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. But it now has abilities we never expected. It can learn through experience -- much the way humans do -- and it won't be long before machines, like their human creators, begin thinking for themselves, creatively.

Clarifai, whose visual recognition software can instantly sort through a travel website's millions of hotel photos, has raised $30 million in a Series B round. The Flatiron-district-based startup plans to use the money to further its development of low-cost and easy-to-use artificial intelligence applications. Silicon Valley stalwart Menlo Ventures led the round, with participation from existing investors, including Union Square Ventures, Lux Capital and Qualcomm Ventures. Founded in 2013, Clarifai has so far raised a total of $41 million. "We've launched new products that move us in this direction of personalizing AI and getting away from this one-size-fits-all approach," said Clarifai founder and CEO Matt Zeiler in an interview.

Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.

Deep Learning is becoming the next big area for companies and universities to explore. Deep Learning libraries are growing and their adoption is expanding. With Google's open sourcing of TensorFlow, there is a massive rise in deep learning adoption. I have started using it for it's very interesting Image Recognition capabilities which can be used out of the box with their ImageRecognition example. Google has released a new TensorFlow library - Image Recognition, Slim. TF-Slim is a lightweight library for defining, training and evaluating complex models in TensorFlow, which should speed up adoption and ease of use.

Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. With advanced analytics, banking may finally getting close to realizing this vision. In 1993, a then revolutionary book, "The One to One Future: Building Relationships One Customer at a Time" was published, proposing the idea that as technology makes it affordable to track individual customers, marketing shifts from finding customers for products to finding products for customers. According to the authors, Don Peppers and Martha Rogers, Ph.D., a company could use technology to gather information about, and to communicate directly with, individuals to form a commercial bond. The book became a bestseller, and was on every marketer's bookshelf … almost a quarter century ago.

When Bob Dylan won the 2016 Nobel Prize in Literature, it shocked the humanitarian world. What's more, Dylan himself hasn't behaved like a traditional Nobel winner: he hasn't commented on the honor and has yet to give an acceptance speech. At least one member of the Nobel panel has called Dylan's silence "rude and arrogant," and the public has been reminded that if he doesn't give a lecture within six months, he won't receive the $900,000 prize money. Selecting Dylan as a Nobel laureate may be contentious, but it's mostly a sign of growth for intellectual society -- at least in Literature, no one is off-limits, not even mumbling masters of wordplay and songwriting. Growing pains are expected as the world of mainstream politics, activism and academia is suddenly forced to consider the potential of new industries, and vice versa.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).

IBM is teaming up with Slack Technologies Inc. to make it easier for companies to build custom chatbots into the startup's workplace-messaging systems, the latest move by Big Blue to add more diverse business cases for its Watson artificial-intelligence technology. The two companies will release a developer toolkit that includes Watson technologies and can integrate easily into Slack, they said in a statement Wednesday. International Business Machines Corp. will also build a chatbot -- a conversation-based application -- that will help IT departments identify and resolve issues without having to leave the Slack platform. The startup's own customer-service bot will incorporate the Watson Conversation system, which includes technologies such as speech-to-text conversion and natural-language processing, or the ability for a computer to understand what a person is saying. "Right now, if you say something to Slackbot in Slack, it will kind of blindly take what you ask it and do a search," Slack Chief Executive Officer Stewart Butterfield said.

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.

IBM, Microsoft Vie for Lead in New-Age Advanced Computing By David Ramel 10/26/2016 Whether it's machine learning, artificial intelligence, cognitive computing or whatever, new-age software development is opening up huge new opportunities, with IBM and Microsoft vying for the lead in this new technological space. During this week's World of Watson conference, IBM unveiled a new Watson Data Platform and made available the Watson Machine Learning Service, among a host of other announcements. Microsoft, meanwhile, stole some of Big Blue's thunder by releasing an updated Microsoft Cognitive Toolkit for "deep learning," yet another moniker in the new movement. Coincidentally, these announcements were made just one day before a news release was issued by research firm IDC to tell the world that "Worldwide Cognitive Systems and Artificial Intelligence Revenues Forecast to Surge Past $47 Billion in 2020." That gives an indication of the kind of stakes that IBM, Microsoft and other leaders are competing for.