Last weekend, I saw my first Christmas ad. And what a Smart Christmas it will be, judging by the haul on offer. Over the past year, companies have been teasing the various connected must-haves for the holidays: bots that can respond to kids' questions and movements, and capture audio and video; an imitation smartwatch that chats with other devices over Bluetooth; not to mention the Barbie Hello Dreamhouse, a pink-and-white smart house for the iconic doll. Not everyone is excited about the intelligence creeping into kids' toys. Privacy activists and developmental psychologists have objected on grounds ranging from security and privacy to fundamental worries about the nature of play.

Last week, Salesforce announced Einstein – their upcoming project to introduce Artificial Intelligence (AI) to their platform. This announcement is significant in bringing AI to the center of the sales ecosystem. While defining Einstein, Salesforce described several core competencies that will be critical in creating a Sales AI. While insights into what Sales AI could look like are a great starting point, the true value of building this technology lies in the end use cases and delivering them in a way people can actually take advantage of today. What does Sales AI actually look like in practice?

Digital is not the destination. Rather, it is laying the foundation for a more profound transformation to come. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?"

Google, ubiquitous in internet search but trailing in consumer hardware, is expected Tuesday to reveal details of its latest efforts to infuse smarts into more of its product offerings. The Alphabet Inc. GOOGL 0.22 % unit aims to integrate its work in artificial intelligence, which allows software to make inferences and decisions, to make products more useful with voice commands that may represent the next step in search. The Mountain View, Calif., company foreshadowed its strategy in May when it announced a device called Home to rival the Echo speaker, Amazon.com Inc. AMZN -0.43 % 's surprise hit that allows users to ask a virtual assistant named Alexa questions and to perform tasks. Home, a voice-controlled speaker, will be powered by what the company calls Google assistant.

If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades! If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it.

IBM said today that it will invest 200 million in a new global headquarters for its Watson Internet of Things business in Munich Germany. The move is part of the strategy of combining Watson -- the supercomputer that beat the world's best human Jeopardy player -- with the Internet of Things (IoT), or using sensors to make everyday objects smart and connected. It's also part of a global plan to invest 3 billion to bring Watson's cognitive computing to IoT. The investment in Munich is one of the company's largest ever in Europe and is in response to growing demand from customers who want to transform their operations with A.I. and IoT. IBM said it has 6,000 global clients using its Watson IoT solutions and services, up from 4,000 eight months ago.

Is your firm prepared to welcome in an era of legal practice using artificial intelligence (AI)? Where is this technology headed and why must law firm leaders fully understand how cognitive computing works in order to benefit from it? With the vast amounts of structured and unstructured data firms have to deal with, there is increasing pressure from nimble technologies and clients to attract AI and cognitive technologies. Understanding the market implications of digital business information and trends is now key to attaining the best possible performance, revenue gains and cost savings. Berwin Leighton Paisner was one of the first firms to embrace AI in the UK, developing its'contract robot' which promised to complete work in seconds that would have otherwise taken a team of paralegals months to do.

Want to build a drone at home? Intel is shipping its Aero Compute Board so you can get your unmanned aerial vehicle in the sky. The Aero Compute Board is priced at a hefty 399 and available on Intel's website. It's a complete drone board system in one unit, but you have to buy certain hardware, like rotor blades, separately. Intel has also started selling its new Robotics Development Kit for 249. The board is meant to be the guts of a robot, providing direction, navigation, decision-making and other capabilities.

After some months reading about deep learning research paper, i feel that there are not many original works like in (bayesian learning, kernel learning). I mean there are many papers just improve something, or combine algorithms, appear a lot even if at Top-venues. What do your think about that? Sorry if i made something wrong.

Latest iteration of Amazon's GPU-powered VMs for machine learning make a big speed leap, but they still use previous-generation Nvidia Kepler GPUs Amazon Web Services has unveiled a new generation of GPU-powered cloud computing instances aimed squarely at customers running machine learning applications. The P2's a major step up from the previous generation of GPU-powered AWS instances, and it has plenty of memory to burn. But it is built with an earlier generation of GPU, so it is less suited for the bleeding-edge machine learning work that needs the most recent advances in GPU technology. Amazon is currently billing the G2 as suitable for "graphics-intensive applications," rather than machine learning specifically. The P2, on the other hand, is definitely for machine learning.