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AI is transforming Google search – the rest of the web is next

162 points by nzonbi 11y ago

PaulHoule 11y ago
With the Google Knowledge Graph they don't need the rest of the web. It's starting to get rare to see organic results to web pages more than most.
lowglow 11y ago
I can't be the only one that considers all these AI articles just smoke and mirror puff pieces to prop up a company's value by capitalizing on the hype (hysteria?), can I? I think the first flag is that the journalists don't seem to really understand the technical capabilities or limitations of current ML/AI applications. They accept grandiose claims at face value because there is no way to measure the real potential of AI (which the promise of seems limitless, so anything appears plausible especially coming from a big company like GOOG). I think there are a couple of really overhyped areas right now, AR/AI/ML and IoT/IoE. Now while I don't mind the attention and money being thrown at tech, I can't help but feel we're borrowing more against promises, hopes, and dreams, while simultaneously under-delivering and I think that's going to hurt tech's image and erode investor confidence sooner than later.
- hiddencost 11y ago
  A lot of things that were impractical 5-10 years ago are now moving out of the domain of the biggest companies to the smaller ones. Applications involving computer vision and speech recognition are now buildable by small companies, which will hopefully yield a proliferation of really interesting novel applications. Sure, we don't have terminator-style AI, but honestly people using the term AI need to shut up, a lot. There is no AI, these days, just massively creaky giant ML systems with a host of ph.d's being thrown onto the fire to keep them running. But the ML applications are super cool.
  - Houshalter 11y ago
    We lost the word "AI" literally decades ago. Everything from search algorithms to machine learning to genetic algorithms to video game bots are called AI. The cool kids use the word "AGI" now. There is a common expression that "AI is whatever computers can't do yet." At one time computers couldn't play board games or do vision, so those things were called AI.
    - throwaway873719 11y ago
      It seems like your first and second paragraphs express opposite opinions. In the former you seem dismayed by the over-application/overhyping of the term "AI"; in the latter you seem frustrated by the high and ever-increasing bar for categorizing systems as "AI". Am I misinterpreting you?
      - Houshalter 11y ago
        My comment can be read two ways, and neither way is wrong. I wasn't really expressing an opinion as much as bringing up relevant facts. People label things that we don't know how to do yet "AI". And then when these hard problems are solved, they seem like they are not really "intelligent". This leads to both overuse and trivialization of the word, but also moving goal posts for the field. And actual progress isn't taken seriously, because nothing feels like intelligence when you understand it. It's all a Mysterious Answer (http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_questions/ http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_...), a codeword for magic (http://lesswrong.com/lw/ix/say_not_complexity/ http://lesswrong.com/lw/ix/say_not_complexity/).
  - joe_the_user 11y ago
    The Chinese curse might be now updated to "may you wind-up dependent on 'really interesting novel applications'..." Machine learning-derived applications are impressive and give a good show until one winds up in a situation where they are expected to work reliably. Sure, it's nice that the insurance company's phone-based, voice-recognition-driven, registration/etc system can understand 99% of the choices people give them - except total failure in that 1% is actually going to leave a large population unserved and angry. Of course the company has keypad backup - except they don't 'cause that would cost the money they claimed voice recognition could save, etc. Machine learning apps are great for situations where 1) You don't expect 100% reliably and the degree of non-reliably doesn't have to even be quantified. 2) Either you are accept that they'll degrade over time and have an army of tuners and massive data collect to keep that from happening OR you are dealing with an environment you completely control. This is kind of the conditions for regular automation - except even more so.
- xxs 11y ago
  >>I can't be the only one that considers all these AI articles just smoke and mirror puff pieces to prop up the companies value by capitalizing on the hype (hysteria?) You're not. So far I have not seen/heard anything remotely resembling AI. Neural nets are just weighted graphs.
  - voodoomagicman 11y ago
    Yeah, but maybe that is all brains are too?
    - riffraff 11y ago
      they might be, but * we still don't know if they are * we still don't know how they effectively work * the scale of the things is far from the computational capabilities of my phone (10^11 neurons and 10^15 synapses for a human brain) (But of course AI means a lot of things, not just "thinking androids")
      - oilywater 11y ago
        Well, in 2025 it will be exactly at the computational capability of a brain.
  - vonnik 11y ago
    Is anyone arguing that machine learning is not AI? The gist of the article is that Google is leaning toward ML/DL and away from the rules engines/Knowledge Graph. The headline is a shorthand, which, although it could be more precise, is not inaccurate.
    - igravious 11y ago
      Interesting that you feel that. The article mentions nothing about Google's Knowledge Graph. I don't have any privileged insight into Google, just the same surface data as the rest of you all - but I would say that, if anything, Google's Knowledge Graph can fit with _both_ a rules engine strategy and a machine learning one. How is Google going to "organise the world's information" unless it has a model of how all the facts in the world line up? That model is the Knowledge Graph. How does Google intend to map queries that it has never seen before to pages in its vast index? With the help of the Knowledge Graph and natural language processing and machine learning. I'm going to try to articulate something here that I've not fully worked out but that I'm sort of intuiting so cut me some slack for the next paragraph :) People keep banging on about machine learning and the impact that it is having. This is undeniable. But we can see even from AlphaGo that a hybrid approach that combines artificial neural nets with some sort of symbolic system outperforms neural nets on their own. For AlphGo that symbolic system is tied to the mechanics of the game of Go. For internet search that symbolic system is a generalised knowledge graph. Do you get what I mean? I'd love to hear what others think …
- hh2222 11y ago
  And ... I can't be the only one that thinks Google search results just aren't that good anymore.
  - scholia 11y ago
    I also think the quality has gone down. But it seems to me it's at least partly deliberate: the quality has gone down because "freshness" has gone up.
    - bones6 11y ago
      Sometimes I get the vibe that it's a weird self-fulfilling prophesy of terrible SERPs. You reward freshness too much, so then people play that game. But you also punish duplicate content at the same time so the best content, if it already exists but is not fresh, naturally has to fall off.
  - jbhatab 11y ago
    I couldn't disagree more. They have only gotten better every single year for me. Undeniably better every year.
  - awqrre 11y ago
    I really wish that I could switch back to google's algo from 10 years ago...
    - minwcnt5 11y ago
      That is pure nostalgia talking. I strongly suspect that if you did you would be appalled and want to switch back after only a few searches. Search in 2016 is leaps and bounds beyond what it was in 2006.
      - CaptSpify 11y ago
        This is very much a YMMV thing, but what improvements have you seen?
- bduerst 11y ago
  We've had artificial intelligence since you could play a computer at chess, but the expectation has always been a mechanized Arnold Schwarzenegger or Hal 9000. The difference today is the scale at which it operates in our daily lives, and the accelerated rate at which it is growing.
- teaneedz 11y ago
  Thank you. Exactly my feelings.
- Fiahil 11y ago
  You can add to this, the gigantic misuse of the term "AI" when journalists really want to say "robotics". I don't mind clever shortcuts when you need to explain something abstract or invisible to non-technical people, but, at some point, somebody will have to tell them they're two completely different fields.
- rsingel 11y ago
  I thought the same thing when I saw the title. And then I saw the byline: Cade Metz. And I read the story. Metz understands tech. Metz got this story right. So while you may be right that many AI articles are smoke-and-mirrors from journalists who don't get the tech, I think you picked the wrong article to make that point about.
  - SixSigma 11y ago
    > The truth is that even the experts don’t completely understand how neural nets work. That is not understanding.
- smira7 11y ago
  The funny part is that it's not illegal for AI to foment.
- tim333 11y ago
  I wouldn't say this particular article is a smoke and mirror puff piece to prop up the companies value. Now other ones you could argue that. 'The Grid' springs to mind. (https://www.quora.com/Has-anyone-heard-about-the-Grid-AI-websites-that-design-themselves-Does-it-really-work-and-can-it-replace-the-manual-way-of-building-a-website https://www.quora.com/Has-anyone-heard-about-the-Grid-AI-web...)
- putaside 11y ago
  Winter is coming.
- paganel 11y ago
  > I think there are a couple of really overhyped areas right now, AR/AI/ML and IoT/IoE. Now while I don't mind the attention and money being thrown at tech, I can't help but feel we're borrowing more against promises, hopes, and dreams, while simultaneously under-delivering and I think that's going to hurt tech's image and erode investor confidence sooner than later. I became interested in natural language processing in the early 2000s, more as a hobby and as part of my personal projects, but even so, I remember that back then most of the AI-related discussions on things like forums and mailing lists were mentioning the AI winter as the big bad wolf that had killed an entire industry. It also killed LISP, they were saying. Interesting to see that that memory seems to have faded away to the distant past.
- bitcuration 11y ago
  yep, this apparently has been a wall street operation than anything else. Google needs the capital to transform and sustain the decline of web search revenue.
ohitsdom 11y ago
> The truth is that even the experts don’t completely understand how neural nets work. I'm no AI/ML expert, but I can't believe this is true... Is it?
- hiddencost 11y ago
  Extremely true.
- oh_sigh 11y ago
  Abstractly, we understand how neural nets work. However, looking at a specific trained neural net, it can be difficult to determine the exact reason why certain weights are they way they are, and what effect they have on the whole. Just like how we can know somewhat how neurons work from a modeling perspective, but when you bundle millions of them together, what exactly each one is doing is not quite clear.
  - hiddencost 11y ago
    We understand how they work mechanically, but not why they work from a theoretical stand-point (which is I assume what you're trying to say by "abstractly"). Why it is that ASGD and backprop converges on a non-convex optimization problem, and what kinds of model topologies make it do better / worse? That's all basically art right now.
    - Houshalter 11y ago
      No that's not what he's trying to say. We know why gradient descent and back-propagation works. But no one understands what an actual trained neural net is doing. You can look at the weights, and you can watch the inputs and outputs, but it is very difficult to understand why it does what it does. It just fit a model to data, but there is no explanation why that model is best. The weights are not interprettable by humans. There have been some attempts at making models which humans can interpret. One program named Eureqa fit the simplest possible mathematical expression possible to a set of samples. A biologist tried it on his data and found that it created an expression which actually fit the data really well. But he couldn't publish it because he couldn't explain why it worked. It just did. But there was no understanding, no explanation.
rifung 11y ago
> At one point, Google ran a test that pitted its search engineers against RankBrain. Both were asked to look at various web pages and predict which would rank highest on a Google search results page. RankBrain was right 80 percent of the time. The engineers were right 70 percent of the time. I don't really understand the point of this metric. Why are they predicting what ranks highest on Google search? Wouldn't a better metric be who predicts the correct place a user was looking for? Is the thinking that if they are using machine learning, than whatever the user is looking for should have bubbled up to the top?
- speeder 11y ago
  I am in the recent days having the impression that Google whatever it is doing is focused more and more in presenting to the user google biggest clients, and hoping that it will be useful. Because I am having more and more trouble finding what I want, people used to consider me a master of google fu, finding whatever random stuff they wanted, now I am struggling, specially after google changed the + and "" meaning (+ went from "mandatory" to mean "google plus search" and " went to mean "literal string" to mean "a sort of mandatory thing") If I need to find some obscure term, I know now that google won't find it, despite finding that same term in the past, finding pages with a certain information on it never happen anymore, even using the "" thing. For example I own a ASUS N46VM laptop with nVidia Optimus... this laptop is terrible, and I am always having to look online how to make it behave properly, before the "+" change, I could type +N46VM and be guaranteed I only would get relevant ifnormation... recently I was desperately searching for some stuff, and found out no matter what I input on google, it returned completely bogus results, where the string N46VM was nowhere in the page, not even in the "time" dimension (ie: if I load the page on archive.org for example and scan every version of it, N46VM never had been on it, google just heuristically decided the page was relevant and gave it to me wrongly). EDIT: I am having some success with DuckDuckGo although their research system is clearly cruder than google, having much less heuristics and whatnot, frequently I find the stuff I want easier on DuckDuckGo anyway, after some pages of browsing results... while on google I browse 40 pages and all of it is completely irrelevant and unrelated (while on DuckDuckGo it shows me 40 pages with the term I want, but in the wrong context).
Houshalter 11y ago
This is very interesting. As late as 2008, Google said they don't use any machine learning in search. Everything was hand engineered with tons of heuristics. They said they didn't trust machine learning, and that it created bizarre failure cases.
- adenadel 11y ago
  Do you have a source for that? It's a really fastcinating claim that I'm interested in reading more about.
  - dhj 11y ago
    I believe it considering 2006-2008 was when all the deep learning pieces came together (some parts were decades old, some 5 years, some 2 years). Google's main push in ML is with deep learning. Although, I would like to see the source too. Tried to find it using Google, but no luck! :)
    - dvlat 11y ago
      "Are Machine-Learned Models Prone to Catastrophic Errors?" http://anand.typepad.com/datawocky/2008/05/are-human-experts-less-prone-to-catastrophic-errors-than-machine-learned-models.html http://anand.typepad.com/datawocky/2008/05/are-human-experts...
  - Houshalter 11y ago
    Here: http://www.zdnet.com/article/peter-norvig-on-googles-mistrust-of-machine-learning/ http://www.zdnet.com/article/peter-norvig-on-googles-mistrus...
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wangii 11y ago
One part of me truly hope Google to success in ML/AI, although I consider Google an evil company. AI, Singularity, they are the most important things in this century. The implication is simply beyond our imagination. I don't care too much if Skynet takes over the earth and kicks human being into the dustbin. If it's the destiny, so be it. Another part of me believe it's a sign of folks don't know what they are doing, writing. How can we achieve AI without understanding? Google will fall apart.
osmode 11y ago
There is a tendency among non-technical admirers of ML to regard deep learning methods as beyond their creators: independent entities that will one day, given refined enough algorithms and enough energy, out-comprehend their human creators and overwhelm humanity with their artificial consciousnesses. The term “neural networks” is itself a misnomer that doesn’t at all reflect the complexity of how human neurons represent and acquire information; it’s simply a term for nonlinear classification algorithms that began catching on once the computing power to run them emerged. The question of whether or not deep neural networks are capable of “understanding” is largely a theoretical concern for the ML practitioner, who spends the bulk of his or her time undertaking the hard work of curating manually labeled data, fine-tuning his or her neural classifier with methods (or hacks) such as dropout, stochastic gradient descent, convolution and recursion, to increase its accuracy by a few fractions of a percentage point. Ten or twenty years from now, I imagine we’ll be dealing with a novel set of ML tools that will evolve with the rise of quantum computing (the term “machine learning” will probably be ancient history, too), but the essence of these methods will probably remain: to train a mathematical model to perform task X while generalizing its performance to the real world. As fascinating and exciting as this era of artificial intelligence is, we should also remember that these algorithms are ultimately sophisticated classifiers that don't "understand" anything at all.
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- proc0 11y ago
  This is true of ANN, and deep learning. They are mathematical models of learning that are finally practical after a couple decades (not to diminish anything the researchers have accomplished which is incredible). Then there are biologically inspired neural networks, like Hierarchical Temporal Memory (HTM), that actually correlate directly to how the cortex in mammals work. These have also demonstrated learning capabilities, and seem a lot more promising in the road map to general AI, in my opinion, because after all we should be piggy-backing on evolution (not that we can't find a mathematical model first). So yeah, the hype is just hype, but it could be justified for the wrong reasons if we see breakthroughs in biologically inspired AI (the Brain Project, to name another example).
reza_n 11y ago
RIP big data. Hello AI. Makes sense, data drives a lot of 'AI' tech. I guess what I find amusing is the push from Google to rebrand themselves as an AI company. My guess is it won't be too long until we see everyone else jumping in the AI branding boat. That will kind of dilute a lot of what is being done.
amelius 11y ago
The current trend seems to be to put a human behind a web API. I guess when AI is sufficiently advanced, those humans can be seamlessly replaced by computers.
hyperpallium 11y ago
I recall google engineers complaining that their clever insightful carefully engineered code was soundly beaten by a statistical approach. The current approaches aren't so much AI as having really, really, ridiculously large datasets.
jonesb6 11y ago
https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies If anyone wants to practice their critical think skills, see how many fallacies you can spot in this article.
inaudible 11y ago
I don't quite understand why people want to dismiss examples of machine learning as valid techniques for understanding the human environment.. It's not as if the human brain was built and guided from nothing, many of the same adaptive principles are as present in our minds as they are in other mammals and equally so from where all the branches divide. Even tiny organisms. And we seems to center the brain at the core of humans intelligence, when there's a range of chemical and metabolic coordination going that might bypass the brain entirely. It's efficient, failure resistant models that matter. We're talking about accelerated learning, finding the models that work out of all those many iterations that fail. You can model it, decompile the results and try to understand and emulate what makes things seem real, but we don't even need to analyze it, because case by case it changes and it's circumstance makes things very different. 'Many ways to skin a cat'. I think the challenge of the future is finding the general API that can negotiate all the things and make all the parts communicate, the kernel if you want. We can determine optimum speech algorithms, babel communication, create seeing eyes that recognize objects, optimize forms that can negotiate physical terrain, work out what is meant in human expression, but it's not until all these units work together that the 'AI' will seem seamless in human terms. All of those parts have discreet forms, they generate a lineage of algorithms from iterations based on code, languages often derived from need. A Lisp might be the best way of interpreting language, a Haskell might be work best for defining strict biomechanics and area physics. Different abstractions are better for the results they are designed to intuit. But when we are to create the ultimate neural net, the composite of all these machine languages that are constantly required to optimize beyond human intelligible understanding, what will be using? What structure will state 'this works good enough' to not bother with the computation any more - in the familiar context of why don't our eyes have faster frame rate, need better detail, or need us to see into UV. What regulates such a machine, and how does a machine understand failure without guidance? I like to think of these questions when I see rough examples posited around potentials in machine learning. Getting one human system sorted is one thing, communicating the results to other sub-systems an optimize concurrent results is another. The data model is too huge to even comprehend! I'm just excited that these things exist, that there are individuals, research groups and companies looking at the what makes us 'us'. It might help us unlock the features of the brain and evolution.. Used for commercial gain - who cares, just a small cog, with revenue to continue development. Just going to add my favourite example of machine learning, not because it's 'best' but because it's so dynamic that you feel the wonder. http://www.goatstream.com/research/papers/SA2013/ http://www.goatstream.com/research/papers/SA2013/
known 11y ago
No alternative to AI for Google http://www.bbc.co.uk/news/technology-23866614 http://www.bbc.co.uk/news/technology-23866614
graycat 11y ago
Tech hype is a little like old spontaneous combustion of some oily rags in the corner: No telling just when they might ignite, but when they do the result can be a big fire, for a short while. Once the hype gets a flicker, there are good sources of more fuel to make the fire bigger. E.g., the situation is old, say, back to the movie Lawrence of Arabia where a news reporter was talking to Prince Faisal and said: "You want your story told, and I desperately want a story to tell.". So, tech people who want their story told get with tech journalists who desperately want a story to tell. One such case doesn't mean very much, but once the fire starts, more techies and more journalists do the same because the fact that there are already lots of stories gives each new story some automatic credibility. But, fairly soon the stories get to be about the same, with little visible progress (usual situation in reality), and interest falls, the bubble bursts, becomes yesterday's news. Then, the world moves on to another source of a hype conflagration, bubble, viral storm, whatever. For AI, by 1985 DARPA funding at the MIT AI Lab had gotten AI going. There were expert systems and more. Lots of hype. In a few years, the fire went out, the bubble burst, and there was AI winter. For the next bubble, say, System-K (right, doesn't mean anything), print up some labels about System-K. Then order a gross of children's bubble bottles, right, soapy water with a plastic stick with a circle at the end good for blowing bubbles. Put the labels on the bottles and send them to various departments at Stanford, start up companies in Silicon Valley, VC firms on Sand Hill Road, and tech journalists. Then stand back and watch the media conflagration for System-K! So, get stories: "System-K -- Next Big Thing" "System-K Deep Background" "Ex-Googlers Respond on System-K" "System-K, Son of AI" "Leading VC Talks about System-K" "Silicon Valley Goes All in on System-K" "System-K, Bigger Than the Internet" "The First System-K Unicorn?" "System-K Trending up"
varelse 11y ago
I can understand Amit Singhal's opposition to replacing hand-coded features with machine learning models. He's right that ML models have bizarre failure cases across large sample sizes, but he's apparently career-endingly wrong to seemingly believe that one cannot do anything about it. He's also wrong IMO to not recognize that hand-crafted signals and features lack bizarre failure cases themselves. IMO this shifts the focus from lovingly hand-crafted signals and features to lovingly hand-crafted loss functions and variants of boosting and training algorithms to address those bizarre failures as they occur. For example, recently much ado was made about minimal changes to the input data to image recognition convolutional nets to spoof the object ID. And the simplest remedy is to augment the training data with these cases and perhaps boost the gradients of outputs that are wrong. It's not perfect, but Google search was never perfect either. Evidence: I was on the Google search team for a bit and we had all sorts of meetings to address such failures as they happened. While I agree that the quality of Google technical searches has declined dramatically recently, I believe there's huge opportunity to fix them by understanding why the ML models are failing (shooting from the hip, I suspect it's a long-tail problem writ large) and changing the loss functions, models and training algorithms to address these failures as they're detected. Anything less IMO is a failure of imagination in an age of 6.6 TFLOPS for ~$1000 and the ability to stuff 8 of them into a $20K server and go wild.
- tyingq 11y ago
  Older quora answer from a googler citing some of Amit's thoughts on the matter: https://www.quora.com/Why-is-machine-learning-used-heavily-for-Googles-ad-ranking-and-less-for-their-search-ranking https://www.quora.com/Why-is-machine-learning-used-heavily-f...

162 points by nzonbi 11y ago

With the Google Knowledge Graph they don't need the rest of the web. It's starting to get rare to see organic results to web pages more than most.

lowglow 11y ago

I can't be the only one that considers all these AI articles just smoke and mirror puff pieces to prop up a company's value by capitalizing on the hype (hysteria?), can I? I think the first flag is that the journalists don't seem to really understand the technical capabilities or limitations of current ML/AI applications. They accept grandiose claims at face value because there is no way to measure the real potential of AI (which the promise of seems limitless, so anything appears plausible especially coming from a big company like GOOG). I think there are a couple of really overhyped areas right now, AR/AI/ML and IoT/IoE. Now while I don't mind the attention and money being thrown at tech, I can't help but feel we're borrowing more against promises, hopes, and dreams, while simultaneously under-delivering and I think that's going to hurt tech's image and erode investor confidence sooner than later.

hiddencost 11y ago
A lot of things that were impractical 5-10 years ago are now moving out of the domain of the biggest companies to the smaller ones. Applications involving computer vision and speech recognition are now buildable by small companies, which will hopefully yield a proliferation of really interesting novel applications. Sure, we don't have terminator-style AI, but honestly people using the term AI need to shut up, a lot. There is no AI, these days, just massively creaky giant ML systems with a host of ph.d's being thrown onto the fire to keep them running. But the ML applications are super cool.
- Houshalter 11y ago
  We lost the word "AI" literally decades ago. Everything from search algorithms to machine learning to genetic algorithms to video game bots are called AI. The cool kids use the word "AGI" now. There is a common expression that "AI is whatever computers can't do yet." At one time computers couldn't play board games or do vision, so those things were called AI.
  - throwaway873719 11y ago
    It seems like your first and second paragraphs express opposite opinions. In the former you seem dismayed by the over-application/overhyping of the term "AI"; in the latter you seem frustrated by the high and ever-increasing bar for categorizing systems as "AI". Am I misinterpreting you?
    - Houshalter 11y ago
      My comment can be read two ways, and neither way is wrong. I wasn't really expressing an opinion as much as bringing up relevant facts. People label things that we don't know how to do yet "AI". And then when these hard problems are solved, they seem like they are not really "intelligent". This leads to both overuse and trivialization of the word, but also moving goal posts for the field. And actual progress isn't taken seriously, because nothing feels like intelligence when you understand it. It's all a Mysterious Answer (http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_questions/ http://lesswrong.com/lw/iu/mysterious_answers_to_mysterious_...), a codeword for magic (http://lesswrong.com/lw/ix/say_not_complexity/ http://lesswrong.com/lw/ix/say_not_complexity/).
- joe_the_user 11y ago
  The Chinese curse might be now updated to "may you wind-up dependent on 'really interesting novel applications'..." Machine learning-derived applications are impressive and give a good show until one winds up in a situation where they are expected to work reliably. Sure, it's nice that the insurance company's phone-based, voice-recognition-driven, registration/etc system can understand 99% of the choices people give them - except total failure in that 1% is actually going to leave a large population unserved and angry. Of course the company has keypad backup - except they don't 'cause that would cost the money they claimed voice recognition could save, etc. Machine learning apps are great for situations where 1) You don't expect 100% reliably and the degree of non-reliably doesn't have to even be quantified. 2) Either you are accept that they'll degrade over time and have an army of tuners and massive data collect to keep that from happening OR you are dealing with an environment you completely control. This is kind of the conditions for regular automation - except even more so.
xxs 11y ago
>>I can't be the only one that considers all these AI articles just smoke and mirror puff pieces to prop up the companies value by capitalizing on the hype (hysteria?) You're not. So far I have not seen/heard anything remotely resembling AI. Neural nets are just weighted graphs.
- voodoomagicman 11y ago
  Yeah, but maybe that is all brains are too?
  - riffraff 11y ago
    they might be, but * we still don't know if they are * we still don't know how they effectively work * the scale of the things is far from the computational capabilities of my phone (10^11 neurons and 10^15 synapses for a human brain) (But of course AI means a lot of things, not just "thinking androids")
    - oilywater 11y ago
      Well, in 2025 it will be exactly at the computational capability of a brain.
- vonnik 11y ago
  Is anyone arguing that machine learning is not AI? The gist of the article is that Google is leaning toward ML/DL and away from the rules engines/Knowledge Graph. The headline is a shorthand, which, although it could be more precise, is not inaccurate.
  - igravious 11y ago
    Interesting that you feel that. The article mentions nothing about Google's Knowledge Graph. I don't have any privileged insight into Google, just the same surface data as the rest of you all - but I would say that, if anything, Google's Knowledge Graph can fit with _both_ a rules engine strategy and a machine learning one. How is Google going to "organise the world's information" unless it has a model of how all the facts in the world line up? That model is the Knowledge Graph. How does Google intend to map queries that it has never seen before to pages in its vast index? With the help of the Knowledge Graph and natural language processing and machine learning. I'm going to try to articulate something here that I've not fully worked out but that I'm sort of intuiting so cut me some slack for the next paragraph :) People keep banging on about machine learning and the impact that it is having. This is undeniable. But we can see even from AlphaGo that a hybrid approach that combines artificial neural nets with some sort of symbolic system outperforms neural nets on their own. For AlphGo that symbolic system is tied to the mechanics of the game of Go. For internet search that symbolic system is a generalised knowledge graph. Do you get what I mean? I'd love to hear what others think …
hh2222 11y ago
And ... I can't be the only one that thinks Google search results just aren't that good anymore.
- scholia 11y ago
  I also think the quality has gone down. But it seems to me it's at least partly deliberate: the quality has gone down because "freshness" has gone up.
  - bones6 11y ago
    Sometimes I get the vibe that it's a weird self-fulfilling prophesy of terrible SERPs. You reward freshness too much, so then people play that game. But you also punish duplicate content at the same time so the best content, if it already exists but is not fresh, naturally has to fall off.
- jbhatab 11y ago
  I couldn't disagree more. They have only gotten better every single year for me. Undeniably better every year.
- awqrre 11y ago
  I really wish that I could switch back to google's algo from 10 years ago...
  - minwcnt5 11y ago
    That is pure nostalgia talking. I strongly suspect that if you did you would be appalled and want to switch back after only a few searches. Search in 2016 is leaps and bounds beyond what it was in 2006.
    - CaptSpify 11y ago
      This is very much a YMMV thing, but what improvements have you seen?
bduerst 11y ago
We've had artificial intelligence since you could play a computer at chess, but the expectation has always been a mechanized Arnold Schwarzenegger or Hal 9000. The difference today is the scale at which it operates in our daily lives, and the accelerated rate at which it is growing.
teaneedz 11y ago
Thank you. Exactly my feelings.
Fiahil 11y ago
You can add to this, the gigantic misuse of the term "AI" when journalists really want to say "robotics". I don't mind clever shortcuts when you need to explain something abstract or invisible to non-technical people, but, at some point, somebody will have to tell them they're two completely different fields.
rsingel 11y ago
I thought the same thing when I saw the title. And then I saw the byline: Cade Metz. And I read the story. Metz understands tech. Metz got this story right. So while you may be right that many AI articles are smoke-and-mirrors from journalists who don't get the tech, I think you picked the wrong article to make that point about.
- SixSigma 11y ago
  > The truth is that even the experts don’t completely understand how neural nets work. That is not understanding.
smira7 11y ago
The funny part is that it's not illegal for AI to foment.
tim333 11y ago
I wouldn't say this particular article is a smoke and mirror puff piece to prop up the companies value. Now other ones you could argue that. 'The Grid' springs to mind. (https://www.quora.com/Has-anyone-heard-about-the-Grid-AI-websites-that-design-themselves-Does-it-really-work-and-can-it-replace-the-manual-way-of-building-a-website https://www.quora.com/Has-anyone-heard-about-the-Grid-AI-web...)
putaside 11y ago
Winter is coming.
paganel 11y ago
> I think there are a couple of really overhyped areas right now, AR/AI/ML and IoT/IoE. Now while I don't mind the attention and money being thrown at tech, I can't help but feel we're borrowing more against promises, hopes, and dreams, while simultaneously under-delivering and I think that's going to hurt tech's image and erode investor confidence sooner than later. I became interested in natural language processing in the early 2000s, more as a hobby and as part of my personal projects, but even so, I remember that back then most of the AI-related discussions on things like forums and mailing lists were mentioning the AI winter as the big bad wolf that had killed an entire industry. It also killed LISP, they were saying. Interesting to see that that memory seems to have faded away to the distant past.
bitcuration 11y ago
yep, this apparently has been a wall street operation than anything else. Google needs the capital to transform and sustain the decline of web search revenue.

ohitsdom 11y ago

> The truth is that even the experts don’t completely understand how neural nets work. I'm no AI/ML expert, but I can't believe this is true... Is it?

hiddencost 11y ago
Extremely true.
oh_sigh 11y ago
Abstractly, we understand how neural nets work. However, looking at a specific trained neural net, it can be difficult to determine the exact reason why certain weights are they way they are, and what effect they have on the whole. Just like how we can know somewhat how neurons work from a modeling perspective, but when you bundle millions of them together, what exactly each one is doing is not quite clear.
- hiddencost 11y ago
  We understand how they work mechanically, but not why they work from a theoretical stand-point (which is I assume what you're trying to say by "abstractly"). Why it is that ASGD and backprop converges on a non-convex optimization problem, and what kinds of model topologies make it do better / worse? That's all basically art right now.
  - Houshalter 11y ago
    No that's not what he's trying to say. We know why gradient descent and back-propagation works. But no one understands what an actual trained neural net is doing. You can look at the weights, and you can watch the inputs and outputs, but it is very difficult to understand why it does what it does. It just fit a model to data, but there is no explanation why that model is best. The weights are not interprettable by humans. There have been some attempts at making models which humans can interpret. One program named Eureqa fit the simplest possible mathematical expression possible to a set of samples. A biologist tried it on his data and found that it created an expression which actually fit the data really well. But he couldn't publish it because he couldn't explain why it worked. It just did. But there was no understanding, no explanation.

rifung 11y ago

> At one point, Google ran a test that pitted its search engineers against RankBrain. Both were asked to look at various web pages and predict which would rank highest on a Google search results page. RankBrain was right 80 percent of the time. The engineers were right 70 percent of the time. I don't really understand the point of this metric. Why are they predicting what ranks highest on Google search? Wouldn't a better metric be who predicts the correct place a user was looking for? Is the thinking that if they are using machine learning, than whatever the user is looking for should have bubbled up to the top?

speeder 11y ago
I am in the recent days having the impression that Google whatever it is doing is focused more and more in presenting to the user google biggest clients, and hoping that it will be useful. Because I am having more and more trouble finding what I want, people used to consider me a master of google fu, finding whatever random stuff they wanted, now I am struggling, specially after google changed the + and "" meaning (+ went from "mandatory" to mean "google plus search" and " went to mean "literal string" to mean "a sort of mandatory thing") If I need to find some obscure term, I know now that google won't find it, despite finding that same term in the past, finding pages with a certain information on it never happen anymore, even using the "" thing. For example I own a ASUS N46VM laptop with nVidia Optimus... this laptop is terrible, and I am always having to look online how to make it behave properly, before the "+" change, I could type +N46VM and be guaranteed I only would get relevant ifnormation... recently I was desperately searching for some stuff, and found out no matter what I input on google, it returned completely bogus results, where the string N46VM was nowhere in the page, not even in the "time" dimension (ie: if I load the page on archive.org for example and scan every version of it, N46VM never had been on it, google just heuristically decided the page was relevant and gave it to me wrongly). EDIT: I am having some success with DuckDuckGo although their research system is clearly cruder than google, having much less heuristics and whatnot, frequently I find the stuff I want easier on DuckDuckGo anyway, after some pages of browsing results... while on google I browse 40 pages and all of it is completely irrelevant and unrelated (while on DuckDuckGo it shows me 40 pages with the term I want, but in the wrong context).

Houshalter 11y ago

This is very interesting. As late as 2008, Google said they don't use any machine learning in search. Everything was hand engineered with tons of heuristics. They said they didn't trust machine learning, and that it created bizarre failure cases.

adenadel 11y ago
Do you have a source for that? It's a really fastcinating claim that I'm interested in reading more about.
- dhj 11y ago
  I believe it considering 2006-2008 was when all the deep learning pieces came together (some parts were decades old, some 5 years, some 2 years). Google's main push in ML is with deep learning. Although, I would like to see the source too. Tried to find it using Google, but no luck! :)
  - dvlat 11y ago
    "Are Machine-Learned Models Prone to Catastrophic Errors?" http://anand.typepad.com/datawocky/2008/05/are-human-experts-less-prone-to-catastrophic-errors-than-machine-learned-models.html http://anand.typepad.com/datawocky/2008/05/are-human-experts...
- Houshalter 11y ago
  Here: http://www.zdnet.com/article/peter-norvig-on-googles-mistrust-of-machine-learning/ http://www.zdnet.com/article/peter-norvig-on-googles-mistrus...
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wangii 11y ago

One part of me truly hope Google to success in ML/AI, although I consider Google an evil company. AI, Singularity, they are the most important things in this century. The implication is simply beyond our imagination. I don't care too much if Skynet takes over the earth and kicks human being into the dustbin. If it's the destiny, so be it. Another part of me believe it's a sign of folks don't know what they are doing, writing. How can we achieve AI without understanding? Google will fall apart.

osmode 11y ago

There is a tendency among non-technical admirers of ML to regard deep learning methods as beyond their creators: independent entities that will one day, given refined enough algorithms and enough energy, out-comprehend their human creators and overwhelm humanity with their artificial consciousnesses. The term “neural networks” is itself a misnomer that doesn’t at all reflect the complexity of how human neurons represent and acquire information; it’s simply a term for nonlinear classification algorithms that began catching on once the computing power to run them emerged. The question of whether or not deep neural networks are capable of “understanding” is largely a theoretical concern for the ML practitioner, who spends the bulk of his or her time undertaking the hard work of curating manually labeled data, fine-tuning his or her neural classifier with methods (or hacks) such as dropout, stochastic gradient descent, convolution and recursion, to increase its accuracy by a few fractions of a percentage point. Ten or twenty years from now, I imagine we’ll be dealing with a novel set of ML tools that will evolve with the rise of quantum computing (the term “machine learning” will probably be ancient history, too), but the essence of these methods will probably remain: to train a mathematical model to perform task X while generalizing its performance to the real world. As fascinating and exciting as this era of artificial intelligence is, we should also remember that these algorithms are ultimately sophisticated classifiers that don't "understand" anything at all.

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proc0 11y ago
This is true of ANN, and deep learning. They are mathematical models of learning that are finally practical after a couple decades (not to diminish anything the researchers have accomplished which is incredible). Then there are biologically inspired neural networks, like Hierarchical Temporal Memory (HTM), that actually correlate directly to how the cortex in mammals work. These have also demonstrated learning capabilities, and seem a lot more promising in the road map to general AI, in my opinion, because after all we should be piggy-backing on evolution (not that we can't find a mathematical model first). So yeah, the hype is just hype, but it could be justified for the wrong reasons if we see breakthroughs in biologically inspired AI (the Brain Project, to name another example).

reza_n 11y ago

RIP big data. Hello AI. Makes sense, data drives a lot of 'AI' tech. I guess what I find amusing is the push from Google to rebrand themselves as an AI company. My guess is it won't be too long until we see everyone else jumping in the AI branding boat. That will kind of dilute a lot of what is being done.

amelius 11y ago

The current trend seems to be to put a human behind a web API. I guess when AI is sufficiently advanced, those humans can be seamlessly replaced by computers.

hyperpallium 11y ago

I recall google engineers complaining that their clever insightful carefully engineered code was soundly beaten by a statistical approach. The current approaches aren't so much AI as having really, really, ridiculously large datasets.

jonesb6 11y ago

https://en.wikipedia.org/wiki/List_of_fallacies https://en.wikipedia.org/wiki/List_of_fallacies If anyone wants to practice their critical think skills, see how many fallacies you can spot in this article.

inaudible 11y ago

I don't quite understand why people want to dismiss examples of machine learning as valid techniques for understanding the human environment.. It's not as if the human brain was built and guided from nothing, many of the same adaptive principles are as present in our minds as they are in other mammals and equally so from where all the branches divide. Even tiny organisms. And we seems to center the brain at the core of humans intelligence, when there's a range of chemical and metabolic coordination going that might bypass the brain entirely. It's efficient, failure resistant models that matter. We're talking about accelerated learning, finding the models that work out of all those many iterations that fail. You can model it, decompile the results and try to understand and emulate what makes things seem real, but we don't even need to analyze it, because case by case it changes and it's circumstance makes things very different. 'Many ways to skin a cat'. I think the challenge of the future is finding the general API that can negotiate all the things and make all the parts communicate, the kernel if you want. We can determine optimum speech algorithms, babel communication, create seeing eyes that recognize objects, optimize forms that can negotiate physical terrain, work out what is meant in human expression, but it's not until all these units work together that the 'AI' will seem seamless in human terms. All of those parts have discreet forms, they generate a lineage of algorithms from iterations based on code, languages often derived from need. A Lisp might be the best way of interpreting language, a Haskell might be work best for defining strict biomechanics and area physics. Different abstractions are better for the results they are designed to intuit. But when we are to create the ultimate neural net, the composite of all these machine languages that are constantly required to optimize beyond human intelligible understanding, what will be using? What structure will state 'this works good enough' to not bother with the computation any more - in the familiar context of why don't our eyes have faster frame rate, need better detail, or need us to see into UV. What regulates such a machine, and how does a machine understand failure without guidance? I like to think of these questions when I see rough examples posited around potentials in machine learning. Getting one human system sorted is one thing, communicating the results to other sub-systems an optimize concurrent results is another. The data model is too huge to even comprehend! I'm just excited that these things exist, that there are individuals, research groups and companies looking at the what makes us 'us'. It might help us unlock the features of the brain and evolution.. Used for commercial gain - who cares, just a small cog, with revenue to continue development. Just going to add my favourite example of machine learning, not because it's 'best' but because it's so dynamic that you feel the wonder. http://www.goatstream.com/research/papers/SA2013/ http://www.goatstream.com/research/papers/SA2013/

known 11y ago

No alternative to AI for Google http://www.bbc.co.uk/news/technology-23866614 http://www.bbc.co.uk/news/technology-23866614

graycat 11y ago

Tech hype is a little like old spontaneous combustion of some oily rags in the corner: No telling just when they might ignite, but when they do the result can be a big fire, for a short while. Once the hype gets a flicker, there are good sources of more fuel to make the fire bigger. E.g., the situation is old, say, back to the movie Lawrence of Arabia where a news reporter was talking to Prince Faisal and said: "You want your story told, and I desperately want a story to tell.". So, tech people who want their story told get with tech journalists who desperately want a story to tell. One such case doesn't mean very much, but once the fire starts, more techies and more journalists do the same because the fact that there are already lots of stories gives each new story some automatic credibility. But, fairly soon the stories get to be about the same, with little visible progress (usual situation in reality), and interest falls, the bubble bursts, becomes yesterday's news. Then, the world moves on to another source of a hype conflagration, bubble, viral storm, whatever. For AI, by 1985 DARPA funding at the MIT AI Lab had gotten AI going. There were expert systems and more. Lots of hype. In a few years, the fire went out, the bubble burst, and there was AI winter. For the next bubble, say, System-K (right, doesn't mean anything), print up some labels about System-K. Then order a gross of children's bubble bottles, right, soapy water with a plastic stick with a circle at the end good for blowing bubbles. Put the labels on the bottles and send them to various departments at Stanford, start up companies in Silicon Valley, VC firms on Sand Hill Road, and tech journalists. Then stand back and watch the media conflagration for System-K! So, get stories: "System-K -- Next Big Thing" "System-K Deep Background" "Ex-Googlers Respond on System-K" "System-K, Son of AI" "Leading VC Talks about System-K" "Silicon Valley Goes All in on System-K" "System-K, Bigger Than the Internet" "The First System-K Unicorn?" "System-K Trending up"

varelse 11y ago

I can understand Amit Singhal's opposition to replacing hand-coded features with machine learning models. He's right that ML models have bizarre failure cases across large sample sizes, but he's apparently career-endingly wrong to seemingly believe that one cannot do anything about it. He's also wrong IMO to not recognize that hand-crafted signals and features lack bizarre failure cases themselves. IMO this shifts the focus from lovingly hand-crafted signals and features to lovingly hand-crafted loss functions and variants of boosting and training algorithms to address those bizarre failures as they occur. For example, recently much ado was made about minimal changes to the input data to image recognition convolutional nets to spoof the object ID. And the simplest remedy is to augment the training data with these cases and perhaps boost the gradients of outputs that are wrong. It's not perfect, but Google search was never perfect either. Evidence: I was on the Google search team for a bit and we had all sorts of meetings to address such failures as they happened. While I agree that the quality of Google technical searches has declined dramatically recently, I believe there's huge opportunity to fix them by understanding why the ML models are failing (shooting from the hip, I suspect it's a long-tail problem writ large) and changing the loss functions, models and training algorithms to address these failures as they're detected. Anything less IMO is a failure of imagination in an age of 6.6 TFLOPS for ~$1000 and the ability to stuff 8 of them into a $20K server and go wild.

tyingq 11y ago
Older quora answer from a googler citing some of Amit's thoughts on the matter: https://www.quora.com/Why-is-machine-learning-used-heavily-for-Googles-ad-ranking-and-less-for-their-search-ranking https://www.quora.com/Why-is-machine-learning-used-heavily-f...