it would have been not only impossible, but stupid impossible like something out of a soft SF novel where little self-flying robots deliver stuff to your house, or you can ask a computer a question in a natural voice and reasonably expect a civil, natural-language answer sourced from global databases. 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.

austinjp 6y ago
a.k.a. "the AI effect", Larry Tesler (mis)quoted by Douglas Hofstadter.
lambdatronics 6y ago
Yes, but you're missing the point. In 1950's sci-fi, those marvels were possible because there was imagined to be something like a general artificial intelligence behind the technology. We've achieved narrow AI, but the perception is that in order to get it, we would already have general AI, which is why people are disappointed.
- robbrown451 6y ago
  Whether 50s sci-fi imagined or implied other things is irrelevant to the question of whether or not the current capability described (point camera at sign, get translation) qualifies as AI. 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".
  - jpttsn 6y ago
    But was it the “capability” that qualified as AI in the 50s? Or was the capability just one example of what the AI could do? Suppose we said we’ve invented Jesus because we’ve invented ways to walk on water and turn it into wine.

pengaru 6y ago

Is it just me, or is AI never capitalized in TFA? Every time I encountered the word while reading it was like a cache miss for my brain...

gwern 6y ago
Do you have NoScript or some other blocker on? As far as I can tell, Economist.com is using smallcaps for all acronyms but for some reason, the smallcaps are implemented solely through JavaScript, so if you read without that, you just see lower-case. The lowercase is because if you have uppercase, smallcaps does nothing - it can't become 'small capitals' because it's already large capitals, as it were. Letters need to be lowercase to be smallcaps. So, that's why you see 'ai' instead of 'AI'. So the 'ai' can properly transform into 'ᴀɪ'. 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.)
- pengaru 6y ago
  Ah, yes, I am using noscript. That explains it, javascript is destroying the web.
  - quicklime 6y ago
    I just disabled JavaScript by setting Firefox's "javascript.enabled" flag to false, and it still renders fine for me. Here's what the HTML source looks like when I grab the page using curl: predicts that artificial intelligence (<small>AI)</small> will add $16trn to the global economy by 2030 So it looks like it's coming through in caps from the server. I don't think a lack of JavaScript by itself is causing the problem...

mattlondon 6y ago

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. 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.

p1esk 6y ago
Things may have changed over the past 5 years or so. Things may have changed over the past 5 weeks or so with GPT-3.

g_airborne 6y ago

Like others are saying the progress towards AGI isn’t great but each individual subdomain is seeing great advances all the time. Object detection, facial recognition and NLP with GPT are much better than they were a few years ago. Each of these can provide business value to a certain degree, but I would agree that something resembling AGI holds the most business value. For this to happen all of the pieces have to be put together somehow - right now research focuses on specific subdomains and improves the SOTA on them. Once someone figures out how to make everything work together, it could mean a second, much larger wave of AI. So the question is, when will that happen?

cjhanks 6y ago

We have also been watching these machine learning models for 6 months: - 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.

Veedrac 6y ago
https://youtu.be/hx7BXih7zx8?t=513 https://youtu.be/hx7BXih7zx8?t=513
- kordlessagain 6y ago
  > complicated when you get to the long tail of it Well, there's the problem right there.
  - lokedhs 6y ago
    Watching this presentation did not make me any more confident about going in a self-driving car. 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?
deleted 6y ago
[deleted]
faitswulff 6y ago
But it has also generated prodigious amounts of erotic fiction so that balances out some of those points, right?
- 6y ago

renewiltord 6y ago

It's limited but so effective! The other day a friend asked for photos of her sister (also a friend) that I had because it's her birthday and she wanted to make a collage. I just searched on Google Photos by her name and it found a bunch because of the face classification. That's some good shit.

andbberger 6y ago
works great until I add epsilon of adversarial noise and turn your sister into an ostrich
- XCSme 6y ago
  I think that the average user doesn't like ostriches that much.
- webmaven 6y ago
  The hard part of that would probably be getting unauthorized access to the photos in order to modify them in place, rather than the adversarial image perturbation per-se.
- renewiltord 6y ago
  Fortunately, my camera is not my adversary so I'm safe.
reportgunner 6y ago
How do you know you haven't missed any good pictures of your sister ?
- renewiltord 6y ago
  You don’t but that’s okay. I have thousands of photos. I don’t need exhaustiveness. I need filtering.

eanzenberg 6y ago

This is so strange. If you use facebook, google, netflix, apple, microsoft, amazon, tesla or a whole host of other products and services you are interfacing with AI all the time, sometimes as the core product of the service. To think there’s no value there is asinine. Comes up a lot on HN. Seems like people who get excited for these types of articles are set in their ways and don’t want to progress forward.

loopz 6y ago
Yet, every "suggestion" is infinitely worse than "My List", that I curated for myself. Interface hiding the fact of dwindling library and quality of content. Chronology gets perverted with random posts that later can't be easily navigated or referenced. Menu-items unexpectedly fade in and out for unclear reasons and lack of anticipation. In many cases these improvements are to user's detriment and confusion, worsening interface, UX and value for money proposition. In too many areas, end user is themselves the target, for ads, manipulation, propaganda, manufactured outrage and lies.
eanzenberg 6y ago
You may even argue that the richest companies of the western world have AI as their core product. May be causation vs. correlation though.
sk0g 6y ago
It's the cycle of AI. Breakthroughs -> it's going to automate EVERYTHING -> wait maybe not -> breakthroughs...
ashtonkem 6y ago
This strikes me more of people pushing back against the hype than a genuine belief that AI is worthless.
tomp 6y ago
Aside from possibly Google, all of these products / services would have just as much, if not more, value without any AI beyond basic statistics.

mlthoughts2018 6y ago

The problem with articles like this is that they don’t properly represent the spectrum of what an ML project can be for a business. 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.

panabee 6y ago

AI is overhyped, but there is much promise. 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.

logicslave 6y ago

Amazon retail is currently in the process of baking advanced deep learning models into every area of their retail process. Its all becoming automated. Just because its not talked about openly doesnt mean its not happening. These people at the economist are completely out of the loop.

aljgz 6y ago

Economist editor to writer: This year, X is on the down slope of hype cycle and no one is talking about it, gimme something ASAP. 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"

LetThereBeNick 6y ago
It sounds like you are ready to program an NLP to generate these articles

mD5pPxMcS6fVWKE 6y ago

Maybe it's because everyone talks/chats every day with "virtual assistants" at banks and every other organization, and never ever finds them useful. Their main purpose is to frustrate you enough so you give up trying to connect to a real person.

ksaj 6y ago
Judging by the number of times and different places I've heard the sentence "Please listen closely, as all our options have changed" even when they haven't changed in years suggests that this is new whine in old bottles.

ilaksh 6y ago

People are going to complain that AI is lame right up until the point that it gets general enough to make them all irrelevant in terms of work productivity. Then rather than modifying society to distribute the gains, they will leave the outdated structures in place and try (too late) to suppress it. 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.

LetThereBeNick 6y ago
With that attitude I would like to pledge my undying allegiance to you

rb808 6y ago

I'm still not sure what successful AI implementations there have been. Stuff like Amazon/Spotify recommendations seem sensible. Is there anything else out there that is impressive?

seibelj 6y ago
Autocomplete on emails which allows me to tab over for common statements. But I agree - the places where AI has improved business can be counted on with one hand, and each finger is extremely thin.
amelius 6y ago
Google Search?
- sixQuarks 6y ago
  Google search has gotten worse over the years though. Perhaps that's because the amount of information is growing exponentially. So much of the search results seems spammy these days.
  - luisvictoria 6y ago
    My guess would be because everyone's doing SEO, and since everyone's competing to be on the top of people's Google searches, they're often inadequate
  - amelius 6y ago
    It's still quite smart though. For example, asking a question about a programming problem often brings up the correct stackoverflow page, even if the wording is completely different.
r_deckard 6y ago
Nvidia's DLSS[0] seems to be pretty successful in achieving its goal, which is to allow games to render on lower resolutions without sacrificing too many details. [0]https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-big-leap-in-ai-rendering/ https://www.nvidia.com/en-us/geforce/news/nvidia-dlss-2-0-a-...

somewhereoutth 6y ago

Previously, when "The computer says no" it was possible that an engineer somewhere might know why the computer said no. With AI, nobody knows why the computer said no.

dbuder 6y ago
They already blame the algo when they made a manual decision. Looking at you Google, you bold faced liars.

cageface 6y ago

I think there's an opportunity right now for new, human curated indexes in the style of the old Yahoo index. AI content generation is getting too good at fooling AI curation and I'm getting less and less value in broad searches on Google. I would pay a monthly fee for hand-vetted lists of the best content on topics I'm interested in.

ipiz0618 6y ago

https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect

dreamcompiler 6y ago

Here we are in 1989 again. 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.

gkolli 6y ago
thank you for this amazing analogy!
tgv 6y ago
My first AI teacher (even before '89) compared solving AI with neural nets to teaching pigs to fly by throwing them from a tower. Improvements come from building higher towers. 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.
- mjburgess 6y ago
  It isn't ever going to model our style of thinking. A "neural network" is just high-dimension linear regression; the idea it has anything to do with the brain is metaphorical nonesense. No algorithm running on digital hardware can emulate the biological process of animal intelligence.
  - machiaweliczny 6y ago
    Why not?
    - mjburgess 6y ago

gwbas1c 6y ago

My general guidelines about AI (Machine learning, programming): 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.

blackrock 6y ago

I heard that some big company was selling an AI powered database. 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.

neonate 6y ago

https://archive.vn/USQ6M https://archive.vn/USQ6M

inimino 6y ago

So now that it's not the latest hype train, we're back to calling it "AI"? For the love of Christ, dear Economist, ML and AI are not the same thing and we have words for both of them. Use your words, people! 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.

shivsinghh900 6y ago

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code4tee 6y ago

In probably 99% of AI/ML use cases the AI/ML is basically just a commodity item and the real “expertise” comes from getting and preparing good datasets for analysis, and having a clear problem to solve. The strategy behind something like AWS SageMaker is based entirely around this idea. 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.

randcraw 6y ago

For a rather complete view of what deep learning still does not do, I recommend the work of Gary Marcus and Ernest Davis. While seeming solely critical, I think they make very good points about the limits inherent in deep learning as we know it now, and how it needs to grow to overcome those deficits. "Rebooting AI" https://www.penguinrandomhouse.com/books/603982/rebooting-ai-by-gary-marcus-and-ernest-davis/ 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 https://arxiv.org/abs/1801.00631 The Next Decade in AI https://arxiv.org/pdf/2002.06177.pdf https://arxiv.org/pdf/2002.06177.pdf How to Build AI We Can Trust https://www.nytimes.com/2019/09/06/opinion/ai-explainability.html 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-gary-marcus https://hbr.org/podcast/2019/10/beyond-deep-learning-with-ga...