Why is deep learning NOT good
https://languagelog.ldc.upenn.edu/nll/?p=40906
Language Log
2018-12-06
Mark Liberman
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<p>« <a href="https://languagelog.ldc.upenn.edu/nll/?p=40894">previous post</a> | <a href="https://languagelog.ldc.upenn.edu/nll/?p=40917">next post</a> » </p>
	<p>At least I think that's what happened here. Gita Jackson, "<a href="https://kotaku.com/tumblrs-new-algorithm-thinks-garfield-is-explicit-conte-1830854912">Tumblr's New Algorithm Thinks Garfield Is Explicit Content</a>", Kotaku 12/4/2018:</p>
<p>Yesterday, Tumblr announced that it will ban all adult content starting December 17th. As users logged into their accounts, they have seen that some of their posts now have a red banner across them, marking them as flagged for explicit content. The problem is, a lot of these posts are hilariously far from being pornographic.</p>
<p>It’s pretty clear that these flags are being done based on an algorithm, and the algorithm is finding false positives. Here’s a list of things that got flagged: a fully clothed woman, a drawing of a dragon, fan-art of of characters from the anime Haikyu!!, art from the children’s book The Princess Who Saved Herself that the author of said book posted, a drawing of a bowl of fruit with mouths, a video of abstract blurs, Garfield.</p>
<blockquote>
<p>GARFIELD IS STRAIGHT UP ILLEGAL ON TUMBLR PASS IT ON <a href="https://t.co/GPxDsbcjoJ">pic.twitter.com/GPxDsbcjoJ</a></p>
<p>— shan murphy (@heyshanmurphy) <a href="https://twitter.com/heyshanmurphy/status/1069703550870142977?ref_src=twsrc%5Etfw">December 3, 2018</a></p></blockquote>
<p>Some additional coverage, mostly focusing on perceived problems with the policy rather than its implementation — "<a href="https://slate.com/technology/2018/12/tumblr-fandom-adult-content-ban-livejournal.html">Fandom’s Fate Is Not Tied to Tumblr’s: If Tumblr doesn’t learn from history, it will be headed for the same fate as LiveJournal</a>"; "<a href="https://www.washingtonpost.com/opinions/2018/12/05/tumblrs-porn-ban-is-depressing-heres-why/?utm_term=.9274e60106d4">Tumblr’s porn ban is depressing. Here’s why.</a>"; "<a href="https://www.washingtonpost.com/technology/2018/12/04/before-tumblr-banned-adult-content-it-was-safe-space-exploring-identity/?utm_term=.2a1e4ec27c58">Before Tumblr announced plan to ban adult content, it was a safe space for exploring identity</a>".</p>
<p>Without any evidence, I'm going to guess that Tumblr execs were freaked out at <a href="https://www.theverge.com/2018/11/20/18104366/tumblr-ios-app-child-pornography-removed-from-app-store">being removed from the App Store over child porn</a>, and accepted the assurances of some overconfident techies whose only knowledge of pattern recognition is that Deep Learning Rulez. So surely pushing a bunch of porn sites through PyTorch would solve the problem, right? Or then again:</p>
<blockquote>
<p>I like imagining the Tumblr anti-porn bot was purposefully badly written. Like some rogue chaotic-good developers that wanted to show the higher ups that it's a horrible idea. Training the AI on Garfield comics and fully dressed women lul</p>
<p>— Dani `$ yarn tart` (@webdevdani) <a href="https://twitter.com/webdevdani/status/1070458920928452609?ref_src=twsrc%5Etfw">December 5, 2018</a></p></blockquote>
<p>No doubt they'll manage to lower the false positive rate over time — but that may not be enough to save the platform.</p>
<p>So far the ban only applies to images, not to text — though allegedly <a href="https://twitter.com/SadistHailey/status/1069784517148270592/photo/1">Tumblr's own text-only announcement of the new policy was flagged?</a> — so we don't have any funny examples of banning bible passages or nursery rhymes. But given Apple's continued policing of the App Store, such things will no doubt be increasingly common in our future.</p>
<p>There's a serious general problem here — how to detect Bad Things on the internet, and what to do about them? And this episode adds to the evidence that we're very far from having real solutions.</p>
<p>Update — as noted in the comments, <a href="https://phonaesthemes.tumblr.com/post/180785532049/those-are-back-vowels-not-widely-spaced-boobs-i">the IPA chart is apparently also flagged as pornographic</a>:</p>
<p><a href="http://languagelog.ldc.upenn.edu/myl/tumblr_flagged_ipachart.png"></a></p>
	<p> 
	December 6, 2018 @ 8:26 am 
· Filed by <a href="https://languagelog.ldc.upenn.edu/nll/?author=2">Mark Liberman</a> under <a href="https://languagelog.ldc.upenn.edu/nll/?cat=299">Elephant semifics</a> 
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 Deep learning stumbles again

 December 6, 2018 @ 8:26 am · Filed by Mark Liberman under Elephant semifics

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 At least I think that's what happened here. Gita Jackson, "Tumblr's New Algorithm Thinks Garfield Is Explicit Content", Kotaku 12/4/2018:

 Yesterday, Tumblr announced that it will ban all adult content starting December 17th. As users logged into their accounts, they have seen that some of their posts now have a red banner across them, marking them as flagged for explicit content. The problem is, a lot of these posts are hilariously far from being pornographic.

 It’s pretty clear that these flags are being done based on an algorithm, and the algorithm is finding false positives. Here’s a list of things that got flagged: a fully clothed woman, a drawing of a dragon, fan-art of of characters from the anime Haikyu!!, art from the children’s book The Princess Who Saved Herself that the author of said book posted, a drawing of a bowl of fruit with mouths, a video of abstract blurs, Garfield.

 GARFIELD IS STRAIGHT UP ILLEGAL ON TUMBLR PASS IT ON pic.twitter.com/GPxDsbcjoJ

 — shan murphy (@heyshanmurphy) December 3, 2018

 Some additional coverage, mostly focusing on perceived problems with the policy rather than its implementation — "Fandom’s Fate Is Not Tied to Tumblr’s: If Tumblr doesn’t learn from history, it will be headed for the same fate as LiveJournal"; "Tumblr’s porn ban is depressing. Here’s why."; "Before Tumblr announced plan to ban adult content, it was a safe space for exploring identity".

 Without any evidence, I'm going to guess that Tumblr execs were freaked out at being removed from the App Store over child porn, and accepted the assurances of some overconfident techies whose only knowledge of pattern recognition is that Deep Learning Rulez. So surely pushing a bunch of porn sites through PyTorch would solve the problem, right? Or then again:

 I like imagining the Tumblr anti-porn bot was purposefully badly written. Like some rogue chaotic-good developers that wanted to show the higher ups that it's a horrible idea. Training the AI on Garfield comics and fully dressed women lul

 — Dani `$ yarn tart` (@webdevdani) December 5, 2018

 No doubt they'll manage to lower the false positive rate over time — but that may not be enough to save the platform.

 So far the ban only applies to images, not to text — though allegedly Tumblr's own text-only announcement of the new policy was flagged? — so we don't have any funny examples of banning bible passages or nursery rhymes. But given Apple's continued policing of the App Store, such things will no doubt be increasingly common in our future.

 There's a serious general problem here — how to detect Bad Things on the internet, and what to do about them? And this episode adds to the evidence that we're very far from having real solutions.

 Update — as noted in the comments, the IPA chart is apparently also flagged as pornographic:

 December 6, 2018 @ 8:26 am · Filed by Mark Liberman under Elephant semifics

 Permalink


 41 Comments

 1. Philip Taylor said,

 December 6, 2018 @ 9:00 am

 Leaving aside the very real problem of false positives, surely a far more important problem is that one man's Bad Things is another's everyday part of life. To some, pornography is bad, to others something normal; to some, any mention of the Tiananmen Square massacre, Independence for Tibet, or the rights of a Muslim minority is So Bad It Must Be Suppressed at All Costs, whilst to others they are things that it essential to discuss. My personal belief is that no social medium which seeks to serve the whole community (as opposed to a social medium set up specifically to address the needs of a particular group) should seek or attempt to pass value judgements on what is Good, or Bad, or Acceptable, at all.

 [(myl) But at the other end of scale, we've got criminal phishing, systematic cyberwarfare based on spreading content from spoofed sources to targeted recipients, etc. etc. Also what Joe Tello said below…]

 2. Philip Taylor said,

 December 6, 2018 @ 9:02 am

 Sorry, please mentally delete "not" in "should not seek" — I forgot that I had used "No social medium" rather than "any social medium". Please bring back (or introduce) the ability to edit one's own content, at least for a couple of days after posting …

 [(myl) Your "not" has been removed. Comment editing is a matter for WordPress — if anyone knows how to add it to this site without major php hacking, please let me know.]

 3. Joe Tello said,

 December 6, 2018 @ 9:19 am

 lol Philip – go ahead de-genderize "one man's Bad Things" while you're editing for accuracy. No indication that the algorithm self-identifies as male.

 4. Philip Taylor said,

 December 6, 2018 @ 9:51 am

 Mark — I know next to nothing about WordPress, but this seems a possible solution.

 Joe — Sorry, for me the aphorism "one man's meat is another man's poison" is so old that it can't (and shouldn't) be expected to conform to today's ideas of Political Correctness.

 5. Joe Tello said,

 December 6, 2018 @ 10:52 am

 lol Philip – perhaps post-modernize your old aphorism with "one person's pharmakon is another's pharmakon". This post is a kind of Socratic treatment of Deep Learning as the Answer to Everything the way writing was critiquedin the Phaedrus.

 6. Ralph Hickok said,

 December 6, 2018 @ 11:05 am

 Perhaps the problem is that Garfield is a pussy cat?

 7. Francois Lang said,

 December 6, 2018 @ 11:21 am

 @Philip Taylor

 One man's meat is another man's poison

 I know this is old hat, but it might still get a chuckle

 One man's Mede is another man's Persian

 8. Kristian said,

 December 6, 2018 @ 11:59 am

 Isn't Tumblr just deciding to go with the same standards that most mainstream social media/blogging sites have? Most of these seem fairly successful at keeping out pornography.

 9. lysa said,

 December 6, 2018 @ 12:45 pm

 @Philip – I'm sorry to hear that the ability to use your preferred phrasing for a certain idiom is important enough to be worth erasing hundreds of women and non-binary people. Having respect for others' identities is just basic decency, not political correctness.

 10. Ellen K. said,

 December 6, 2018 @ 2:20 pm

 lol Philip – go ahead de-genderize "one man's Bad Things" while you're editing for accuracy. No indication that the algorithm self-identifies as male.

 Or even that it identifies as human.

 11. Philip Taylor said,

 December 6, 2018 @ 2:29 pm

 Lysa — to my mind, they are not "erased" at all; they are subsumed. For centuries, it has been the rule that "the male encompasses the female", so in (for example) the apophthegm "Man proposes, God disposes" (the title of one of Turner's more striking paintings), "Man" clearly refers to any human of either sex or none.

 12. lysa said,

 December 6, 2018 @ 3:05 pm

 @Philip – If you look "through the hat band," so to speak, I think you'll see that that's not quite the case.

 13. Philip Taylor said,

 December 6, 2018 @ 3:16 pm

 Genuinely puzzled. Lysa — I was not familiar with the idiom "to look through one's hat band", so I asked Google, and as far as I can see it is not attested. Would you be kind enough to explain, please ?

 14. Trogluddite said,

 December 6, 2018 @ 3:27 pm

 Have Tumblr's coders not heard of "Rule 34"? ;-)

 15. Chandra said,

 December 6, 2018 @ 3:49 pm

 @Philip Taylor – Legions of non-male people have for decades been asserting that we do not appreciate being "subsumed" under male nomenclature as it does, in fact, contribute to our erasure, whether or not you, as a man, are able to understand that experience. You are of course entirely free to continue to prioritize archaism and tradition over respecting the wishes of half or more of the population, but please do not try to tell us what we do or do not experience.

 16. lysa said,

 December 6, 2018 @ 3:51 pm

 @Philip – Where I come from, "looking through the hatband" has the connotation of being "on the fence," so to speak.

 17. Paul Garrett said,

 December 6, 2018 @ 6:04 pm

 (As @lysa says, it's just about basic decency, not "political correctness", the latter … as we know all too well … a phrase most often used dismissively by people _not_ on the receiving end of marginalization…)

 18. Philip Taylor said,

 December 6, 2018 @ 6:15 pm

 Chandra — I would never dream of trying to tell you (or anyone) what you do or do not experience: how could I possibly know ? I simply put my position, with which I accept that not necessarily everyone will agree. But we are discussing language and my argument is that it is a mistake to allow (or worse, require) long-established idioms to be sacrificed on the altar of Newspeak or Political Correctness (e.g., "rule of thumb", "manhole cover" or "accident blackspot") [1].
 ——–
 [1] https://www.thetimes.co.uk/article/rule-of-thumb-blacklisted-as-pcs-go-pc-9x8rmz0v7fg

 19. chris said,

 December 6, 2018 @ 7:34 pm

 we don't have any funny examples of banning bible passages or nursery rhymes
 Or serious examples, either. Even granting for the sake of argument the premise that sexual content is somehow *more* banworthy than (e.g.) God ordering people to commit genocide and them obeying, ISTR the Bible *also* has explicit sexual content in it.

 Nursery rhyme examples might be limited to false positives, IDK. Unless there are nursery rhymes about grass-mud horses (and if there aren't yet, I'm sure someone can invent some).

 @Ralph Hickok: Well, you have to admit he IS fully nude in that picture. I predict it will be quite tricky to draw a line on which beings are allowed to appear nude, when fictional characters are involved. How much consideration should be given to deliberate omission of crucial details, as (not) seen in this picture?

 Defining pornography is famously beyond the capacity of even some well-educated humans, who can merely claim "I know it when I see it". What hope does the present state of artificial intelligence have?

 If Tumblr doesn't realize they're *at least* going to need an appeal process for false positives, then they are egregious fools who haven't made even a cursory study of the history of attempts to filter user-generated content. Not even mentioning the possibility that the Internet will "route around it" by a substantial fraction of their userbase deserting them for a less prudish competitor.

 20. J.W. Brewer said,

 December 6, 2018 @ 7:51 pm

 Through the magic of the google books corpus, you can find the phrase "one person's meat is another person's poison" in a piece by Edwin L. Sabin titled "Stomachitis" published in 1911 in Lippincott's Monthly Magazine. Presumably neither Mr. Sabin nor his editors were responding to the sort of politicized controversy about gendered language that has arisen in more recent decades, and if he'd chosen the more conventional phrasing it would presumably have been printed in that form.

 That said, the google books n-gram viewer shows "one person's meat" to be much rarer (like 95%+ less common) than "one man's meat" well into the 21st century.

 If anyone is interested in returning to the subject of the original post, you can google up a story from 2009 reporting that "Garfield is obscene" in the judgment of whatever then-state-of-the-art internet filtering software was being used by the Communist authorities in mainland China. (That's the preview but I'm having trouble clicking through to the actual website.) So maybe Tumblr has just reached the level of competence the PRC was at a decade ago?

 21. Y said,

 December 6, 2018 @ 8:29 pm

 Headline in the Guardian today: "Thanks for the vegan idioms, Peta, but there are bigger fish to fry."

 22. Rebecca said,

 December 6, 2018 @ 10:49 pm

 @chris: cartoon animal nudity will indeed be tricky to process, given the way some clothed cartoon animals dress. Mickey Mouse seems pretty conventional, but they better not train their algorithms on Pooh or Donald Duck

 23. Brett said,

 December 6, 2018 @ 11:26 pm

 @Rebecca: There is an old joke about the fact that Mickey Mouse wears pants but no shirt, while Donald Duck wears a shirt but no pants.

 24. rcalmy said,

 December 6, 2018 @ 11:49 pm

 Bringing it closer to home, Gretchen McCulloch at All Things Linguistic reblogged the image of a vowel chart that got flagged. Apparently the algorithm sees something salacious in IPA symbols.

 [(myl) From here:

 ]

 ]

 25. Michèle Sharik Pituley said,

 December 7, 2018 @ 10:31 am

 @Chandra & lysa — thank you! I, too, am so tired of being told that I’m “subsumed” or “encompassed” or “included” in male-gendered language. It’s not rocket surgery to make language inclusive and to show respect to “the other half” of humanity; it’s just basic decency.

 Also so tired of the term “political correctness”. Excluding groups of people because you don’t want to change your habits is rude. Yes, long-standing habits of language are difficult to change, but that doesn’t mean one shouldn’t try. If one is quoting something of historical importance (such as Shakespeare or Chaucer) then fine, but make it clear it’s a quote (so the meat/poison thing might be ok if it is clearly a quote) and not just general obtuseness or cluelessness.

 Language changes. Try to keep up. Thank you.

 /rant

 Ps please bring back the option to subscribe to comments. The disappearance of this option has been mentioned several times now (by me & by others), but never addressed or even acknowledged. Is it a WP thing? Or a deliberate change?

 26. Philip Taylor said,

 December 7, 2018 @ 2:18 pm

 Michèle, Lysa, Chandra, et al. I am sorry that you feel excluded when a writer such as myself uses idioms such as "One man's meat is another man's poison", or "Man proposes, God disposes". Exclusion is certainly not intended (quite the reverse, in fact), but I accept that it might be inferred. Nonetheless, I shall continue to use such phrases, since whilst language itself might evolve, idiolects, at least for those of a certain age, tend to be rather more conservative. But I would ask you two questions, if I may ? (q1) What is your position on "mankind", when referring to the entire human race ? For me, the term is completely unexceptionable, and for this I have the support of the Oxford English Dictionary, which I regard as authoritative in such matters :

 1. The human species. As a collective noun: human beings in general.

 and (2) what is your position on "waitperson" ? I have seen this used in writing on more than one occasion (but never heard anyone use it in speech), and it always strikes me as one of the most ridiculous and facile additions to our language that there has ever been. We have waiters (who are male) and waitresses (who are female), and ignoring for the purposes of my question the small percentage (circa 0,4%) of trans-gender individuals who might perform this rôle, are there really perceived problems with identifying the sex of the rôle-holder as we always have ? Already the term "actress" is under attack (especially in the Guardian) — how many more such useful words/distinctions are we to lose, I am forced to wonder.

 P.S. Does anyone really say "waitperson" in real life ? Serious question.

 27. Michèle Sharik Pituley said,

 December 7, 2018 @ 4:43 pm

 I’m almost 50 years old and have learned to use gender-neutral language, so age is no excuse. Also: intent is not magic.

 To answer your questions:

 I prefer “humanity” to “mankind”. “Humankind” seems a bit awkward, though I concede it might be useful in formal situations where “humanity” might be considered too casual.

 I waited tables for years to put myself through university. At the restaurant in which I worked, we were all “servers” or sometimes “the wait staff” but never “wait people” or “a waitperson”, which just sounds awkward to me. (I waited tables from 1987-1996, so a simple gender-neutral option existed way back then.)

 I think it’s important to ask: Why is it so important to distinguish people by sex or gender? Why can’t we all just be people? Why do you find this distinction “useful”? (I mean in a general sense. Of course a distinction can be useful in a specific sense, but now that I think about it, maybe only if it’s the only or most obvious distinction — and then can’t you just say “third one from the left”? This bears more thought, which unfortunately I don’t have time for right now, as I’m leaving for the evening.)

 FWIW, I feel the same way about “Congressperson”. I prefer “Representative” or “Congressional Representative” if I have to be more specific for some reason. That’s what they’re supposed to *do*, after all.

 28. Nipo said,

 December 7, 2018 @ 5:24 pm

 @Michèle Sharik Pituley

 I have nothing to do with the comments you replied to, but I want to reply your question "Why is it so important to distinguish people by sex or gender?"

 Not so surprisingly, I don't. There is no point. However, in other languages there are other approaches than de-genderifying the language.

 E.g. in Swedish there are still sjuksköterskor (sick-carer-ess-es), the female nurses, as well as sjukskötare (sick-carer). There are still "fröken" (miss) in the day care. However, the day care "miss" can be named Peter, and the difference between the different nurses seems to be in where they work at – every nurse in mental health are called "sick-carers" and every other nurse is a "sick-carer-ess".

 An ex-chairman of Finnish parliament was a woman – she chose to be addressed as "Mrs. Chairman", because she found the alternatives awkward. Also, a national worry in Finland is that it is hard to find male law-men (laki/mies, lawyer) to hire anymore.

 Of course there are no "proper" genders in either Swedish or Finnish, so our troubles are not exactly the same. Finnish has no genders to speak of, and Swedish genders are neutrum and utrum. Speakers of neither language seem to actually differentiate between the people by their genitals, even if the language still has the words to do so. Even though there is no point of denoting whether a server is a man or woman or machine, one can also choose what one hears.

 But could someone explain to a non-native English learner: why is the word "server" gender-neutral but "waiter" is not? They seem to be built the very same way.

 29. Philip Taylor said,

 December 7, 2018 @ 6:30 pm

 Michèle asked "why is it so important to distinguish people by sex or gender ?". In some cases it is important, in other cases far less so. For a case where it is important, consider the stage or film — you could not employ a actress if the role required a man, nor could you employ a actor if the role required a woman. In the case of waiter/waitress it is less important (although my wife, who runs a hotel, would undoubtedly advertise for a waiter or a waitress but not for "a person to wait at table" or any similar circumlocution), but it is traditional, as are, for example 'bus conductor/conductress (the latter frequently referred to as a "clippy" — there was no analogous term for a conductor), governor/governess (again, an important distinction), tailor/tailoress (unimportant) and so on.

 As to whether one can (or should) deliberately adapt one's speech or writing to conform to current norms, this must surely be a personal choice, and my choice is informed by discussions with women of my own age group (70+), very few of whom feel excluded by expressions such as "one man's …" and most of whom find "waitperson" as ridiculous as I do. I have little doubt that as one goes down the age spectrum, the ratio of those who feel excluded to those who do not will rise, and I think that such people are perfectly entitled to expect members of their own age group to conform to the norms to which that age group subscribes, but by the same token I do not think that they are entitled to expect their elders to adapt their speech just to conform to the expectations of, or to cater to the sensibilities of, a younger generation.

 30. chris said,

 December 7, 2018 @ 10:49 pm

 But could someone explain to a non-native English learner: why is the word "server" gender-neutral but "waiter" is not? They seem to be built the very same way.
 An interesting point. I think the only plausible answer is historical: because there is a female-specific form "waitress" in recent/current usage, "waiter" became effectively male-specific.

 If "waitress" had never existed, then we probably wouldn't see "waiter" as any more gendered than "driver" or "painter" or "singer".

 31. Chandra said,

 December 7, 2018 @ 10:52 pm

 @Philip Taylor –

 1) I simply put my position, with which I accept that not necessarily everyone will agree. My point is that you can’t have a valid “position” on other people’s experiences. It’s not an opinion or a feeling, it’s a thing that actually happens, and you can no more “disagree” with it than I can “disagree” with what you ate for breakfast this morning.

 2) I say “humankind” or “humanity”, and “server”. I’m not sure why you bringing up dictionaries, as the question at hand is about social context, not definitions.
 Also most transgender people prefer to be referred to by binary gender terms; you’re thinking of nonbinary people. What do you deem the critical percentage point for a group of people to be worthy of basic consideration?

 3) As I said, you’re free to continue doing whatever you want. I’m also free to continue holding opinions about what your words and actions say about you. Frankly I couldn’t care less about outdated aphorisms; it’s the way you’ve chosen to respond to people’s requests for more inclusive language, on a public forum whose readership is comprised of more than just 70-year-olds, that is most telling.
 Both my parents are in their 70s and have no difficulty with this concept. Also it’s interesting to note that you had no trouble whatsoever substituting “Bad Things”/”everyday part of life” for “meat”/”poison” in said aphorism – almost as if you’re perfectly capable of being flexible with language after all, when it suits you.

 32. Philip Taylor said,

 December 8, 2018 @ 4:10 am

 Chandra —

 "My point is that you can’t have a valid “position” on other people’s experiences" — I agree. My "position" was simply concerned with which words and phrases I preferred to use. As I explained previously, I have never sought to comment on other's experiences.

 "What do you deem the critical percentage point for a group of people to be worthy of basic consideration". Infinitely small. I sought only to exclude them from the domain of my question, not from consideration.

 "it’s the way you’ve chosen to respond to people’s requests for more inclusive language" — My response is simply that I hear their requests, but that I will continue decide for myself which words or phrases I will use, and that my decisions will be based primarily on my own feelings but influenced by discussions with people (in this context, primarily but not solely women) of my own social circle and age group.

 "you’re perfectly capable of being flexible with language when it suits you". Agreed — when I choose to do so, not when others seek to dictate it.

 33. Chandra said,

 December 9, 2018 @ 12:40 am

 Your position was that we are not erased. That is an incorrect statement about our experiences.

 No one is dictating anything to you. We have expressed how this kind of language affects us – as we are well within our rights to do when someone decides to pull “political correctness” into a discussion that had nothing at all to do with that in the first place – and you have made it abundantly clear that you don’t care enough to change your habits. I am now stating for the third time that you are free to do whatever you want, which is exactly the opposite of dictating what you should do. We are also free to express why we disapprove. If you dislike our disapproval, that is your own problem to deal with.

 34. Philip Taylor said,

 December 9, 2018 @ 2:20 am

 Chandra, I clearly wrote "To my mind, they [i.e., you] are not 'erased' at all; they [you] are subsumed". Thus this is not a statement about your experiences, about which I know absolutely nothing, but about my perception of the phrase and what its use entails. Can you see the difference ? And of course I don't dislike your disapproval, I just choose to ignore it when deciding what words or phrases I will use..

 35. speedwell said,

 December 9, 2018 @ 4:15 am

 About the idea that changing outdated language is "sacrificing it on the altar of political correctness" (leaving out the question of whether "political correctness" is actually a meaningful term instead of simply a sneer at people with manners): It's only "sacrificial" in the same sense that, when you change your underwear, you "sacrifice" the old knickers to the washing machine.

 36. ajay said,

 December 10, 2018 @ 12:31 pm

 If "waitress" had never existed, then we probably wouldn't see "waiter" as any more gendered than "driver" or "painter" or "singer".

 Or indeed "editor" and "aviator", both of which used to have female forms which are now completely unused.

 37. Chandra said,

 December 10, 2018 @ 2:02 pm

 @Philip Taylor –

 To my mind you didn’t have eggs for breakfast this morning. Can you see that your perception is utterly irrelevant to the situation?

 Perhaps you mean to say that you did not have the intention of erasing anyone. That may be so, but your intention doesn’t change our reality, in which plenty of research shows that male-coded terms used as a supposed generic in fact cause people to assume the referents are men (e.g. Moulton, Robinson & Elias (1978); Miller & James (2009)), and reinforce biased views of women (e.g. Briere & Lanktree (1983)).

 We are demonstrably erased, and this kind of language is demonstrably harmful to us. It is incredibly frustrating and exhausting to have people with no direct experience of these effects constantly talk over us – implying that we are merely being frivolously difficult (i.e. “politically correct”) when we ask for consideration – instead of actually listening and trying to understand.

 And on that note, I have no further energy for this debate and will not be returning to this thread to read or respond.

 38. Peter said,

 December 10, 2018 @ 2:59 pm

 chris wrote:

 Nursery rhyme examples might be limited to false positives, IDK.

 Given the back-stories that are supposed to be behind many nursery rhymes, I expected that it wouldn't be hard to find something of a salacious nature, and indeed the original text of "Rub-a-dub-dub" does seem to refer to gentlemen observing young ladies in their bath.

 Of course, one could also cite the lyric of Tom Lehrer's song "Smut":

 When correctly viewed
 Everything is lewd
 I could tell you things about Peter Pan
 And the Wizard of Oz – there's a dirty old man!

 39. Philip Anderson said,

 December 11, 2018 @ 6:09 pm

 Philip Taylor:
 I agree that we would usually refer to a woman taking a female role as an actress, in that context, but she would also be an actor by profession or vocation so you would be employing an actor. But other artistic professions have long lost their gendered forms, e.g. authoress and poetess.

 Some of your "traditional" forms were dated when I was young (perhaps you were a young fogey?); I can barely remember bus conductors, but a train might have a (male or female) conductor, never a 'conductress'. Is "clippy" still current in the North of England? I have only met it in a short story.

 There is an important distinction between governors and governesses, but it is not primarily a gender issue; a governess was, and perhaps still is in some frozen-in-time families, a chaperone-cum-teacher, whereas a male or female governor governs something or somewhere, e.g. a school or the Bank of England.

 Why should your choice of terminology only be informed by discussions with women of your own age group? One of the reasons comments are allowed here is so that we can listen to people with different experiences and points of view, including on our own language.

 40. Philip Anderson said,

 December 11, 2018 @ 6:12 pm

 Peter:
 Remember that most origin stories for nursery rhymes are modern inventions.

 41. Philip Taylor said,

 December 12, 2018 @ 8:57 am

 I think that this digression has gone on far too long (and I am sure others will agree with me), so I apologise in advance for what follows. Let me respond to Peter's questions, and then I promise to remain silent on this topic.

 'Bus conductor/conductress/clippy — my father was a 'bus driver, and as a child I would frequently travel on his 'bus; he had a favourite clippy with whom he preferred to work, and she was known as "Jack's clippy". This was in South-East London.

 As to "why [my] choice of terminology should be informed only by discussions with women of [my] own age group ?", there is no reason at all why it should be; it simply is. Though as I explained earlier, not solely informed, just mainly. I think that we tend to choose our friends on the basis of shared values, and most of my friends have little interest in adopting modern usages such as "Ms", "Chair" (for Chairman) or "actor" for an actress. I fully accept that these views are by no means fashionable, and I believe that those who hold different views are fully entitled to state them, which is why I earlier said to Chandra "Of course I don't dislike your disapproval, I just choose to ignore it when deciding what words or phrases I will use …". Or to mis-quote Voltaire "I disagree with virtually everything that you say, but I will defend to the death your right to say it".

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« previous post | next post » 
	At least I think that's what happened here. Gita Jackson, "Tumblr's New Algorithm Thinks Garfield Is Explicit Content", Kotaku 12/4/2018:
Yesterday, Tumblr announced that it will ban all adult content starting December 17th. As users logged into their accounts, they have seen that some of their posts now have a red banner across them, marking them as flagged for explicit content. The problem is, a lot of these posts are hilariously far from being pornographic.
It’s pretty clear that these flags are being done based on an algorithm, and the algorithm is finding false positives. Here’s a list of things that got flagged: a fully clothed woman, a drawing of a dragon, fan-art of of characters from the anime Haikyu!!, art from the children’s book The Princess Who Saved Herself that the author of said book posted, a drawing of a bowl of fruit with mouths, a video of abstract blurs, Garfield.
GARFIELD IS STRAIGHT UP ILLEGAL ON TUMBLR PASS IT ON pic.twitter.com/GPxDsbcjoJ 
— shan murphy (@heyshanmurphy) December 3, 2018  
Some additional coverage, mostly focusing on perceived problems with the policy rather than its implementation — "Fandom’s Fate Is Not Tied to Tumblr’s: If Tumblr doesn’t learn from history, it will be headed for the same fate as LiveJournal"; "Tumblr’s porn ban is depressing. Here’s why."; "Before Tumblr announced plan to ban adult content, it was a safe space for exploring identity".
Without any evidence, I'm going to guess that Tumblr execs were freaked out at being removed from the App Store over child porn, and accepted the assurances of some overconfident techies whose only knowledge of pattern recognition is that Deep Learning Rulez. So surely pushing a bunch of porn sites through PyTorch would solve the problem, right? Or then again:
I like imagining the Tumblr anti-porn bot was purposefully badly written. Like some rogue chaotic-good developers that wanted to show the higher ups that it's a horrible idea. Training the AI on Garfield comics and fully dressed women lul
— Dani `$ yarn tart` (@webdevdani) December 5, 2018  
No doubt they'll manage to lower the false positive rate over time — but that may not be enough to save the platform.
So far the ban only applies to images, not to text — though allegedly Tumblr's own text-only announcement of the new policy was flagged? — so we don't have any funny examples of banning bible passages or nursery rhymes. But given Apple's continued policing of the App Store, such things will no doubt be increasingly common in our future.
There's a serious general problem here — how to detect Bad Things on the internet, and what to do about them? And this episode adds to the evidence that we're very far from having real solutions.
Update — as noted in the comments, the IPA chart is apparently also flagged as pornographic:
   
	 
	December 6, 2018 @ 8:26 am 
· Filed by Mark Liberman under Elephant semifics 
	
	
	Permalink
Language Log
https://languagelog.ldc.upenn.edu/nll/?p=40906&utm_source=dlvr.it&utm_medium=twitter
https://andrewjtaggart.com/2019/11/15/the-roomba-aint-a-flying-car/
The Roomba Ain’t A Flying Car
2019-11-15
Andrew Taggart
<div><div>
	<p><strong>Roomba Roomba Roomba!</strong></p>
<p>My wife Alexandra and I recently bought a Roomba iRobot vacuum cleaner. Ours is Model 690, not Model 980. I’ll try to say something about why that makes a difference below.</p>
<p>Our Roomba, named “Sopapilla II,” took its maiden voyage around the house yesterday, and we were amazed by how well it cleaned. Our hardwood floors, area rugs, Turkish rugs, Saltillo tiles, and other surfaces have never looked or felt so good on bare feet. We plan to let it do its dandiest in our guest quarters as well.</p>
<p>All this is great–but here’s the rub. According to Gary Marcus and Ernest Davis in their excellent book <em>Rebooting AI: Building Artificial Intelligence We Can Trust</em> (2019), deep learning on its own is not going to get us to where we want to go. More pithily, and now famously, put by Peter Thiel: “We wanted <em>flying cars</em>, instead we got <em>140 characters</em>.”</p>
<p><strong>What? No Driverless Cars! Get Outta Here!</strong></p>
<p>It might help to know that Roomba uses deep learning. In <em>Rebooting AI</em>, Marcus and Davis argue that (1) deep learning has been an important step in the right direction, yet (2) deep learning on its own will not be sufficient to get us what we want and so (3) we’ll need to combine “classical AI” (top-down approaches) with machine learning (bottom-up) approaches.</p>
<p>Why? Take the example of driverless cars. Marcus and Davis point out that an environment like a city is <em>open-ended</em> in the sense that new objects, movements, and scenarios present themselves to us each day. Because it is open-ended, complex, and variable, human beings need to constantly adjust to the liquid situation they find themselves in. But deep learning, on its own, is only able to handle <em>bounded</em> environments. An Arizona highway on a sunny day is not like a New York street in Midtown or near Penn Station. While the former tends to be a bounded, determinate environment, the latter is filled with surprises. And, Marcus and Davis argue, no amount of deep learning on its own will enable an AI to deal, as human beings do, with surprises of the kind to be found in Times Square. For, by definition, <a href="https://andrewjtaggart.com/2013/07/10/on-the-importance-of-being-surprised-part-1/">a surprise is just the thing that we didn’t see coming</a>; here and now it confounds our understanding and challenges our expectations. In the face of surprise, therefore, we need to adjust our map, our understanding, our expectations accordingly–and often swiftly.</p>
<p>But deep learning, which is somewhat like a blank slate that builds up certain patterns over time due to Big Data, computing power, and algorithms cannot account for, and behave smartly in the face of, unknown unknowns. For Marcus and Davis, this means that deep learning on its own won’t be enough to get us to driverless cars, robot servants, and more.</p>
<p>To handle the complexity that ordinary and extraordinary situations throw at us, they urge, AI will need some top-town reasoning skills, mapping skills, and the like. In philosophical lingo, empiricism will need to reacquaint itself with rationalism; the two will need to get married.</p>
<p><strong>Deep Learning <em>And</em> Classical AI</strong></p>
<p>I got an intuitive sense of the limits of deep learning during our Roomba 690’s maiden voyage yesterday. I’d just gotten out of the shower and, sure enough, it was stumbling and bumbling into the bathroom. Naked, I sat on the counter after the shower until it had cleaned some and bumbled on out of the bathroom. You see that it has no <em>concept</em> of privacy.</p>
<p>According to MIT Tech Review, as of 2015, <a href="https://www.technologyreview.com/s/541326/the-roomba-now-sees-and-maps-a-home/">“The Roomba [980] now seeks and maps a home.”</a> This is a step in the right direction, and evidently our 690 is behind the times. We read: “The navigation approach used by the 980 is known as simultaneous location and mapping, or SLAM, which means it builds a map as it goes along and refers back to it.” Therefore, as I vaguely understand it, it seems to have powers that are roughly akin to human perception, to human conceptualization, and to some feedback between perception and conceptualization. At least as of 2015, it didn’t (and my hunch is is that it still doesn’t) know how to classify <em>kinds of things</em> such as cats, dogs, and humans. The latter all have properties (is furry, likes to hiss, purrs when petted, etc.) that, if known, would help to determine the robot’s behavior.</p>
<p><strong>Appropriate Technology And A Better Tomorrow</strong></p>
<p>In closing, I’d like to come back to Thiel’s quote: “We wanted <em>flying cars</em>, instead we got <em>140 characters</em>.” He’s alluding to the juxtaposition of the grand vision of the Space Age (“We wanted <em>flying cars</em>“) with the crappy actuality of social media (“instead we got <em>140 characters</em>“). His point is that this third AI season isn’t close to being ambitious enough.</p>
<p>While I’m not sure that Thiel’s imagined future resembles my own (I discuss my utopia at the end of this <a href="https://www.ihmc.us/lectures/20190314/">IHMC talk</a>), I do, generally speaking, appreciate his insight, his floating of orthodoxy, and his candor. Therefore, I wonder what appropriate forms of technology could contribute to a post-<a href="https://andrewjtaggart.substack.com/">Total Work world</a>.</p>
<p>What, I ask, would be an appropriate technology, vetted by wisdom, (a) that would enable human beings to diminish the amount of toil they perform regularly, (b) that could free us up so that we could engage with <a href="https://www.youtube.com/watch?v=F7UonZl-Gis">contemplative questions about the nature of reality</a>, and (c) that could also free us up so that we could act decisively and wisely–not just for the sake of <em>homo sapiens </em>but for the sake of all sentient beings–in this time of ecocide?</p>
<p>I welcome any appropriate technology that can be something that would knock us out of our slumber, the slumber that the Armenian mystic Gurdjieff, almost 100 years ago, warned us about.</p>
	</div>||||I|||| Skip to content

 Andrew James Taggart, Practical Philosopher, Ph.D.

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 The Roomba Ain’t A Flying Car

 The Roomba Ain’t A Flying Car

 November 15, 2019November 15, 2019 Andrew Taggart

 Roomba Roomba Roomba!

 My wife Alexandra and I recently bought a Roomba iRobot vacuum cleaner. Ours is Model 690, not Model 980. I’ll try to say something about why that makes a difference below.

 Our Roomba, named “Sopapilla II,” took its maiden voyage around the house yesterday, and we were amazed by how well it cleaned. Our hardwood floors, area rugs, Turkish rugs, Saltillo tiles, and other surfaces have never looked or felt so good on bare feet. We plan to let it do its dandiest in our guest quarters as well.

 All this is great–but here’s the rub. According to Gary Marcus and Ernest Davis in their excellent book Rebooting AI: Building Artificial Intelligence We Can Trust (2019), deep learning on its own is not going to get us to where we want to go. More pithily, and now famously, put by Peter Thiel: “We wanted flying cars, instead we got 140 characters.”

 What? No Driverless Cars! Get Outta Here!

 It might help to know that Roomba uses deep learning. In Rebooting AI, Marcus and Davis argue that (1) deep learning has been an important step in the right direction, yet (2) deep learning on its own will not be sufficient to get us what we want and so (3) we’ll need to combine “classical AI” (top-down approaches) with machine learning (bottom-up) approaches.

 Why? Take the example of driverless cars. Marcus and Davis point out that an environment like a city is open-ended in the sense that new objects, movements, and scenarios present themselves to us each day. Because it is open-ended, complex, and variable, human beings need to constantly adjust to the liquid situation they find themselves in. But deep learning, on its own, is only able to handle bounded environments. An Arizona highway on a sunny day is not like a New York street in Midtown or near Penn Station. While the former tends to be a bounded, determinate environment, the latter is filled with surprises. And, Marcus and Davis argue, no amount of deep learning on its own will enable an AI to deal, as human beings do, with surprises of the kind to be found in Times Square. For, by definition, a surprise is just the thing that we didn’t see coming; here and now it confounds our understanding and challenges our expectations. In the face of surprise, therefore, we need to adjust our map, our understanding, our expectations accordingly–and often swiftly.

 But deep learning, which is somewhat like a blank slate that builds up certain patterns over time due to Big Data, computing power, and algorithms cannot account for, and behave smartly in the face of, unknown unknowns. For Marcus and Davis, this means that deep learning on its own won’t be enough to get us to driverless cars, robot servants, and more.

 To handle the complexity that ordinary and extraordinary situations throw at us, they urge, AI will need some top-town reasoning skills, mapping skills, and the like. In philosophical lingo, empiricism will need to reacquaint itself with rationalism; the two will need to get married.

 Deep Learning And Classical AI

 I got an intuitive sense of the limits of deep learning during our Roomba 690’s maiden voyage yesterday. I’d just gotten out of the shower and, sure enough, it was stumbling and bumbling into the bathroom. Naked, I sat on the counter after the shower until it had cleaned some and bumbled on out of the bathroom. You see that it has no concept of privacy.

 According to MIT Tech Review, as of 2015, “The Roomba [980] now seeks and maps a home.” This is a step in the right direction, and evidently our 690 is behind the times. We read: “The navigation approach used by the 980 is known as simultaneous location and mapping, or SLAM, which means it builds a map as it goes along and refers back to it.” Therefore, as I vaguely understand it, it seems to have powers that are roughly akin to human perception, to human conceptualization, and to some feedback between perception and conceptualization. At least as of 2015, it didn’t (and my hunch is is that it still doesn’t) know how to classify kinds of things such as cats, dogs, and humans. The latter all have properties (is furry, likes to hiss, purrs when petted, etc.) that, if known, would help to determine the robot’s behavior.

 Appropriate Technology And A Better Tomorrow

 In closing, I’d like to come back to Thiel’s quote: “We wanted flying cars, instead we got 140 characters.” He’s alluding to the juxtaposition of the grand vision of the Space Age (“We wanted flying cars“) with the crappy actuality of social media (“instead we got 140 characters“). His point is that this third AI season isn’t close to being ambitious enough.

 While I’m not sure that Thiel’s imagined future resembles my own (I discuss my utopia at the end of this IHMC talk), I do, generally speaking, appreciate his insight, his floating of orthodoxy, and his candor. Therefore, I wonder what appropriate forms of technology could contribute to a post-Total Work world.

 What, I ask, would be an appropriate technology, vetted by wisdom, (a) that would enable human beings to diminish the amount of toil they perform regularly, (b) that could free us up so that we could engage with contemplative questions about the nature of reality, and (c) that could also free us up so that we could act decisively and wisely–not just for the sake of homo sapiens but for the sake of all sentient beings–in this time of ecocide?

 I welcome any appropriate technology that can be something that would knock us out of our slumber, the slumber that the Armenian mystic Gurdjieff, almost 100 years ago, warned us about.

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 Create a website or blog at WordPress.com
Roomba Roomba Roomba! 
My wife Alexandra and I recently bought a Roomba iRobot vacuum cleaner. Ours is Model 690, not Model 980. I’ll try to say something about why that makes a difference below.
Our Roomba, named “Sopapilla II,” took its maiden voyage around the house yesterday, and we were amazed by how well it cleaned. Our hardwood floors, area rugs, Turkish rugs, Saltillo tiles, and other surfaces have never looked or felt so good on bare feet. We plan to let it do its dandiest in our guest quarters as well.
All this is great–but here’s the rub. According to Gary Marcus and Ernest Davis in their excellent book Rebooting AI: Building Artificial Intelligence We Can Trust (2019), deep learning on its own is not going to get us to where we want to go. More pithily, and now famously, put by Peter Thiel: “We wanted flying cars, instead we got 140 characters.”
 What? No Driverless Cars! Get Outta Here! 
It might help to know that Roomba uses deep learning. In Rebooting AI, Marcus and Davis argue that (1) deep learning has been an important step in the right direction, yet (2) deep learning on its own will not be sufficient to get us what we want and so (3) we’ll need to combine “classical AI” (top-down approaches) with machine learning (bottom-up) approaches.
Why? Take the example of driverless cars. Marcus and Davis point out that an environment like a city is open-ended in the sense that new objects, movements, and scenarios present themselves to us each day. Because it is open-ended, complex, and variable, human beings need to constantly adjust to the liquid situation they find themselves in. But deep learning, on its own, is only able to handle bounded environments. An Arizona highway on a sunny day is not like a New York street in Midtown or near Penn Station. While the former tends to be a bounded, determinate environment, the latter is filled with surprises. And, Marcus and Davis argue, no amount of deep learning on its own will enable an AI to deal, as human beings do, with surprises of the kind to be found in Times Square. For, by definition, a surprise is just the thing that we didn’t see coming; here and now it confounds our understanding and challenges our expectations. In the face of surprise, therefore, we need to adjust our map, our understanding, our expectations accordingly–and often swiftly.
But deep learning, which is somewhat like a blank slate that builds up certain patterns over time due to Big Data, computing power, and algorithms cannot account for, and behave smartly in the face of, unknown unknowns. For Marcus and Davis, this means that deep learning on its own won’t be enough to get us to driverless cars, robot servants, and more.
To handle the complexity that ordinary and extraordinary situations throw at us, they urge, AI will need some top-town reasoning skills, mapping skills, and the like. In philosophical lingo, empiricism will need to reacquaint itself with rationalism; the two will need to get married.
 Deep Learning And Classical AI 
I got an intuitive sense of the limits of deep learning during our Roomba 690’s maiden voyage yesterday. I’d just gotten out of the shower and, sure enough, it was stumbling and bumbling into the bathroom. Naked, I sat on the counter after the shower until it had cleaned some and bumbled on out of the bathroom. You see that it has no concept of privacy.
According to MIT Tech Review, as of 2015, “The Roomba [980] now seeks and maps a home.” This is a step in the right direction, and evidently our 690 is behind the times. We read: “The navigation approach used by the 980 is known as simultaneous location and mapping, or SLAM, which means it builds a map as it goes along and refers back to it.” Therefore, as I vaguely understand it, it seems to have powers that are roughly akin to human perception, to human conceptualization, and to some feedback between perception and conceptualization. At least as of 2015, it didn’t (and my hunch is is that it still doesn’t) know how to classify kinds of things such as cats, dogs, and humans. The latter all have properties (is furry, likes to hiss, purrs when petted, etc.) that, if known, would help to determine the robot’s behavior.
 Appropriate Technology And A Better Tomorrow 
In closing, I’d like to come back to Thiel’s quote: “We wanted flying cars, instead we got 140 characters.” He’s alluding to the juxtaposition of the grand vision of the Space Age (“We wanted flying cars“) with the crappy actuality of social media (“instead we got 140 characters“). His point is that this third AI season isn’t close to being ambitious enough.
While I’m not sure that Thiel’s imagined future resembles my own (I discuss my utopia at the end of this IHMC talk), I do, generally speaking, appreciate his insight, his floating of orthodoxy, and his candor. Therefore, I wonder what appropriate forms of technology could contribute to a post-Total Work world.
What, I ask, would be an appropriate technology, vetted by wisdom, (a) that would enable human beings to diminish the amount of toil they perform regularly, (b) that could free us up so that we could engage with contemplative questions about the nature of reality, and (c) that could also free us up so that we could act decisively and wisely–not just for the sake of homo sapiens but for the sake of all sentient beings–in this time of ecocide?
I welcome any appropriate technology that can be something that would knock us out of our slumber, the slumber that the Armenian mystic Gurdjieff, almost 100 years ago, warned us about.
The Roomba Ain’t A Flying Car
https://andrewjtaggart.com/2019/11/15/the-roomba-aint-a-flying-car/
https://techcrunch.com/2016/10/26/the-darker-side-of-machine-learning/
The darker side of machine learning
2016-10-26
David Riggs; Ben Dickson
<div><div>
	<div>
	<p>
	Ben Dickson is a software engineer and the founder of <a href="https://bdtechtalks.com/">TechTalks</a>.	</p>
	<div>
	<p>More posts by this contributor</p>
	</div>
	</div>
<p>While machine learning is introducing innovation and change to <a href="https://techcrunch.com/2016/07/01/exploiting-machine-learning-in-cybersecurity/">many</a> <a href="https://techcrunch.com/2016/07/13/how-iot-and-machine-learning-can-make-our-roads-safer/">sectors</a>, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy.</p>
<p>From rooting out your <a href="http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/">intimate and embarrassing secrets</a> to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven’t uttered and actions you haven’t taken.</p>
<p>Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands. This is a reminder that while we further delve into the seemingly countless possibilities of this exciting new technology, we should keep our eyes open for the repercussions and unwanted side-effects.</p>
<h2>When facial recognition technology goes awry</h2>
<p>Neural networks and deep learning algorithms that process images <a href="http://thenextweb.com/apps/2011/08/19/10-great-uses-of-image-and-face-recognition/#gref">are working wonders</a> to make our social media platforms, search engines, gaming consoles and authentication mechanisms smarter.</p>
<p>But can they also be put to ill-use? <a href="http://www.dailydot.com/layer8/facial-recognition-privacy-selfies/">Facial recognition app FindFace proved that it can</a>. Rolled out in Russia earlier this year, the app allows anyone to use its <a href="http://observer.com/2016/05/facial-recognition-findface-ntech/">extremely efficient facial recognition capability</a> to identify anyone who has a profile in <a href="https://vk.com/">VK.com</a>, the social media platform known as the “Russian Facebook,” which boasts more than 200 million user accounts in Eastern Europe.</p>
<p>Its untethered access to the VK’s vast image database quickly turned FindFace into an attractive application for a number of different purposes. Within weeks of its launch, FindFace had acquired hundreds of thousands of users, and the Moscow law enforcement was slated to rent the service to enhance its network of 150,000 surveillance cameras.</p>
<p>But it was also put to sinister use by online vigilantes who used the technology to harass unfortunate victims, and there is concern that authoritarian regimes will use the same technology to identify dissidents and protestors in rallies and demonstrations. <a href="https://www.theguardian.com/technology/2016/may/17/findface-face-recognition-app-end-public-anonymity-vkontakte">In an interview with the Guardian</a>, the creators of the app said they were open to offers by the FSB, the Russian security service.</p>
<p>Experts at Kaspersky Labs have shared some tips on <a href="https://blog.kaspersky.com/findface-experiment/11916/">how to circumvent facial recognition apps</a> such as FindFace, but the proposed poses and angles are somewhat awkward.</p>
<p>This warrants more discreetness in posting pictures on social media, as they can quickly find their way into the repositories of one of the many data-gobbling machine learning engines that are roaming across the internet. And who knows where it’s going to resurface after that?</p>
<h2>Machine learning that peeks behind the pixels</h2>
<p>Blurring and pixelation are common techniques used to preserve privacy in images and video. They’re practices that have proven their effectiveness in obscuring faces, license plates and writings from the human eye.</p>
<p>But it seems that machine learning can see through the pixels.</p>
<p>Researchers at University of Texas at Austin and Cornell Tech recently succeeded in training an image recognition <a href="https://www.wired.com/2016/09/machine-learning-can-identify-pixelated-faces-researchers-show/">machine learning algorithm that can undermine the privacy benefits of content-masking techniques</a> such as pixelation and blurring. What’s worrying, the researchers underlined, is that the feat was accomplished with mainstream machine learning techniques that are widely known and available, and could be put to nefarious use by bad actors.</p>
<div>
	<blockquote>
	This doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it.	</blockquote>
	</div>
	
<p>The team used the technology to attack some of the most well-known image obfuscation techniques, such as YouTube’s blur tool, standard mosaicing (or pixelation) and a popular JPEG encryption tool called Privacy Photo Sharing (P3).</p>
<p>The algorithm doesn’t actually reconstruct the obfuscated object, but if it has it in its database, it is very likely to be able to identify its blurred version. After having been trained, the neural network was able to identify faces, objects and handwritten text with accuracy rates as high as 90 percent.</p>
<p>The researchers’ goal was to warn the tech community about the privacy implications of advanced machine learning. Richard McPherson, one of the researchers, warned that <a href="https://www.inverse.com/article/20908-neural-networks-can-see-past-pixelated-blurred-images">similar methods might be used to bypass voice obfuscation techniques</a>.</p>
<p>According to the scientists, the only way to bypass machine learning identification would be to use black boxes to completely obscure the parts of the image that need to be redacted, or to cover those areas with some other random image before blurring them in order to avoid the identification of the real image in case the obfuscation is defeated.</p>
<p>The resulting scene might not be as appealing as before, but at least it can provide you with guaranteed privacy.</p>
<h2>An algorithm that imitates your handwriting</h2>
<p>Handwriting forgery has always been a complicated task, one that’ll take even the most proficient fraudsters considerable time and practice to master. But it’ll only take a computer a few samples of your handwriting <a href="http://www.dailydot.com/layer8/handwriting-algorithm-privacy-security/">to discern your writing style —</a> <a href="http://www.dailydot.com/layer8/handwriting-algorithm-privacy-security/">and imitate it</a>.</p>
<p>Researchers at the University College London have developed a program called <a href="https://www.ucl.ac.uk/news/news-articles/0816/110816-my-text-in-your-handwriting">My Text in Your Handwriting</a>, which analyzes as little as a paragraph’s worth of handwritten script, and then starts to generate text that is authentically similar to the same person’s handwriting.</p>
<div>
	<blockquote>
	We must consider that while we cherish and harness the full power of machine learning … we also must speculate on and prepare ourselves for the broader implications.	</blockquote>
	</div>
	
<p>The technique is not flawless. It needs assistance and fine-tuning by a human, and it will not slip past forensic examiners and scientists. But it is by far the most accurate replication of human handwriting to date. In a test that involved people who had prior knowledge of the technology, participants were fooled by the artificial handwriting 40 percent of the time. That number is likely to drop as the technology becomes more enhanced.</p>
<p>The UCL researchers have iterated <a href="https://youtu.be/3mAKZaOPbBo?t=4m30s">a number of settings in which the technology can be put to novel use</a>, such as helping stroke victims formulate letters or translating comic books into different languages.</p>
<p>But the same technology can be put to more sinister uses, such as forging legal and historical documents and creating false evidence. The algorithm was used to generate text in the handwritings of Abraham Lincoln, Frida Kahlo and Arthur Conan Doyle decades and centuries after their deaths.</p>
<p>In <a href="http://www.digitaltrends.com/cool-tech/handwriting-replication-algorithm/">an interview with Digital Trends</a>, lead researcher Dr. Tom Haines admitted that the algorithm was likely to fool the untrained eye.</p>
<h2>Machine learning that impersonates you</h2>
<p>Chatbots, machine learning robots that can understand and generate natural language, <a href="http://venturebeat.com/2016/06/29/rise-of-the-chatbots-and-why-you-should-care/">have been on the rise lately</a>, and are revolutionizing a number of sectors. Among other fields, online and mobile customer service, weather reporting, restaurant reservation, news and shopping are being streamlined thanks to chatbots, and there’s a possibility that in the near future they will <a href="https://techcrunch.com/2015/09/29/forget-apps-now-the-bots-take-over/">eliminate the myriad apps</a> you have to install on your smartphone.</p>
<p>But chatbot apps also can serve a totally different use, as companies like <a href="https://luka.ai/">Luka</a> have proven. The firm, which offers high-end, conversational, <a href="https://techcrunch.com/2016/04/08/luka-with-4-42-million-in-series-a-releases-ai-powered-chat-bots/">AI-powered chatbots</a>, has been tapping into machine learning technology to create bots based on real human beings, dead or alive.</p>
<p>Luka recently presented a chatbot that talks like the characters from <a href="http://www.recode.net/2016/4/24/11586346/silicon-valley-hbo-chatbots-for-season-3-premier">HBO’s Silicon Valley</a>. The characters’ lines were fed into the neural networks that power the bots, which analyzed their language patterns and learned to say things as they would.</p>
<p>In a more ambitious — and spookier — project, Luka used its technology to, after a fashion, <a href="http://arstechnica.co.uk/information-technology/2016/07/luka-ai-chatbot-speaking-to-the-dead-mind-uploading/">reincarnate a dead person</a> by using his text messages, social media conversations and other sources of information to train their chatbot. This is something that is becoming possible as new generations tend to generate more and more online data.</p>
<p>While both use cases are harmless, the same technology can be used to mimic live, non-fictitious people, as the company is aiming to do. This could mean that with enough research and monitoring, a malicious actor can create your alter ego and start impersonating you in online conversations.</p>
<p>And if you think that your voice is still yours, you just need to take a look at <a href="https://techcrunch.com/2016/09/09/googles-wavenet-uses-neural-nets-to-generate-eerily-convincing-speech-and-music/">Google’s WaveNet technology</a>, which uses neural nets to generate convincingly realistic speech. Combined with Luka’s conversation technology, it can be used to make phone calls on your behalf.</p>
<h2>Have you gotten the shivers yet?</h2>
<p>Don’t worry though, this doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it. Its advantages and benefits far outweigh its negative trade-offs. However, we must consider that while we cherish and harness the full power of machine learning to make our lives and businesses more comfortable and efficient, we also must speculate on and prepare ourselves for the broader implications, especially where ethics and privacy are concerned. Many things as we know them today will be changed thanks to machine learning. Are we ready for it?</p>
	</div>||||I|||| The darker side of machine learning

 Ben Dickson 6 years

 Ben Dickson
 Contributor
 Ben Dickson is a software engineer and the founder of TechTalks.
 More posts by this contributor
 * Monetizing computing resources on the blockchain
 * Unlocking the potential of eye tracking technology

 While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy.

 From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven’t uttered and actions you haven’t taken.

 Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands. This is a reminder that while we further delve into the seemingly countless possibilities of this exciting new technology, we should keep our eyes open for the repercussions and unwanted side-effects.

 When facial recognition technology goes awry

 Neural networks and deep learning algorithms that process images are working wonders to make our social media platforms, search engines, gaming consoles and authentication mechanisms smarter.

 But can they also be put to ill-use? Facial recognition app FindFace proved that it can. Rolled out in Russia earlier this year, the app allows anyone to use its extremely efficient facial recognition capability to identify anyone who has a profile in VK.com, the social media platform known as the “Russian Facebook,” which boasts more than 200 million user accounts in Eastern Europe.

 Its untethered access to the VK’s vast image database quickly turned FindFace into an attractive application for a number of different purposes. Within weeks of its launch, FindFace had acquired hundreds of thousands of users, and the Moscow law enforcement was slated to rent the service to enhance its network of 150,000 surveillance cameras.

 But it was also put to sinister use by online vigilantes who used the technology to harass unfortunate victims, and there is concern that authoritarian regimes will use the same technology to identify dissidents and protestors in rallies and demonstrations. In an interview with the Guardian, the creators of the app said they were open to offers by the FSB, the Russian security service.

 Experts at Kaspersky Labs have shared some tips on how to circumvent facial recognition apps such as FindFace, but the proposed poses and angles are somewhat awkward.

 This warrants more discreetness in posting pictures on social media, as they can quickly find their way into the repositories of one of the many data-gobbling machine learning engines that are roaming across the internet. And who knows where it’s going to resurface after that?

 Machine learning that peeks behind the pixels

 Blurring and pixelation are common techniques used to preserve privacy in images and video. They’re practices that have proven their effectiveness in obscuring faces, license plates and writings from the human eye.

 But it seems that machine learning can see through the pixels.

 Researchers at University of Texas at Austin and Cornell Tech recently succeeded in training an image recognition machine learning algorithm that can undermine the privacy benefits of content-masking techniques such as pixelation and blurring. What’s worrying, the researchers underlined, is that the feat was accomplished with mainstream machine learning techniques that are widely known and available, and could be put to nefarious use by bad actors.

 This doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it.

 The team used the technology to attack some of the most well-known image obfuscation techniques, such as YouTube’s blur tool, standard mosaicing (or pixelation) and a popular JPEG encryption tool called Privacy Photo Sharing (P3).

 The algorithm doesn’t actually reconstruct the obfuscated object, but if it has it in its database, it is very likely to be able to identify its blurred version. After having been trained, the neural network was able to identify faces, objects and handwritten text with accuracy rates as high as 90 percent.

 The researchers’ goal was to warn the tech community about the privacy implications of advanced machine learning. Richard McPherson, one of the researchers, warned that similar methods might be used to bypass voice obfuscation techniques.

 According to the scientists, the only way to bypass machine learning identification would be to use black boxes to completely obscure the parts of the image that need to be redacted, or to cover those areas with some other random image before blurring them in order to avoid the identification of the real image in case the obfuscation is defeated.

 The resulting scene might not be as appealing as before, but at least it can provide you with guaranteed privacy.

 An algorithm that imitates your handwriting

 Handwriting forgery has always been a complicated task, one that’ll take even the most proficient fraudsters considerable time and practice to master. But it’ll only take a computer a few samples of your handwriting to discern your writing style — and imitate it.

 Researchers at the University College London have developed a program called My Text in Your Handwriting, which analyzes as little as a paragraph’s worth of handwritten script, and then starts to generate text that is authentically similar to the same person’s handwriting.

 We must consider that while we cherish and harness the full power of machine learning … we also must speculate on and prepare ourselves for the broader implications.

 The technique is not flawless. It needs assistance and fine-tuning by a human, and it will not slip past forensic examiners and scientists. But it is by far the most accurate replication of human handwriting to date. In a test that involved people who had prior knowledge of the technology, participants were fooled by the artificial handwriting 40 percent of the time. That number is likely to drop as the technology becomes more enhanced.

 The UCL researchers have iterated a number of settings in which the technology can be put to novel use, such as helping stroke victims formulate letters or translating comic books into different languages.

 But the same technology can be put to more sinister uses, such as forging legal and historical documents and creating false evidence. The algorithm was used to generate text in the handwritings of Abraham Lincoln, Frida Kahlo and Arthur Conan Doyle decades and centuries after their deaths.

 In an interview with Digital Trends, lead researcher Dr. Tom Haines admitted that the algorithm was likely to fool the untrained eye.

 Machine learning that impersonates you

 Chatbots, machine learning robots that can understand and generate natural language, have been on the rise lately, and are revolutionizing a number of sectors. Among other fields, online and mobile customer service, weather reporting, restaurant reservation, news and shopping are being streamlined thanks to chatbots, and there’s a possibility that in the near future they will eliminate the myriad apps you have to install on your smartphone.

 But chatbot apps also can serve a totally different use, as companies like Luka have proven. The firm, which offers high-end, conversational, AI-powered chatbots, has been tapping into machine learning technology to create bots based on real human beings, dead or alive.

 Luka recently presented a chatbot that talks like the characters from HBO’s Silicon Valley. The characters’ lines were fed into the neural networks that power the bots, which analyzed their language patterns and learned to say things as they would.

 In a more ambitious — and spookier — project, Luka used its technology to, after a fashion, reincarnate a dead person by using his text messages, social media conversations and other sources of information to train their chatbot. This is something that is becoming possible as new generations tend to generate more and more online data.

 While both use cases are harmless, the same technology can be used to mimic live, non-fictitious people, as the company is aiming to do. This could mean that with enough research and monitoring, a malicious actor can create your alter ego and start impersonating you in online conversations.

 And if you think that your voice is still yours, you just need to take a look at Google’s WaveNet technology, which uses neural nets to generate convincingly realistic speech. Combined with Luka’s conversation technology, it can be used to make phone calls on your behalf.

 Have you gotten the shivers yet?

 Don’t worry though, this doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it. Its advantages and benefits far outweigh its negative trade-offs. However, we must consider that while we cherish and harness the full power of machine learning to make our lives and businesses more comfortable and efficient, we also must speculate on and prepare ourselves for the broader implications, especially where ethics and privacy are concerned. Many things as we know them today will be changed thanks to machine learning. Are we ready for it?

Ben Dickson is a software engineer and the founder of TechTalks.	
	
	More posts by this contributor
	
	
While machine learning is introducing innovation and change to many sectors, it also is bringing trouble and worries to others. One of the most worrying aspects of emerging machine learning technologies is their invasiveness on user privacy.
From rooting out your intimate and embarrassing secrets to imitating you, machine learning is making it hard to not only hide your identity but also keep ownership of it and prevent from being attributed to you words you haven’t uttered and actions you haven’t taken.
Here are some of the technologies that might have been created with good-natured intent, but can also be used for evil deeds when put into the wrong hands. This is a reminder that while we further delve into the seemingly countless possibilities of this exciting new technology, we should keep our eyes open for the repercussions and unwanted side-effects.
When facial recognition technology goes awry
Neural networks and deep learning algorithms that process images are working wonders to make our social media platforms, search engines, gaming consoles and authentication mechanisms smarter.
But can they also be put to ill-use? Facial recognition app FindFace proved that it can. Rolled out in Russia earlier this year, the app allows anyone to use its extremely efficient facial recognition capability to identify anyone who has a profile in VK.com, the social media platform known as the “Russian Facebook,” which boasts more than 200 million user accounts in Eastern Europe.
Its untethered access to the VK’s vast image database quickly turned FindFace into an attractive application for a number of different purposes. Within weeks of its launch, FindFace had acquired hundreds of thousands of users, and the Moscow law enforcement was slated to rent the service to enhance its network of 150,000 surveillance cameras.
But it was also put to sinister use by online vigilantes who used the technology to harass unfortunate victims, and there is concern that authoritarian regimes will use the same technology to identify dissidents and protestors in rallies and demonstrations. In an interview with the Guardian, the creators of the app said they were open to offers by the FSB, the Russian security service.
Experts at Kaspersky Labs have shared some tips on how to circumvent facial recognition apps such as FindFace, but the proposed poses and angles are somewhat awkward.
This warrants more discreetness in posting pictures on social media, as they can quickly find their way into the repositories of one of the many data-gobbling machine learning engines that are roaming across the internet. And who knows where it’s going to resurface after that?
Machine learning that peeks behind the pixels
Blurring and pixelation are common techniques used to preserve privacy in images and video. They’re practices that have proven their effectiveness in obscuring faces, license plates and writings from the human eye.
But it seems that machine learning can see through the pixels.
Researchers at University of Texas at Austin and Cornell Tech recently succeeded in training an image recognition machine learning algorithm that can undermine the privacy benefits of content-masking techniques such as pixelation and blurring. What’s worrying, the researchers underlined, is that the feat was accomplished with mainstream machine learning techniques that are widely known and available, and could be put to nefarious use by bad actors.
	
	This doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it.	
	
	
The team used the technology to attack some of the most well-known image obfuscation techniques, such as YouTube’s blur tool, standard mosaicing (or pixelation) and a popular JPEG encryption tool called Privacy Photo Sharing (P3).
The algorithm doesn’t actually reconstruct the obfuscated object, but if it has it in its database, it is very likely to be able to identify its blurred version. After having been trained, the neural network was able to identify faces, objects and handwritten text with accuracy rates as high as 90 percent.
The researchers’ goal was to warn the tech community about the privacy implications of advanced machine learning. Richard McPherson, one of the researchers, warned that similar methods might be used to bypass voice obfuscation techniques.
According to the scientists, the only way to bypass machine learning identification would be to use black boxes to completely obscure the parts of the image that need to be redacted, or to cover those areas with some other random image before blurring them in order to avoid the identification of the real image in case the obfuscation is defeated.
The resulting scene might not be as appealing as before, but at least it can provide you with guaranteed privacy.
An algorithm that imitates your handwriting
Handwriting forgery has always been a complicated task, one that’ll take even the most proficient fraudsters considerable time and practice to master. But it’ll only take a computer a few samples of your handwriting to discern your writing style — and imitate it.
Researchers at the University College London have developed a program called My Text in Your Handwriting, which analyzes as little as a paragraph’s worth of handwritten script, and then starts to generate text that is authentically similar to the same person’s handwriting.
	
	We must consider that while we cherish and harness the full power of machine learning … we also must speculate on and prepare ourselves for the broader implications.	
	
	
The technique is not flawless. It needs assistance and fine-tuning by a human, and it will not slip past forensic examiners and scientists. But it is by far the most accurate replication of human handwriting to date. In a test that involved people who had prior knowledge of the technology, participants were fooled by the artificial handwriting 40 percent of the time. That number is likely to drop as the technology becomes more enhanced.
The UCL researchers have iterated a number of settings in which the technology can be put to novel use, such as helping stroke victims formulate letters or translating comic books into different languages.
But the same technology can be put to more sinister uses, such as forging legal and historical documents and creating false evidence. The algorithm was used to generate text in the handwritings of Abraham Lincoln, Frida Kahlo and Arthur Conan Doyle decades and centuries after their deaths.
In an interview with Digital Trends, lead researcher Dr. Tom Haines admitted that the algorithm was likely to fool the untrained eye.
Machine learning that impersonates you
Chatbots, machine learning robots that can understand and generate natural language, have been on the rise lately, and are revolutionizing a number of sectors. Among other fields, online and mobile customer service, weather reporting, restaurant reservation, news and shopping are being streamlined thanks to chatbots, and there’s a possibility that in the near future they will eliminate the myriad apps you have to install on your smartphone.
But chatbot apps also can serve a totally different use, as companies like Luka have proven. The firm, which offers high-end, conversational, AI-powered chatbots, has been tapping into machine learning technology to create bots based on real human beings, dead or alive.
Luka recently presented a chatbot that talks like the characters from HBO’s Silicon Valley. The characters’ lines were fed into the neural networks that power the bots, which analyzed their language patterns and learned to say things as they would.
In a more ambitious — and spookier — project, Luka used its technology to, after a fashion, reincarnate a dead person by using his text messages, social media conversations and other sources of information to train their chatbot. This is something that is becoming possible as new generations tend to generate more and more online data.
While both use cases are harmless, the same technology can be used to mimic live, non-fictitious people, as the company is aiming to do. This could mean that with enough research and monitoring, a malicious actor can create your alter ego and start impersonating you in online conversations.
And if you think that your voice is still yours, you just need to take a look at Google’s WaveNet technology, which uses neural nets to generate convincingly realistic speech. Combined with Luka’s conversation technology, it can be used to make phone calls on your behalf.
Have you gotten the shivers yet?
Don’t worry though, this doesn’t necessarily mean that machine learning is an evil technology that is putting an end to privacy as we know it. Its advantages and benefits far outweigh its negative trade-offs. However, we must consider that while we cherish and harness the full power of machine learning to make our lives and businesses more comfortable and efficient, we also must speculate on and prepare ourselves for the broader implications, especially where ethics and privacy are concerned. Many things as we know them today will be changed thanks to machine learning. Are we ready for it?
The darker side of machine learning
https://techcrunch.com/2016/10/26/the-darker-side-of-machine-learning/
https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063?gi=d0fc46fef7bb
Why Deep Learning over Traditional Machine Learning?
2019-01-22T05:48:44Z
Sambit Mahapatra
<html><body><div><div><figure></figure><div><a href="https://sambit9238.medium.com/?source=post_page-----1b6a99177063--------------------------------"><div><p></p></div></a><a href="https://towardsdatascience.com/?source=post_page-----1b6a99177063--------------------------------"><div><p></p></div></a></div><p><strong>Artificial Intelligence </strong>is on a rage! All of a sudden every one, whether understands or not, is talking about it. Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts <strong>Machine Learning</strong> and <strong>Deep Learning</strong>. But lately,<strong> Deep Learning</strong> is gaining much popularity due to it’s supremacy in terms of accuracy when trained with huge amount of data.</p><p>Just to show you the kind of attention <strong>Deep Learning</strong> is getting, here is the Google trend for the keyword:</p><figure><figcaption>Trend of “Deep Learning” in google</figcaption></figure><p>The software industry now-a-days moving towards machine intelligence. <strong>Machine Learning</strong> has become necessary in every sector as a way of making machines intelligent. In a simpler way, <strong><em>Machine Learning is set of algorithms that parse data, learn from them, and then apply what they’ve learned to make intelligent decisions</em></strong><em>.</em></p><p>Examples of <strong>Machine Learning</strong> are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook recognises your friend’s face in a digital photo. Or how a customer service representative will know if you’ll be satisfied with their support before you even take a CSAT survey.</p><p>The thing about traditional <strong>Machine Learning</strong> algorithms is that as complex as they may seem, they’re still machine like. They need lot of domain expertise, human intervention only capable of what they’re designed for; nothing more, nothing less. For AI designers and the rest of the world, that’s where deep learning holds a bit more promise.</p><h2><strong>What is Deep Learning?</strong></h2><p>Practically, <strong>Deep Learning</strong> is a subset of <strong>Machine Learning<em> that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.</em></strong></p><p>Elaborately, a deep learning technique learn categories incrementally through it’s hidden layer architecture, defining low-level categories like letters first then little higher level categories like words and then higher level categories like sentences. In the example of image recognition it means identifying light/dark areas before categorizing lines and then shapes to allow face recognition. Each neuron or node in the network represents one aspect of the whole and together they provide a full representation of the image. Each node or hidden layer is given a weight that represents the strength of its relationship with the output and as the model develops the weights are adjusted.</p><figure><figcaption>Deep Learning Architecture</figcaption></figure><h2><strong>Distinctive Features Of Deep Learning</strong></h2><p>A big advantage with deep learning, and a key part in understanding why it’s becoming popular, is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. As per <strong>Andrew Ng</strong>, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project, “<strong><em>The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.</em></strong>”</p><figure></figure><p><strong>Deep Learning</strong> requires high-end machines contrary to traditional <strong>Machine Learning</strong> algorithms. GPU has become a integral part now to execute any <strong>Deep Learning</strong> algorithm.</p><p>In traditional <strong>Machine learning </strong>techniques, most of the applied features need to be identified by an domain expert in order to reduce the complexity of the data and make patterns more visible to learning algorithms to work. The biggest advantage <strong>Deep Learning</strong> algorithms as discussed before are that they try to learn high-level features from data in an incremental manner. This eliminates the need of domain expertise and hard core feature extraction.</p><figure></figure><p>Another major difference between <strong>Deep Learning</strong> and <strong>Machine Learning </strong>technique is the problem solving approach.<strong> Deep Learning</strong> techniques tend to solve the problem end to end, where as <strong>Machine learning</strong> techniques need the problem statements to break down to different parts to be solved first and then their results to be combine at final stage. For example for a multiple object detection problem, <strong>Deep Learning </strong>techniques like <strong>Yolo net t</strong>ake the image as input and provide the location and name of objects at output. But in usual <strong>Machine Learning </strong>algorithms like<strong> SVM</strong>, a bounding box object detection algorithm is required first to identify all possible objects to have the <strong>HOG</strong> as input to the learning algorithm in order to recognize relevant objects.</p><figure></figure><p>Usually, a <strong>Deep Learning</strong> algorithm takes a long time to train due to large number of parameters. Popular <strong>ResNet </strong>algorithm takes about two weeks to train completely from scratch. Where as, traditional <strong>Machine Learning</strong> algorithms take few seconds to few hours to train. The scenario is completely reverse in testing phase. At test time,<strong> Deep Learning </strong>algorithm takes much less time to run. Whereas, if you compare it with k-nearest neighbors (a type of machine learning algorithm), test time increases on increasing the size of data. Although this is not applicable on all machine learning algorithms, as some of them have small testing times too.</p><p>Interpretability is the main issue why many sectors using other<strong> Machine Learning </strong>techniques over <strong>Deep Learning</strong>. Let’s take an example. Suppose we use deep learning to calculate the relevance score of a document. The performance it gives is quite excellent and is near human performance. But there’s is an issue. It does not reveal why it has given that score. Indeed mathematically you can find out which nodes of a deep neural network were activated, but we don’t know what there neurons were supposed to model and what these layers of neurons were doing collectively. So we fail to interpret the results. Which is not in case of <strong>Machine Learning</strong> algorithms like decision trees, logistic regression etc.</p><h2><strong>When to use Deep Learning or not over others?</strong></h2><ol><li><strong>Deep Learning </strong>out perform other techniques if the <strong><em>data size is large</em></strong>. But with small data size, traditional <strong>Machine Learning</strong> algorithms are preferable.</li><li><strong>Deep Learning</strong> techniques need to have <strong><em>high end infrastructure</em></strong> to train in reasonable time.</li><li>When there is <strong><em>lack of domain understanding for feature introspection</em></strong>, <strong>Deep Learning</strong> techniques outshines others as you have to worry less about feature engineering.</li><li><strong>Deep Learning </strong>really shines when it comes to <strong><em>complex problems such as image classification, natural language processing, and speech recognition</em></strong>.</li></ol><p><strong>References-</strong></p></div></div></body></html>
Artificial Intelligence is on a rage! All of a sudden every one, whether understands or not, is talking about it. Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it’s supremacy in terms of accuracy when trained with huge amount of data. Just to show you the kind of attention Deep Learning is getting, here is the Google trend for the keyword:  Trend of “Deep Learning” in google  The software industry now-a-days moving towards machine intelligence. Machine Learning has become necessary in every sector as a way of making machines intelligent. In a simpler way,  Machine Learning is set of algorithms that parse data, learn from them, and then apply what they’ve learned to make intelligent decisions  .  Examples of Machine Learning are everywhere. It’s how Netflix knows which show you’ll want to watch next or how Facebook recognises your friend’s face in a digital photo. Or how a customer service representative will know if you’ll be satisfied with their support before you even take a CSAT survey. The thing about traditional Machine Learning algorithms is that as complex as they may seem, they’re still machine like. They need lot of domain expertise, human intervention only capable of what they’re designed for; nothing more, nothing less. For AI designers and the rest of the world, that’s where deep learning holds a bit more promise.  What is Deep Learning?  Practically, Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.   Elaborately, a deep learning technique learn categories incrementally through it’s hidden layer architecture, defining low-level categories like letters first then little higher level categories like words and then higher level categories like sentences. In the example of image recognition it means identifying light/dark areas before categorizing lines and then shapes to allow face recognition. Each neuron or node in the network represents one aspect of the whole and together they provide a full representation of the image. Each node or hidden layer is given a weight that represents the strength of its relationship with the output and as the model develops the weights are adjusted.  Deep Learning Architecture   Distinctive Features Of Deep Learning  A big advantage with deep learning, and a key part in understanding why it’s becoming popular, is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. As per Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project, “ The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms. ”    Deep Learning requires high-end machines contrary to traditional Machine Learning algorithms. GPU has become a integral part now to execute any Deep Learning algorithm. In traditional Machine learning techniques, most of the applied features need to be identified by an domain expert in order to reduce the complexity of the data and make patterns more visible to learning algorithms to work. The biggest advantage Deep Learning algorithms as discussed before are that they try to learn high-level features from data in an incremental manner. This eliminates the need of domain expertise and hard core feature extraction.   Another major difference between Deep Learning and Machine Learning technique is the problem solving approach. Deep Learning techniques tend to solve the problem end to end, where as Machine learning techniques need the problem statements to break down to different parts to be solved first and then their results to be combine at final stage. For example for a multiple object detection problem, Deep Learning techniques like Yolo net take the image as input and provide the location and name of objects at output. But in usual Machine Learning algorithms like SVM, a bounding box object detection algorithm is required first to identify all possible objects to have the HOG as input to the learning algorithm in order to recognize relevant objects.   Usually, a Deep Learning algorithm takes a long time to train due to large number of parameters. Popular ResNet algorithm takes about two weeks to train completely from scratch. Where as, traditional Machine Learning algorithms take few seconds to few hours to train. The scenario is completely reverse in testing phase. At test time, Deep Learning algorithm takes much less time to run. Whereas, if you compare it with k-nearest neighbors (a type of machine learning algorithm), test time increases on increasing the size of data. Although this is not applicable on all machine learning algorithms, as some of them have small testing times too. Interpretability is the main issue why many sectors using other Machine Learning techniques over Deep Learning. Let’s take an example. Suppose we use deep learning to calculate the relevance score of a document. The performance it gives is quite excellent and is near human performance. But there’s is an issue. It does not reveal why it has given that score. Indeed mathematically you can find out which nodes of a deep neural network were activated, but we don’t know what there neurons were supposed to model and what these layers of neurons were doing collectively. So we fail to interpret the results. Which is not in case of Machine Learning algorithms like decision trees, logistic regression etc.  When to use Deep Learning or not over others?    Deep Learning out perform other techniques if the  data size is large . But with small data size, traditional Machine Learning algorithms are preferable.  Deep Learning techniques need to have  high end infrastructure  to train in reasonable time. When there is  lack of domain understanding for feature introspection , Deep Learning techniques outshines others as you have to worry less about feature engineering.  Deep Learning really shines when it comes to  complex problems such as image classification, natural language processing, and speech recognition .   References-
Why Deep Learning over Traditional Machine Learning?
https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063
https://venturebeat.com/datadecisionmakers/this-is-what-makes-deep-learning-so-powerful/
This is what makes deep learning so powerful
2022-03-27
Abhishek Gupta; Talentica Software
<div><div>
	<section>
	<p><time>March 27, 2022 8:10 AM</time>
	</p>
	</section>
	<div>
	<p></p><div><p><em>Image Credit: onurdongel/Getty</em></p></div>	</div>
	</div><div>
	<article>
	<div>
	<div>
<p><em>Check out all the on-demand sessions from the Intelligent Security Summit <a href="https://avolio.swapcard.com/intelligentsecuritysummit2022/registrations/Start?utm_source=vb&amp;utm_medium=boiler&amp;utm_content=ondemand&amp;utm_campaign=IS22_BoilerPlates">here</a></em>.</p>
<hr />
</div><p>The use of <a href="https://venturebeat.com/2021/07/05/the-future-of-deep-learning-according-to-its-pioneers/">deep learning</a> has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to <a href="https://www.globenewswire.com/news-release/2021/03/23/2197902/0/en/Deep-Learning-System-Market-Size-to-Reach-USD-93-34-Billion-in-2028-Improvements-in-Computing-Power-and-Reduction-in-Hardware-Costs-will-Drive-the-Industry-Growth-says-Emergen-Rese.html">Emergen Research</a>, which expects deep learning to become a $93 billion market by 2028.</p>
<p>But what exactly is deep learning and how does it work?</p>
<p>Deep learning is a subset of machine learning which uses <a href="https://venturebeat.com/2021/05/25/the-business-value-of-neural-networks/">neural networks</a> to perform learning and predictions. Deep learning has shown amazing performance in various tasks, whether it be text, time series or computer vision. The success of deep learning comes primarily from the availability of large data and compute power. However, it is more than that, which makes deep learning far better than any of the classical machine learning algorithms.</p>
<h2>Deep learning: Neural networks and functions</h2>
<p>A neural network is an interconnected network of neurons with each neuron being a limited function approximator. This way, neural networks are considered as <em>universal function approximators</em>. If you recall from high school math, a function is a mapping from input space to an output space. A simple sin(x) function is mapping from angular space (-180<sup>o</sup> to 180<sup> o</sup> or 0<sup> o</sup> to 360<sup> o</sup>) to real number space (-1 to 1).</p>
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 <p><a href="https://avolio.swapcard.com/intelligentsecuritysummit2022/registrations/Start?utm_source=vb&amp;utm_medium=incontent&amp;utm_content=ondemand&amp;utm_campaign=IS22_InContent">
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 </p></div><p>Let’s see why neural networks are considered to be universal function approximators. Each neuron learns a limited function: f(.) = g(W*X) where W is the weight vector to be learned, X is the input vector and g(.) is a non-linear transformation.  W*X can be visualized as a line (being learned) in high-dimensional space (hyperplane) and g(.) can be any non-linear differentiable function like sigmoid, tanh, ReLU, etc. (commonly used in the deep learning community). Learning in neural networks is nothing but finding the optimum weight vector W. As an example, in y = mx+c, we have 2 weights: m and c. Now, depending on the distribution of points in 2D space, we find the optimum value of m &amp; c which satisfies some criteria: the difference between predicted y and actual points is minimal for all data points.</p>
<h2>The layer effect</h2>
<p>Now that each neuron is a nonlinear function, we stack several such neurons in a “layer” where each neuron receives the same set of inputs but learn different weights W. Therefore, each layer has a set of learned functions: [f1, f2, …, fn], which are called as hidden layer values. These values are again combined, in the next layer: h(f1, f2, …, fn)  and so on. This way, each layer is composed of functions from the previous layer (something like h(f(g(x)))). It has been shown that through this composition, we can learn any non-linear complex function.</p>
<p>Deep learning is a neural network with many hidden layers (usually identified by &gt; 2 hidden layers). But effectively, what deep learning is a complex composition of functions from layer to layer, thereby finding the function that defines a mapping from input to output. For example, if the input is an image of a lion and output is the image classification that the image belongs to the class of lions, then deep learning is learning a function that maps image vectors to classes. Similarly, input is word sequence and output is whether the input sentence has a positive/neutral/negative sentiment. Therefore, deep learning is learning a map from input text to output classes: neutral or positive or negative.</p>
<h2>Deep learning as interpolation</h2>
<p>From a biological interpretation, humans process images of the world by hierarchically interpreting them bit by bit, from low-level features like edges and contours to high-level features like objects and scenes. Function composition in neural networks is in line with that, where each function composition is learning complex features about an image. The most common neural network architecture that is used for images is Convolutional Neural Network (CNN), which learns those features in a hierarchical fashion and then a fully connected neural network classifies image features into different classes.</p>
<p>By using high school math again, given a set of data points in 2D, we try to fit a curve through interpolation that somewhat represents a function defining those data points. The more complex the function we fit (in interpolation, for example, determined through polynomial degree), the more it fits the data; however, the less it generalizes for a new data point. This is where deep learning faces challenges and what is generally referred to as an overfitting problem: fitting to data as much as possible, but compromising in generalization. Almost all architectures in deep learning had to handle this important factor to be able to learn a general function that can perform equally well on unseen data.</p>
<p>A deep learning pioneer, Yann LeCun (creator of the convolutional neural network and ACM Turing award winner) posted on his <a href="https://twitter.com/ylecun/status/1409940043951742981?lang=en">Twitter handle</a> (based on a paper): “Deep Learning is not as impressive as you think because it is mere interpolation resulting from glorified curve fitting. But in high dimensions, there is no such thing as interpolation. In high dimensions, everything is extrapolation.” Thus, as part of function learning, deep learning is doing nothing but interpolation or in some cases, extrapolation. That’s all! </p>
<h2>The learning aspect</h2>
<p>So, how do we learn this complex function? Well, it completely depends on the problem at hand and that’s what determines the neural network architecture. If we are interested in image classification, then we use CNN. If we are interested in time-dependent predictions or text then we use RNN or transformers and if we have a dynamic environment (like car driving) then we use reinforcement learning. Apart from this, learning involves handling different challenges:</p>
<ul><li>Ensuring the model learns general function and doesn’t just fit to train data; this is handled by using regularization</li><li>Depending on the problem at hand, choice of the loss function is made; loosely speaking, the loss function is error function between what we want (true value) and what we currently have (current prediction).</li><li>Gradient descent is the algorithm used for converging to an optimal function; deciding learning rate becomes challenging because when we are away from optimal, we want to move faster to optimal, and when we are near-optimal, then we want to move slower to ensure we converge to optimal and global minima.</li><li>High number of hidden layers need to handle the vanishing gradient problem; architectural changes like skip connections and appropriate non-linear activation function helps to solve it.</li></ul>
<h2>Compute challenges</h2>
<p>Now that we know deep learning is simply a learning complex function, it brings other compute challenges:</p>
<ul><li>To learn a complex function, we require a large amount of data</li><li>To process large data, we require fast compute environments</li><li>We need an infrastructure that supports such environments</li></ul>
<p>Parallel processing with <a href="https://venturebeat.com/2022/02/18/deci-simplifies-deep-learning-apps-on-cpus/">CPUs</a> is not enough to compute millions or billions of weights (also called parameters of DL). Neural networks requires learning weights that require vector (or tensor) multiplications. That is where GPUs come in handy, as they can do parallel vector multiplications very fast. Depending on the deep learning architecture, data size, and task at hand, we sometimes require 1 GPU, and sometimes, several of them, a decision data scientist needs to make based on known literature or by measuring the performance on 1 GPU.</p>
<p>With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from one vector space to another vector space. That’s what makes deep learning such a powerful tool for any machine learning task.</p>
<p><em>Abhishek Gupta is the principal data scientist at </em><a href="https://www.talentica.com/"><em>Talentica Software</em></a><em>.</em></p>
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 This is what makes deep learning so powerful

 Abhishek Gupta, Talentica Software
 March 27, 2022 8:10 AM
 * 
 * 
 * 
 Image Credit: onurdongel/Getty

 Check out all the on-demand sessions from the Intelligent Security Summit here.

 The use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.

 But what exactly is deep learning and how does it work?

 Deep learning is a subset of machine learning which uses neural networks to perform learning and predictions. Deep learning has shown amazing performance in various tasks, whether it be text, time series or computer vision. The success of deep learning comes primarily from the availability of large data and compute power. However, it is more than that, which makes deep learning far better than any of the classical machine learning algorithms.

 Deep learning: Neural networks and functions

 A neural network is an interconnected network of neurons with each neuron being a limited function approximator. This way, neural networks are considered as universal function approximators. If you recall from high school math, a function is a mapping from input space to an output space. A simple sin(x) function is mapping from angular space (-180o to 180 o or 0 o to 360 o) to real number space (-1 to 1).

 Event

 Intelligent Security Summit On-Demand

 Learn the critical role of AI & ML in cybersecurity and industry specific case studies. Watch on-demand sessions today.

 Watch Here

 Let’s see why neural networks are considered to be universal function approximators. Each neuron learns a limited function: f(.) = g(W*X) where W is the weight vector to be learned, X is the input vector and g(.) is a non-linear transformation. W*X can be visualized as a line (being learned) in high-dimensional space (hyperplane) and g(.) can be any non-linear differentiable function like sigmoid, tanh, ReLU, etc. (commonly used in the deep learning community). Learning in neural networks is nothing but finding the optimum weight vector W. As an example, in y = mx+c, we have 2 weights: m and c. Now, depending on the distribution of points in 2D space, we find the optimum value of m & c which satisfies some criteria: the difference between predicted y and actual points is minimal for all data points.

 The layer effect

 Now that each neuron is a nonlinear function, we stack several such neurons in a “layer” where each neuron receives the same set of inputs but learn different weights W. Therefore, each layer has a set of learned functions: [f1, f2, …, fn], which are called as hidden layer values. These values are again combined, in the next layer: h(f1, f2, …, fn) and so on. This way, each layer is composed of functions from the previous layer (something like h(f(g(x)))). It has been shown that through this composition, we can learn any non-linear complex function.

 Deep learning is a neural network with many hidden layers (usually identified by > 2 hidden layers). But effectively, what deep learning is a complex composition of functions from layer to layer, thereby finding the function that defines a mapping from input to output. For example, if the input is an image of a lion and output is the image classification that the image belongs to the class of lions, then deep learning is learning a function that maps image vectors to classes. Similarly, input is word sequence and output is whether the input sentence has a positive/neutral/negative sentiment. Therefore, deep learning is learning a map from input text to output classes: neutral or positive or negative.

 Deep learning as interpolation

 From a biological interpretation, humans process images of the world by hierarchically interpreting them bit by bit, from low-level features like edges and contours to high-level features like objects and scenes. Function composition in neural networks is in line with that, where each function composition is learning complex features about an image. The most common neural network architecture that is used for images is Convolutional Neural Network (CNN), which learns those features in a hierarchical fashion and then a fully connected neural network classifies image features into different classes.

 By using high school math again, given a set of data points in 2D, we try to fit a curve through interpolation that somewhat represents a function defining those data points. The more complex the function we fit (in interpolation, for example, determined through polynomial degree), the more it fits the data; however, the less it generalizes for a new data point. This is where deep learning faces challenges and what is generally referred to as an overfitting problem: fitting to data as much as possible, but compromising in generalization. Almost all architectures in deep learning had to handle this important factor to be able to learn a general function that can perform equally well on unseen data.

 A deep learning pioneer, Yann LeCun (creator of the convolutional neural network and ACM Turing award winner) posted on his Twitter handle (based on a paper): “Deep Learning is not as impressive as you think because it is mere interpolation resulting from glorified curve fitting. But in high dimensions, there is no such thing as interpolation. In high dimensions, everything is extrapolation.” Thus, as part of function learning, deep learning is doing nothing but interpolation or in some cases, extrapolation. That’s all!

 The learning aspect

 So, how do we learn this complex function? Well, it completely depends on the problem at hand and that’s what determines the neural network architecture. If we are interested in image classification, then we use CNN. If we are interested in time-dependent predictions or text then we use RNN or transformers and if we have a dynamic environment (like car driving) then we use reinforcement learning. Apart from this, learning involves handling different challenges:

 * Ensuring the model learns general function and doesn’t just fit to train data; this is handled by using regularization
 * Depending on the problem at hand, choice of the loss function is made; loosely speaking, the loss function is error function between what we want (true value) and what we currently have (current prediction).
 * Gradient descent is the algorithm used for converging to an optimal function; deciding learning rate becomes challenging because when we are away from optimal, we want to move faster to optimal, and when we are near-optimal, then we want to move slower to ensure we converge to optimal and global minima.
 * High number of hidden layers need to handle the vanishing gradient problem; architectural changes like skip connections and appropriate non-linear activation function helps to solve it.

 Compute challenges

 Now that we know deep learning is simply a learning complex function, it brings other compute challenges:

 * To learn a complex function, we require a large amount of data
 * To process large data, we require fast compute environments
 * We need an infrastructure that supports such environments

 Parallel processing with CPUs is not enough to compute millions or billions of weights (also called parameters of DL). Neural networks requires learning weights that require vector (or tensor) multiplications. That is where GPUs come in handy, as they can do parallel vector multiplications very fast. Depending on the deep learning architecture, data size, and task at hand, we sometimes require 1 GPU, and sometimes, several of them, a decision data scientist needs to make based on known literature or by measuring the performance on 1 GPU.

 With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from one vector space to another vector space. That’s what makes deep learning such a powerful tool for any machine learning task.

 Abhishek Gupta is the principal data scientist at Talentica Software.

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March 27, 2022 8:10 AM
	
	
	
	    Image Credit: onurdongel/Getty  	
	 
	
	
	
 Check out all the on-demand sessions from the Intelligent Security Summit here .
 The use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.
But what exactly is deep learning and how does it work?
Deep learning is a subset of machine learning which uses neural networks to perform learning and predictions. Deep learning has shown amazing performance in various tasks, whether it be text, time series or computer vision. The success of deep learning comes primarily from the availability of large data and compute power. However, it is more than that, which makes deep learning far better than any of the classical machine learning algorithms.
Deep learning: Neural networks and functions
A neural network is an interconnected network of neurons with each neuron being a limited function approximator. This way, neural networks are considered as universal function approximators. If you recall from high school math, a function is a mapping from input space to an output space. A simple sin(x) function is mapping from angular space (-180o to 180 o or 0 o to 360 o) to real number space (-1 to 1).
 Event
  Intelligent Security Summit On-Demand
Learn the critical role of AI &amp; ML in cybersecurity and industry specific case studies. Watch on-demand sessions today.
  
 Watch Here 
   Let’s see why neural networks are considered to be universal function approximators. Each neuron learns a limited function: f(.) = g(W*X) where W is the weight vector to be learned, X is the input vector and g(.) is a non-linear transformation.  W*X can be visualized as a line (being learned) in high-dimensional space (hyperplane) and g(.) can be any non-linear differentiable function like sigmoid, tanh, ReLU, etc. (commonly used in the deep learning community). Learning in neural networks is nothing but finding the optimum weight vector W. As an example, in y = mx+c, we have 2 weights: m and c. Now, depending on the distribution of points in 2D space, we find the optimum value of m &amp; c which satisfies some criteria: the difference between predicted y and actual points is minimal for all data points.
The layer effect
Now that each neuron is a nonlinear function, we stack several such neurons in a “layer” where each neuron receives the same set of inputs but learn different weights W. Therefore, each layer has a set of learned functions: [f1, f2, …, fn], which are called as hidden layer values. These values are again combined, in the next layer: h(f1, f2, …, fn)  and so on. This way, each layer is composed of functions from the previous layer (something like h(f(g(x)))). It has been shown that through this composition, we can learn any non-linear complex function.
Deep learning is a neural network with many hidden layers (usually identified by &gt; 2 hidden layers). But effectively, what deep learning is a complex composition of functions from layer to layer, thereby finding the function that defines a mapping from input to output. For example, if the input is an image of a lion and output is the image classification that the image belongs to the class of lions, then deep learning is learning a function that maps image vectors to classes. Similarly, input is word sequence and output is whether the input sentence has a positive/neutral/negative sentiment. Therefore, deep learning is learning a map from input text to output classes: neutral or positive or negative.
Deep learning as interpolation
From a biological interpretation, humans process images of the world by hierarchically interpreting them bit by bit, from low-level features like edges and contours to high-level features like objects and scenes. Function composition in neural networks is in line with that, where each function composition is learning complex features about an image. The most common neural network architecture that is used for images is Convolutional Neural Network (CNN), which learns those features in a hierarchical fashion and then a fully connected neural network classifies image features into different classes.
By using high school math again, given a set of data points in 2D, we try to fit a curve through interpolation that somewhat represents a function defining those data points. The more complex the function we fit (in interpolation, for example, determined through polynomial degree), the more it fits the data; however, the less it generalizes for a new data point. This is where deep learning faces challenges and what is generally referred to as an overfitting problem: fitting to data as much as possible, but compromising in generalization. Almost all architectures in deep learning had to handle this important factor to be able to learn a general function that can perform equally well on unseen data.
A deep learning pioneer, Yann LeCun (creator of the convolutional neural network and ACM Turing award winner) posted on his Twitter handle (based on a paper): “Deep Learning is not as impressive as you think because it is mere interpolation resulting from glorified curve fitting. But in high dimensions, there is no such thing as interpolation. In high dimensions, everything is extrapolation.” Thus, as part of function learning, deep learning is doing nothing but interpolation or in some cases, extrapolation. That’s all! 
The learning aspect
So, how do we learn this complex function? Well, it completely depends on the problem at hand and that’s what determines the neural network architecture. If we are interested in image classification, then we use CNN. If we are interested in time-dependent predictions or text then we use RNN or transformers and if we have a dynamic environment (like car driving) then we use reinforcement learning. Apart from this, learning involves handling different challenges:
 Ensuring the model learns general function and doesn’t just fit to train data; this is handled by using regularization Depending on the problem at hand, choice of the loss function is made; loosely speaking, the loss function is error function between what we want (true value) and what we currently have (current prediction). Gradient descent is the algorithm used for converging to an optimal function; deciding learning rate becomes challenging because when we are away from optimal, we want to move faster to optimal, and when we are near-optimal, then we want to move slower to ensure we converge to optimal and global minima. High number of hidden layers need to handle the vanishing gradient problem; architectural changes like skip connections and appropriate non-linear activation function helps to solve it. 
Compute challenges
Now that we know deep learning is simply a learning complex function, it brings other compute challenges:
 To learn a complex function, we require a large amount of data To process large data, we require fast compute environments We need an infrastructure that supports such environments 
Parallel processing with CPUs is not enough to compute millions or billions of weights (also called parameters of DL). Neural networks requires learning weights that require vector (or tensor) multiplications. That is where GPUs come in handy, as they can do parallel vector multiplications very fast. Depending on the deep learning architecture, data size, and task at hand, we sometimes require 1 GPU, and sometimes, several of them, a decision data scientist needs to make based on known literature or by measuring the performance on 1 GPU.
With the use of proper neural network architecture (number of layers, number of neurons, non-linear function, etc.) along with large enough data, a deep learning network can learn any mapping from one vector space to another vector space. That’s what makes deep learning such a powerful tool for any machine learning task.
 Abhishek Gupta is the principal data scientist at   Talentica Software  . 
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This is what makes deep learning so powerful
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The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe
2016-09-09
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 <h3>The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe</h3>
 <p>technologyreview.com</p>
 <p>In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines.</p>
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 Jana Vembunarayanan

 2325 days ago

 Fascinating piece.

 The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe

 technologyreview.com

 In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines.

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The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe
 technologyreview.com
 In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines.
The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe
https://getpocket.com/@jvembuna/share/1245579