One by one, the skills that separate us from machines are falling into the machines' column. First there was chess, then Jeopardy!, then Go, then object recognition, face recognition, and video gaming in general. You could be forgiven for thinking that humans are becoming obsolete. But try any voice recognition software and your faith in humanity will be quickly restored. Though good and getting better, these systems are by no means perfect.

Yoshua Bengio, one of the leading figures behind the rise of deep learning, is launching a Silicon Valley-style startup incubator dedicated to this enormously influential form of artificial intelligence. The incubator, Element.AI, will help build companies from AI research that emerges from the University of Montreal, where Bengio is a professor, and nearby McGill University, and he says this is just part of his efforts to develop an "AI ecosystem" in Montreal. Bengio says the Canadian city offers "the biggest concentration in the world" of academic researchers exploring deep learning, the breed of AI that now plays such an important role inside the likes of Google, Facebook, and Microsoft. "Element.AI will help entrepreneurs get started in that high-growth area, with a team of experts--and my help--to steer those companies in the right direction," he says. According to Bengio, about 100 researchers are exploring deep learning at the University of Montreal and about 50 others are doing similar work at McGill.

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.

Hollywood's depiction of the future is practically the present. The Jetsons' lifestyle has become Elon Musk's passion project; the self-driving car from I, Robot is now being tested on roads; and even the hoverboard from Back to the Future can be purchased by consumers. And as the evolution of technology turns fiction into reality, brands are forced to keep pace with their customers. "You have this symbiotic rise of technology and consumer expectations," Norman de Greve, SVP and CMO of CVS Health, said at a recent CMO Panel hosted by creative consultancy Lippincott in New York. So, how can brands meet consumer demand when those demands and expectations are constantly changing?

After you make predictions, you need to know if they are any good. There are standard measures that we can use to summarize how good a set of predictions actually are. Knowing how good a set of predictions is, allows you to make estimates about how good a given machine learning model of your problem, In this tutorial, you will discover how to implement four standard prediction evaluation metrics from scratch in Python. You must estimate the quality of a set of predictions when training a machine learning model. Performance metrics like classification accuracy and root mean squared error can give you a clear objective idea of how good a set of predictions is, and in turn how good the model is that generated them.

With numerous machine learning algorithms to experiment with, this relatively simple graph can quickly help you shortlist a handful of algorithms. Any then you may give Scikit a try. With numerous machine learning algorithms to experiment with, this relatively simple graph can quickly help you shortlist a handful of algorithms.

Widespread adoption of cognitive systems and artificial intelligence across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. According to the Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide from IDC, the market for cognitive/AI solutions will experience a compound annual growth rate of 55.1% over the 2016-2020 forecast period. "Software developers and end user organisations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process," said David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC. "Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for "Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organisations to be planning and undertaking strategies that incorporate these wide-ranging technologies. Identifying, understanding, and acting on the use cases, technologies, and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant." The ability to recognise and respond to data flows using algorithms and rule-based logic enables cognitive/AI systems to automate a broad range of functions across many industries. The use cases that are attracting the most investment in 2016 are automated customer service agents, quality management investigation and recommendation systems, diagnosis and treatment systems, and fraud analysis and investigation. The use cases that will experience the fastest revenue growth over the next five years are public safety and emergency response, pharmaceutical research and discovery, diagnosis and treatment systems, supply and logistics, quality management investigation and recommendation systems, and fleet management. The use cases that are attracting the most investment in 2016 are automated customer service agents, quality management investigation and recommendation systems, diagnosis and treatment systems, and fraud analysis and investigation. The use cases that will experience the fastest revenue growth over the next five years are public safety and emergency response, pharmaceutical research and discovery, diagnosis and treatment systems, supply and logistics, quality management investigation and recommendation systems, and fleet management. "Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," said Jessica Goepfert, program director, Customer Insights and Analysis at IDC. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies.

For his 4th appearance on the show, we talk with author Calum Chace about the future of AI, big data, machine learning, security, privacy, bad credit, and the joys of politics in the US and UK. Review The Future Episode 64 - Calum Chace on Is it Time to Start Worrying About AI?

During today's fourth quarter 2016 earnings call, Apple CEO Tim Cook was asked about Siri, artificial intelligence, home assistants vs. mobile assistants, and balancing AI with security, which led to some interesting new insights into Siri's popularity and Apple's privacy stance. According to Cook, Apple is now getting more than 2 billion Siri requests per week. "It's very large," he said, "and to the best of our knowledge, we've shipped more assistant enabled devices than anyone out there." He went on to highlight Apple's efforts to deliver a great Siri experience around the world. While most AI services are limited to the United States, Siri is available in many countries. "We put a lot of energy into that," Cook said.

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