? ? ?日本日立在2015年開(kāi)發(fā)了工廠機(jī)器人弃秆，約有復(fù)印機(jī)大小，可穿過(guò)倉(cāng)庫(kù)地板瞬哼，兩只手臂在升降機(jī)上自行升高或降低，每只手臂上都有一個(gè)相機(jī)租副。它不僅是一個(gè)可以從架子上取下瓶子的機(jī)器人坐慰，快速而精湛地執(zhí)行看似簡(jiǎn)單的任務(wù)。現(xiàn)在用僧，人類(lèi)和機(jī)器一起管理倉(cāng)庫(kù)结胀。總有一天责循，這樣的機(jī)器人可能完全取代倉(cāng)庫(kù)工人糟港。

? ? ? ?亞馬遜也配置了倉(cāng)庫(kù)Kiva機(jī)器人,把貨物從貨架上拿走，并把貨架運(yùn)送給人類(lèi)院仿，將效率提高到四倍秸抚，和人類(lèi)在工廠中并肩工作。機(jī)器人被嚴(yán)格地與人類(lèi)工作人員隔離 歹垫，一方面是為了保護(hù)人類(lèi)剥汤，另一方面是為了地防止機(jī)器人混淆其工作，以嚴(yán)格控制機(jī)器人排惨。

? ? ? ? ?機(jī)器人可以做的越來(lái)越多 吭敢，可以是生菜選擇者、調(diào)酒師暮芭、醫(yī)院搬運(yùn)工等鹿驼。由于機(jī)器人硬件技術(shù)升級(jí)了欲低，具有更好且更便宜的傳感器壹粟，實(shí)質(zhì)上改善了機(jī)器人的“眼睛”涡扼、“指尖觸摸”。

? ? ? ? ? ? 過(guò)去幾年里，人工智能（AI）進(jìn)步真正開(kāi)始加速舷蟀。在狹義AI算法中，可以過(guò)濾電子郵件垃圾郵件面哼，或者識(shí)別Facebook照片中的臉野宜。處理器變得更快，數(shù)據(jù)集更大魔策，更好地學(xué)習(xí)如何改進(jìn)自己的算法匈子。IBM的沃森，在比賽中擊敗人類(lèi)冠軍的頭條新聞Jeopardy闯袒，已經(jīng)比診斷肺癌的醫(yī)生更好了虎敦。

? ? 世界機(jī)器人“人口”迅速擴(kuò)大，工業(yè)機(jī)器人的銷(xiāo)售額每年增長(zhǎng)約13％政敢，這意味著機(jī)器人“出生率”每五年就會(huì)翻一番其徙。長(zhǎng)期以來(lái)，美國(guó)以外的制造業(yè)到新興市場(chǎng)去尋用的廉價(jià)工人喷户， 現(xiàn)在機(jī)器人將令生產(chǎn)返回到美國(guó)唾那。很多經(jīng)濟(jì)學(xué)家認(rèn)為，機(jī)器人和AI必將帶來(lái)了一個(gè)新奇的經(jīng)濟(jì)趨勢(shì)褪尝，發(fā)達(dá)國(guó)家和新興市場(chǎng)國(guó)的經(jīng)濟(jì)發(fā)展比較,人工智能應(yīng)用率將是一個(gè)重要指標(biāo)闹获。

Rise of the robots: What advances mean for workers

It's about the size and shape of a photocopier.

Emitting a gentle whirring noise, it travels across the warehouse floor while two arms raise or lower themselves on scissor lifts, ready for the next task.

Each arm has a camera on its knuckle. The left one eases a cardboard box forward on the shelf, the right reaches in and extracts a bottle.

Like many new robots, it's from Japan. Hitachi showcased it in 2015 and hopes to be selling it by 2020.

50 Things That Made the Modern Economy highlights the inventions, ideas and innovations which have helped create the economic world we live in.

It is broadcast on the BBC World Service. You can find more information about the programme's sources and listen online or subscribe to the programme podcast.

It's not the only robot that can pick a bottle off a shelf - but it's as close as robots have yet come to performing this seemingly simple task as speedily and dextrously as a good old-fashioned human.

One day, robots like this might replace warehouse workers altogether.

For now, humans and machines run warehouses together.

In Amazon depots, Kiva robots scurry around, not picking things off shelves, but carrying the shelves to humans for them to select things.

In this way, Kiva robots can improve efficiency up to fourfold.

Robots and humans work side-by-side in factories, too.

Factories have had robots since 1961, when General Motors installed the first Unimate, a one-armed automaton that was used for tasks like welding.

But until recently, robots were strictly segregated from human workers - partly to protect the humans, and partly to stop them confusing the robots, whose working conditions had to be strictly controlled.

With some new robots, that's no longer necessary.

Take Rethink Robotics' Baxter.

'Reshoring' trend

Baxter can generally avoid bumping into humans, or falling over if humans bump into it. Cartoon eyes indicate to human co-workers where it's about to move.

Historically, industrial robots needed specialist programming, but Baxter can learn new tasks from its co-workers.

The world's robot population is expanding quickly - sales of industrial robots are growing by around 13 per cent a year, meaning the robot "birth rate" is almost doubling every five years.

There has long been a trend to "offshore" manufacturing to cheaper workers in emerging markets. Now, robots are part of the "reshoring" trend that is returning production to established centres.

They do more and more things - they're lettuce-pickers, bartenders, hospital porters.

But they're still not doing as much as we'd once expected.

In 1962 - a year after the Unimate was introduced - the American cartoon The Jetsons imagined Rosie, a robot maid doing all the household chores. That prospect still seems remote.

The progress that has happened is partly thanks to improved robot hardware, including better and cheaper sensors - essentially improving a robot's eyes, the touch of its fingertips, and its balance.

But it's also about software: robots are getting better brains.

And it's about time, too. Machine thinking is another area where initial high expectations encountered early disappointments.

Attempts to invent artificial intelligence are generally dated to 1956, and a summer workshop at Dartmouth College for scientists with a pioneering interest in "machines that use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves".

Processors have become faster, data sets bigger, and programmers better at writing algorithms that can learn how to improve themselves.

That capacity for self-improvement worries some thinkers like Bostrom. What will happen if and when we create artificial general intelligence - a system which could apply itself to any problem, as humans can?

Will it rapidly turn itself into a superintelligence? How would we keep it under control?

That's not an imminent concern, at least. Human-level artificial general intelligence is still about, ooh, 20 years away.

But narrow AI is already transforming the economy.

For years, algorithms have been taking over white-collar drudgery in areas like book-keeping and customer service. And more prestigious jobs are far from safe.

IBM's Watson, which hit the headlines for beating human champions at the game show Jeopardy!, is already better than doctors at diagnosing lung cancer.

Software is getting to be as good as experienced lawyers at predicting what lines of argument are most likely to win a case.

'Secular stagnation'

Robo-advisers dispense investment advice.

Algorithms routinely churn out news reports on the financial markets and sports - although, luckily for me, it seems they can't yet write feature articles about technology and economics.

Some economists reckon robots and AI explain a curious economic trend.

Erik Brynjolfsson and Andrew McAfee argue there's been a "great decoupling" between jobs and productivity - how efficiently an economy takes inputs, like people and capital, and turns them into useful stuff.

Historically, better productivity meant more jobs and higher wages.

But Brynjolfsson and McAfee argue that's no longer the case in the United States. Since the turn of the century, US productivity has been improving, but jobs and wages haven't kept pace.

Some economists worry that we're experiencing "secular stagnation" - where there's not enough demand to spur economies into growing, even with interest rates at or below zero.

Technology destroying jobs is nothing new - it's why, 200 years ago, the Luddites went around destroying technology.

"Luddite" has become a term of mockery because technology has always, eventually, created new jobs to replace the ones it destroyed. Better jobs. Or at least, different jobs.

What happens this time remains debatable. It's possible that some of the jobs humans will be left doing will actually be worse.

That's because technology seems to be making more progress at thinking than doing: robots' brains are improving faster than their bodies.

Martin Ford, author of Rise Of The Robots, points out that robots can land aeroplanes and trade shares on Wall Street, but still can't clean toilets.

So perhaps, for a glimpse of the future, we should look not to Rosie the Robot but to another device now being used in warehouses: the Jennifer Unit.

It's a computerised headset that tells human workers what to do, down to the smallest detail.

If you have to pick 19 identical items from a shelf, it'll tell you to pick five, then five, then five, then four. That leads to fewer errors than saying "pick 19".

If robots beat humans at thinking, but humans beat robots at picking things off shelves, why not control a human body with a robot brain?

It may not be a fulfilling career choice, but you can't deny the logic.

Tim Harford writes the Financial Times's Undercover Economist column. 50 Things That Made the Modern Economy is broadcast on the BBC World Service. You can find more information about the programme's sources and listen online or subscribe to the programme podcast.