ឥឡូវនេះអាចក្លែងបន្លំឈ្នះការប្រកួតមួយ, ទទួលស្គាល់មុខរបស់អ្នក, សូម្បីតែប្តឹងឧទ្ធរណ៍ប្រឆាំងសំបុត្រកន្លែងចតរថយន្តរបស់អ្នក. ប៉ុន្តែវាអាចធ្វើវត្ថុសូម្បីតែមនុស្សរកឃើញយ៉ាងដូចម្តេច?
វីដេអូមួយ, សំរាប់ខ្ញុំ, ផ្លាស់ប្តូរអ្វីគ្រប់យ៉ាង. វាជាការប្រកួតវីដេអូពីអាយុ Atari បំបែកចេញ, មួយដែលអ្នករុញចេវមួយនៅខាងឆ្វេងនិងស្ដាំនៅខាងក្រោមនៃអេក្រង់, ព្យាយាមបំផ្លាញឥដ្ឋដោយលោតចូលទៅក្នុងពួកគេដែលមានគ្រាប់បាល់. អ្នកប្រហែលជាបានអានអំពីកីឡាករនៃការប្រកួតនេះ: ក្បួនដោះស្រាយបង្កើតឡើងដោយ Deepmind, ក្រុមហ៊ុនក្លែងបន្លំរបស់ប្រទេសអង់គ្លេសដែលមានកម្មវិធី AlphaGo ផងដែរបានផ្តួលដែលល្អបំផុតដែលមិនធ្លាប់មានចូរទៅកីឡាករ, លោក Lee Sedol, កាលពីដើមឆ្នាំនេះ.
ប្រហែលជាអ្នកបានរំពឹងថានឹងកុំព្យូទ័រមួយដើម្បីជាការល្អនៅហ្គេមកុំព្យូទ័រ? នៅពេលពួកគេដឹងថាអ្វីដែលត្រូវធ្វើ, ពួកគេពិតជាធ្វើវាបានកាន់តែលឿននិងកាន់តែច្រើនជាប់លាប់ជាងណាមួយរបស់មនុស្ស. កីឡាករផ្ទុះទើ្ប DeepMind របស់មិនបានដឹងអ្វី, ទោះជាយ៉ាងណា. វាមិនត្រូវបានកម្មវិធីដោយមានការណែនាំអំពីរបៀបដែលការលេងហ្គេម; វាមិនត្រូវបានគេប្រាប់សូម្បីតែពីរបៀបប្រើវត្ថុបញ្ជា. ទាំងអស់វាមានគឺរូបភាពនៅលើអេក្រង់និងពាក្យបញ្ជាដើម្បីព្យាយាមដើម្បីទទួលបានពិន្ទុជាច្រើនដូចជាអាចធ្វើទៅបាន.
អង្គការឃ្លាំមើល វីដេអូ. ជាដំបូង, ចេនេះអនុញ្ញាតឱ្យការធ្លាក់ចុះគ្រាប់បាល់ចូលទៅក្នុង oblivion, ការដឹងមិនប្រសើរ. នៅទីបំផុត, គ្រាន់តែ mucking អំពី, វាគោះបាល់ត្រឡប់មកវិញ, បំផ្លាញឥដ្ឋមួយនិងទទួលបានមួយពិន្ទុ, ដូច្នេះវាទទួលស្គាល់នេះនិងធ្វើវាបានកាន់តែច្រើនជាញឹកញាប់. បន្ទាប់ពីការអនុវត្តចំនួនពីរម៉ោង, ឬប្រហែល 300 ការប្រកួត, វាបានក្លាយទៅជាការល្អយ៉ាងខ្លាំង, ល្អប្រសើរជាងអ្នកឬខ្ញុំនឹងធ្លាប់ត្រូវបាន. បន្ទាប់មក, បន្ទាប់ពីការអំពី 600 ការប្រកួត, អ្វីដែលទទួលបាន spooky. ក្បួនដោះស្រាយបានចាប់ផ្តើមក្នុងគោលបំណងចំណុចដូចគ្នាដែរ, ម្តងហើយម្តងទៀត, ក្នុងគោលបំណងដើម្បីចូលក្នុងឥដ្ឋ burrow លំហដែលនៅពីក្រោយការចូលទៅក្នុង. នៅពេលដែលមាន, ក្នុងនាមជាកីឡាករផ្ទុះទើ្បណាមួយដឹង, គ្រាប់បាល់នេះនឹងស្ទុះងើបឡើងនៅជុំវិញសម្រាប់ខណៈពេលដែលមួយ, ការប្រមូលពិន្ទុ. វាជាយុទ្ធសាស្រ្តល្អដែលកុំព្យូទ័រដែលបានមកឡើងជាមួយនឹងនៅលើខ្លួនរបស់វាផ្ទាល់.
"នៅពេលដែលអ្នកស្រាវជ្រាវបានឃើញដូច្នោះរបស់យើង, ដែលពិតពួកគេរន្ធត់,"នាយកប្រតិបត្តិ DeepMind របស់, Demi Hassabis, ប្រាប់ទស្សនិកជននៅក្នុងសន្និសីទមួយនៅទីក្រុងប៉ារីសបច្ចេកវិទ្យា. អ្នកអាច មើលការធ្វើបាតុកម្មរបស់ខ្លួន, ផងដែរ, និងឮសំណើចនិងអបអរសាទរនៅពេលដែលម៉ាស៊ីនតួលេខចេញពីយុទ្ធសាស្រ្តរបស់ burrows របស់ខ្លួន. កុំព្យូទ័រនេះបានក្លាយជាឆ្លាតវៃ, បន្តិចដូចជាពួកយើង.
«បញ្ញាសិប្បនិមិត្ត "គឺគ្រាន់តែអំពីចំណាស់និងឃោសនាបំផ្លើសច្រើនបំផុតនៃឃ្លាភ្ញាក់ផ្អើលរបស់កុំព្យូទ័រទាំងអស់. គំនិតនេះបានត្រៀមរួចម្តងធ្ងន់ធ្ងរជាលើកដំបូងដោយ លោក Alan លំអ នៅ គ្រឿងម៉ាស៊ីនកុំព្យូទ័រនិងស៊ើបការណ៍សម្ងាត់, នេះ 1950 ក្រដាសនៅក្នុងការដែលគាត់បានស្នើឡើងនូវអ្វីដែលត្រូវបានគេស្គាល់ថាជា ការធ្វើតេស្ត្ព: ប្រសិនបើម៉ាស៊ីនមួយដែលអាចបញ្ចុះបញ្ចូលអ្នកតាមរយៈការសន្ទនាថាវាជាមនុស្ស, វាត្រូវបានគេធ្វើការជាច្រើនដូចជាមនុស្សណាមួយដែលអាចធ្វើបានដើម្បីបញ្ជាក់ថាវាត្រូវបានគេគិតយ៉ាងពិតប្រាកដ. ប៉ុន្តែ AI ពាក្យនេះមិនត្រូវបានគេប្រើជាទូទៅរហូតដល់ 1955, ពេលណា គណិតវិទូអាមេរិចលោក John McCarthy សម្រាប់អ្នកជំនាញបានស្នើសន្និសិទមួយ. នេះបានកើតឡើងនៅឆ្នាំបន្ទាប់, ហើយចាប់តាំងពីពេលនោះមកវាលនេះត្រូវបានរត់នៅលើវដ្តពីរទសវត្សរ៍ប្រហែលជក់ចិត្តនិងភាពអស់សង្ឃឹមនៃ. (ក្រុមអ្នកស្រាវជ្រាវបានសូម្បីតែមានរយៈពេលថ្មី - "រដូវរងារអាយ» - ដើម្បីរៀបរាប់អំពីអក្ខរាវិរុទ្ធរបស់ខ្លួនចេញពីម៉ូដ. ទសវត្សរ៍ឆ្នាំ 1970 និងទសវត្សរ៍ឆ្នាំ 1990 ត្រូវបានគេអាក្រក់ពិសេស។)
សព្វថ្ងៃនេះមានជា Mania ថ្មី, ដែលមើលទៅខុសប្លែកពីអ្នកដទៃនោះ: វាសមនៅក្នុងហោប៉ៅរបស់អ្នក. ទូរស័ព្ទមួយអាចផ្តួលម្ចាស់ជើងឯកអុកពិភពលោក, ទទួលស្គាល់បទចម្រៀងនៅលើវិទ្យុនិងរូបភាពនៃកូនរបស់អ្នក, និងបកប្រែទៅជាភាសាសំលេងរបស់អ្នកផ្សេងទៀត. មនុស្សយន្តរូបភាពនៅទីនេះជាមួយលោកណៅ Yotam Ottolenghi អាចដើរជើងពីរ, និយាយ, ស្វែងរកគ្រាប់បាល់ជាមួយនិងការរាំសូម្បីតែ. (វាជាមនុស្សយន្ត, បើទោះបីជា, មិន AI: វាមិនអាចរចនាម៉ឺនុយ។)
ឮអំពីកម្រិតខ្ពស់នៅក្នុង AI, អ្នកមិនចាំបាច់ជាអ្នកជំនាញក្នុងការប្រាប់អ្នកដើម្បីឱ្យមានការរំភើប, ឬការភ័យខ្លាច. អ្នកគ្រាន់តែចាប់ផ្តើមដើម្បីទទួលបានអារម្មណ៍: ស៊ើបការណ៍សម្ងាត់គឺនៅទីនេះ. ច្បាស់ណាស់ក្រុមហ៊ុន Google ទទួលបានអារម្មណ៍, ផងដែរ, ដោយសារតែវាបានទិញសម្រាប់ $ 650m DeepMind មួយពាក្យចចាមអារាម. នៅ 2013, ហ្វេសប៊ុកបានចាប់ផ្តើមគម្រោងរបស់ខ្លួនផ្ទាល់, ជាមួយនឹងផែនការដើម្បីធ្វើការអភិវឌ្ឍមុខនិងការទទួលស្គាល់ភាសាសម្រាប់តំបន់បណ្ដាញនេះធម្មជាតិ. អ្នកអភិវឌ្ឍន៍បានចាប់ផ្តើមការងារនៅលើ chatbots ឆ្លាតវៃរួចទៅហើយ, អ្នកប្រើប្រាស់ហ្វេសប៊ុកដែលនឹងអាចហៅកម្មវិធីផ្ញើសាររបស់ខ្លួនដោយប្រើសេវា.
រហូតមកដល់ពេល, កុំព្យូទ័រមិនត្រូវបាន "ឆ្លាតវៃ" នៅទាំងអស់, ឬតែតូចចង្អៀតដូច្នេះ. ពួកគេបានធ្វើយ៉ាងល្អនៅឯភារកិច្ចងាយស្រួលដែល dazzle ពួកយើង, ដូចជាគណិតវិទ្យា, ប៉ុន្តែនៅពេលពួកអ្នកដែលយើងអាក្រក់មើលស្រាល, ដែលបានក្លាយទៅជាមានការលំបាកយ៉ាងខ្លាំង. ទង្វើនៃការដើរគឺជាអ្វីដែលមនុស្សយន្តសម័យទំនើបដូចទារកនិងរៀននៅតែតស៊ូជាមួយ; ភារកិច្ច Potter មូលដ្ឋានក្តីសុបិន្តឆ្ងាយ. «ឧទាហរណ៍មួយគឺភាពងាយស្រួលជាមួយនឹងការដែលអ្នកឬខ្ញុំអាចធ្វើឱ្យតែមួយពែងក្នុងផ្ទះបាយរបស់នរណាម្នាក់ផ្សេងទៀត,"បាននិយាយថា សាស្រ្តាចារ្យលោក Alan Winfield, roboticist នៅឯសាកលវិទ្យាល័យ West នៃប្រទេសអង់គ្លេសមួយ. "មិនមានមនុស្សយន្តនៅលើភពផែនដីដែលអាចធ្វើបាននេះ។ "
ដើម្បីយល់ហេតុអ្វីបានជាត្រូវបានមនុស្សគឺមានការលំបាកដូច្នេះ, គិតអំពីរបៀបដែលអ្នកអាចទទួលបានកុំព្យូទ័រមួយដើម្បីទទួលស្គាល់មនុស្សពីរូបថត. ដោយគ្មាន AI, អ្នកមានដើម្បីដឹងថាតើអ្នកធ្វើវាដោយខ្លួនឯងជាលើកដំបូង, ក្នុងគោលបំណងដើម្បីសរសេរកម្មវិធីកុំព្យួទ័រ. អ្នកមានដើម្បីប្រមូលនិងគិតអំពីលំនាំដែលអាចធ្វើបានទាំងអស់, ពណ៍មុខនិងរាង, និងពីរបៀបដែលពួកគេបានផ្លាស់ប្តូរនៅក្នុងពន្លឺហើយនៅមុំផ្សេងគ្នា - និងអ្នកមានដើម្បីដឹងថាអ្វីដែលជាការសំខាន់និងអ្វីដែលគ្រាន់តែភក់នៅលើកញ្ចក់នេះ. ជាមួយនឹង AI, អ្នកមិនមានដើម្បីពន្យល់: អ្នកគ្រាន់តែផ្តល់ឱ្យភ្នំនៃទិន្នន័យពិតប្រាកដមួយទៅកុំព្យូទ័រមួយនិងអនុញ្ញាតឱ្យវារៀន. របៀបដែលអ្នករចនាកម្មវិធីដែលបានរៀនសូត្រនៅតែជាបញ្ហា Esoteric, អ្នកវិទ្យាសាស្រ្តកុំព្យូទ័រនៅខេត្តស្វែងរកបន្ទាប់ពីមួយចំនួន, ប៉ុន្តែវាជាការច្បាស់ណាស់ពួកគេបានទទួលនៅលើការឈ្នះដោយការធ្វើរចនាសម្ព័ន្ធនៃទិន្នន័យដោយផ្អែករលុងដំណើរការរចនាសម្ព័ន្ធនៅលើខួរក្បាល. (នេះត្រូវបានគេហៅថា "ការរៀនសូត្រយ៉ាងជ្រាលជ្រៅ" ។) ដូចជាសម្រាប់ភ្នំនៃទិន្នន័យពិតប្រាកដ, ល្អ, នោះហើយជាអ្វីដែល Google, ហ្វេសប៊ុក, ក្រុមហ៊ុន Amazon, Uber និងការទាំងអស់ដែលនៅសល់កើតឡើងបាននៅក្បែរ.
នៅដំណាក់កាលនេះ, 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, ដូច្នេះ he built some software to listen for him. ឥឡូវនេះ, whenever Newlan’s name is mentioned, his computer instantly sends him a transcript of the last half-minute, waits 15 វិនាទី, 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
ជាការប្រសើរណាស់, 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. They could add whatever else they liked, but those four had to be in the finished dish, which I would cook and eat. Chef Watson didn’t hesitate, instantly giving me two pasta sauces. Ottolenghi was more circumspect. “When I got the challenge I thought, ‘This is not going to work,’” he tells me.
I thought the same. Or at least I thought I would end up eating two dishes that managed to be OK despite their ingredients, rather than because of them. In fact – and you’ll think me a creep, but so what – Ottolenghi’s recipe was a revelation: liver and onion and a tequila reduction, served with an apple, radish, beetroot and chicory slaw, with a wasabi and yoghurt dressing. The dish may make little sense on paper, 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, សីតុណ្ហភាព, 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.”
Watson was first built by IBM to win the television gameshow Jeopardy! នៅ 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.) ជាចុងក្រោយ, 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. នៅចុងបញ្ចប់, 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.
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," គាត់និយាយ. “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.”
សាលក្រម 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 អេកូរបស់ក្រុមហ៊ុន Amazon, 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. នេះ 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 ឬ 15 ប៉ុន្មានឆ្នាំកន្លងមក, 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, ផងដែរ, 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:
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”.
ផ្ទុយទៅវិញ, Steinberg digs more meaningfully into the numbers, showing that Jamie Vardy not only scored 24 គ្រាប់បាល់បញ្ចូលទី, 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, ជាការពិតណាស់, 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.
សាលក្រម Steinberg is a much better writer, unless you want 20 data-heavy articles in 10 នាទី.
The painting test
A laptop wants me to smile. “It’s in a good mood," Simon Colton បាននិយាយថា. 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, ជាក់ស្តែង, 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, គឺ Sarah Jane Moon, an artist who exhibits with the Royal Society of Portrait Painters. She doesn’t want to see my teeth, ទាំង. “We paint from life,"នាងបាននិយាយ, “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, ម្យ៉ាងវិញទៀត, 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?
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, ទម្រង់, 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 គិត looks like a nose.”
“What the software does is break it down into colour regions,” Colton says.
"ចាស, យ៉ាងពិតប្រាកដ,” 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, ខ្ញុំគិតថា. 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?
សាលក្រម 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, និង it’s already a decade old. In many ways it typifies where AI has got to. Useful, ប្រាកដ; 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, បកប្រែ (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% ភាសាអង់គ្លេស). “Most of our growth, and actually most of our traffic, comes from developing or emerging markets such as Brazil, ឥណ្ឌូនេស៊ី, ប្រទេសឥណ្ឌា, ថៃ,” says Barak Turovsky, head of product management and user experience at Google Translate. It’s surprisingly popular for dating, ផងដែរ, 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. មិនយូរប៉ុន្មាន, Turovsky says, they will deploy new deep learning algorithms, which will produce much more fluent translations.
ទោះអញ្ចឹងក្ដី, there are limits, and some seem fundamental when you talk to a human translator and realise how subtle their work is. Ros Schwartz និង 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. ថា, 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.”
សាលក្រម Some bullseyes and howlers from Google Translate, while Schwartz and De Freyman are fluent and exact.
guardian.co.uk ©អាណាព្យាបាលព័ត៌មាន & ប្រព័ន្ធផ្សព្វផ្សាយលីមីតធីត 2010