Artificial Intelligence Bulletin – Human Job Quotas
Each week on a Wednesday Nikolas Badminton, Futurist highlights the top stories from the past week relating to the incredible rise of artificial intelligence and its application in society, the workplace, in cities, and in our lives.
In Artificial Intelligence Bulletin – Human Job Quotas we look at legislating for quotas of human workers, battling Wiki Bots, medicine disrupted, artificial problems, and please don’t hire a CAIO.
Rise of robotics will upend laws and lead to human job quotas, study says
Innovation in artificial intelligence and robotics could force governments to legislate for quotas of human workers, upend traditional working practices and pose novel dilemmas for insuring driverless cars, according to a report by the International Bar Association.
The survey, which suggests that a third of graduate level jobs around the world may eventually be replaced by machines or software, warns that legal frameworks regulating employment and safety are becoming rapidly outdated.
The competitive advantage of poorer, emerging economies – based on cheaper workforces – will soon be eroded as robot production lines and intelligent computer systems undercut the cost of human endeavour, the study suggests.
While a German car worker costs more than €40 (£34) an hour, a robot costs between only €5 and €8 per hour. “A production robot is thus cheaper than a worker in China,” the report notes. Nor does a robot “become ill, have children or go on strike and [it] is not entitled to annual leave”.
Read more at The Guardian
Wiki Bots That Feud for Years Highlight the Troubled Future of A.I.
Heads up, humans: Automated software bots patrolling Wikipedia regularly engage in fights that can last for years, according to new research.
The autonomous software agents are tasked with correcting spelling, maintaining links, or even undoing digital vandalism on web pages. But when two bots find themselves with conflicting missions, they can glitch out into patterns that disrupt service.
The behavior is significant, said researchers from the University of Oxford and the Alan Turning Institute, because it suggests that even the most basic kinds of automated programs and artificial intelligence agents can display unpredictable behavior when interacting with one another.
The researchers tracked the behavior of Wikipedia’s autonomous edit bots between 2001-2010 on 13 different language editions of the popular online encyclopedia. They found that the edit bots’ behavior was often unpredictable as they virtually crossed paths while doing their jobs. For instance, two edit bots programmed to make conflicting changes to a webpage would circle back and make edits over and over, each undoing the other’s work in a potentially infinite loop.
“We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other’s edits and these sterile ‘fights’ may sometimes continue for years,” the researchers wrote.
Read more at Seeker
A.I. VERSUS M.D. – What happens when diagnosis is automated?
“It’s easy to diagnose a stroke once the brain is dead and gray,” she said. “The trick is to diagnose the stroke before too many nerve cells begin to die.” Strokes are usually caused by blockages or bleeds, and a neuroradiologist has about a forty-five-minute window to make a diagnosis, so that doctors might be able to intervene—to dissolve a growing clot, say. “Imagine you are in the E.R.,” Lignelli-Dipple continued, raising the ante. “Every minute that passes, some part of the brain is dying. Time lost is brain lost.”
She glanced at a clock on the wall, as the seconds ticked by. “So where’s the problem?” she asked.
Strokes are typically asymmetrical. The blood supply to the brain branches left and right and then breaks into rivulets and tributaries on each side. A clot or a bleed usually affects only one of these branches, leading to a one-sided deficit in a part of the brain. As the nerve cells lose their blood supply and die, the tissue swells subtly. On a scan, the crisp borders between the anatomical structures can turn hazy. Eventually, the tissue shrinks, trailing a parched shadow. But that shadow usually appears on the scan several hours, or even days, after the stroke, when the window of intervention has long closed. “Before that,” Lignelli-Dipple told me, “there’s just a hint of something on a scan”—the premonition of a stroke.
Read more at The New Yorker
Artificial Intelligence and Artificial Problems
Former US Treasury Secretary Larry Summers recently took exception to current US Treasury Secretary Steve Mnuchin’s views on “artificial intelligence” (AI) and related topics. The difference between the two seems to be, more than anything else, a matter of priorities and emphasis.
Mnuchin takes a narrow approach. He thinks that the problem of particular technologies called “artificial intelligence taking over American jobs” lies “far in the future.” And he seems to question the high stock-market valuations for “unicorns” – companies valued at or above $1 billion that have no record of producing revenues that would justify their supposed worth and no clear plan to do so.
Summers takes a broader view. He looks at the “impact of technology on jobs” generally, and considers the stock-market valuation for highly profitable technology companies such as Google and Apple to be more than fair.
I think that Summers is right about the optics of Mnuchin’s statements. A US treasury secretary should not answer questions narrowly, because people will extrapolate broader conclusions even from limited answers. The impact of information technology on employment is undoubtedly a major issue, but it is also not in society’s interest to discourage investment in high-tech companies.
Read more at Project Syndicate
Please Don’t Hire a Chief Artificial Intelligence Officer
Every serious technology company now has an Artificial Intelligence team in place. These companies are investing millions into intelligent systems for situation assessment, prediction analysis, learning-based recognition systems, conversational interfaces, and recommendation engines. Companies such as Google, Facebook, and Amazon aren’t just employing AI, but have made it a central part of their core intellectual property.
As the market has matured, AI is beginning to move into enterprises that will use it but not develop it on their own. They see intelligent systems as solutions for sales, logistics, manufacturing, and business intelligence challenges. They hope AI can improve productivity, automate existing process, provide predictive analysis, and extract meaning from massive data sets. For them, AI is a competitive advantage, but not part of their core product. For these companies, investment in AI may help solve real business problems but will not become part of customer facing products. Pepsi, Wal-Mart and McDonalds might be interested in AI to help with marketing, logistics or even flipping burgers but that doesn’t mean that we should expect to see intelligent sodas, snow shovels, or Big Macs showing up anytime soon.
Read more at the Harvard Business Review