In a crowded media industry, where anyone can create a website and deliver content to a large amount of readers, relevance has become more important for publishers than ever before. Publishers must deliver the right content, to the right readers, at the right time, and in the manner that they want to consume it. While this may seem like a tall order, artificial intelligence solutions are making it easier for publishers to deliver content tailored to individual readers. "We're talking about building AI that helps engage more meaningful interactions," said Boomtrain CEO and co-founder Nick Edwards during a recent webinar. "It can connect your readers to the most relevant and engaging content."

In this post, I'm going to share a new way to get a diagnosis of who you are as a brand persona using a highly-objective artificial intelligence named Watson. Read on to learn more about what Watson can do for you!

Watson, can you grow into a multibillion-dollar business and become the engine of IBM's resurgence? IBM is betting its future that the answer is yes. Its campaign to commercialize Watson, the company's version of artificial intelligence technology, stands out, even during the current A.I. frenzy in the tech industry. IBM has invested billions of dollars in its Watson business unit, created at the start of 2014, which now employs an estimated 10,000 workers. Its big-ticket marketing push includes clever television ads that feature Watson trading quips with famous people like Serena Williams and Bob Dylan. And Watson, after a slow start, has shown its mettle by assisting in daunting tasks like diagnosing cancer.

It can teach itself, The Next Web reported (17 Oct 2016): "In a significant step forward for artificial intelligence, Alphabet's hybrid system -- called a Differential Neural Computer (DNC) -- uses the existing data storage capacity of conventional computers while pairing it with smart AI and a neural net capable of quickly parsing it." The AI also knows how to optimise its memory to accelerate future searching-learning. The Next Web added: "Instead of having to learn every possible outcome to find a solution, DeepMind can derive an answer from prior experience, unearthing the answer from its internal memory rather than from outside conditioning and programming." In other AI news the British Socialist newspaper the Morning Star commented on the #Singularity and AI, regarding concern about powerful multinationals shaping AI (17 Oct 2016): "Technologies shaping our world and determining the sustainability of human civilisation are commissioned by wealthy corporations. So uploaded human intelligence, machine learning and systems designed without human agency -- and perhaps without human values -- are ideas we all need to understand and influence."

Egged on by Samsung's recent acquisition of the team behind Siri, the company has chosen Ruslan Salakhutdinov, a formidable computer science professor from the esteemed Carnegie Mellon University, to be the director of artificial intelligence research at Apple. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA He may continue to work on Carnegie for research and tinker with Macs and iPhones simultaneously. It remains unspecified what the job description exactly is for someone helming Apple's AI research front, but as per Recode's observations, it'll most likely study the context behind questions that users ask Apple's voice-activated virtual assistant Siri. The professor's latest research that dabbles with contextual derivatives behind a user's' voice input supports this.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.

The Google logo is displayed on a sign outside of the Google headquarters in Mountain View, California. Google's artificial intelligence (AI) platform DeepMind revolutionizes the field, being now capable of learning based on information already possessed. DeepMind is able of learning, or better said of teaching itself, based on data it already possesses. According to The Next Web, this is a significant step forward for artificial intelligence, a real breakthrough that revolutionizes the field. DeepMind technology is based on Alphabet's hybrid system called Differential Neural Computer (DNC).

IBM's artificial intelligence platform Watson will work with doctors in Germany attempting to solve some complex medical cases. It will be based at the Undiagnosed and Rare Diseases Centre at the University Hospital in Marburg. So far, Watson has looked at half a dozen cases, but it is unclear how many it has correctly diagnosed. AI systems are increasingly being used in healthcare, with Google's DeepMind partnering several UK hospitals. The Watson partnership, with private hospital group Rhon-Klinkum AG, will be piloted from the end of the year.

When the Google self-driving-car project began about a decade ago, the company made a strategic decision to build its technology on expensive lidar and detailed mapping. Even today, Google's self- driving technology still relies on those two pillars. While that approach is great up to a point--we have good algorithms for using lidar and camera data to localize a car on the map--it's still not good enough. Driving on complicated, ever-changing streets involves perception and decision-making skills that are inherently uncertain (see "Your Driverless Ride Is Arriving"). Now an artificial-intelligence technology called deep learning is being used to address the problem.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.