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A new study by Pew Internet Research takes a hard look at how innovations in robotics and artificial intelligence will impact the future of work. To reach their conclusions, Pew researchers invited 12,000 experts (academics, researchers, technologists, and the like) to answer two basic questions:
Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?
To what degree will AI and robotics be parts of the ordinary landscape of the general population by 2025?
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A new study by Pew Internet Research takes a hard look at how innovations in robotics and artificial intelligence will impact the future of work. To reach their conclusions, Pew researchers invited 12,000 experts (academics, researchers, technologists, and the like) to answer two basic questions:

  • Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?
  • To what degree will AI and robotics be parts of the ordinary landscape of the general population by 2025?

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EyeQuant is a startup which isn’t too unusual in the fact that it deals with machine learning and artificial intelligence and has big-name clients like Google and Spotify. But the company’s current fascination—using machine learning to train their AI to recognize bad aesthetics and poor website design—takes it into uncharted waters. “We use machine learning and computational neuroscience to build predictive models of how humans look at web ites,” founder Fabian Stelzer told Co.Labs. “We focused on attention before but we are now branching out to more general things like why people prefer one image instead of another or what are the factors that drive trustworthiness of image.” And he’s betting that machines can be trained to detect web pages that most of us think are ugly.
Read More>

EyeQuant is a startup which isn’t too unusual in the fact that it deals with machine learning and artificial intelligence and has big-name clients like Google and Spotify. But the company’s current fascination—using machine learning to train their AI to recognize bad aesthetics and poor website design—takes it into uncharted waters. “We use machine learning and computational neuroscience to build predictive models of how humans look at web ites,” founder Fabian Stelzer told Co.Labs. “We focused on attention before but we are now branching out to more general things like why people prefer one image instead of another or what are the factors that drive trustworthiness of image.” And he’s betting that machines can be trained to detect web pages that most of us think are ugly.

Read More>

"In 5 years, a computer system could know what you like to eat better than you do. A machine that experiences flavor will determine the precise chemical structure of food and why people like it. Not only will it get you to eat healthier, but it will also surprise us with unusual pairings of foods that are designed to maximize our experience of taste and flavor. Digital taste buds will help you to eat smarter." - How Creative Can Computers Be?

"In 5 years, a computer system could know what you like to eat better than you do. A machine that experiences flavor will determine the precise chemical structure of food and why people like it. Not only will it get you to eat healthier, but it will also surprise us with unusual pairings of foods that are designed to maximize our experience of taste and flavor. Digital taste buds will help you to eat smarter." - How Creative Can Computers Be?