One goal of machine learning algorithms is to use past information to predict the future. The advantage of machine learning over traditional analytics is the ability for the machine-learning algorithm to automatically build a good model, saving time, preventing overfitting, and generally being more robust. To do this the algorithm builds a model, calculates the error rate of the model, adjusts parameters to lower the error rate, and iterates again, 'learning' from its mistakes. There's a step in between: calculating the error rate requires us to split our dataset into a training and test dataset, in which we train the model on the training dataset, and calculate the error rate on the test dataset. We need to do this because if we calculate the error rate while training on the entire dataset we would get a low error rate since the model is trained on that specific data, and this would be misleading for predicting future, unknown data.

We have seen a fast growing interest in current activities around AI startups and research in the last couple of months. Headlines like "2016 was the year AI came of age", "AI was everywhere in 2016", and "The Great A.I. Awakening" were all over the media in the ending weeks of 2016 and we are curious about what 2017 will bring. I found particularly interesting that the current applications, future potential, and possible risks even attracted interest beyond the tech community through TV shows like Westworld, coverage on traditional media and even Obama's farewell address. Sadly, for many of us tech enthusiasts here in Europe, we sometimes feel like there is way less movement on this side of the Atlantic than in the Silicon Valley. However, with major acquisitions like DeepMind, Magic Pony Technology, Movidius, Vision Factory, and Dark Blue Labs, Europe has shown that it is actually leading the way in AI and machine learning.

Ford is investing $1 billion in a secretive artificial intelligence startup headed by former Google and Uber execs to advance its self-driving car efforts. The startup, Argo AI, was founded by Bryan Salesky, the former director of hardware for Google's self-driving-car efforts, and Peter Rander, Uber's engineering lead at its autonomous cars center. The $1 billion investment will be spread out over five years as Ford looks to commercialize its self-driving technology by 2021. Argo AI will leverage roboticists and engineers from inside and outside of Ford to develop a virtual driver system for the autonomous vehicles in 2021, Ford wrote in a press release. The virtual driver system will use machine learning, a branch of machine learning that improves with experience, to act as the brain of Ford's self-driving cars.

Netflix instant streaming is both one of the best and the worst products to be developed in the recent years. Best, because its helped make sitting at home for eight hours on a Saturday, binge watching the latest "water cooler TV" almost feel productive. The worst, because Netflix isn't smart enough (yet) to understand my mood and my general indecisiveness with "easy" decisions, I suffer from analysis paralysis when deciding what to watch. Should I watch something silly? Let me spend 20 minutes searching through a seemingly endless selection of mediocre movies I have never heard of eventually to settle on an episode of Arrested Development I've already seen 14 times.

Amazon's latest artificial intelligence tool is a piece of image recognition software that can learn to guess a human's age. The feature is powered by Amazon's Rekognition platform, which is a developer toolkit that exists as part of the company's AWS cloud computing service. So long as you're willing to go through the process of signing up for a basic AWS account -- that entails putting in credit card info but Amazon won't charge you -- you can try the age-guessing software for yourself. In what sounds like a smart move on Amazon's end, the tool gives a wide range instead of trying to pinpoint a specific number, along with the likelihood that the subject of the image is smiling or wearing glasses. Microsoft tried the latter approach back in 2015 with its own AI tool, resulting in some hilariously bad estimates that exposed fundamental weaknesses in how these types of image recognition algorithms function.

If you attend a protest in Washington, D.C., nowadays, better plan on leaving your cellphone at home. That is, unless you want police to confiscate it, mine it for incriminating information and then gather even more data from their BFF -- Facebook. At least one person arrested during protests on Inauguration Day got an email from Facebook's Law Enforcement Response Team alerting them that investigators wanted access to their data. Another received a Facebook data subpoena. The email was basically a countdown to when Facebook inevitably handed that data over to D.C. police. That is, unless the respondent figured out how to file an objection within a 10-day window.

Notice: Faculty members who are planning to apply for funding are encouraged to login to the My Research Application (MRA) on-line system well in advance of the submission deadline to ensure that there are no problems with your login credentials. The information in the on-line form will be automatically routed to the appropriate internal approvers for review and endorsement. If you encounter problems, please consult the My Research - Application User Guide for additional information or contact the RAISE help desk (416-946-5000, RAISE@utoronto.ca). The Royal Bank of Canada (RBC) has announced two Research Prizes ($25,000/prize) to be given to Faculty members working in the field of Fintech. The Royal Bank of Canada (RBC) Faculty Research Prize will support research and innovation at the University of Toronto.

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We are excited to announce that an AWS Deep Learning AMI for Ubuntu is now available in the AWS Marketplace in addition to the Amazon Linux version. The AWS Deep Learning AMI, now available on AWS Marketplace, lets you run deep learning in the Cloud, at any scale. Launch instances of pre-installed, open source deep learning frameworks, including Apache MXNet, to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques. The AWS Deep Learning AMI lets you create managed, auto-scaling clusters of GPUs for large-scale training, or run inference on trained models using the latest versions of MXNet, TensorFlow, Caffe, Theano, Torch, and Keras. With the addition of an Ubuntu version, you have the choice to run on the operating system of your choice.

Ford Motor Co. plans to invest $1 billion US over the next five years in tech startup Argo AI to help the Detroit automaker reach its goal of producing a self-driving vehicle for commercial ride sharing fleets by 2021, the companies announced on Friday. The investment in Pittsburg-based Argo AI, founded by former executives on self-driving teams at Google and Uber, will make Ford the company's largest shareholder. Ford chief executive officer Mark Fields said the investment is in line with previous announcements on planned capital expenditures. Argo AI, which focuses on artificial intelligence and robotics, will help build what Ford calls its "virtual driver system" at the heart of the fully autonomous car Ford said last year it would develop by 2021. "With Argo AI's agility and Ford's scale we're combining the benefits of a technology start up with the experience and discipline we have at Ford," Fields said at a press conference.