7 ms·

Facebook Must Really Suck At Machine Learning

122 points by ktamiola 10y ago

deleted 10y ago
[deleted]
mentat 10y ago
I challenge anyone to put together a robust definition of what "real news" is. This is a fundamentally unsolved problem at the philosophical level.
- hota_mazi 10y ago
  Reporting an event that actually happened. How hard a concept is this, really?
  - mentat 10y ago
    And that report is just the facts? I haven't seen real news in a couple decades then. Better just filter it all.
    - yosito 10y ago
      Fake news is saying that George Soros has voting machines that are rigging the election. It's just not true no matter how much people want to hear it. Real news is saying that Trump wants to address immigration by building a wall. People may disagree about what that means, but it's still a true story.
  - yosito 10y ago
    You can't tell me that Clinton lost the election. You're just biased against democrats. ;)
  - pixl97 10y ago
    Very. Prove I went to a restaurant yesterday. You could show a video of me at a restaurant, but is it from yesterday, or some other day. Is it really me there, or someone that looked like me.
    - hota_mazi 10y ago
      You are describing the difficulty of proving that an event really happened. The discussion is about the definition of "real news". Two very different things.
      - dingaling 10y ago
        Well perhaps that is the problem. "The news" used to be about events that had happened, often several days in the past due to the propagation speed of information. Nowadays "the news" also includes: 1. What someone says might be happening right now. 2. What someone says about something that happened, or which might be happening right now. 3. What someone thinks might happen next. None of which are necessarily true or grounded in fact but all of which are valued by the media in the race to be first with the headlines. So perhaps "real news" could be defined as "events which have occurred and for which we have objective proof" and everything else is just "editorial".
  - tomp 10y ago
    Most mainstream media post interpretations, not facts. E.g. that Trump is "racist" (based on some comments about some, not even all, Mexican, who aren't even a race!).
- sulam 10y ago
  I'd start with: news posted on sites that are fake. What's a fake site, you say? Well, how about one that claims to be a site that it is obviously not. IOW, this is very similar to a problem we expect companies to be able to solve: identifying phishing sites. If a site claims to be ABC News, a known media entity, and it clearly is not, I'd argue anything posted on it should be considered fake.
  - sndean 10y ago
    This is probably a good way to decide, since it's relatively easy to determine whether a new site is fake. Another example, the "Denver Guardian": http://www.denverpost.com/2016/11/05/there-is-no-such-thing-as-the-denver-guardian/ http://www.denverpost.com/2016/11/05/there-is-no-such-thing-...
- daveFNbuck 10y ago
  You don't need a robust definition to implement something practical that deals with the clear cases.
- sytelus 10y ago
  Challenge accepted. Define "news" as probabilistic statement of an event. Define "real news" as news with probability > e, where e is some chosen threshold value.
- username223 10y ago
  Fortunately, Facebook only has to solve the much simpler problem of identifying clearly fake news. This is like determining that a site is a link farm, not that it is a content farm. (I think Google has even made decent progress on the latter, harder problem, though I still see ask.com results sometimes.)
- burkaman 10y ago
  Anything pertaining to recent events that has more facts than opinions, and no falsehoods.
myf01d 10y ago
Facebook actually sucks at most things, but it doesn't matter as long as the stock goes up and it generates more revenues.
tall 10y ago
It's not a talent problem. It's a culture/ management issue.
BucketSort 10y ago
This angers the LeCun
mattzito 10y ago
My suspicion is that it's not that Facebook sucks at machine learning, but that they are scared of public backlash when their algorithm accidentally takes a story from Breitbart and classifies it (arguably correctly) as fake news. At that point Facebook is derided as a tool of the liberal msm (after all it's run by liberal Silicon Valley technocrats), and from there eyeballs go elsewhere and Facebook is harmed. Better for them to feign ignorance and lie to themselves.
- tomp 10y ago
  Funny you mention Breitbart, it's one of the rare media that didn't predict a landslide victory for Hillary.
  - notheguyouthink 10y ago
    I don't have a dog in this show, but wouldn't that be quite reasonable/expected if they are skewed towards Republican/Trump?
    - snewk 10y ago
      i think the bigger question in wake of hillary's missing landslide, is whether facebook may actually be classifying CNN et. al. as 'fake' news.
      - tomp 10y ago
        A better example would be NYT or WSJ, as CNN is obviously fake news [1]. [1] https://www.washingtonpost.com/news/volokh-conspiracy/wp/2016/10/17/remember-its-illegal-to-possess-wikileaks-clinton-emails-but-its-different-for-the-media-says-cnns-chris-cuomo/ https://www.washingtonpost.com/news/volokh-conspiracy/wp/201...
      - serge2k 10y ago
        Poor polling data doesn't mean you are publishing fake news.
    - 10y ago
betolink 10y ago
Their news feed sucks big time, and its' not just a facebook issue, most social media outsmarted themselves with ML.
- grzm 10y ago
  My (limited) experience with feeds online has been disappointing. Do you have examples of good feed implementations, especially at larger scales, such as what Facebook works at? What tech do they use?
  - tedmiston 10y ago
    Feedly's Today page kind of feels like a news feed, though of RSS feeds which I've subscribed to by hand. That said, I believe it's just selecting the most viewed stories over a very recent period from your most popular feeds (some ambiguity here).
- betolink 10y ago
  People change, we like different stuff every day, with these curated feeds we eventually end up seeing only a few things because the algorithms are designed to predict patterns using very very subjective parameters.
jwtadvice 10y ago
The problem is much more difficult than creating a classifier. The purpose of filtering news is to control the impact, not to control the tactic. The last time Facebook issued a surge to control 'spam' posts for the same government concerns they seek to deal with "fake news" today, they took out pro-Sanders articles, censored sharing of the Snowden documents, prevented people from linking to Wikileaks, and shut down Facebook organizing for May Day protests. Facebook regularly censors and directs conversation online around the world to the whims of various regimes and administrations, but it's just not easy "use machine learning". The issue is that machine learning is inherently modeled to solve decision problems in spaces that separate high level 'features' from low level observations. The study and practice of machine learning thus far has modeled noise, uncertainty and complex state spaces. However, machine learning classifiers do not themselves have a concept of adversarial data: this led, for example, to spammers who would "poison" machine learning classifiers by biasing them to learn the wrong things, so that they could get their spam through. Similarly, not too long ago, Microsoft put a conversation bot on the web that 'learned the wrong things' because people approached the bot in an attempt to trick it. Facebook's requirement for battling unsanctioned opinions is that it solves this problem - it hasn't been solved yet. Facebook needs to continuously monitor the adapting activity and patterns of people who are trying to say things that governments and Facebook customers don't want people saying. Basically: machine learning doesn't adapt but people do, and naive attempts to get machine learning to adapt make it vulnerable to being tricked, because classifier theory inherently has no concept of adversarial data.
- mathperson 10y ago
  umm no!...I don't know too much about this sub-field..but there are machine methods designed explicitly for adversarial data. it even has a wikipedia page! I don't know if facebook's methods implement any of this yet..but your statements about machine learning not modelling this just ain't so. https://en.wikipedia.org/wiki/Adversarial_machine_learning https://en.wikipedia.org/wiki/Adversarial_machine_learning http://www.cs.virginia.edu/~evans/ http://www.cs.virginia.edu/~evans/
  - jwtadvice 10y ago
eip 10y ago
That's what they want you to think.
nkurz 10y ago
It would help me if the author were to point more examples of what he means by "fake news". As it is, I can't really tell if he's really complaining about "Onion" style satire, copypasta with clickbait titles, evil government propaganda, or just political opinions that he disagrees with. Does a public training set exist that makes it clear what he's talking about? Does the division point he wants actually exist? Snopes published an article yesterday asking that asks the question more clearly: There is much bad news in the online world, but not all of it is "fake" (i.e., completely fabricated information that has little or no intersection with real-world events). There are also partisan political sites that take nuggets of real news and spin them into highly distorted, clickbait articles. There are sites that misleadingly repackage old news as if it were current information. There are sites that aggregate articles from a variety of dubious and questionable sources. There are sites (especially in the fields of health and science) that believe they're presenting pertinent information but are woefully inaccurate in their information-gathering and reporting. These forms of news are all bad in one way or another, but broadly classifying all such information as "fake news" clouds an already confusing issue. http://www.snopes.com/2016/11/17/we-have-a-bad-news-problem-not-a-fake-news-problem/ http://www.snopes.com/2016/11/17/we-have-a-bad-news-problem-...
- sulam 10y ago
  Here's an easy example: http://abcnews.com.co/donald-trump-protester-speaks-out-i-was-paid-to-protest/ http://abcnews.com.co/donald-trump-protester-speaks-out-i-wa... Google is probably just as easy to fool as Facebook. This was the first hit on a search for "protesters paid to protest", a story that is demonstrably fake: https://www.washingtonpost.com/news/the-intersect/wp/2016/11/17/facebook-fake-news-writer-i-think-donald-trump-is-in-the-white-house-because-of-me/?hpid=hp_rhp-top-table-main_fake-news-845a%3Ahomepage%2Fstory https://www.washingtonpost.com/news/the-intersect/wp/2016/11... Immediate red flags should be: This isn't ABC News's website. There are statements in the article like: "No one still has an AOL email address except people that would vote for Hillary Clinton" and from a made up Trump supporter quoted saying "I knew those weren’t real protesters, they were too organized and smart..." and (awesomely) "I think I was paid more than the other protesters because I was white and had taken classes in street fighting and boxing a few years back". I mean, this may well be parody, but it gets picked up as real news and he's making real money off it, impersonating a real news site, which is the point at which it cross the line from parody to fake, IMO.
eva1984 10y ago
No. Google is fooled by fake news too. http://www.cbsnews.com/news/googles-top-search-result-for-final-election-numbers-leads-to-fake-news-site/ http://www.cbsnews.com/news/googles-top-search-result-for-fi... I have some problem with those claims that offer little to none details. Use the above link as an example, how do we teach machine to verify this news? First, we need to teach them to formulate a question, in this case, 'Who has a larger number of popular vote [as of now]?', then it needs to find its way to verify the number from reliable sources, possibly need to struggle between conflicted sources as well. This is no trivial, if we solve this, we might as well solve the problem of understanding human language as a whole. The mentioning of Princeton students' Hackthon project is really offbeat, if the author really bothers to look into what that project actually is, nothing more than an API mashup. https://github.com/anantdgoel/HackPrincetonF16/blob/master/backend/imageverify.py https://github.com/anantdgoel/HackPrincetonF16/blob/master/b... Disclaimer: I worked for a major news app, and the effort to battle fake news or low quality non-trustworthy news is ever growing. While application of machine learning is promising, human, especially experienced human is still the real workhorse for this problem Edit: Typo.
- alanbernstein 10y ago
  You're focusing on the content. Look at the source. 70news.wordpress.com should not be at the top of anybody's list of reliable news sources. Look at the topic (e.g. from simple keywords). Look at the references (urls) mentioned in the article. Look at the timeliness. There is plenty of information available to machine learning algorithms, without even needing to touch the natural language aspect.
  - BinaryIdiot 10y ago
    > You're focusing on the content. That's the only thing you can focus on. Why should someone be punished and labeled as fake if they're on a wordpress.com subdomain? Domain is just out, even spammy or weird looking domains may end up being popular / culturally fit. You would be preventing good journalistic folks from having any type of reach if you want to test against domains names, something that arguable may go away as far as the user is concerned in the next decade. I'm unconvinced there is anything but content that you can really go off of. Keywords are not even used in search results, why would they determine news legitimacy? Even references are not always useful.
yosito 10y ago
My first reaction to this was to object to using machine learning to validate news stories, since it's very hard for a machine to go out into the world and verify that a story is based on reality. But the idea that fake news on Facebook is similar to the content farmed / bot generated / spammy stuff Google tries to eliminate, makes me think. The curious uptick of fake news over the last few months makes me wonder if Facebook either tried to, or was complicit in swaying public opinion before the election. I've long suspected that Google/Facebook technically have enough power to effectively decide an election by manipulating public opinion. But if they were going to do so, they'd attempt to do it in a way that doesn't make them look culpable. Someone should investigate.
deepnotderp 10y ago
Ummm,considering that until recently, FB was second only to google in Deep learning, i would beg to differ.
tumbas 10y ago
we'll they have some top talent money can buy there. Including member of RU academy of science. They can't suck just like that. These people made history and are most probably being watched by any group with any interest in machine learning.
danieltillett 10y ago
The fundamental problem is the mixing of opinion and facts into the same story combined with a complete lack of interest and care by writers in finding out what happened. I want and will pay for a news service which only reports facts (no opinions) and which is concerned with accuracy. There are specialty services that provide this in certain niche areas, but no general service.
snipethunder 10y ago
I think there is also a incentive problem. Internally, from what I know, the performance of a team is always driven much how much metric it can drive, how much incremental revenue it can generate, how much more engagement this feature will drive, etc. The number of contents taken down doesn't seem like a sexy metric, so I assume no team wants to take this unsexy task.
sprobertson 10y ago
Most of the other ML applications listed (driving, voice recognition, content recommendation) are somewhat easy tasks for humans, if not at scale... Reading, understanding and fact checking a news article is comparatively pretty difficult.
kutkloon7 10y ago
Too bad that this clickbait is upvoted. Both the premises reasoning in the article are not sound; filtering fake news is not at all similar to spam filtering, anf even if it were, it still wouldn't be easy as a result. Then there is the false dilemma, and there are probably more fallacies in there, but I won't bother to read this utter bullshit anymore.
braindead_in 10y ago
One person's fake news is another person's entertainment. The problem is not a technical issue. It is a cultural issue. ML cannot solve a cultural issue.
- tedmiston 10y ago
  It'd be cool if I could adjust a parameter on how gossip-y or factual I want my news sources to be. Google News seems to have something in place with the "Interested in? / Not interested" underneath some story abstracts.
  - grzm 10y ago
    Another knob I'd like would be terseness. Sometimes I want a nice, wandering, more literary piece. Sometimes I just want the facts.
    - tedmiston 10y ago
      That would be incredible. The Information is an unpopular, I think even blacklisted, news source here due to their paywall. But they do a really great job writing a very terse and high signal two-sentence takeaway for each article. I'm not a subscriber anymore (too pricy IMO), but here are some recent examples: Surge-Price Builder Leaves Uber By Amir Efrati · Monday Oct 17, 2016 > THE TAKEAWAY: One of Uber’s secret weapons in recent years was an economics professor, Keith Chen. He overhauled Uber’s surge-price algorithm and made other key contributions that helped riders, and Uber, save money. How Dropbox Doubled Down on Business Market By Steve Nellis · Thursday Sep 15, 2016 > THE TAKEAWAY: Dropbox founder Drew Houston has come to terms with the fact that his company probably isn’t the next consumer tech giant like Facebook or Google. That was reflected in a decision halfway through last year to sharpen the company’s focus on the business market, including shutting off consumer-focused apps like Carousel. I would love to have that for all tech news.
      - grzm 10y ago
        I've heard good things about The Information as well. One of the reasons they're able to do that, I suspect, is because it's behind a paywall: they can pay their journalists for quality content. There are ML systems out there for summarization. I wonder how long until the systems are good enough to provide this as well. The examples I've seen (e.g., Summly, which uses SRI tech, IIRC), are likely good enough if you just want the facts. I still want it to be well-written. Ouch. I just checked out the subscription rates for The Information. AFAICT, you can't even see the rates without supplying your name and email address. :/ Edit to add: From other sources on the internet, it looks like subscription rates are $399/year.
bitL 10y ago
A question - does anyone utilize the fact observed in cognitive neuroscience in robotics, that the best learning happens in organically growing systems, not in universal systems where you run a huge batch on a clean, pre-configured state? So far I see everyone designing the structure of an ML system in advance and hoping for the best (e.g. define the whole structure in TensorFlow in the beginning), instead of allowing the system to self-organize as the learning happens.
- tedmiston 10y ago
  Are you referring to reinforcement learning [1] as opposed to "standard" supervised learning? [1]: https://en.wikipedia.org/wiki/Reinforcement_learning https://en.wikipedia.org/wiki/Reinforcement_learning
cocktailpeanuts 10y ago
What a misleading title. The real title says "must suck at" (which probably is insinuating that they probably don't suck), and somehow it's been fabricated into this clickbait title.
- sctb 10y ago
  We reverted the title from the submitted “Facebook Sucks at Machine Learning”.
msie 10y ago
Facebook let itself be bullied by the right.
gr_thrwy 10y ago
Leaving aside the Fake News controversy, given the number of fake accounts polluting their social graph, I would say yes, FB does suck at ML.
psadri 10y ago
My respect for Elad Gil just dropped a notch.
- deleted 10y ago
  [deleted]
- eladgil 10y ago
  Sorry that is your reaction. Will email you to discuss.
mirekrusin 10y ago
When person A says something publicly that is fake and newspaper writes article quoting the person - is the article fake or not? It's very blurred indeed. What would probably help is extracting list of (main) statements/assumptions made and give them fact-probability score (ideally with references) - and show it to people. But this is not trivial exercise, is it? I mean, once we have technology that's good in doing it, well... it means we can probably rely on this AI to make 80% of decisions to run the country.
anonu 10y ago
I think fake news is actually a somewhat intractable problem with the level of ML today... News is usually considered "breaking" and new... meaning there's nothing out there to cross-reference it with. Even if you were able to extract "facts" from an article - you might find it difficult to find any other source (that you trust) that would also be reporting those facts. It is a very difficult problem... I think we will eventually get to a point where we can flag potentially fake news. But were still a few years away with our current technology.
mcbits 10y ago
Facebook's job is to show you what people in your network are sharing with you, filtered to what seems most likely to keep you engaged with the site. It's not to act as peer reviewers for the "news". All these people suddenly complaining about "fake news" are really complaining about the unwashed masses' content preferences and world views. Maybe the urge to complain is justified, but it's aimed in the wrong direction.
whoopdedo 10y ago
If Facebook has a fake news problem it's not in small part because Facebook users will share fake news articles more than real news. Google's decision to not serve AdWords on fake sites is because those sites were taking advantage of the high traffic fake news generates to make money. Fake news is popular. And the entire social economy is based on facilitating that which is popular. Because the more eyes you can drive to your content the more advertising you can sell. While Google vilifies the website for making the fake news, they also profited by selling the AdWords those sites hosted. While Facebook says it's difficult to distinguish legitimate news from fake news, they make more money from the frequently shared fake news than the less popular real news. The conflation of article content with advertising gives the advertising brokers a disincentive to prevent fake articles. Until readers stop clicking on the fake stories, but one glance at a typical supermarket checkout aisle will tell you that isn't going to happen. If Google or Facebook are serious about not encouraging fake news they will stop using the content of a webpage to drive the price of ads. But that obviously isn't going to happen as targeted advertising is their raison d'etre. TLDR, Facebook has met the enemy, and he is Facebook.
captn3m0 10y ago
Surprised no one has pointed out Google's aproach to the problem: https://blog.google/topics/journalism-news/labeling-fact-check-articles-google-news/ https://blog.google/topics/journalism-news/labeling-fact-che... They leave the fact-check to the publisher and ask them to use http://pending.schema.org/ClaimReview http://pending.schema.org/ClaimReview schema to tag news as fact-checked. Already in use by some publications. Announced almost a month before the election day (Oct 13).
throwaway9834 10y ago
There is a large amount of (possibly dubious) research indicating it is possible to detect whether a speaker is lying by examining movements and changes in his face and/or body. There has been a large amount of successful research using neural networks to classify human behaviors (facial expressions, lip reading, etc.) Is it possible to create a system to act as a high quality lie detector? Such software could be a poweful tool in studying crime, politics and business.

122 points by ktamiola 10y ago

deleted 10y ago

[deleted]

I challenge anyone to put together a robust definition of what "real news" is. This is a fundamentally unsolved problem at the philosophical level.

hota_mazi 10y ago
Reporting an event that actually happened. How hard a concept is this, really?
- mentat 10y ago
  And that report is just the facts? I haven't seen real news in a couple decades then. Better just filter it all.
  - yosito 10y ago
    Fake news is saying that George Soros has voting machines that are rigging the election. It's just not true no matter how much people want to hear it. Real news is saying that Trump wants to address immigration by building a wall. People may disagree about what that means, but it's still a true story.
- yosito 10y ago
  You can't tell me that Clinton lost the election. You're just biased against democrats. ;)
- pixl97 10y ago
  Very. Prove I went to a restaurant yesterday. You could show a video of me at a restaurant, but is it from yesterday, or some other day. Is it really me there, or someone that looked like me.
  - hota_mazi 10y ago
    You are describing the difficulty of proving that an event really happened. The discussion is about the definition of "real news". Two very different things.
    - dingaling 10y ago
      Well perhaps that is the problem. "The news" used to be about events that had happened, often several days in the past due to the propagation speed of information. Nowadays "the news" also includes: 1. What someone says might be happening right now. 2. What someone says about something that happened, or which might be happening right now. 3. What someone thinks might happen next. None of which are necessarily true or grounded in fact but all of which are valued by the media in the race to be first with the headlines. So perhaps "real news" could be defined as "events which have occurred and for which we have objective proof" and everything else is just "editorial".
- tomp 10y ago
  Most mainstream media post interpretations, not facts. E.g. that Trump is "racist" (based on some comments about some, not even all, Mexican, who aren't even a race!).
sulam 10y ago
I'd start with: news posted on sites that are fake. What's a fake site, you say? Well, how about one that claims to be a site that it is obviously not. IOW, this is very similar to a problem we expect companies to be able to solve: identifying phishing sites. If a site claims to be ABC News, a known media entity, and it clearly is not, I'd argue anything posted on it should be considered fake.
- sndean 10y ago
  This is probably a good way to decide, since it's relatively easy to determine whether a new site is fake. Another example, the "Denver Guardian": http://www.denverpost.com/2016/11/05/there-is-no-such-thing-as-the-denver-guardian/ http://www.denverpost.com/2016/11/05/there-is-no-such-thing-...
daveFNbuck 10y ago
You don't need a robust definition to implement something practical that deals with the clear cases.
sytelus 10y ago
Challenge accepted. Define "news" as probabilistic statement of an event. Define "real news" as news with probability > e, where e is some chosen threshold value.
username223 10y ago
Fortunately, Facebook only has to solve the much simpler problem of identifying clearly fake news. This is like determining that a site is a link farm, not that it is a content farm. (I think Google has even made decent progress on the latter, harder problem, though I still see ask.com results sometimes.)
burkaman 10y ago
Anything pertaining to recent events that has more facts than opinions, and no falsehoods.

myf01d 10y ago

Facebook actually sucks at most things, but it doesn't matter as long as the stock goes up and it generates more revenues.

tall 10y ago

It's not a talent problem. It's a culture/ management issue.

BucketSort 10y ago

This angers the LeCun

mattzito 10y ago

My suspicion is that it's not that Facebook sucks at machine learning, but that they are scared of public backlash when their algorithm accidentally takes a story from Breitbart and classifies it (arguably correctly) as fake news. At that point Facebook is derided as a tool of the liberal msm (after all it's run by liberal Silicon Valley technocrats), and from there eyeballs go elsewhere and Facebook is harmed. Better for them to feign ignorance and lie to themselves.

tomp 10y ago
Funny you mention Breitbart, it's one of the rare media that didn't predict a landslide victory for Hillary.
- notheguyouthink 10y ago
  I don't have a dog in this show, but wouldn't that be quite reasonable/expected if they are skewed towards Republican/Trump?
  - snewk 10y ago
    i think the bigger question in wake of hillary's missing landslide, is whether facebook may actually be classifying CNN et. al. as 'fake' news.
    - tomp 10y ago
      A better example would be NYT or WSJ, as CNN is obviously fake news [1]. [1] https://www.washingtonpost.com/news/volokh-conspiracy/wp/2016/10/17/remember-its-illegal-to-possess-wikileaks-clinton-emails-but-its-different-for-the-media-says-cnns-chris-cuomo/ https://www.washingtonpost.com/news/volokh-conspiracy/wp/201...
    - serge2k 10y ago
      Poor polling data doesn't mean you are publishing fake news.
  - 10y ago

betolink 10y ago

Their news feed sucks big time, and its' not just a facebook issue, most social media outsmarted themselves with ML.

grzm 10y ago
My (limited) experience with feeds online has been disappointing. Do you have examples of good feed implementations, especially at larger scales, such as what Facebook works at? What tech do they use?
- tedmiston 10y ago
  Feedly's Today page kind of feels like a news feed, though of RSS feeds which I've subscribed to by hand. That said, I believe it's just selecting the most viewed stories over a very recent period from your most popular feeds (some ambiguity here).
betolink 10y ago
People change, we like different stuff every day, with these curated feeds we eventually end up seeing only a few things because the algorithms are designed to predict patterns using very very subjective parameters.

jwtadvice 10y ago

The problem is much more difficult than creating a classifier. The purpose of filtering news is to control the impact, not to control the tactic. The last time Facebook issued a surge to control 'spam' posts for the same government concerns they seek to deal with "fake news" today, they took out pro-Sanders articles, censored sharing of the Snowden documents, prevented people from linking to Wikileaks, and shut down Facebook organizing for May Day protests. Facebook regularly censors and directs conversation online around the world to the whims of various regimes and administrations, but it's just not easy "use machine learning". The issue is that machine learning is inherently modeled to solve decision problems in spaces that separate high level 'features' from low level observations. The study and practice of machine learning thus far has modeled noise, uncertainty and complex state spaces. However, machine learning classifiers do not themselves have a concept of adversarial data: this led, for example, to spammers who would "poison" machine learning classifiers by biasing them to learn the wrong things, so that they could get their spam through. Similarly, not too long ago, Microsoft put a conversation bot on the web that 'learned the wrong things' because people approached the bot in an attempt to trick it. Facebook's requirement for battling unsanctioned opinions is that it solves this problem - it hasn't been solved yet. Facebook needs to continuously monitor the adapting activity and patterns of people who are trying to say things that governments and Facebook customers don't want people saying. Basically: machine learning doesn't adapt but people do, and naive attempts to get machine learning to adapt make it vulnerable to being tricked, because classifier theory inherently has no concept of adversarial data.

mathperson 10y ago
umm no!...I don't know too much about this sub-field..but there are machine methods designed explicitly for adversarial data. it even has a wikipedia page! I don't know if facebook's methods implement any of this yet..but your statements about machine learning not modelling this just ain't so. https://en.wikipedia.org/wiki/Adversarial_machine_learning https://en.wikipedia.org/wiki/Adversarial_machine_learning http://www.cs.virginia.edu/~evans/ http://www.cs.virginia.edu/~evans/
- jwtadvice 10y ago

eip 10y ago

That's what they want you to think.

nkurz 10y ago

It would help me if the author were to point more examples of what he means by "fake news". As it is, I can't really tell if he's really complaining about "Onion" style satire, copypasta with clickbait titles, evil government propaganda, or just political opinions that he disagrees with. Does a public training set exist that makes it clear what he's talking about? Does the division point he wants actually exist? Snopes published an article yesterday asking that asks the question more clearly: There is much bad news in the online world, but not all of it is "fake" (i.e., completely fabricated information that has little or no intersection with real-world events). There are also partisan political sites that take nuggets of real news and spin them into highly distorted, clickbait articles. There are sites that misleadingly repackage old news as if it were current information. There are sites that aggregate articles from a variety of dubious and questionable sources. There are sites (especially in the fields of health and science) that believe they're presenting pertinent information but are woefully inaccurate in their information-gathering and reporting. These forms of news are all bad in one way or another, but broadly classifying all such information as "fake news" clouds an already confusing issue. http://www.snopes.com/2016/11/17/we-have-a-bad-news-problem-not-a-fake-news-problem/ http://www.snopes.com/2016/11/17/we-have-a-bad-news-problem-...

sulam 10y ago
Here's an easy example: http://abcnews.com.co/donald-trump-protester-speaks-out-i-was-paid-to-protest/ http://abcnews.com.co/donald-trump-protester-speaks-out-i-wa... Google is probably just as easy to fool as Facebook. This was the first hit on a search for "protesters paid to protest", a story that is demonstrably fake: https://www.washingtonpost.com/news/the-intersect/wp/2016/11/17/facebook-fake-news-writer-i-think-donald-trump-is-in-the-white-house-because-of-me/?hpid=hp_rhp-top-table-main_fake-news-845a%3Ahomepage%2Fstory https://www.washingtonpost.com/news/the-intersect/wp/2016/11... Immediate red flags should be: This isn't ABC News's website. There are statements in the article like: "No one still has an AOL email address except people that would vote for Hillary Clinton" and from a made up Trump supporter quoted saying "I knew those weren’t real protesters, they were too organized and smart..." and (awesomely) "I think I was paid more than the other protesters because I was white and had taken classes in street fighting and boxing a few years back". I mean, this may well be parody, but it gets picked up as real news and he's making real money off it, impersonating a real news site, which is the point at which it cross the line from parody to fake, IMO.

eva1984 10y ago

No. Google is fooled by fake news too. http://www.cbsnews.com/news/googles-top-search-result-for-final-election-numbers-leads-to-fake-news-site/ http://www.cbsnews.com/news/googles-top-search-result-for-fi... I have some problem with those claims that offer little to none details. Use the above link as an example, how do we teach machine to verify this news? First, we need to teach them to formulate a question, in this case, 'Who has a larger number of popular vote [as of now]?', then it needs to find its way to verify the number from reliable sources, possibly need to struggle between conflicted sources as well. This is no trivial, if we solve this, we might as well solve the problem of understanding human language as a whole. The mentioning of Princeton students' Hackthon project is really offbeat, if the author really bothers to look into what that project actually is, nothing more than an API mashup. https://github.com/anantdgoel/HackPrincetonF16/blob/master/backend/imageverify.py https://github.com/anantdgoel/HackPrincetonF16/blob/master/b... Disclaimer: I worked for a major news app, and the effort to battle fake news or low quality non-trustworthy news is ever growing. While application of machine learning is promising, human, especially experienced human is still the real workhorse for this problem Edit: Typo.

alanbernstein 10y ago
You're focusing on the content. Look at the source. 70news.wordpress.com should not be at the top of anybody's list of reliable news sources. Look at the topic (e.g. from simple keywords). Look at the references (urls) mentioned in the article. Look at the timeliness. There is plenty of information available to machine learning algorithms, without even needing to touch the natural language aspect.
- BinaryIdiot 10y ago
  > You're focusing on the content. That's the only thing you can focus on. Why should someone be punished and labeled as fake if they're on a wordpress.com subdomain? Domain is just out, even spammy or weird looking domains may end up being popular / culturally fit. You would be preventing good journalistic folks from having any type of reach if you want to test against domains names, something that arguable may go away as far as the user is concerned in the next decade. I'm unconvinced there is anything but content that you can really go off of. Keywords are not even used in search results, why would they determine news legitimacy? Even references are not always useful.

yosito 10y ago

My first reaction to this was to object to using machine learning to validate news stories, since it's very hard for a machine to go out into the world and verify that a story is based on reality. But the idea that fake news on Facebook is similar to the content farmed / bot generated / spammy stuff Google tries to eliminate, makes me think. The curious uptick of fake news over the last few months makes me wonder if Facebook either tried to, or was complicit in swaying public opinion before the election. I've long suspected that Google/Facebook technically have enough power to effectively decide an election by manipulating public opinion. But if they were going to do so, they'd attempt to do it in a way that doesn't make them look culpable. Someone should investigate.

deepnotderp 10y ago

Ummm,considering that until recently, FB was second only to google in Deep learning, i would beg to differ.

tumbas 10y ago

we'll they have some top talent money can buy there. Including member of RU academy of science. They can't suck just like that. These people made history and are most probably being watched by any group with any interest in machine learning.

danieltillett 10y ago

The fundamental problem is the mixing of opinion and facts into the same story combined with a complete lack of interest and care by writers in finding out what happened. I want and will pay for a news service which only reports facts (no opinions) and which is concerned with accuracy. There are specialty services that provide this in certain niche areas, but no general service.

snipethunder 10y ago

I think there is also a incentive problem. Internally, from what I know, the performance of a team is always driven much how much metric it can drive, how much incremental revenue it can generate, how much more engagement this feature will drive, etc. The number of contents taken down doesn't seem like a sexy metric, so I assume no team wants to take this unsexy task.

sprobertson 10y ago

Most of the other ML applications listed (driving, voice recognition, content recommendation) are somewhat easy tasks for humans, if not at scale... Reading, understanding and fact checking a news article is comparatively pretty difficult.

kutkloon7 10y ago

Too bad that this clickbait is upvoted. Both the premises reasoning in the article are not sound; filtering fake news is not at all similar to spam filtering, anf even if it were, it still wouldn't be easy as a result. Then there is the false dilemma, and there are probably more fallacies in there, but I won't bother to read this utter bullshit anymore.

braindead_in 10y ago

One person's fake news is another person's entertainment. The problem is not a technical issue. It is a cultural issue. ML cannot solve a cultural issue.

tedmiston 10y ago
It'd be cool if I could adjust a parameter on how gossip-y or factual I want my news sources to be. Google News seems to have something in place with the "Interested in? / Not interested" underneath some story abstracts.
- grzm 10y ago
  Another knob I'd like would be terseness. Sometimes I want a nice, wandering, more literary piece. Sometimes I just want the facts.
  - tedmiston 10y ago
    That would be incredible. The Information is an unpopular, I think even blacklisted, news source here due to their paywall. But they do a really great job writing a very terse and high signal two-sentence takeaway for each article. I'm not a subscriber anymore (too pricy IMO), but here are some recent examples: Surge-Price Builder Leaves Uber By Amir Efrati · Monday Oct 17, 2016 > THE TAKEAWAY: One of Uber’s secret weapons in recent years was an economics professor, Keith Chen. He overhauled Uber’s surge-price algorithm and made other key contributions that helped riders, and Uber, save money. How Dropbox Doubled Down on Business Market By Steve Nellis · Thursday Sep 15, 2016 > THE TAKEAWAY: Dropbox founder Drew Houston has come to terms with the fact that his company probably isn’t the next consumer tech giant like Facebook or Google. That was reflected in a decision halfway through last year to sharpen the company’s focus on the business market, including shutting off consumer-focused apps like Carousel. I would love to have that for all tech news.
    - grzm 10y ago
      I've heard good things about The Information as well. One of the reasons they're able to do that, I suspect, is because it's behind a paywall: they can pay their journalists for quality content. There are ML systems out there for summarization. I wonder how long until the systems are good enough to provide this as well. The examples I've seen (e.g., Summly, which uses SRI tech, IIRC), are likely good enough if you just want the facts. I still want it to be well-written. Ouch. I just checked out the subscription rates for The Information. AFAICT, you can't even see the rates without supplying your name and email address. :/ Edit to add: From other sources on the internet, it looks like subscription rates are $399/year.

bitL 10y ago

A question - does anyone utilize the fact observed in cognitive neuroscience in robotics, that the best learning happens in organically growing systems, not in universal systems where you run a huge batch on a clean, pre-configured state? So far I see everyone designing the structure of an ML system in advance and hoping for the best (e.g. define the whole structure in TensorFlow in the beginning), instead of allowing the system to self-organize as the learning happens.

tedmiston 10y ago
Are you referring to reinforcement learning [1] as opposed to "standard" supervised learning? [1]: https://en.wikipedia.org/wiki/Reinforcement_learning https://en.wikipedia.org/wiki/Reinforcement_learning

cocktailpeanuts 10y ago

What a misleading title. The real title says "must suck at" (which probably is insinuating that they probably don't suck), and somehow it's been fabricated into this clickbait title.

sctb 10y ago
We reverted the title from the submitted “Facebook Sucks at Machine Learning”.

msie 10y ago

Facebook let itself be bullied by the right.

gr_thrwy 10y ago

Leaving aside the Fake News controversy, given the number of fake accounts polluting their social graph, I would say yes, FB does suck at ML.

psadri 10y ago

My respect for Elad Gil just dropped a notch.

deleted 10y ago
[deleted]
eladgil 10y ago
Sorry that is your reaction. Will email you to discuss.

mirekrusin 10y ago

When person A says something publicly that is fake and newspaper writes article quoting the person - is the article fake or not? It's very blurred indeed. What would probably help is extracting list of (main) statements/assumptions made and give them fact-probability score (ideally with references) - and show it to people. But this is not trivial exercise, is it? I mean, once we have technology that's good in doing it, well... it means we can probably rely on this AI to make 80% of decisions to run the country.

anonu 10y ago

I think fake news is actually a somewhat intractable problem with the level of ML today... News is usually considered "breaking" and new... meaning there's nothing out there to cross-reference it with. Even if you were able to extract "facts" from an article - you might find it difficult to find any other source (that you trust) that would also be reporting those facts. It is a very difficult problem... I think we will eventually get to a point where we can flag potentially fake news. But were still a few years away with our current technology.

mcbits 10y ago

Facebook's job is to show you what people in your network are sharing with you, filtered to what seems most likely to keep you engaged with the site. It's not to act as peer reviewers for the "news". All these people suddenly complaining about "fake news" are really complaining about the unwashed masses' content preferences and world views. Maybe the urge to complain is justified, but it's aimed in the wrong direction.

whoopdedo 10y ago

If Facebook has a fake news problem it's not in small part because Facebook users will share fake news articles more than real news. Google's decision to not serve AdWords on fake sites is because those sites were taking advantage of the high traffic fake news generates to make money. Fake news is popular. And the entire social economy is based on facilitating that which is popular. Because the more eyes you can drive to your content the more advertising you can sell. While Google vilifies the website for making the fake news, they also profited by selling the AdWords those sites hosted. While Facebook says it's difficult to distinguish legitimate news from fake news, they make more money from the frequently shared fake news than the less popular real news. The conflation of article content with advertising gives the advertising brokers a disincentive to prevent fake articles. Until readers stop clicking on the fake stories, but one glance at a typical supermarket checkout aisle will tell you that isn't going to happen. If Google or Facebook are serious about not encouraging fake news they will stop using the content of a webpage to drive the price of ads. But that obviously isn't going to happen as targeted advertising is their raison d'etre. TLDR, Facebook has met the enemy, and he is Facebook.

captn3m0 10y ago

Surprised no one has pointed out Google's aproach to the problem: https://blog.google/topics/journalism-news/labeling-fact-check-articles-google-news/ https://blog.google/topics/journalism-news/labeling-fact-che... They leave the fact-check to the publisher and ask them to use http://pending.schema.org/ClaimReview http://pending.schema.org/ClaimReview schema to tag news as fact-checked. Already in use by some publications. Announced almost a month before the election day (Oct 13).

throwaway9834 10y ago

There is a large amount of (possibly dubious) research indicating it is possible to detect whether a speaker is lying by examining movements and changes in his face and/or body. There has been a large amount of successful research using neural networks to classify human behaviors (facial expressions, lip reading, etc.) Is it possible to create a system to act as a high quality lie detector? Such software could be a poweful tool in studying crime, politics and business.