Self learning AI 

The Economist reports that AI programs can now learn by itself and beat human opponents in game contests.
The latest AI can work things out without being taught

IN 2016 Lee Sedol, one of the world’s best players of Go, lost a match in Seoul to a computer program called AlphaGo by four games to one. It was a big event, both in the history of Go and in the history of artificial intelligence (AI). Go occupies roughly the same place in the culture of China, Korea and Japan as chess does in the West. After its victory over Mr Lee, AlphaGo beat dozens of renowned human players in a series of anonymous games played online, before re-emerging in May to face Ke Jie, the game’s best player, in Wuzhen, China. Mr Ke fared no better than Mr Lee, losing to the computer 3-0.

For AI researchers, Go is equally exalted. Chess fell to the machines in 1997, when Garry Kasparov lost a match to Deep Blue, an IBM computer. But until Mr Lee’s defeat, Go’s complexity had made it resistant to the march of machinery. AlphaGo’s victory was an eye-catching demonstration of the power of a type of AI called machine learning, which aims to get computers to teach complicated tasks to themselves.

AlphaGo learned to play Go by studying thousands of games between expert human opponents, extracting rules and strategies from those games and then refining them in millions more matches which the program played against itself. That was enough to make it stronger than any human player. But researchers at DeepMind, the firm that built AlphaGo, were confident that they could improve it. In a paper just published in Nature they have unveiled the latest version, dubbed AlphaGo Zero. It is much better at the game, learns to play much more quickly and requires far less computing hardware to do well. Most important, though, unlike the original version, AlphaGo Zero has managed to teach itself the game without recourse to human experts at all.


How Retailers Are Watching Shoppers Emotions

Bricks and mortar retailers are using AI and cameras to monitor shoppers in the hope that this drives higher sales.

All they have to do is get around the privacy issues to make this more mainstream.

The Economist explains.

How retailers are watching shoppers’ emotions

CCTV, thermal-imaging cameras, EEG caps and other kit boost sales

FOR eight months up to this April, a French bookstore chain had video in a Paris shop fed to software that scrutinises shoppers’ movements and facial expressions for surprise, dissatisfaction, confusion or hesitation. When a shopper walked to the end of an aisle only to return with a frown to a bookshelf, the software discreetly messaged clerks, who went to help. Sales rose by a tenth.

The bookseller wants to keep its name quiet for now. Other French clients of the Paris startup behind the technology,, are testing it in research shops that are not open to the public. They include Aéroports de Paris, an airport owner; LVMH, a luxury conglomerate; and Carrefour, a chain of hypermarkets. In a test at a Mothercare shop in Tallinn, Estonia, software from Realeyes, an emotion-detection firm based in London, showed that shoppers who entered smiling spent a third more than others.

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Used Car Trade in Values Keep Falling

Used car trade-in values just keep falling

This is just relentless: Wholesale prices of used vehicles up to eight years old going through auctions across the US dropped another 1.5% in April from the prior month.

It pushed the seasonally adjusted Used Vehicle Price Index by J.D. Power Valuation Services(formerly known as NADA Used Car Guide) down to 109.9. The 10th month in a row of declines.

The index is down 7.1% year-over-year and down over 13% from its peak in mid-2014. It’s at the lowest level since September 2010, when prices were still spiking from the cash-for-clunkers program which had eliminated a whole generation of often perfectly good cars. In that sense, values are just now beginning to normalize (chart by J.D. Power Valuation Services):


The Limitations Of Big Data

Accessibility, formats and granularity are some of the key issues hindering actionable datasets.

Fortune explains.

The Real Limitations of Big Data

“Every revolution in science—from the Copernican heliocentric model to the rise of statistical and quantum mechanics, from Darwin’s theory of evolution and natural selection to the theory of the gene—has been driven by one and only one thing: access to data.”

That was the eye-opening opening of a keynote address given yesterday by the brilliant John Quackenbush, a professor of biostatistics and computational biology at Dana-Farber Cancer Institute who has a dual professorship at the Harvard T.H. Chan School of Public Health and ample other academic credits after his name.

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We Don’t Do Lunch

Consumers are increasingly avoiding lunch.

Going Out for Lunch Is a Dying Tradition

Restaurants suffer as people eat at their desks; no more three-martini sit-down meals

The U.S. restaurant industry is in a funk. Blame it on lunch.

Americans made 433 million fewer trips to restaurants at lunchtime last year, resulting in roughly $3.2 billion in lost business, according to market-research firm NPD Group Inc. It was the lowest level of lunch traffic in at least four decades.

While that loss in traffic is a… Continue reading