A change in political leadership with Donald Trump's presidential victory and GOP control of Congress has raised expectation of policy shifts that could affect the regulatory compliance process. The incoming administration is promising to work to "dismantle the Dodd-Frank Act and replace it with new policies to encourage economic growth and job creation." This scenario would have plusses and minuses. On one hand, bank stocks are on the rise because of Trump's promise to lessen regulation. On the other hand, a complete dismantling of Dodd-Frank would mean that banks would have to overhaul the compliance processes that they have spent billions of dollars to put in place over the past six years.

Enabling autonomous vehicles and personalizing advertising are two of the highest opportunity use cases for machine learning today. Additional use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics; predicting personalized health outcomes, and optimizing merchandising strategy in retail. McKinsey identified 120 potential use cases of machine learning in 12 industries and surveyed more than 600 industry experts on their potential impact. They found an extraordinary breadth of potential applications for machine learning. Each of the use cases was identified as being one of the top three in an industry by at least one expert in that industry.

When conversation with my non-data scientist friends turns to AI it's almost inevitable that at least one will remark on the wonders of Watson. To many of the uninformed, Watson is synonymous with AI and clearly it's already here. So without getting so technical that their eyes glaze over, and that can happen pretty fast, here's a little bit of explanation you can use if you're caught in the same circumstance. The Watson that lives in the imagination of so many folks is the Watson that won the widely televised contest on Jeopardy in 2011. Fewer people are aware that the month following its televised debut, Watson went to Washington and played an untelevised set of matches against members of the House of Representatives where it also won.

Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.

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.