Self-driving cars might fill the roads a lot sooner if carmakers can put aside their rivalries and share the data that would teach computers how to drive safely. MobileEye, an Israeli company that supplies advanced computer hardware and software to many carmakers to enable cars to spot objects on the road, is now developing ways to train cars to drive themselves. The effort involves feeding computers huge quantities of driving behavior into a vast, highly realistic simulation, so that they can learn how to drive for themselves. And MobileEye aims to have different customers contribute the data that their vehicles collect. "If you want to leverage many, many cars, you need to leverage as many carmakers as possible," says Amnon Shashua, cofounder and CTO of MobileEye.

Much has been written about DevOps and its ability to speed up time-to-value and innovation. Machine learning is no different. New approaches and algorithms--for example, deep learning--are coming out all the time, and data scientists are trying them out through code and relying less on GUI-based interfaces. After the new approach has been tested out in a sandbox environment with limited scope, it's time to move toward development, QA, and finally, production. Each one of these environments can be automated with DevOps through tools like Jenkins, Puppet, Chef, Ansible, and Docker.

Dr. Alessandro Negro is the Chief Scientist at GraphAware. He has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. Before joining the team, Alessandro has gained over 10 years of experience in software development and spoke at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento. Your email address will not be published.

Now that we have training and test datasets sampled, initially preprocessed and available in the data grid, we can close Web Notebook and start experimenting with different techniques and algorithms by submitting Spark applications. For our first baseline approach let's take a single feature device_conn_type and logistic regression algorithm: We will explain a little bit more what happens here. At first, we load the training dataset from the data grid, which we prepared and saved earlier with Web Notebook. Then we use StringIndexer and OneHotEncoder to map a column of categories to a column of binary vectors. For example, with 4 categories of device_conn_type, an input value of the second category would map to an output vector of [0.0, 1.0, 0.0, 0.0, 0.0].

Microsoft is jumping on the artificial intelligence bandwagon with the formation of a new research group that will seek to make the technology more accessible via its Azure cloud while helping to deliver new capabilities across applications, services and infrastructure. The infrastructure portion of the effort focuses on combining the processing engines like GPUs and FPGAs designed to improve network connectivity as ways to boost AI performance running on Microsoft's Azure Cloud. Microsoft (NASDAQ: MSFT) said last week Harry Shum, a 20-year company veteran who worked on the Bing search and Cortana intelligence personal assistant projects, would head the AI initiative. More than 5,000 computer scientists and engineers work for Microsoft's AI and Research Group. Microsoft's AI initiative seeks to "democratize" AI technology through a focus on agents, applications, services and infrastructure.

The go to place for quick and easy translations – Google Translate – just received a huge upgrade with Deep Learning algorithms boosting its translation capabilities and reducing errors by 60%. Google's experiments with neural machine translation pays off in a big manner. Like most translation services, Google Translate too relied on breaking down sentences into smaller phrases or groups of words and then translated these phrases which were later joined together to produce the output. With Neural Machine Translation, Google Translate can translate entire sentences without breaking them in phrases. This new approach has been said to reduce errors by at least 60 percent compared to the previous phrase based approach.

Omron's table tennis robot is getting smarter Forpheus, the mighty table tennis robot developed by Japan's Omron, is getting smarter. An updated version on show at the Ceatec electronics show this week has artificial intelligence to become a tougher opponent. In the new version, the robot attempts to rank a player according to their perceived skill as a beginner, intermediate player or advanced. It does this by looking at the speed of the served ball, its trajectory, rotation and the body motion of the player with cameras, and does so with 90 percent accuracy, according to Omron. The machine uses that information to customize its return ball, softer and easy for beginners, faster and more unpredictable for advanced players. The use of artificial intelligence has also improved the robot's game.

Like many of the students around them, robots at Carnegie Mellon University are constantly learning -- learning how to think, how to move, and how to be more like humans. "It's sort of this wonderland of innovation," says 60 Minutes producer Nichole Marks in the video above. "Everywhere you go, every corner of the campus, there are robots -- robots in the hallways, robots picking things up, robots talking to you." Marks and correspondent Charlie Rose visited the Carnegie Mellon campus in Pittsburgh while reporting their two-part story on artificial intelligence, or A.I., for this week's episode of 60 Minutes. What they found in the old steel town was a glimpse into the future, says Rose.

The following script is from "Artificial Intelligence," which aired on Oct. 9, 2016. Charlie Rose is the correspondent. The search to improve and eventually perfect artificial intelligence is driving the research labs of some of the most advanced and best-known American corporations. They are investing billions of dollars and many of their best scientific minds in pursuit of that goal. All that money and manpower has begun to pay off. In the past few years, artificial intelligence -- or A.I. -- has taken a big leap -- making important strides in areas like medicine and military technology. What was once in the realm of science fiction has become day-to-day reality. You'll find A.I. routinely in your smart phone, in your car, in your household appliances and it is on the verge of changing everything. On 60 Minutes Overtime, Charlie Rose explores the labs at Carnegie Mellon on the cutting edge of A.I. See robots learning to go where humans can'... It was, for decades, primitive technology.

Tom Fawcett is Principal Data Scientist at Silicon Valley Data Science. Co-author of the popular book Data Science for Business, Tom has over 20 years of experience applying machine learning and data mining in practical applications. He is a veteran of companies such as Verizon and HP Labs, and an editor of the Machine Learning Journal. A basic mantra in statistics and data science is correlation is not causation, meaning that just because two things appear to be related to each other doesn't mean that one causes the other. This is a lesson worth learning.