Nvidia Founder, President and CEO Jen-Hsun Huang introduces the Nvidia Spot, a USD 49.95 microphone and speaker that will let owners use Google Assistant anywhere in a home, as he delivers a keynote address at CES 2017 (Photo: Ethan Miller/Getty Images) LAS VEGAS--Nvidia is best known for the high-end computer graphics cards prized by hardcore gamers. If co-founder and CEO Jen-Hsun Huang delivers on his bold vision, more people are likely to recognize Nvidia as the powerhouse behind artificial intelligence in your home and in your vehicle. Clad in his trademark black leather jacket, Huang delivered a high energy opening night keynote address Wednesday night at CES, assuming a prestigious speaking slot that for years was reserved for Microsoft's Bill Gates and later his successor Steve Ballmer. Nvidia (NVDA) is already a star on Wall Street. It is coming off a two-year hot streak, with a particularly sizzling 224% gain in 2016 that made it the top performing stock in the S&P 500.

Sifting through lots of documents can be difficult and time consuming. Without an abstract or summary, it can take minutes just to figure out what the heck someone is talking about in a paper or report. And, if you need to get through hundreds of documents – good luck. Summarizer is an algorithm that extracts sentences from a text document, determines which are most important, and returns them in a readable and structured way. Automatic text summarization is part of the field of natural language processing, which is how computers can analyze, understand, and derive meaning from human language.

Honda's NeuV (New Electric Utility Vehicle) is a combination of ideas from the Japanese automaker, ranging from autonomous driving, to car sharing, electric charging, and connectivity. But perhaps the most ambitious idea put forth by Toyota in its latest concept car goes by the name of Hana (Honda Automated Network Assistant). It is meant to be a helper as well as a companion. "Artificial intelligence is becoming more and more important in vehicles because we want to kind of have a relationship with the vehicle rather than the vehicle just doing things on its own," says Nick Renner, who helped design NeuV. Hana can sense a driver's emotions through heart rate monitoring and face recognition, as well as carry on a conversation.

Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017, by Matthew Mayo Data Science Trends To Look Out For In 2017, by Andrew Dipper 50 Data Science, Machine Learning Cheat Sheets, updated, by Thuy T. Pham Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017 Why Deep Learning is Radically Different From Machine Learning 4 Cognitive Bias Key Points Data Scientists Need to Know 4 Reasons Your Machine Learning Model is Wrong (and How to Fix It) Big Data: Main Developments in 2016 and Key Trends in 2017 The 5 Basic Types of Data Science Interview Questions

In this article we will discuss the machine learning method called "decision trees", moving quickly over the usual "how decision trees work" and spending time on "why decision trees work." We will write from a computational learning theory perspective, and hope this helps make both decision trees and computational learning theory more comprehensible. The goal of this article is to set up terminology so we can state in one or two sentences why decision trees tend to work well in practice. Newcomers to data science are often disappointed to learn that the job of the data scientist isn't tweaking and inventing new machine learning algorithms. In the "big data" world supervised learning has been a solved problem since at least 1951 (see [FixHodges1951] for neighborhood density methods, see [GordonOlshen1978] for k-nearest neighbor and decision tree methods).

The year of (insert trend here) sounds familiar to anyone involved in the tech world. A new innovation arrives, it dominates the news cycle, and its popularity is followed by a chorus of talking heads declaring it as the product of the year. Reality is quite different: Tech trends don't really follow a yearly cycle and new computing paradigms usually take a lot more than a year to become indispensable parts of the industry. Take cloud computing for example: We said 2011 was going to be the year the cloud went completely mainstream. Now in 2017 we can see that, while definitely a fundamental piece of the IT puzzle, cloud computing still hasn't taken over and it's not likely to ever do so.

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.

It's hard to see anything other than artificial intelligence being the next frontier of the edtech front. More specifically, machine learning has quickly gained the potential to overhaul what educators can accomplish with technology due to how rapidly the field has advanced within the last two years. Using machine learning, truly "smart" education solutions that can perform intelligent, self-informed actions are poised to change how we facilitate learning inside and outside of K-12 and higher education classrooms. Here's four ways artificial intelligence is going to do just that: As edtech authority Eric Sheninger (1) has noted, digital tools can boost student collaboration while they grapple with class material. This increased and streamlined student engagement with other students, as well as the subject matter at hand, has been known to produce more holistic and deeper learning.

CES has turned into the first car show of the year, with major automakers choosing to show off upcoming features in Las Vegas. Microsoft wants to help make cars more intelligent, and it unveiled a new suite of services Thursday to do so. The Connected Vehicle Platform brings together a smorgasbord of services from Microsoft, including Azure IoT Hub, Cortana Intelligence Suite, Microsoft Dynamics and many others. In addition, Office 365, Skype for Business and Cortana can be integrated with the platform. Microsoft frequently packages cloud services as suites, then markets them for kick-starting particular applications.

New KDnuggets Cartoon examines what can happen when you combine a Self-Driving Car and Machine Learning technology such as Recommendations ... Car: "No, I will not take you to A-1 Steakhouse. I recommend you go to the gym first, and then to V8 vegetarian restaurant" This cartoon was ably drawn by Jon Carter. Here is KDnuggets Big Data, Data Mining, and Data Science Cartoon page Recent KDnuggets Cartoons: Cartoon: Thanksgiving, Big Data, and Turkey Data Science. Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Facebook data science experiments and Cats Cartoon: It all started with the iPhone answering my email Cartoon: Where humans are still ahead of Deep Learning Cartoon: A solution for Data Scientists allergies caused by Big Data Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Facebook data science experiments and Cats Cartoon: It all started with the iPhone answering my email Cartoon: Where humans are still ahead of Deep Learning Cartoon: A solution for Data Scientists allergies caused by Big Data