In the age of Facebook Messenger, WhatsApp, Slack and AI assistant (Cortana, Siri, Ok Google) we can finally communicate intelligently with our software. With growing Artificial Intelligence, Chatbots are helping us to find products, places, food and even solving customer service issues. Chatbots can convince you of remarkable things like the necessity of clicking a link or they are humans, not robots. Automation using bots allows companies to minimize their costs while still maintaining a high price point, thanks to the ease of use and quality of service. Brands capitalizing on digital experience are using chatbots to gain competitive advantage.

IBM says new Watson Data Platform will'bring machine learning to the masses' Don't be afraid of artificial intelligence, says VC Ben Horowitz IBM says new Watson Data Platform will'bring machine learning to the masses' IBM expands Watson's reach with data platform, iOS integration, bots, education efforts Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time. We won't share your personal information with anyone.

I hate my alarm clock, and I bet you hate yours. They're machines that drag you from your comfortable slumber into the cold drudgery of everyday life. I don't think I could ever like an alarm clock. But could I be impressed by one? And Bonjour, by French design house Holi, is a deeply impressive alarm clock in the making.

Mountain View, California-based Drive.ai is a startup created by former lab mates from Stanford University's Artificial Intelligence Lab. Originally founded in 2015 by Carol Reiley and Fred Rosenzweig, Drive.ai raised $12 million in Series A funding earlier this year to develop deep learning algorithms to control the operation of autonomous vehicles. Building on experience gained from the DARPA Grand Challenge, Google and other self-driving pioneers programmed the first self-driving car to rely primarily on light detection and ranging (LIDAR), which is a remote sensing method that uses pulses of laser light to measure distances, and detailed mapping. Although this has worked pretty well, the current technology is expensive. Making autonomous vehicles easier to manufacture with less expensive parts will make them more affordable.

While transportation, hospitality, and financial services are all industries being disrupted by technology, the next big area poised for massive, tech-driven change may be the human workforce. "We are going to move from people to things," explained Jane Fraser, CEO of Citigroup's Latin America business, speaking Monday at Fortune's Most Powerful Women Summit in Laguna Niguel, Calif. "We are expecting 500 billion objects to become connected to the internet and this automation is going to hollow out middle and working class jobs," explained Fraser. "Technology is replacing these jobs." The technology Fraser is referring to is artificial intelligence--the machine learning that powers driverless cars and other intelligent machines that are slowly taking over human tasks.

As a San Francisco resident, I've often been awed and inspired by the sight of the Golden Gate Bridge. A team from MIT's Media Lab ran a photo of the landmark through its "Nightmare Machine" and now it looks like a moving, tentacled monster that will grab and crush any car that dares to cross it. The Nightmare Machine uses deep-learning algorithms (and possibly evil spirits) to turn ordinary images of people and places into scary ones. To help the AI learn maximum spookiness, the public is invited to rate the faces as "scary" or "not scary." The Halloween-perfect project comes from MIT Media Lab's Scalable Cooperation group, which studies how technology is reshaping the nature of human cooperation.

The world's largest education company is leveraging IBM's Watson platform as it tries to take college tutoring from campus libraries to the virtual world. Pearson is partnering with Armonk, New York-based International Business Machines Corp. to use the Watson artificial intelligence product as an online tutor for college courseware. The companies on Tuesday announced a pilot project that's already underway in the U.S. and is set to expand through 2017 and 2018. Both companies declined to disclose terms, costs or revenue projections from the venture. The project is part of Pearson's efforts to shift its business into the digital age, as it struggles with slumping textbook sales and lower college enrollments in the U.S. IBM is seeking to drive revenue growth by developing practical applications for Watson, its software that wooed the sector five years ago by beating two human champions on the TV game show Jeopardy!

The same internal, deep learning tools that Microsoft engineers used to build its human-like speech recognition engine, as well as consumer products like Skype Translator and Cortana, are now available for public use. Redmond announced today that it is open-sourcing the Cognitive Toolkit that has led to many key developments coming out of its dedicated AI division. In other words: anyone can now train their own artificial intelligence. Formerly known as the CNTK, Microsoft says the beta version of the Cognitive Toolkit is not only faster than previous incarnations, but it is also beats out competing deep learning toolkits – especially when crunching large datasets across multiple machines. On a more practical level for startups and hobbyists, Microsoft says the platform is flexible enough to run on a solo laptop -- just in case you don't have a server farm loaded with NVIDIA GPUs at your disposal.

We approach this problem as a classification problem, predicting two classes -- whether the flight will be delayed, or whether it will be on time. Broadly speaking, in machine learning and statistics, classification is the task of identifying the class or category to which a new observation belongs, on the basis of a training set of data containing observations with known categories. Classification is generally a supervised learning problem. Since this is a binary classification task, there are only two classes. To solve this categorization problem, we will build an experiment using Azure ML Studio.

The next phase is augmentation and modification. As media planners and strategists are freed from mechanical tasks, they can focus on understanding how media mix can inform creative work. Right now, all the creative work is done up front; once a campaign launches, it becomes a matter of optimizing placement and timing. Down the road, machine learning may help recognize when the content itself is the problem, and also campaign workflows that are more responsive to news events, for example stopping a programmatic run to lead on-the-fly creative that resonates with a stunt that just went viral at Burning Man or an October surprise in the political world.