In our previous analysis, we discussed how Intel is competing with Nvidia in the data center coprocessor market. In this analysis, we specifically discuss details about the product offering of the two companies in this market that differentiates the two companies. It is worth noting that a coprocessor can offload processor-intensive tasks from the CPU, resulting in improved system performance. Further, it is economical to have a coprocessor for a specific task, as compared to having a costly integrated CPU that caters to a large number of functionalities even when you don't need them. Nvidia is the leading manufacturer of GPU(Graphics Processing Unit), a processor with multiple cores that is optimized for the compute-intensive functions involved in processing graphics.

There is an entire field of math dedicated to them. Connections between a group and an object like the square are called group actions. But… What is a group? Not all graphs are groups. Only a very special kind of graph is.

Why Samsung May Release A Foldable Smartphone Next Year -- Even If It Won't Make Them Any Money By all accounts, 2016 has been an extraordinary year for Silicon Valley. Not only have the technology behemoths mustered a growing influence on Capitol Hill, their sheer market capitalization also testifies to one undeniable fact: They are the ones who change the world. The tech industry's missions are unapologetic and filled with passion. Their corporate myths are often wrapped up in their early days as startups. That some awkward twenty-year-old could turn their social ineptness into their biggest advantage and build a global enterprise from their garage is the highest expression of the American dream.

At least, that seems to be the industry perspective. Humans are rash, impulsive and tainted by personal bias. So why not just replace them with machines? Apparently, that's what Facebook thought when they fired their entire team of news editors for political bias, replacing them instead with a new algorithm. That did not go well.

The Internet of Things will challenge the status quo of how IT and development organizations operate. Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and shared the must-have mindsets for removing complexity from the development process, accelerating application delivery times, and ensuring that developers will become heroes (not bottlenecks) in the IoT revolution. Speaker Bio Craig Sproule is the CEO of Metavine where he is responsible for the company's vision, product development and customer success.

Things, not Strings Entity-centric views on enterprise information and all kinds of data sources provide means to get a more meaningful picture about all sorts of business objects. This method of information processing is as relevant to customers, citizens, or patients as it is to knowledge workers like lawyers, doctors, or researchers. People actually do not search for documents, but rather for facts and other chunks of information to bundle them up to provide answers to concrete questions. Strings, or names for things are not the same as the things they refer to. Still, those two aspects of an entity get mixed up regularly to nurture the Babylonian language confusion.

When creating a new AI-based app, there are many generic problems that are already being solved by other companies, for example face and gesture detection. Unless this is the main business and focus of the company, they will prefer to look for an out-of-the-box AI-as-a-service solution which will save them time, expertise and money. This type of solutions are called AI platforms and give their users many out-of-the-box services, such as computer vision (feature/face and gesture detection), natural language processing (NLP), speech to text, and translations between different language. See also: Artificial intelligence: a force for good or bad? Many companies including Google and Amazon sell this kind of AI services.

WHAT: French grocery chain Monoprix creates an almost exact remake of Amazon's recent promo video for its tech-powered grocery store of the future. WHY WE CARE: Not long ago, Amazon unveiled its plans for a beta version of its new cashierless grocery shopping experience called Go. Here, Monoprix sidesteps all the "computer vision," "deep learning algorithms," and "sensor fusion much like you'd find in self-driving cars" that Amazon touted about Go, and instead trolls the tech giant with an almost exact remake of the Go promo--actor doppelgangers dressed in the same outfits, similarly framed shots--with a human solution to the whole cashier line-up problem. And they deliver your groceries in an hour. Is there an Amazon drone for that yet?

A fleet of self-driving Uber cars left for Arizona on Thursday after they were banned from California roads over safety concerns. The announcement came after Arizona Gov. Doug Ducey took to social media on Wednesday and Thursday touting Arizona as an alternative to California for the ride-hailing company to test out its self-driving cars. Ducey, a Republican, sent tweets advertising Arizona's friendly business environment, saying Uber should ditch California for the Grand Canyon state. Uber said in a statement that it had shipped its cars to Arizona and will be expanding its self-driving pilot program in the next few weeks. The company hasn't announced a date when the cars will be tested, nor did it provide details about how many cars were included. Uber previously had 16 self-driving cars registered in California.

One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It's a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result. The network seems like it is able to learn concepts about 3D space and the structure of the objects it's drawing, and because it's generating images rather than numbers it gives us a better sense about how the network "thinks" as well. I happened to stumble upon the Radboud Faces Database some time ago, and wondered if something like this could be used to generate and interpolate between faces as well. To implement this, I adapted a version of the "1s-S-deep" model from the chairs paper.