Man Machine v: Bisa Komputer Cook, Tulis lan Paint Luwih kita?

Man v Machine: Can Computers Cook, Write and Paint Better Than Us?

Artificial intelligence saiki bisa menang game sing, ngenali pasuryan sampeyan, malah mréntahaké marang tiket parkir Panjenengan. Nanging bisa apa kuwi malah manungsa golek angel?


Powered by Guardian.co.ukArtikel iki judul “mesin v Man: bisa komputer cook, nulis lan Paint luwih saka us?” iki ditulis dening Leo Benediktus, kanggo Guardian ana 4 Juni 2016 08.00 UTC

salah video, nggo aku, diganti kabeh. Iku dawane saka game Atari lawas breakout, ing ngendi geser paddle kiwa lan nengen ing sisih ngisor layar, nyoba kanggo numpes bricks dening sehat werni menyang wong. Sampeyan bisa uga wis maca bab pemain game: algoritma dikembangaké dening Deepmind, perusahaan Intelligence Ponggawa Inggris kang program AlphaGo uga ngalahake siji saka paling tau Go pemain, Lee Sedol, sadurungé taun.

Mbok nyana komputer dadi apik ing game komputer? Sawise padha ngerti apa apa, wong mesthi nindakaken luwih cepet lan luwih terusan saka sembarang manungsa. Breakout pamuter DeepMind kang sumurup apa-apa, Nanging. Nanging iki ora diprogram kanthi instruksi cara karya game; iki malah ora marang carane nggunakake kontrol. Kabeh iku wis ana ing gambar ing layar lan printah kanggo nyoba kanggo njaluk minangka akeh TCTerms sabisa.

Watch video. Pertamane, paddle ngijini gulung werni menyang lalen, ngerti ora luwih. pungkasanipun, mung mucking bab, iku tansah thothok-thothok ing werni bali, karusakane bata lan nemu titik, supaya mangerteni lan ora luwih asring. Sawise laku rong jam ', utawa babagan 300 game, iku wis dadi akeh apik, luwih saka sampeyan utawa aku bakal tau dadi. banjur, sawise bab 600 game, iku spooky. algoritma wiwit ngarahake ing titik sing padha, terus terusan, supaya burrow liwat bricks menyang papan konco. Sawise ana, minangka pemain Breakout mangerténi, werni bakal seger watara kanggo nalika, kumpul TCTerms free. Iku strategi sing apik komputer teka munggah karo dhewe.

"Nalika peneliti kita weruh iki, sing bener cingak wong,"CEO DeepMind kang, Demis Hassabis, marang para rawuh ing konferensi teknologi ing Paris. sampeyan bisa nonton demonstrasi, banget, lan krungu ngguyu lan keplok nalika mesin tokoh-tokoh metu strategi burrowing sawijining. komputer wis dadi pinter, dicokot kaya kita.

"Artificial intelligence" iku mung bab sing paling tuwa lan paling hyped phrases Buzz kabeh komputerisasi kang. idea iki pisanan mooted akeh dening Alan Turing ing Mesin komputasi lan Intelligence, ing 1950 kertas kang ngajokaken apa dadi dikenal minangka test Turing: yen mesin bisa gawe uwong yakin sampeyan liwat obrolan sing ana manungsa, iku dilakoni minangka akeh minangka sembarang manungsa bisa mbuktekaken iku saestu mikir. Nanging istilah AI iki ora umum digunakake nganti 1955, kapan matématikawan American John McCarthy ngajokaken konferensi kanggo ahli. Iki njupuk Panggonan ing taun iki, lan wiwit banjur lapangan wis mbukak ing roughly siklus loro-dekade mania lan nglokro. (Peneliti malah duwe istilah anyar - "AI mangsa" - kanggo njlèntrèhaké disebutake sawijining metu saka fashion. Taun 1970-an lan 1990-an padha utamané atos.)

Dina iki ana sing mania anyar, kang katon beda saka liyane: mathuk ing kanthong. A telpon bisa ngalahake juara catur donya, ngenali lagu ing radio lan gambar saka anak, lan nerjemahake swara menyang basa liyane. Robot Nao sing digambaraké ing kéné karo Yotam Ottolenghi bisa lumaku ing loro sikil, nganggo, golek bal malah tari. (Iku robot, sanadyan, ora AI: iku ora bisa desain menu.)

Hearing bab maju ing AI, sampeyan ora perlu pakar kanggo pitutur marang kowe dadi bungah, utawa wedi. Sampeyan mung miwiti kanggo njaluk koyo: Intelligence punika. Cetha Google tak roso, banget, amarga tuku DeepMind kanggo $ 650m dikabarake. Ing 2013, Facebook dibukak project dhewe, karo plans kanggo berkembang pangenalan basa rai lan alami situs. Developers wis dipunwiwiti karya ing chatbots cerdas, kang kedhaftar Facebook bakal bisa kanggo nyeluk nggunakake layanan Messenger sawijining.

Supaya adoh, komputer durung "cerdas" ing kabeh, utawa mung narrowly supaya. Padha wis apik ing tugas gampang sing dazzle kita, kayata maths, nanging ala ing wong kita njupuk kanggo diwenehake, kang nguripake metu dadi akeh hard. Tumindak kang lumampah soko robot modern sinau kaya bayi lan isih perjuangan karo; tugas pottering dhasar tetep impen adoh. "Salah sijine yaiku ease karo kang sampeyan utawa aku bisa nggawe tuwung saka tèh ing pawon wong liya iku,"ngandika Professor Alan Winfield, a roboticist ing Universitas West Inggris. "Ana ora robot ing planet sing bisa nindakake iki."

Kanggo ngerti kok manungsa iku dadi angel, mikir bab carane sampeyan bisa njaluk komputer kanggo ngenali wong saka foto. tanpa AI, sampeyan kudu ngerti carane apa iku dhewe pisanan, supaya program komputer. Sampeyan kudu ngumpulake lan mikir bab kabeh pola bisa, lan manéka warna pasuryan, lan carane wong ngganti ing cahya lan ing sudhut sing beda - lan sampeyan kudu ngerti apa iku wujud lan apa namung lendhut ing lensa. kanthi AI, sampeyan ora duwe kanggo nerangake: sampeyan mung menehi gunung data nyata kanggo komputer lan supaya iku sinau. Carane desain lunak learning tetep prakara Esoteric, provinsi sawetara ilmuwan komputer sought sawise, nanging cetha padha wis tak ing kanggo sing menang kanthi devising struktur data-Processing adhedhasar kahanan ing struktur ing otak. (Iki diarani "learning jero".) Minangka kanggo gunung data nyata, uga, sing apa Google, Facebook, Amazon, Uber lan kabeh liyane kelakon wis gemlethek.

Ing tataran iki, aku durung ngerti kang migunakake saka AI bakal nguripake metu paling. Josh Newlan, a coder California makarya ing Shanghai, tak bosen karo ngrungokake telpon konferensi telas, supaya kang dibangun sawetara lunak kanggo ngrungokake kanggo wong. Saiki, kapan jeneng Newlan ingkang kasebut, komputer kang enggal dikirim marang transcript saka-setengah menit pungkasan, ngenteni 15 detik, banjur muter rekaman saka wong matur, "Sorry, Aku ora kelingan mikropon ana ing bisu. "Paling taun, Josh Browder, remaja Inggris, dibangun pengacara Ponggawa free sing Appeals marang karcis parkir; plans kanggo mbangun liyane kanggo nuntun pengungsi liwat sistem legal manca. Kemungkinan sing ... Inggih, Mungkin algoritma bisa count kemungkinan.

Dadi bakal pikiran mesin siji dina outstrip kita dhewe? Peneliti Aku pitutur marang sing ngati-ati, lan njupuk loro kanggo nandheske apa mesin sing ora bisa apa. Nanging aku mutusaké kanggo nyelehake AI kanggo test: bisa iku jangka meal uga Ottolenghi? Bisa iku Paint ngadeg sandi? Teknologi isih artificially cerdas - utawa iku miwiti dadi pinter, tenanan?

Test masak

Inggih, Aku bakal ngomong iku ora nggegirisi. Manungsa wis dadi kula elek. Senajan ing bebener jeneng sing IBM Chef Watson menehi sajian iki ("Ayam ati Savoury Sauce") kira minangka appetising minangka pantes.

Dadi sing padha Chef Watson, lan kanggo Guardian Weekend dhewe chef-pawarto Yotam Ottolenghi, Aku wis nyetel cukup tugas. Aku takon kanggo sajian adhedhasar papat úa sing ketoke kagungane ono cedhak saben liyane: livers pitik, yoghurt Yunani, wasabi lan tequila. Padha bisa nambah apa wae liya padha disenengi, nanging sing papat wis dadi ing sajian rampung, kang aku cook lan mangan. Chef Watson ora ragu-ragu, enggal menehi kula loro pasta sauces. Ottolenghi ana liyane circumspect. "Nalika aku tak tantangan Aku panginten, 'Iki ora arep bisa,' "Piyambakipun dhateng kula.

Aku panginten padha. Utawa ing paling Aku bakal mungkasi munggah mangan loro pasugatan sing ngatur dadi OK senadyan úa sing, tinimbang amarga wong. Ing kasunyatan - lan sampeyan bakal ngarani aku penjilat a, nanging supaya apa - resep Ottolenghi kang ana wahyu: ati lan trikatuka lan abang tequila, dadi karo apel, Lobak, beetroot lan chicory slaw, karo wasabi lan yoghurt klamben. sajian bisa nggawe sethitik pangertèn ing kertas, nanging aku kamangsa koyo plateful sing saben unsur duweke. (Lan vinaigrette thickened karo yoghurt lan wasabi tinimbang sawi: akeh, dipun cobi.) Ottolenghi dhateng kula resep namung cendhak whisker saka publishable.

Ing bab punika, sing sajian banjur dicekel lan kang tim telung dina kanggo nyampurnakaké. Padha bisa kanggo rasa lan ngrembug roso, tekstur, werna, Suhu, ing cara sing Watson ora bisa - senadyan ana uga "diskusi" bab nambah mekanisme saran ing mangsa, Chef engineer timbal Watson, Florian Pinel, dhateng kula. "A resep kuwi bab Komplek,"Ngandika Ottolenghi. "Iku angel kanggo kula malah ngerti carane komputer bakal pendekatan iku."

pasugatan Chef Watson Yotam Ottolenghi lan
pasugatan Chef Watson Yotam Ottolenghi lan Motret: Jay Brooks kanggo Guardian

Watson was first built by IBM to win the television gameshow Jeopardy! ing 2011. In some ways it was a misleading challenge, because for a computer the tough part of a quiz is understanding the questions, not knowing the answers; for humans, it’s the other way around. But Watson won, and its technology began to be applied elsewhere, including as a chef, generating new recipes based on 10,000 real examples taken from Bon Appétit magazine.

First the software had to “ingest” these recipes, as the Watson team put it. A lot of computation went into understanding what the ingredients were, how they were prepared, how long they were cooked for, in order to be able to explain how to use them in new dishes. (The process can still go awry. Even now Chef Watson recommends an ingredient called “Mollusk”, which it helpfully explains is “the sixth full-length album by Ween”.)

A bigger problem was trying to give the machine a sense of taste. “It’s easy enough for a computer to create a novel combination,” Pinel says, “but how can it evaluate one?” Watson was taught to consider each ingredient as a combination of specific flavour compounds – of which there are thousands – and then to combine ingredients that had compounds in common. (This principle, food pairing, is well established among humans.) Akhire, the software generates step-by-step instructions that make sense to a human cook. The emphasis is on surprises rather than practical meal planning. “Chef Watson is really there to inspire you,” Pinel explains. Each recipe comes with the reminder to “use your own creativity and judgment”.

And I need to. The first step is to “toast flat-leaf parsley”, which just isn’t a good idea. I am making, effectively, a slow-cooked spiced pork and beef ragu, including all my four ingredients, yet Watson oddly also includes cucumber and keeps telling me to “season with allspice”, which I refuse to do on principle. In the end, I have a rich sauce with a flavour rather close to the farmyard, but not uneatable. I can’t taste the wasabi or the tequila, which I’m glad about.

Yotam Ottolenghi with Nao robot
Yotam Ottolenghi with Nao robot loaned courtesy of Heber primary school, London. Motret: Jay Brooks. Styling: Lee Flude

Watson is clever and the task is tough, but I am ready to say that this is no more than a bit of fun for food nerds, until Ottolenghi stops me. “I think the idea of slow-cooking the livers with a bit of meat is great,"Kang ngandika. “It intensifies the flavour. Everything will come together. If I had to start afresh with this recipe, obviously the yoghurt doesn’t fit – but I would leave the orange skin there, a few of the spices. I don’t think it’s a very bad recipe. It could work.”

Putusan Watson hides the weirdness of the ingredients, but Ottolenghi makes them sing.

The writing test

Put little Wordsmith next to the fearsome machines of IBM and Google, and it looks as computationally advanced as a pocket calculator. Yet while Watson fumbles through its apprenticeship, Wordsmith is already at work. If you read stock market reports from the Associated Press, or Yahoo’s sports journalism, there is a good chance you’ll think they were written by a person.

Wordsmith is an artificial writer. Developed by a company in North Carolina called Automated Insights, it plucks the most interesting nuggets from a dataset and uses them to structure an article (or email, or product listing). When it comes across really big news, it uses more emotive language. It varies diction and syntax to make its work more readable. Even a clumsy robot chef can have its uses, but writing for human readers must be smooth. Hooked up to a voice-recognition device such as Amazon’s Echo, Wordsmith can even respond to a spoken human question – about the performance of one’s investments, say – with a thoughtfully spoken answer, announcing what’s interesting first, and leaving out what isn’t interesting at all. If you didn’t know the trick, you’d think Hal 9000 had arrived.

The trick is this: Wordsmith does the part of writing that people don’t realise is easy. Locky Stewart from Automated Insights gives me a tutorial. You write into Wordsmith a sentence such as, “New ABC figures show that the New York Inquirer’s circulation rose 3% in April.” Then you play around. Ing 3% has come from your data, so you select the word “rose” and write a rule, known as a “branch”, which will change the word “rose” to the phrase “shot up” if the percentage is more than 5%. Then you branch “rose” to become “fell” if the percentage is negative. If the percentage is -5% or lower, “rose” becomes “plummeted”.

Then you feed it synonyms. So “plummeted” can also be “fell sharply by”. “The Inquirer’s circulation” can be “circulation at the Inquirer”. “Shot up” can be “soared” and so on. Then you add more sentences, perhaps about online traffic, or about which days’ print copies sold best, or about comparisons year-on-year. Then you get clever. You tell Wordsmith to put the sentences with the most newsworthy information first, defined perhaps as those that feature the greatest percentage changes. Maybe you add a branch to say that a result is “the best/worst performance among the quality titles”. Hell, you can even teach it some old Fleet Street tricks, so that if circulation plummets the piece begins “Editor Charles Kane is facing fierce criticism as”, but if circulation has “shot up” this becomes “Charles Kane has silenced critics with news that”. Insert “more” or “again” or “continues” if you get the same thing two months in a row.

“The artificial intelligence is actually the human intelligence that is building the network of logic,” Stewart says, “the same network you would use when writing a story. It could have been developed 10 utawa 15 taun ago, in code, but to make it work at this scale has only been possible lately.” Clearly it takes longer to prepare an article on Wordsmith than to write one conventionally, but once you’ve done so, the computer can publish a fresh newspaper circulation story every month, on every newspaper, within seconds of receiving the information. It can publish millions of stories in minutes – or publish only some of them, if the data doesn’t reach a given threshold of newsworthiness. Thus it becomes an automated editor, banget, with adjustable tastes in thoroughness, frequency and hysteria.

For Wordsmith’s task, I suggest football: it’s a field that produces a lot of data and has a readership that wants personalised articles. Guardian football writer Jacob Steinberg volunteers to take on the computer, and I provide a table of facts from the recent Premier League: last season’s league position and this season’s position at Christmas and at the end, goals scored and conceded, top scorer’s name and total, value of summer transfers and a quote from the manager.

Working solely from this data, computer and human must each write a review of the season for a given club. Steinberg chooses Leicester City on the basis that its numbers should contain a story that anyone would see. Wordsmith doesn’t need to choose. It will do all 20.

And in fact both computer and human quickly produce quite similar work:

Leicester City footballer Jamie Vardy

Both Steinberg and Wordsmith deliver dramatic first sentences. Perhaps keen to sound authentic, Automated Insights use some clever tricks to put feeling into the latter’s article, astutely guessing that Leicester were “hoping to finish in the top 10 after a 14th place finish last season”. I look through Wordsmith’s other articles and Southampton, having finished seventh last season, have “eyes on a European spot”, while Manchester City “began the season dreaming of a league title after finishing second”.

Conversely, Steinberg digs more meaningfully into the numbers, showing that Jamie Vardy not only scored 24 gol, but that this was a higher percentage of his team’s goals than was managed by all but two other players. Knowing how Wordsmith works, mesthi, one could easily set it up to do the same. In fact looking through it, Steinberg’s entire article could have been created by a skilled Wordsmith programmer – with the exception of one line. “It’s a magical season,” he quotes the Leicester manager as saying, before adding, “justifiably so, given that a summer expenditure of £26.7m on transfers made them the eighth lowest spenders”. That “justifiably so” shows a writer who actually understands what he is writing.

Putusan Steinberg is a much better writer, unless you want 20 data-heavy articles in 10 menit.

The painting test

A laptop wants me to smile. “It’s in a good mood," Simon Colton ngandika. He knows because he’s the scientist who programmed it. We are in the Science Museum in London, where the Painting Fool, as it is called, is giving a public demonstration. It’s important that I don’t show my teeth, Colton says, because something about the light makes them look green to the Painting Fool.

From my toothless smile the laptop creates a “conception” of what it would like to paint, based on its mood. The mood comes from a “sentiment analysis” of recent Guardian articles, as it happens (on average reading the Guardian is a downer, ketoke, apart from the stuff about gardening). Yesterday the Fool was in such a bad mood that it sent someone away unpainted; today it is feeling “positive”.

Next the Fool attempts to paint with a simulated brush and a simulated hand (bener, an image of Colton’s hand) on the screen behind me. It learned to reflect its mood from the work of Dan Ventura, another computer scientist, at Brigham Young University in Utah, who trained a neural network to recognise the emotional attributes of images by sitting thousands of people in front of tens of thousands of paintings and asking them to tag each one with whatever adjectives came to mind. The Fool now knows that bright colours reflect a good mood, and “pencils with tight hatching” create a picture that is “cold”. When it is done, it prints out a page with a typed self-critique. “Overall, this is quite a bright portrait,” it says. “That’s OK, but my style has lowered the level of bright here. So I’m a bit annoyed about that.”

Here along with us, intrigued but too busy at her easel to watch, punika Sarah Jane Moon, an artist who exhibits with the Royal Society of Portrait Painters. She doesn’t want to see my teeth, salah siji. “We paint from life,"Dheweke ngandika, “and you can’t hold a smile for sitting upon sitting. That’s why all the traditional portraits show quite relaxed features.”

The Painting Fool is a special machine, and even slightly famous, but I can’t deny that Moon is almost all of why I’m excited to be here. The feeling of being painted by a real person, having them look at you and think about you, is exciting and flattering. Sentiment analysis and training data, on the other hand, don’t add up to anything whose view of me I care about, and the finished portraits do not change my mind. Moon’s is a lovely, real thing, which feels straight away like one person seen by another. The Fool’s three efforts have qualities I like, but mostly they look like photographs that have gone through some kind of software filter. Colton insists the Fool is here “to learn to be better” but I look and think: so what?

Painting of Leo Benedictus by Sarah Jane Moon
Leo Benedictus as seen by Sarah Jane Moon…
Painting of Leo Benedictus by the Painting Fool computer
…and as imagined by the Painting Fool laptop. Motret: Murray Ballard

Then I think some more. For one thing, it turns out that art is more mechanical than I’d realised. “I try to look at Leo as an abstract set of shapes, forms, werna, tones,” Moon tells Colton, “to get away from the fact that that’s a nose. Because when you start to do that, you get caught up in what you mikir looks like a nose.”

“What the software does is break it down into colour regions,” Colton says.

"Ya, persis,” Moon agrees. “I think that’s what the best painters do. It’s transcribing.” Afterwards she tells me she felt a kind of “kinship” with the software as they worked side by side.

More importantly, I realise that what matters isn’t how the machine paints; it’s how I see. Moon I understand, Aku. She’s a person and I know how that feels, so I care about her picture. But what does it feel like to be the Painting Fool? Is that what its portraits are trying to tell me?

Putusan Moon’s painting is far richer; the Fool is still learning and has centuries of practice to go.

The translation test

Google Translate was the first piece of proper science fiction to come true, lan it’s already a decade old. In many ways it typifies where AI has got to. Useful, manawa; impressive, without question; but still clunky as hell, despite big improvements.

If you haven’t used it, it works like this: enter text or web links in any of 103 supported languages and you get a rough translation seconds later in any of the others. The app on your phone will transcribe what you say and then speak it back, translated (32 languages supported); it can replace the text of a foreign language sign or menu wherever you point the camera. No explanation is needed of how cool that is (and it’s free).

Globally, half a billion people use Google Translate each month, mostly those who don’t speak English (which is 80% of people) but who want to understand the internet (which is 50% Inggris). “Most of our growth, and actually most of our traffic, comes from developing or emerging markets such as Brazil, Indonesia, India, Thailand,” says Barak Turovsky, head of product management and user experience at Google Translate. It’s surprisingly popular for dating, banget, he adds. “Things like ‘I love you’ and ‘You have beautiful eyes’, that’s very prevalent.”

The software has always used a form of statistical machine learning: scouring the internet for already translated text – UN declarations, EU documents – and mapping the likelihood of certain words and phrases corresponding to one another. The more data it gathers, the better it gets, but the improvement levelled off a couple of years ago. Soon, Turovsky says, they will deploy new deep learning algorithms, which will produce much more fluent translations.

malah supaya, there are limits, and some seem fundamental when you talk to a human translator and realise how subtle their work is. Ros Schwartz lan Anne de Freyman volunteer for this task. Both are professional French/English translators, and I need two because, in order to judge how good the translation is without being fluent in both languages, we need to translate twice – once out of English into French, once back again. Google Translate keeps no memory of the original and can do the same thing.

I choose a short passage of distinctive but not especially wild or ambiguous prose from the beginning of Herzog by Saul Bellow. Translators normally require context, so I tell Schwartz and De Freyman that it comes from a famous mid-century American novel.

Within a few days, Schwartz and De Freyman return a very smooth facsimile of the original text. Here and there some nuances have not survived, but the passage remains a pleasure to read, and the main meanings come across exactly.

Google Translate takes only a few seconds, and the result is both impressive and inadequate, weirdly good in places, in others weirdly bad – turning “he” into “it” and concocting the idea that Herzog is in love. Miraculously, it keeps “cracked” as a description of the hero. French has no word that combines the sense of “broken” and “mad” that cracked coveys in English, so De Freyman makes it “cinglé”, which comes back from Schwartz as “crazy”.

“Google Translate would look at statistical probability and say, what does ‘cracked’ mean?” Turovsky explains. “And statistically, it will try to decide whether it means ‘cracked’ or ‘crazy’ or whatever. That, for a machine, is a non-trivial task.” Nor is it simple for a human, even though we find it easy. You’d have to ask whether Bellow could have meant that Herzog was “cracked” as in physically fractured. Then you’d have to assume not, because human bodies don’t generally do that. So you’d wonder what he did mean and assume instead, if you were not already familiar with the usage, that he must mean “crazy”, because you understand the rest of what you’ve read. But to do all this, wouldn’t Google Translate have to be pretty much conscious, I ask? Turovsky laughs. “I don’t think I’m qualified to answer that question.”

Putusan Some bullseyes and howlers from Google Translate, while Schwartz and De Freyman are fluent and exact.

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