- Email: [email protected]
Facebook maps out the future of work
For more articles, visit newscientist.com
Garry Kasparov is a former world chess champion, the chairman of the Human Rights Foundation and author of Winter is Coming (PublicAffairs)
South Africa in 1985, doctors were struck off for failure to act in the deaths of detainees even though they broke no law. Sticking to the law, or “following orders”, is not a defence for unethical conduct for doctors – and nor should it ever be. As opinions harden against asylum seekers and refugees in Europe, doctors there must be wary of being coerced into becoming servants of the state at the expense of their primary duty to patients. They should boycott any service that seeks to compromise them in this way. n David Berger is a UK doctor now working in Australia
INSIGHT Artificial intelligence
Albert Gea / Reuters
in chess, while a human is only a slip away from catastrophe. No machine suffers complacency, anxiety and exhaustion. When I lost the decisive sixth game to Deep Blue in 1997 I was under huge pressure and played like it. It was the worst game of my career. Despite that, it was an exciting time, the culmination of interest in mastering the game mechanically dating back to the 18th-century hoax chess machine the Turk. Today AlphaGo represents a machine learning project with real AI implications and deserves wide attention. Se-dol may be so much stronger than AlphaGo that human fallibilities won’t be decisive yet. Go also has many more possible moves each turn than chess and is less dynamic, factors that work against machine success. But I’m afraid the writing is on the wall. Today, a decent laptop running a free chess program would crush Deep Blue and any human grandmaster. The jump from chess machines being predictable and weak to terrifyingly strong took just a dozen years. Go, your clock is ticking. n
humans make maps in the 21st century. One approach is a project called Open Street Map, which uses volunteer labour to trace satellite photographs by hand, picking out roads and houses. It can create maps of entirely unmapped regions in a few days, and these have been used all over the world, often for disaster response. But it would take decades for a human team of any size to carry out mapping on the scale that Facebook’s AI system has demonstrated. Facebook’s map-making AI is just one of probably thousands of narrow AIs – those trained to focus on a single task – churning through human projects around the planet, faster and on larger scales than we ever could. The CERN particle physics laboratory near Geneva, Switzerland, is using deep learning to find patterns in the mass of its collision data; pharmaceutical –Who are you calling ambitious?– companies are using it to find new drug ideas in data sets that no human could plumb. What’s exciting is that all neural networks can scale up like Facebook’s mapping AI. Have a narrow AI that can spot the signs of cancer in a scan? Good: if you have the data, you can now search for cancer in every human on Earth in a few hours. An AI that internet to areas that don’t have it. knows how to spot a crash in the It’s a dubious starting point, but markets? Great: it can watch all 20 of whatever you think about Facebook’s the world’s major stock exchanges at internet colonialism, the company’s the same time, as well as the share drones won’t be able to beam Wi-Fi prices of individual companies. to the disconnected until they know The real power of narrow AI isn’t in where they are. what it can do, because its performance The model was able to map is almost never as good as a human’s 20 countries after being trained on would be. The maps that Facebook’s just 8000 human-labelled satellite AI produces are nowhere near as “The model was able to map good as those that come out of 20 countries after being companies that rely on humans such trained on just 8000 as custom map developer Mapbox. photos from one nation” But smart systems being built in labs at Google, Facebook and Microsoft are powerful because of the speed and photos from a single nation – and scale at which they operate. The future Facebook’s data-science team wasn’t of human work may be determined by even trying to go quickly. whether it is better to do an averageThe company says it has now quality job many thousands of times improved the process to the point at a second or a human-quality job once which it could do the same mapping in a few hours. Assuming it had the every few minutes. photos, this means it could map Earth AI is here – and it’s real and powerful. in about six days. That’s something But humans are still in control. We’re we still haven’t managed to do fully. just all about to get some extremely Using its AI, Facebook aped how clever help. n
Facebook maps out the future of work Hal Hodson
WE HAVE just learned that Facebook’s artificial-intelligence software can probably map more in a week than humanity has over our entire history. In a blog post, the social network announced that its AI system took two weeks to build a map that covers 4 per cent of our planet. That’s satellite photos of 14 per cent of Earth’s land surface – an area covering 21.6 million square kilometres – digested and traced into a digital representation of the roads, buildings and settlements they show. This is the starkest example we have seen so far of the most important phenomenon in technology – computers doing human work really fast. It will have massive implications for how we acquire knowledge, cooperate on large projects and even understand the world. The stated goal of Facebook’s datascience team is to build maps to help the social network plan how to deliver
5 March 2016 | NewScientist | 27
Garry Kasparov is a former world chess champion, the chairman of the Human Rights Foundation and author of Winter is Coming (PublicAffairs)
South Africa in 1985, doctors were struck off for failure to act in the deaths of detainees even though they broke no law. Sticking to the law, or “following orders”, is not a defence for unethical conduct for doctors – and nor should it ever be. As opinions harden against asylum seekers and refugees in Europe, doctors there must be wary of being coerced into becoming servants of the state at the expense of their primary duty to patients. They should boycott any service that seeks to compromise them in this way. n David Berger is a UK doctor now working in Australia
INSIGHT Artificial intelligence
Albert Gea / Reuters
in chess, while a human is only a slip away from catastrophe. No machine suffers complacency, anxiety and exhaustion. When I lost the decisive sixth game to Deep Blue in 1997 I was under huge pressure and played like it. It was the worst game of my career. Despite that, it was an exciting time, the culmination of interest in mastering the game mechanically dating back to the 18th-century hoax chess machine the Turk. Today AlphaGo represents a machine learning project with real AI implications and deserves wide attention. Se-dol may be so much stronger than AlphaGo that human fallibilities won’t be decisive yet. Go also has many more possible moves each turn than chess and is less dynamic, factors that work against machine success. But I’m afraid the writing is on the wall. Today, a decent laptop running a free chess program would crush Deep Blue and any human grandmaster. The jump from chess machines being predictable and weak to terrifyingly strong took just a dozen years. Go, your clock is ticking. n
humans make maps in the 21st century. One approach is a project called Open Street Map, which uses volunteer labour to trace satellite photographs by hand, picking out roads and houses. It can create maps of entirely unmapped regions in a few days, and these have been used all over the world, often for disaster response. But it would take decades for a human team of any size to carry out mapping on the scale that Facebook’s AI system has demonstrated. Facebook’s map-making AI is just one of probably thousands of narrow AIs – those trained to focus on a single task – churning through human projects around the planet, faster and on larger scales than we ever could. The CERN particle physics laboratory near Geneva, Switzerland, is using deep learning to find patterns in the mass of its collision data; pharmaceutical –Who are you calling ambitious?– companies are using it to find new drug ideas in data sets that no human could plumb. What’s exciting is that all neural networks can scale up like Facebook’s mapping AI. Have a narrow AI that can spot the signs of cancer in a scan? Good: if you have the data, you can now search for cancer in every human on Earth in a few hours. An AI that internet to areas that don’t have it. knows how to spot a crash in the It’s a dubious starting point, but markets? Great: it can watch all 20 of whatever you think about Facebook’s the world’s major stock exchanges at internet colonialism, the company’s the same time, as well as the share drones won’t be able to beam Wi-Fi prices of individual companies. to the disconnected until they know The real power of narrow AI isn’t in where they are. what it can do, because its performance The model was able to map is almost never as good as a human’s 20 countries after being trained on would be. The maps that Facebook’s just 8000 human-labelled satellite AI produces are nowhere near as “The model was able to map good as those that come out of 20 countries after being companies that rely on humans such trained on just 8000 as custom map developer Mapbox. photos from one nation” But smart systems being built in labs at Google, Facebook and Microsoft are powerful because of the speed and photos from a single nation – and scale at which they operate. The future Facebook’s data-science team wasn’t of human work may be determined by even trying to go quickly. whether it is better to do an averageThe company says it has now quality job many thousands of times improved the process to the point at a second or a human-quality job once which it could do the same mapping in a few hours. Assuming it had the every few minutes. photos, this means it could map Earth AI is here – and it’s real and powerful. in about six days. That’s something But humans are still in control. We’re we still haven’t managed to do fully. just all about to get some extremely Using its AI, Facebook aped how clever help. n
Facebook maps out the future of work Hal Hodson
WE HAVE just learned that Facebook’s artificial-intelligence software can probably map more in a week than humanity has over our entire history. In a blog post, the social network announced that its AI system took two weeks to build a map that covers 4 per cent of our planet. That’s satellite photos of 14 per cent of Earth’s land surface – an area covering 21.6 million square kilometres – digested and traced into a digital representation of the roads, buildings and settlements they show. This is the starkest example we have seen so far of the most important phenomenon in technology – computers doing human work really fast. It will have massive implications for how we acquire knowledge, cooperate on large projects and even understand the world. The stated goal of Facebook’s datascience team is to build maps to help the social network plan how to deliver
5 March 2016 | NewScientist | 27