In the middle of all of the autonomous car promises, slightly thinner and brighter televisions, and appliances that spy on you in as many different ways as they possibly can were a small handful of social robots. These are robots designed to interact with you at home. People responding to IEEE Spectrum's live twitter feeds as we covered each announcement, pointed out that these little white social home robots all look kinda similar to each other, and they also look kinda similar to that little white social home robot that managed to raise $3.7 million on Indiegogo in September of 2014: Jibo. To show what we're talking about (if you haven't been following along with our CES coverage, and you totally should be), here are three new social home robots (Kuri, Mykie, and Hub) that were announced Wednesday, along with Jibo for comparison. Big heads on small bodies.

For cord cutters who want nothing but the best, Nvidia's $200 Shield TV console has always been the box of choice for streaming videos and even games. At CES 2017, the company announced a new-and-improved version that adds in HDR support, refines gaming and entertainment options, and even transforms the Android TV device into a voice-controlled rival of the Amazon Echo. We've already covered all the details revealed during Nvidia's CES 2017 press conference, but I just spent over an hour at Nvidia's suite to learn more about the new Shield TV's every nook and cranny. Here are some initial impressions, and a look at some of the more nitty-gritty improvements that weren't mentioned during the keynote. Before we even get into flashy features like smart-home controls and Google Assistant, the second-gen Shield TV packs worthwhile quality-of-life improvements over its predecessor.

Twitter can sometimes feel like a language of its own, but one lecturer is using the social media site as a tool to teach Arabic. In Mahammed Bouabdallah's classes at the University of Westminster, London, students are set simple tasks using Twitter to complement their lessons. Bouabdallah publishes a photo or link and asks students to comment on it in Arabic, or runs a Twitter poll about events happening in Arabic-speaking countries and discusses the results in class. Sometimes, they will use Twitter's built-in translation tool and judge its accuracy. "They have to tweet outside the class, and we discuss it inside the class," says Bouabdallah. His own research suggests that Twitter is popular as a language learning tool, with 80 per cent of surveyed students responding positively to its use.

The velocity of change in the marketing industry is accelerating, but what we see today is elementary when we consider the potential of what comes next. This session provides a glimpse into the future of marketing, and the opportunities that exist for those who can harness the power of artificial intelligence and cognitive technology like IBM's Watson. They will be able to do more with less, run personalized campaigns of unprecedented complexity, and analyze massive data sets to predict outcomes. The opportunities are endless for those with the will and vision to transform the industry. Attendees will: - Learn what the disruption of other industries can teach us about the inevitable impact artificial intelligence will have on the marketing industry.

To whom does pharma turn when facing cutting-edge research challenges such as designing algorithms to comb unstructured EHR data hunting for undiagnosed patients? Who helps glean patient insights from digital data streams such as social media? Who is designing the value-based frameworks behind pharma's latest wave of performance-based agreements with payers? These are just some of the tasks being fielded by data scientists, a new kind of insight and analytics professional showing up in the biopharma ranks. These experts, skilled as they are in advanced data techniques, are being seriously courted by the industry. It's with diversity in mind -- diversity not only in skill set, but also in experience," reports Sandy Allerheiligen, Merck VP of predictive and economic modeling, at September's AI and the Near-term seminar organized by the consultancy Luminary Labs.

Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or "predictive modeling", clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. The hope that comes with this discipline is that including the experience into its tasks will eventually improve the learning. But this improvement needs to happen in such a way that the learning itself becomes automatic so that humans like ourselves don't need to interfere anymore is the ultimate goal. There are close ties between this discipline and Knowledge Discovery, Data Mining, Artificial Intelligence (AI) and Statistics. Typical applications can be classified into scientific knowledge discovery and more commercial ones, ranging from the "Robot Scientist" to anti-spam filtering and recommender systems. But above all, you will know this discipline because it's one of the topics that you need to master if you want to excel in data science. Today's scikit-learn tutorial will introduce you to the basics of Python machine learning: step-by-step, it will show you how to use Python and its libraries to explore your data with the help of matplotlib, work with the well-known algorithms KMeans and Support Vector Machines (SVM) to construct models, to fit the data to these models, to predict values and to validate the models that you have build. The first step to about anything in data science is loading in your data.

Four leading car brands have announced deals with three tech giants to add virtual assistants to new cars. Microsoft's Cortana netted two of the deals, the others went to Amazon's Alexa and Google's Assistant. The announcements were made at the CES tech show in Las Vegas. One analyst said there would be a "battle of the giants" over the adoption of virtual assistants in 2017, since they can be built in to a variety of appliances. Nissan and BMW have opted to work with Microsoft to bring Cortana to selected vehicles in the near future.

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TYSONS, Va.--(BUSINESS WIRE)--CSC (NYSE: CSC) today announced the expansion of its strategic alliance with Microsoft through the advancement of Industrial Machine Learning (IML) solutions that enable data scientists to produce new data-derived enterprise insights, leading to better and more timely business decisions. Most large, data-rich companies fail to benefit from information on an enterprise-wide scale because they are not equipped to effectively capture and analyze available data sources. A recent survey from PwC and Iron Mountain found that 57 percent of businesses are unable to extract significant value from their information, and 23 percent are unable to derive any real value at all. Regardless of size, geography or sector, businesses are struggling to fully realize value from their information. "CSC has been working with Microsoft on industry-specific approaches to the problem of creating data science that works," said Dan Hushon, chief technology officer, CSC.

As promised, FoldiMate showed off its laundry-folding robot at CES 2017 in Las Vegas. This machine is intended to be a household robot that makes folding clothes hassle-free for families. FoldiMate is among the companies that are in attendance at the big tech trade show this week. After teasing about its upcoming family robot for a while, the California-based company is finally showing off its invention to the public. The FoldiMate Family robot is designed to be a machine that takes care of clothes once they are washed.