I started college as a psychology major. While I was working in a cognitive science research lab, the graduate student who advised me asked me to analyze some data in MATLAB. I had never coded before, and I thought analyzing that data was super hard. I felt like I was bad at programming, and I hated being bad at it. So I decided to declare a computer science major.

Feel free to forward this email or share it with your network. IN THE BURGEONING field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as “black box†systems: read more. Today Google is announcing a "Google Assistant" that essentially performs the same tasks as other Google interfaces do, but in a conversational mode. We are in the middle of a technological revolution. Artificial intelligence (AI) has the potential to fundamentally transform the world around us… Read more.

While programming in our current day and age isn't going away – and probably isn't even shrinking anytime soon – there are exciting new opportunities on the horizon for programmers to come. Namely, with machine learning becoming ever more important, we're sure to see new "trainer" roles growing and eventually becoming part of the profession that deals with artificial intelligence. In a traditional computing environment, you program a computer and "tell" it how to do everything step-by-step. You're creating a functionality that teaches it how to learn. A great amount of the work in this area will be in discovering how to extract relevant data out of the system.

Jonathan Schaeffer is a fellow of the Association for the Advancement of Artificial Intelligence and dean of the faculty of science, University of Alberta. In McKinsey & Co.'s report Disruptive Technologies, technologies containing artificial intelligence (AI) are expected to create tens of trillions of dollars in economic impact by the year 2025. Perhaps, but it is hard to imagine any industry that won't be affected by software applications with "smart" capabilities. For several decades, Canada has been a global powerhouse in AI research. The Universities of Alberta, Toronto and Montreal have world-class AI research centres, and several other universities have strong programs.

Text-to-speech systems are familiar in the modern world in navigation apps, talking clocks, telephone answering systems, and so on. Traditionally these have been created by recording a large database of speech from a single individual and then recombining the utterances to make new phrases. The problem with these systems is that it is difficult to switch to a new speaker or change the emphasis in their words without recording an entirely new database. So computer scientists have been working on another approach. Their goal is to synthesize speech in real time from scratch as it is required.

Today sees the publication of the ICO's updated paper on big data and data protection. What's changed in the two and a half years since we first visited this topic? The complexity and opacity of these types of processing operations mean that it's often hard to know what's going on behind the scenes. This can be problematic when personal data is involved, especially when decisions are made that have significant effects on people's lives. The combination of these factors has led some to call for new regulation of big data, AI and machine learning, to increase transparency and ensure accountability.

I read Artificial Intelligence for Humans, Volume 1 and then ordered volumes 2 and 3. What I like about this series is the same thing I like about Volume 2, that it's very readable. For someone without a math background, and limited programming prowess, I can understand the concepts. The only things about the book that I don't like are: 1) Some of the context is missing. For instance, I can understand Genetic Algorithms, Partical Swarm Optimization, and Ant Colony Optimization as concepts and I think I could basically code them if I needed to. I would say his forte is explaining the ideas and the math in plain language.

On Tuesday, at the 2017 Google Cloud Next conference in San Francisco, two key themes dominated Google's roadmap for its future in cloud: Machine learning and data analytics. The conference, held at the Moscone Center, saw Google executives like Google Cloud senior vice president Diane Greene, Google CEO Sundar Pichai, and Alphabet chairman Eric Schmidt take the stage to explain the mission of Google Cloud. Over the past year, Greene said, Google Cloud engineers have done 500 releases on the platform, and the company has ramped up partnerships as well. "Google Cloud is a natural extension of our mission to make information accessible and useful," Pichai said. SEE: Google Cloud Platform: The smart person's guide One of those partnerships was with analytics giant SAP. Green spoke with SAP's Bernd Leukert on stage at the event, where he explained that many of SAP's business products, like SAP HANA, are now generally available, and certified, on the Google Cloud Platform. However, SAP will remain custodian of that data, even though it is run in Google Cloud.

Artificial intelligence is playing an increasingly essential role in the enterprise, however, more and more businesses find themselves struggling to keep up. One of our most important goals is to make machine learning a transformational tool for organizations of any size, industry or sophistication. We're seeing customers making it part of their wider data analytics strategy, with early adopters like Airbnb, Airbus, Disney and Ocado serving as inspirational use cases.Today at Google Cloud Next '17 we're excited to announce new products, research and education programs to ensure machine learning is accessible to all businesses, data scientists and developers. We're also thrilled to welcome Kaggle to Google Cloud. Home to the world's largest community of data scientists and machine learning enthusiasts, Kaggle is used by more than 800,000 data experts to explore, analyze and understand the latest updates in machine learning and data analytics.

The same artificial intelligence that may soon drive your new car is being adapted to help drive interventional radiology care for patients. Researchers at the University of California, Los Angeles (UCLA), have used advanced artificial intelligence, also called machine learning, to create a "chatbot" or Virtual Interventional Radiologist (VIR). This device communicates automatically with a patient's physicians and can quickly offer evidence-based answers to frequently asked questions. The scientists will present their research today at the Society of Interventional Radiology's 2017 annual scientific meeting in Washington, D.C. This breakthrough will allow clinicians to give patients real-time information on interventional radiology procedures as well as planning the next step of their treatment.