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Algorithms were supposed to free us from our unconscious mistakes. But now there’s a new set of problems to solve. "Although AI decision-making is often regarded as inherently objective, the data and processes that inform it can invisibly bake inequality into systems that are intended to be equitable. Avoiding that bias requires an understanding of both very complex technology and very complex social issues." (from the article) Via Sharrock 
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Without humans as a guide, current AI is no more capable than a computer without software.
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In the first part of this blog post, I reported on the opening of the new Berkeley Institute for Data Science (BIDS). Today, I am going to share with you some of the ways that UC Berkeley scientist...
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Data Science Association
Waldo Conti-Bosso's curator insight,
October 10, 2013 5:39 PM
I believe that, science will not resolve every problem we have in the world, but without science, will be much more dificult.
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Photo by Jesse Means The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries. My...
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we might need a “data humanities” and a “data rhetoric” that paralleled the emergence of data science
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Starting a business is a dangerous thing. A larger competitor might undercut your prices. Someone might sue you for patent infringement. Someone else could sue you because your products don’t do what you said it would. Or, well, the market may have no interest in what you’re selling. According to the U.S. Bureau of Labor Statistics, about half of all businesses fail within five years.
But Thomas Thurston thinks data science could remove a fair amount of the risk. For the past nine years, he’s been honing techniques for evaluating business plans statistically rather than intuitively. He calls it business model simulation, and you can think of it as something akin to Moneyball for investors.
He says his simulations correctly predicted that Snapchat, Uber, and Airbnb would be big—and that they’re now right about 66 percent of the time when predicting that a company will still exist within five years. When predicating that a company will fail, he adds, they’re right 88 percent of the time.
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What critical skills do IT professionals need to develop if they are going to grow into future data scientists?
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New York University University of California-Berkeley and the University of Washington are launching a $37.8 million project to boost the numbers of...
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They find truth, intrigue and drama in what is dry-as-dust information to us, says Shraddha Uchil, who met a few of these cyberspace alchemists. - Lifestyle dna
luiy's curator insight,
August 30, 2013 12:33 PM
So what skills would a person need to become a good data scientist?
Academically, a basic foundation in computer science, mathematics and statistics is sufficient, says Bhattacharji. However, the alchemy of these skills is what differentiates the boys from the men.
Agreeing with him, Harsh Singhal, a data scientist at LinkedIn, says, “Data scientists come from multi-disciplinary backgrounds. They can code, create visuals, convert data into products and they can tell stories.” In other words, a data scientist has to be a storyteller. Data science is all about being curious and spotting trends by looking beyond the obvious.
Say, for instance, Flipkart, arguably India's most popular e-commerce portal, wants to know which of its customers are most likely to commit credit card frauds. How do they go about it? “If you look at a customer's files, all the raw data is available,” says Regunath B, the principal architect at Flipkart. But what’s just raw data to the unskilled is a juicy bag of information to a data scientist. And here’s where he works his magic.
“A data scientist uses algorithms to predict possible fraud, and keeps the company updated,” he adds. Thus, a data scientist must be able to understand problems from the business' perspective. |
