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5 non-health tech stories you should care about this week

EU and U.S. agree to changes to rules on international data transfer, Google Ventures looks for AI in Europe, but end users don’t seem quite ready for cognitive computing.

European Union

It’s time again to take a look at what you may have missed in the world of technology outside healthcare. In the wake of Thursday’s Brexit vote, this week’s tech roundup has a decidedly European flavor, er, flavour.

Here are five interesting general technology stories from the past seven days that people in healthcare should pay attention to. These issues could have an impact on health tech in the future.

1. “EU, United States agree on changes to strengthen data transfer pact” (Reuters)

The European Union and the United States have agreed [to] changes to a data transfer pact that is key to transatlantic business, including stricter rules for companies holding information on Europeans and clearer limits on U.S. surveillance.

The revised EU-U.S. Privacy Shield was sent for review by European member states overnight. They are expected to hold a vote in early July, several EU sources said, at which point it will enter into force.

Cross-border data transfers by businesses include payroll and human resources information as well as lucrative data used for targeted online advertising, which is of particular importance to tech companies.

2. “Google Ventures is on the hunt for the next DeepMind in Europe” (Business Insider)

[Google Ventures partner Tom] Hulme said he is interested in a specific type of AI known as AGI, or artificial general intelligence. AGI— the area DeepMind operates in — is an emerging field focused on the building of “thinking machines.” These machines can be thought of as general-purpose systems with intelligence comparable to that of the human mind. While this was the original goal of artificial intelligence (AI), the mainstream of AI research has turned towards more niche applications and is often referred to as applied AI.

“Applied [AI] is injected in some way into most businesses now,” said Hulme. “Two years ago at accelerator demo, every pitch deck said big data in it. This year every pitch deck says artificial intelligence.”

3. “Companies, employees not quite ready for cognitive technology wave of robotics, AI, machine learning” (ZDNet)

Forrester’s report had an optimistic tone. Customers will need human advice more than ever, people can provide nuance better than machines, and displacement and transformation will happen at the same time.

Nevertheless, sales jobs will take a hit from customer self service after office and admin staff falls away. Professional roles like doctors and scientists will go away more slowly. Management, business and financial jobs will be most resistant to the robots. One banker did note that robots and automation will close the books at the end of quarter and fiscal year instead of the 2,000 humans today.

4. “Dell Sells Off Software to Double Down on Its Riskiest Business” (Wired)

The sale is a strange move for a company trying to reinvent itself in a business environment that’s rapidly changing thanks to cloud computing and mobile devices. Dell went private in 2013 in part to give itself the freedom to make radical changes to its business model, including going big on software. But then it went big—really big—on hardware when it agreed to acquire EMC last year for a record-breaking $67 billion. By jettisoning its software group, Dell appears to be going all-in on hardware, the most vulnerable part of its business.

5. “Twitter Buys Magic Pony For $150M: Not An Enchanted Steed, But A Machine Learning Firm” (Tech Times)

Twitter revealed that it has acquired Magic Pony, which is not some sort of an enchanted steed but rather a company focused on machine learning and visual processing technology.

The purchase of Magic Pony, which is based in London, will expand the machine learning capabilities of the video technology of the social media platform while also adding significant talent to Twitter’s Cortex team. This team is made up of researchers, data scientists and engineers who use machine learning to make improvements to Twitter’s offerings.

Photo: Flickr user Yukiko Matsuoka

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