David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. FORTUNE: We hear a lot of terms on the AI front these days--"artificial intelligence," "machine learning," "deep learning," "unsupervised learning," and the one IBM uses to describe Watson: "cognitive computing." KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text. Deep learning was needed when we wanted to read an X-ray. And all of that has led to this concept of artificial intelligence--though at IBM, we tend to say, in many cases, that it's not artificial as much as it's augmented.

Artificial intelligence (AI) is already becoming entrenched in many facets of everyday life, and is being tapped for a growing array of core business applications, including predicting market and customer behavior, automating repetitive tasks and providing alerts when things go awry. As technology becomes more sophisticated, the use of AI will continue to grow quickly in the coming years, as explored in a new eMarketer report, "Artificial Intelligence 2016: What's Now, What's New and What's Next" (eMarketer PRO customers only). In its most widely understood definition, AI involves the ability of machines to emulate human thinking, reasoning and decision-making. A May 2015 survey of US business executives by Narrative Science found that 31% of respondents believed AI was "technology that thinks and acts like humans." Other conceptions included "technology that can learn to do things better over time," "technology that can understand language" and "technology that can answer questions for me."

Last Thursday, I had the honor to be one of the warmup acts for President Obama at the White House Frontiers Conference at Carnegie Mellon University in Pittsburgh. Here is the prepared text and slides from the talk I delivered there. As you'll see if you watch the video, what I ended up saying isn't exactly what I had written out in advance, but it is reasonably close. Let me know if you like this format for sharing talks.) Hearing that Bob Dylan just won the Nobel Prize for Literature, how could I not begin this talk with his famous line, "Something is happening here, but you don't know what it is, do you, Mr. Jones?"

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature.

The largest actors union in Hollywood officially called a strike early Friday morning against several prominent video game companies after the two sides failed to reach an agreement on an increase in compensation for performers who do voice-over and motion-capture work for popular games. SAG-AFTRA said Friday the work stoppage began at 12:01 a.m. Friday and covers games made by the companies that went into production after Feb. 17, 2015. Many of the most sophisticated games take years to develop and bring to market, and employ large casts of actors over that development process. Members of SAG-AFTRA are planning to picket one of the companies -- Electronic Arts -- at its location in Playa Vista on Monday.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

Salesforce has a history of staying close to the cutting edge of technology, so it shouldn't be surprising that it announced an artificial intelligence initiative recently, it dubbed Einstein. We caught up with some key members of the Einstein team at Dreamforce earlier this month and asked them to explain the new technology for us. Einstein isn't a product so much as a set of intelligence functionality that underlies the entire Salesforce platform, and while the types of functionality that it's enabling now are somewhat limited, the idea is to provide a base on top of which the company can continue to add new capabilities into the future. Today, Einstein can provide information like predictive lead scoring and opportunity insights, which alert a rep how a deal is trending -- the kinds of information many CRM applications have been offering for some time -- but as the technology develops, the company sees a much bigger role for it. As John Ball, GM of Einstein at Salesforce explains, it's really aimed at making life easier for users.

The smart energy startup Verdigris announced today that it has raised 6.7 million to scale production of its Einstein smart sensor and frequency detectors. The sensors are used to predict the failure of machines and improve energy efficiency. Factories, manufacturing facilities, and other large buildings using Verdigris technology reduce energy use 8 to 22 percent, CEO Mark Chung told VentureBeat in a phone interview. The Einstein frequency detector from Verdigris made its debut in August. "Rather than take a big data approach where we study thousands of motors and this is the failure pattern, we instead take a physics based model which is looking at a signal through our sensors," Chung said.

This press release is submitted and shown here in its original form, unedited by Furniture/Today. One of those newest technologies is the Recommendations API from Microsoft Cognitive Services. DutchCrafters has used this machine learning technology to offer customers product recommendations that meet the customers' preferences out of the thousands of products available on its website. DutchCrafters' effective use of Microsoft Cognitive Services, boosting its conversion rate by three-times for those using the recommendation results, caught Microsoft's eye as one of its success stories. DutchCrafters is the flagship ecommerce site of Sarasota-based niche retailer JMX Brands.

We are happy to announce that we can finally answer the question on how to cite mlr properly in publications. Our paper on mlr has been published in the open-access Journal of Machine Learning Research (JMLR) and can be downloaded on the journal home page. The paper gives a brief overview of the features of mlr and also includes a comparison with similar toolkits. For an in-depth understanding we still recommend our excellent online mlr tutorial which is now also available as a PDF on arxiv.org Once mlr 2.10 hits CRAN you can retrieve the citation information from within R: