There is no doubt the technology landscape is changing at a frenetic pace, and staying ahead of the major trends reshaping today's world will ultimately separate the winners from the losers. We have all heard the mantra of these days, "you either disrupt yourself or you will get disrupted." The risk of becoming irrelevant has never been greater. It's the role of chief information officers to look beyond today's solutions and be ready for what lies ahead. However, the list of innovations can be endless and keeping up with the latest emerging technologies and new tools is no easy task.

Artificial Intelligence, the possibility of merging consciousness with computers, and singularity are discussed in this mind expanding conversation with Dr. Susan Schneider. Are we prepared to face the implications of the success of our own technological innovations? Is the universe teeming with postbiological super Artificial Intelligence? Can silicon based entities bond with carbon based lifeforms? Explore the philosophical questions of superintelligence on the Antidote, hosted by Michael Parker.

Data science is first and foremost a talent-based discipline and capability. Platforms, tools and IT infrastructure play an important but secondary role. Nevertheless, software and technology companies around the globe spend significant amounts of money talking business managers into buying or licensing their products which often times results in unsatisfying outcomes that do not come close to realizing the full potential of data science. Talent is key - but unfortunately very rare and hard to identify. If you are trying to hire a data scientist these days you are facing the serious risk of recruiting someone with the wrong or an insufficient skill set. On top of things, talent is even more crucial for small or medium-sized companies whose data science teams are likely to stay relatively small. Wasting one or two head counts on wrong profiles might render an entire team inefficient.

RSNA 2016 is an essential appointment for radiologists like Eliot Siegel, M.D., who appeared at an event sponsored by healthcare and consumer product giant Philips to tout the virtues of applying artificial intelligence and machine learning to medical imaging. Increasingly, healthcare organizations are leveraging analytics to gain insights that solve inefficiencies and streamline workflows. Access our guide now for the 6 components of a healthcare analytics plan, how to get employees invested in analytics, and more. Corporate E-mail Address: This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.

Microsoft has released a set of 100,000 questions and answers that artificial intelligence (AI) researchers can use to create systems that can read and answer questions as precisely as a human. "The dataset is called MS MARCO, which stands for Microsoft MAchine Reading COmprehension, and can be used to teach artificial intelligence systems to recognise questions and formulate answers and, eventually, to create systems that can come up with their own answers based on unique questions they have not seen before," said Microsoft in a blog post. By providing realistic questions and answers, the researchers said they can train systems to better deal with the nuances and complexities of questions regular people actually ask, including those queries that have no clear answer or multiple possible answers. "Our dataset is designed not only using real-world data but also removing such constraints so that the new-generation deep learning models can understand the data first before they answer questions," added Li Deng, Partner Research Manager of Microsoft's Deep Learning Technology Centre. The MS MARCO dataset is available for free to any researcher who wants to download it and use it for non-commercial applications, Microsoft said.

It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago. Today we're going to take a look at 3 industries on the cusp of disruption through the adoption of AI and NLP technologies;

Adam Coates, director of Baidu Research's Silicon Valley AI Lab, isn't worried about artificial intelligence taking over the world. "I will tell you why I am not actually afraid of AI right now," he said at the InformationWeek Elite 100 Conference in the Four Seasons Hotel in Las Vegas on Tuesday, May 3. "I don't go around seeing every Camero on the street thinking that's a Transformer. I know what's under the hood. It's self-evident the technology is just not capable of doing something like that today." Not everyone in the audience was entirely convinced there's no need for fear.

Deep learning works by providing AI systems with huge amounts of data so it can learn to identify patterns and recognize important features, trends, and "decisions" to be made in processes such as playing a game or rendering a diagnosis. Deep learning has already made huge strides in detecting financial transaction fraud, making voice recognition systems like Siri work, and recognizing faces and patterns in photographs.

It won't be long before connected cars serve two masters: owners and manufacturers. And one software maker thinks it can build car brains smart enough to work for them all. If you drive an Entune-equipped Toyota (most of them) or a Lexus with Enform, you're already using connected-car software from UIEvolution (recently renamed Xevo). Besides getting a new name, Xevo is also about to acquire Surround.io, More: Connected cars: will you need an in-car ad blocker?

The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, bagging, and boosting. So how do ensemble methods work and why are they superior to individual models? They average out biases: If you average a bunch of democratic-leaning polls and republican-leaning polls together, you will get an average something that isn't leaning either way. They reduce the variance: The aggregate opinion of a bunch of models is less noisy than the single opinion of one of the models. In finance, this is called diversification -- a mixed portfolio of many stocks will be much less variable than just one of the stocks alone.