Just had someone in the IRC channel today who mined… 7500BTC….
and then threw the hard drive away.
We encouraged him to take a trip to the landfill and see if he could find it, maybe hire someone(s) to help him. He said he’s going to make a few calls.
I felt so bad for him. That’s $6 million and counting.
[…] He went to the “recycling centre” and they showed him around. The hard drive, if it was there, would be buried under around 4 feet of mud and waste, in an area the size of a soccer field. The cost of closing the centre, hiring diggers, and searching for it would be too high, and then the chances of finding it are still not excellent.
He’s based in Newport, in South Wales, United Kingdom.
Just as optimisation algorithms come in handy when people are swamped by vast numbers of permutations, so statistical algorithms help firms to grapple with complex datasets. Dunnhumby, a data-analysis firm, uses algorithms to crunch data on customer behaviour for a number of clients. Its best-known customer (and majority-owner) is Tesco, a British supermarket with a Clubcard loyalty-card scheme that generates a mind-numbing flow of data on the purchases of 13m members across 55,000 product lines. To make sense of it all, Dunnhumby’s analysts cooked up an algorithm called the rolling ball.
It works by assigning attributes to each of the products on Tesco’s shelves. These range from easy-to-cook to value-for-money, from adventurous to fresh. In order to give ratings for every dimension of a product, the rolling-ball algorithm starts at the extremes: ostrich burgers, say, would count as very adventurous. The algorithm then trawls through Tesco’s purchasing data to see what other products (staples such as milk and bread aside) tend to wind up in the same shopping baskets as ostrich burgers do. Products that are strongly associated will score more highly on the adventurousness scale. As the associations between products become progressively weaker on one dimension, they start to get stronger on another. The ball has rolled from one attribute to another. With every product categorised and graded across every attribute, Dunnhumby is able to segment and cluster Tesco’s customers based on what they buy.
The rolling-ball algorithm is in its fourth version. Refinements occur every year or two, to add new attributes or to tweak the maths. All these data then feed into a variety of decisions, such as the ranges to put into each store and which products should sit next to each other on the shelves. “All this sophisticated data analysis and it comes down to where you put the biscuits,” laments Martin Hayward, director of consumer strategy at Dunnhumby.The rolling-ball algorithm is in its fourth version. Refinements occur every year or two, to add new attributes or to tweak the maths. All these data then feed into a variety of decisions, such as the ranges to put into each store and which products should sit next to each other on the shelves. “All this sophisticated data analysis and it comes down to where you put the biscuits,” laments Martin Hayward, director of consumer strategy at Dunnhumby.
Exposure to video games had no effect on behavior, attention or emotional issues.
Watching 3 or more hours of television at age 5 did lead to a small increase in behavioral problems in youngsters between 5 and 7.
Neither television nor video games lead to attentional or emotional problems.
There was no difference between boys and girls in the survey results.
Google no longer understands how its “deep learning” decision-making computer systems have made themselves so good at recognizing things in photos.
This means the internet giant may need fewer experts in future as it can instead rely on its semi-autonomous, semi-smart machines to solve problems all on their own.
The claims were made at the Machine Learning Conference in San Francisco on Friday by Google software engineer Quoc V. Le in a talk in which he outlined some of the ways the content-slurper is putting “deep learning” systems to work.
"Deep learning" involves large clusters of computers ingesting and automatically classifying data, such as pictures. Google uses the technology for services like Android voice-controlled search, image recognition, and Google translate, among others. […]
What stunned Quoc V. Le is that the machine has learned to pick out features in things like paper shredders that people can’t easily spot – you’ve seen one shredder, you’ve seen them all, practically. But not so for Google’s monster.
Learning “how to engineer features to recognize that that’s a shredder – that’s very complicated,” he explained. “I spent a lot of thoughts on it and couldn’t do it.” […]
This means that for some things, Google researchers can no longer explain exactly how the system has learned to spot certain objects, because the programming appears to think independently from its creators, and its complex cognitive processes are inscrutable. This “thinking” is within an extremely narrow remit, but it is demonstrably effective and independently verifiable.