Людина v машина: Може Комп'ютери Кук, Писати і малювати краще нас?

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

Штучний інтелект тепер може виграти гру, розпізнавати обличчя, навіть оскаржити квиток парковки. Але це може зробити речі навіть люди знаходять хитрий?


Працює на Guardian.co.ukЦя стаття під назвою “Людина v машина: можна готувати комп'ютери, писати і малювати краще, ніж у нас?” була написана Лео Benedictus, для The Guardian в суботу 4 червня 2016 08.00 УНІВЕРСАЛЬНЕ ГЛОБАЛЬНЕ ЧАС

одне відео, для мене, все змінилося. Це кадри зі старої гри Atari втечу, той, де ви ковзати лопатка вліво і вправо вздовж нижньої частини екрана, намагаючись зруйнувати цеглини, стрибучий м'яч в них. Ви можете прочитати про гравця гри: алгоритм, розроблений DeepMind, британський штучний інтелект компанії, чиї AlphaGo програма також побили одного з найбільших коли-небудь Go гравців, чи Седол, на початку цього року.

Можливо, ви очікуєте, комп'ютер, щоб бути хорошим в комп'ютерних іграх? Після того, як вони знають, що робити, вони, звичайно, робити це швидше і більш послідовно, ніж будь-яка людина. Поворотний гравець DeepMind взагалі нічого не знають, проте. Він не був запрограмований з інструкціями, як гра працює; це навіть не сказали, як використовувати елементи управління. Все це було було зображення на екрані і команду, щоб спробувати отримати якомога більше очок, як це можливо.

годинник відео. По-перше, лопать дозволяє падіння м'яч в Лету, не знаючи, чи не краще. в кінці кінців, просто відведенням про, вона вибиває м'яч назад, руйнує цеглу та отримує точку, тому він визнає це і робить його більш часто. Після практики двох годин, або про 300 гри, він став серйозно добре, краще, ніж ви або я коли-небудь буде. Потім, після того, як про 600 гри, речі стають привид. Алгоритм починається спрямований на тому ж місці, знову і знову, для того, щоб рити через цеглу в простір позаду. Одного разу там, як будь-який гравець знає Breakout, м'яч буде відскакувати навколо деякий час, збір вільних точок. Це хороша стратегія, що комп'ютер придумали сам по собі.

"Коли наші дослідники побачили це, що насправді їх в шоці,"Генеральний директор компанії DeepMind в, Деміс Хассабіс, розповів аудиторії на технологічній конференції в Парижі. Ти можеш стежити за його демонстрацію, теж, і почути сміх і оплески, коли машина з'ясовує стратегію Burrowing. Комп'ютер став розумним, трохи схожі на нас.

"Штучний інтелект" тільки про найстаріших і найбільш роздутими з гудіння фраз всіх обчислювальних в. Ідея була вперше обговорювалася всерйоз Алан Тьюринг в Обчислювальні машини й розум, the 1950 документ, в якому він запропонував те, що стало відомо як тест Тьюринга: якщо машина зможе переконати вас через розмову, що це була людина, вона робить стільки, скільки кожна людина могла, щоб довести, що це було дійсно думати. Але термін штучний інтелект не був зазвичай використовується до тих пір, 1955, коли Американський математик Джон Маккарті запропонували провести конференцію для фахівців. Це сталося в наступному році, і з тих пір поле закінчилася приблизно на два десятиліття циклу манії і відчаю. (Дослідники навіть новий термін - "зима AI" - описати свої заклинання з моди. У 1970-і і 1990-і роки були особливо важкими.)

Сьогодні є нова манія, яка зовні відрізняється від інших: вона поміщається в кишені. Телефонний може обіграти чемпіона світу з шахів, розпізнавати пісні на радіо і фотографії ваших дітей, і перевести свій голос на іншу мову. Нао робот зображений тут з Yotam Оттоленгі може ходити на двох ногах, говорити, знайти м'яч і навіть танець. (це робот, хоча, НЕ А.І.: він не може створити меню.)

Слух про досягнення в AI, Вам не потрібен фахівець, щоб сказати вам, щоб бути порушені, або налякав. Ви просто почати, щоб отримати почуття: інтелект тут. Ясно, що Google отримав почуття, теж, тому що він купив DeepMind за $ 650 млн за чутками. в 2013, Facebook запустила свій власний проект, з планами з розвитку розпізнавання особи і природна мова для сайту. Розробники вже почали роботу над інтелектуальними chatbots, якої користувачі Facebook зможуть викликати його за допомогою служби повідомлень.

Досі, комп'ютери не були "розумним" на всіх, або тільки вузько так. Вони були гарні в простих завданнях, які засліплюють нас, таких як математика, але погано на ті, які ми вважаємо цілком очевидним, які виявляються серйозно важко. Акт ходьби є чимось сучасні роботи вчаться як діти і до сих пір боротьба з; основні завдання залишаються Пораючись далекі мрії. "Одним з прикладів є легкість, з якою ви або я міг би зробити чашку чаю в кухні когось іншого,"говорить Професор Алан Вінфілд, робототехніки в Університеті Західної Англії. "Існує не робот на планеті, які могли б зробити це."

Для того, щоб зрозуміти, чому бути людиною настільки важко, думати про те, як ви могли б змусити комп'ютер розпізнавати людей з фотографій. Без штучного інтелекту, ви повинні знати, як ви це робите собі в першу чергу, для того, щоб запрограмувати комп'ютер. Ви повинні зібрати і думати про всі можливі моделей, кольори і форми граней, і як вони змінюються в світлі і під різними кутами - і ви повинні знати, що є істотним, а що це просто бруд на об'єктиві. За допомогою AI, Вам не потрібно пояснювати: ви просто дати гору реальних даних на комп'ютер і дайте йому дізнатися. Як розробити програму навчання залишається езотеричним питанням, в провінції кілька потребують комп'ютерних вчених, але це ясно, що у них є на переможця шляхом розробки структур обробки даних на основі вільно на структурах в головному мозку. (Це називається "глибоке навчання".) Що стосується гір реальних даних, Ну, це те, що Google, facebook, Амазонка, Uber and all the rest happen to have lying around.

At this stage, we don’t yet know which uses of AI will turn out best. Josh Newlan, a California coder working in Shanghai, got bored with listening to endless conference calls, так he built some software to listen for him. Зараз, whenever Newlan’s name is mentioned, his computer instantly sends him a transcript of the last half-minute, waits 15 секунд, then plays a recording of him saying, “Sorry, I didn’t realise my microphone was on mute.” Last year, Josh Browder, a British teenager, built a free artificial lawyer that appeals against parking tickets; he plans to build another to guide refugees through foreign legal systems. The possibilities are… Well, maybe an algorithm can count the possibilities.

So will machine minds one day outstrip our own? The researchers I speak to are cautious, and take pains to emphasise what their machines can’t do. But I decided to put AI to the test: can it plan a meal as well as Ottolenghi? Can it paint my portrait? Is technology still artificially intelligent – or is it starting to be intelligent, for real?

The cooking test

Добре, I will say it isn’t horrible. Humans have served me worse. Although in truth the name that IBM’s Chef Watson gives this dish (“Chicken Liver Savoury Sauce”) is about as appetising as it deserves.

To be fair to Chef Watson, and to Guardian Weekend’s own chef-columnist Yotam Ottolenghi, I had set them quite a task. I asked for a dish based on four ingredients that seemed to belong nowhere near each other: chicken livers, Greek yoghurt, wasabi and tequila. Вони могли б додати, що ще їм подобається, але ці чотири мали бути в готову страву, які я б готувати і є. Шеф Уотсон не вагаючись, миттєво дає мені дві Соуси. Оттоленгі був більш обачним. "Коли я отримав виклик я думав, "Це не буде працювати,"Він каже мені,.

Я думав, що те ж саме. Або, принаймні, я думав, що в кінцевому підсумку є два блюда, які вдалося ОК, незважаючи на їх інгредієнти, а не через них. Насправді - і ви будете вважати мене повзати, але так, що - рецепт Ottolenghi був одкровенням: Печінка і цибулю і зменшення текіла, служив з яблуком, редис, буряком і цукром Slaw, з васабі і йогуртовой заправкою. Блюдо може мало сенсу на папері, but I devoured a plateful feeling that every element belonged. (And vinaigrette thickened with yoghurt and wasabi instead of mustard: seriously, give it a try.) Ottolenghi tells me the recipe is just a whisker short of publishable.

The thing is, that dish took him and his team three days to perfect. They were able to taste and discuss flavours, textures, colours, temperatures, in a way that Watson can’t – although there have been “discussions” about adding a feedback mechanism in future, Chef Watson’s lead engineer, Florian Pinel, tells me. “A recipe is such a complex thing,” Ottolenghi says. “It’s difficult for me even to understand how a computer would approach it.”

Yotam Ottolenghi and Chef Watson’s dishes
Yotam Ottolenghi and Chef Watson’s dishes Фотографія: Jay Brooks for the Guardian

Watson was first built by IBM to win the television gameshow Jeopardy! в 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.) в кінці кінців, 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, Лондон. Фотографія: 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,"він говорить. “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.”

Вердикт 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 Амазонки 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. The 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 або 15 багато років тому, 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, теж, 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 goals, 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, звичайно, 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.

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

The painting test

A laptop wants me to smile. “It’s in a good mood," Simon Colton говорить. 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, мабуть, 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 (actually, 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, є Sarah Jane Moon, an artist who exhibits with the Royal Society of Portrait Painters. She doesn’t want to see my teeth, або. “We paint from life,"вона говорить, “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, з іншого боку, 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. Фотографія: 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, colours, 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 думати looks like a nose.”

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

"Так, точно,” 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.

Що ще більш важливо, I realise that what matters isn’t how the machine paints; it’s how I see. Moon I understand, Я думаю. 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?

Вердикт 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, і it’s already a decade old. In many ways it typifies where AI has got to. Useful, впевнений; 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% Англійська). “Most of our growth, and actually most of our traffic, comes from developing or emerging markets such as Brazil, Indonesia, Індія, Таїланд,” says Barak Turovsky, head of product management and user experience at Google Translate. It’s surprisingly popular for dating, теж, 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.

Навіть так, there are limits, and some seem fundamental when you talk to a human translator and realise how subtle their work is. Ros Schwartz і 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. що, 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. Таким чином, ви б цікаво, що він мав на увазі, і замість того, щоб припустити,, якщо ви не були вже знайомі з використанням, що він повинен означати "божевільний", тому що ви розумієте інше, що ви читали. Але все це робити, НЕ Google Translate повинні бути в значній мірі свідомим, я запитую? Туровський сміється. "Я не думаю, що я кваліфікований, щоб відповісти на це питання."

Вердикт Деякі bullseyes і Howlers від Google Translate, в той час як Шварц і De Freyman вільно володіють і точним.

guardian.co.uk © Guardian News & Media Limited 2010

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