Seattle-based machine learning startup Dato, which originally launched as GraphLab, announced today that it has changed its name again. Now, the company is known as Turi, an ode to computer science legend Alan Turing. This past September, Turi -- then Dato -- found itself in a trademark infringement argument with fellow tech startup Datto after Dato changed its name from GraphLab in January 2015. Datto, which has been offering data backup and recovery services from its Connecticut headquarters since 2007, first complained about Dato's new name just weeks after the company finished its rebranding from GraphLab. Datto argued that Dato's new name would cause confusion.

This is the third in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can subscribe at the bottom of the page. Data science companies are increasingly looking at portfolios when making hiring decisions. One of the reasons for this is that a portfolio is the best way to judge someone's real-world skills. The good news for you is that a portfolio is entirely within your control. If you put some work in, you can make a great portfolio that companies are impressed by. The first step in making a high-quality portfolio is to know what skills to demonstrate. Any good portfolio will be composed of multiple projects, each of which may demonstrate 1-2 of the above points. This is the third post in a series that will cover how to make a well-rounded data science portfolio.

"Highly autonomous cars and everything they connect to will require powerful and reliable electronic brains to make them smart enough to navigate traffic and avoid accidents," Intel Chief Executive Brian Krzanich said last Friday at a joint news conference announcing the alliance. It is too early to say which other carmakers would join the alliance, BMW Chief Executive Harald Krueger said at a news conference on the outskirts of Munich on Friday. But creating common technology standards would help all manufacturers update their vehicles faster, Intel Chief Executive Brian Krzanich said. Auto camera and software maker Mobileye will supply its Road Experience Management (REM) technology and make its latest EyeQ5 chip available to be deployed on Intel computing platforms.

When most of us think of artificial intelligence in the workplace, we imagine automated assembly lines of robots managed by an algorithm. LinkedIn's Reid Hoffman has a different idea. In an essay for MIT Sloan Management Review, Hoffman describes human applications for the technology. Among other things, it would help to use data science to improve the way we onboard new team members, organize workflow, and communicate about performance. Addressing the question of how technology will change management practices over the next five years, Hoffman explains how the use of a "knowledge graph" will become standard management practice.

In the workplace of the future, "humans may supplement the skills of machines -- and not the other way around," predicts Columbia's Bernd Schmitt. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Is the convergence between artificial and human intelligence, which once seemed like just a gleam in the eyes of computer scientists and science fiction authors, almost upon us? And if robots become as clever as we are, how will the role of managers change? Bernd Schmitt, the Robert D. Calkins Professor of International Business at Columbia Business School, thinks the convergence is coming, and that managers have to start preparing now.

Google and the U.K.'s government health service have partnered to study whether computers can be trained to spot degenerative eye problems early enough to prevent blindness. Google DeepMind, the London-based artificial intelligence unit owned by Alphabet, announced a research partnership today with the National Health Service to gain access to a million anonymous eye scans. DeepMind will use the data to train its computers to identify eye defects. The aim is to give doctors a digital tool that can read an eye-scan test and recognize problems faster. Earlier detection of eye disorders related to diabetes and age-related macular degeneration could allow doctors to prevent loss of vision in many people, according to a statement by DeepMind Tuesday announcing the project with the Moorfields Eye Hospital NHS Foundation Trust.

George Burgess, the founder of Gojimo, a revision app, explores how artificial intelligence can be used within education. Artificial Intelligence (AI) has dominated tech news in 2016, from Google's ground breaking AlphaGo to Microsoft's racist Tay and Amazon's Echo. This has led to a heated debate on what it means for the human race, from socioeconomic concerns about loss of jobs in the fourth industrial revolution to moral, philosophical and even religious questions about our understanding of human consciousness. Rather than stray into these murky waters, I think it is best to concentrate on the sectors where AI can make a quantifiable and significant difference without threatening livelihoods or invoking metaphysics. One such area is education.

Take one major trend spanning the business and technology worlds, add countless vendors and consultants hoping to cash in, and what do you get? In the world of big data, the surrounding hype has spawned a brand-new lingo. Read on for a glossary of sorts highlighting some of the main data types you should understand. The shining star in this constellation of terms is "fast data," which is popping up with increasing frequency. It refers to "data whose utility is going to decline over time," said Tony Baer, a principal analyst at Ovum who says he coined the term back in 2012.

Posted by Lance Olson, Partner Director of Program Management in the Data Group at Microsoft. Cortana Intelligence with Bing Predicts preview is an end-to-end consulting program that brings the power of Microsoft's unique corpus of social, search and web data to let customers enrich and augment their Cortana Intelligence Suite solutions resulting in more accurate outcomes across a wide variety of business problems. The program springs from the highly successful and well-regarded Bing Predicts consumer experience, where Bing correctly predicted every knockout game of the 2014 soccer World Cup and 95% of the 2014 US mid-term elections. This program was born when we saw the opportunity to take the unique data assets that we have from our consumer businesses and help our commercial customers. Our team interprets these datasets to help you gain unique insights such as localized consumer preferences, user sentiment, customer demographics, macroeconomic indicators predictions and industry trends.

So NVIDIA engineer Robert Bond is using deep learning -- and our Jetson TX1 development platform -- to recognize cats and turn on his home's sprinkler system to gently shoo the visitors away. "My wife is a gardener and she likes her garden to be tidy and clean," says Bond, 65, a system software engineer who has been at NVIDIA for more than eight years. Bond quickly dismissed the idea of trapping the cats -- which just seemed "unneighborly" -- and decided to go with a more technical solution. Bond is no stranger to deep learning or Jetson. Last year, he build a system that shines a harmless 5 milliwatt laser beam on the ants that occasionally scuttle across his kitchen floor (see "How One NVIDIA Built a Jetson-Powered Laser'Ant Annoyer'").