Lukas Biewald was able to build a robot that “sees” with $100 and TensorFlow…do you think you could also? I challenge our subscribers to have a go and blog about their experience. We haven’t decided on a prize yet but email me if you’re keen to get involved.
A few days ago we had this video published by Researchers at Berkley University, showing an interactive system that allows you to manipulate visual content in a natural and realistic way. Now you can get started yourself!
Does computer automation lead to major job losses? This column is aiming to answer this question. It comes to the conclusion that automation does not appear to have a major effect on overall employment, but is associated with job losses for low-wage occupations and job gains for high-wage occupations.
In a trial conducted by Nokia, Deutsche Telekom and the Technical University of Munich achieved a transmission rate of one terabyte over fiber optics using realistic network conditions. This means the technology is 1000x faster than Google Fiber which provides one Gbps.
Jigsaw, a subsidiary of Alphabet is building an open source AI tool designed to filter out abusive language. We have seen a similar technology before by Yahoo, trained on a million Yahoo article comments. Google claims that its technology can identify abusive comments with “more than 92 percent certainty and a 10 percent false-positive rate”. However, Andy Greenberg from Wired didn’t have such good results when he tried the software himself..
According to PwC, Data-driven companies are more likely to report significant improvements in decision making. The more companies identify themselves as data-driven the better they perform. In order to succeed, they have to make the transition from data to action, for example: Uber uses algorithms for real-time monitoring of traffic and trip times to balance demand and supply for ride sourcing – and to adjust fees accordingly.
With more and more data being generated, data visualisation allows an effective form of communication. This extensive learning report that is made up of five chapters, talks about the rise of data visualisation, its contributions to research, when it should be utilised, how to enhance its effectivity and points out areas where further research is needed.