Artificial Intelligence (AI) is one of the most exciting and controversial developments in science and technology. While the recognised applications of AI are limited, think Google and Siri, many believe it is poised to make significant contributions to organizations as it is used to advance and accelerate decision-making. Can we apply cognitive technology to amplify our intelligence and improve the effectiveness and efficiency of medicine? To enhance the patient experience and improve engagement? To improve diagnostic accuracy and personalize treatment with remarkable precision?

AI technologies have clear value paths in marketing, including sales and enhancing customer experience. When most companies think about artificial intelligence (AI)--and its many sub-categories, such as machine learning, natural language processing (NLP), or cognitive computing--they think about applications such as IBM's Watson technology being used to diagnose patients. What they often miss, according to Fern Halper, VP and senior research director of advanced analytics at TDWI, is that "marketing is often one of the first departments in an organization to use advanced analytics." In a recent webinar, Halper discussed the current potential and future pathways for marketing analytics with David Stodder, senior research director of business intelligence at TDWI, and Wilson Raj, the global director of customer intelligence with SAS. They all agreed that among all the different business departments, applying AI to marketing could provide the most immediate, recognizable return on investment .

If you're into enterprise, cloud computing -- and data, analytics, machine learning, and conversational interfaces -- this article is for you. It's my report from the 2016 World of Watson conference, written for both those who missed the event and those who attended. IBM stated 17,000 Las Vegas attendance and there was a lot to absorb! At World of Watson, I saw computing's future… and also technologies that I associate with a pre-cognitive past. I encountered a tech giant with vision, amazing alliances, and unique computing assets… and I saw lingering limitations I wouldn't have expected. I'll open this report with three key IBM messaging points, followed by three observations about products and positioning. I'll close with four points where, in my view, IBM hasn't yet hit the mark.

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A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who wants to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with. All algorithms are implemented in Python, using numpy, scipy and autograd.

Bill Gates, Elon Musk, and Stephen Hawking all have something in common: All three have gone on the record sharing their concerns and fears about artificial intelligence and robotics. While these technologies hold a great deal of promise, and will have a real impact on our future, it's important for us to understand the ramifications they could have for all of us, particularly in terms of labor. My first big concern about AI was recently highlighted in a New York Times piece by John Markoff, who wrote that while AI has great potential for good, it could also be abused by criminals who might use it for their nefarious goals. The growing sophistication of computer criminals can be seen in the evolution of attack tools like the widely used malicious program known as Blackshades, according to Mr. Goodman. The author of the program, a Swedish national, was convicted last year in the United States.

We now experience life through an algorithmic lens. Whether we realize it or not, machine learning algorithms shape how we behave, engage, interact, and transact with each other and with the world around us. Deep learning is the next advance in machine learning. While machine learning has traditionally been applied to textual data, deep learning goes beyond that to find meaningful patterns within streaming media and other complex content types, including video, voice, music, images, and sensor data. Deep learning enables your smartphone's voice-activated virtual assistant to understand spoken intentions.

BBC Click's Spencer Kelly meets a robot getting ready to help visitors to the 2020 Tokyo Olympics with language translation. Japan is embracing the use of robots across a wide range of industries. The country has an ageing population and is increasingly looking for robots to replace people in the workforce. Fumiyuki Hashimoto of Fujitsu says Japanese people see robots and artificial intelligence as one. Robots are perceived as something that can protect and support humans.

Agilience mission is to find people and stories that matter to you, to your community, to your business. Their ambition is to nurture communities by pointing out key authorities who inspire and educate, and they do with Agilience Authority Index. Here we review their list of top 10 social media authorities in Artificial Intelligence and Machine Learning. Driven by the crazy idea that #DataScience must be simple & profitable. Tweets are mine & don't represent my employer's opinion.

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