The Federal Trade Commission's Bureau of Consumer Protection confirmed Monday that has undertaken a non-public investigation into Facebook's data practices, according to a statement from Tom Pahl, the agency's acting director. The announcement comes just over a week after The New York Times and the The Guardian published explosive reports about the reported improper use of data belonging to 50 million Facebook users by the Trump-campaign affiliated data firm Cambridge Analytica. This isn't the first time the FTC has investigated the social network's data practices. In 2011, Facebook agreed to settle charges--though admitted no actual fault--that it "deceived consumers by telling them they could keep their information on Facebook private, and then repeatedly allowing it to be shared and made public," among other overreaches. The settlement barred Facebook from making further deceptive privacy claims, required it obtain a user's explicit approval before changing the way it handles their data, and mandated that Facebook receive periodic assessments of its privacy practices by third-party auditors for the next 20 years.

Artificial intelligence promises to be a paradigm shift for many applications from manufacturing to finance, and from defense to education. Given the vast potential, focus on AI has sharpened around the world, including in China. Decision makers in Beijing and around the country are paying attention and have begun shaping a legal and policy regime that favors the development of AI. Research and investment in AI on both sides of the Pacific has led to cross-border collaboration – both in terms of talent and capital. Last December, Google announced that it will open an AI research center in Beijing, in part to leverage AI talent there. A month earlier, San Diego-based Qualcomm announced a strategic investment in SenseTime, a Chinese company specializing in facial-recognition software.

Google's artificial intelligence technology is being used by the U.S. Department of Defense to analyze drone footage, a rare and controversial move by a company that's actively limited its work with the military in the past.

In January Google's parent company, Alphabet, announced the launch of Chronicle – an artificial intelligence-based solution for the cybersecurity industry – promising "the power to fight cyber crime on a global scale." There are mixed opinions on the value and readiness of artificial intelligence (AI) in our industry. Just last year Google's own Heather Adkins, director of information security and privacy addressed the crowd at TechCrunch Disrupt 2017 and criticized the over use of artificial intelligence for the cybersecurity industry. Adkins argued that the implementation of artificial intelligence relies too heavily on feedback, "to learn what is good and bad…but we're not sure what good and bad is." She went on to say that companies should invest in more human talent and less technology.

Today I am back at the Data Fest Data Summit 2018, for the second day. I'm here with my EDINA colleagues James Reid and Adam Rusbridge and we are keen to meet people interested in working with us, so do say hello if you are here too! I'm liveblogging the presentations so do keep an eye here for my notes, updated throughout the event. As usual these are genuinely live notes, so please let me know if you have any questions, comments, updates, additions or corrections and I'll update them accordingly. We've just opened with a video on Ecometrica and their Data Lab supported work on calculating water footprints. I'd like to start by thanking our sponsors, who make this possible. And also I wanted to ask you about your highlights from yesterday. These include Eddie Copeland from Nesta's talk, discussion of small data, etc. Data science has a huge impact for the business world, but also for societal good. I wanted to talk about the 5 i's of data science for social good: So, the number one, is the Interest. The data can attrat people to engage with a problem. Everything we do is digital now. And all this information is useful for something. No matter what your passion, you can follow this as a data scientist. I wanted to give an example here… My background is astrophysics and I love teaching people about the world, but my day job has always been other things. About 20 years ago I was working in data science at NASA and we saw an astronomical – and I mean it, we were NASA – growth in data. And we weren't sure what to do with it, and a colleague told me about data mining. It seemed interesting but I just wasn't getting what the deal was. We had a lunch talk from a professor at Stanford, and she came in and filled the board with equations… She was talking about the work they were doing at IBM in New York. And then she said "and now I'm going to tell you about our summer school" – where they take kids from inner city kids who aren't interested in school, and teach them data science. Deafening silence from the audience… And she said "yes, we teach the staff data mining in the context of what means most for these students, what matters most. And she explained: street basketball. So IBM was working on a software called IBM Advanced Calc specifically predicting basketball strategy. And the kids loved basketball enough that they really wanted to work in math and science… And I loved that, but what she said next changed my life. My PhD research was on colliding galaxy. It was so exciting… I loved teaching and I was so impressed with what she had done.

During this past week, AI Singapore signed three Memorandum of Understanding (MOUs), reiterating AI Singapore's commitment to pursue research, develop skills and talent, as well as embrace new technologies in AI-related areas. The three MOUs were signed with the National Trades Union Congress (NTUC), Intel Corporation and PwC Singapore. AI Singapore [1] was launched in May 2017 by the National Research Foundation (NRF) of Singapore with up to S$150 million in funding to catalyse, synergise and boost Singapore's AI capabilities. AI Singapore's key programmes include fundamental research, addressing Grand Challenges and conducting 100 experiments in collaboration with companies to tackle real-life business problems. It also runs an AI Apprenticeship programme through a collaboration with the Infocomm Media Development Authority (IMDA).

Artificial intelligence promises to be a paradigm shift for many applications from manufacturing to finance, and from defense to education. Given the vast potential, focus on AI has sharpened around the world, including in China. Decision makers in Beijing and around the country are paying attention and have begun shaping a legal and policy regime that favors the development of AI. Research and investment in AI on both sides of the Pacific has led to cross-border collaboration – both in terms of talent and capital. Last December, Google announced that it will open an AI research center in Beijing, in part to leverage AI talent there. A month earlier, San Diego-based Qualcomm announced a strategic investment in SenseTime, a Chinese company specializing in facial-recognition software.

For D3 templates for producing many of these chart types in FT style, see our Visual Vocabulary repo. The full content of the poster, along with links to related material, including research and examples of best practice. This is a work in progress. Emphasise variations ( /-) from a fixed reference point. Typically the reference point is zero but it can also be a target or a long-term average. Can also be used to show sentiment (positive/neutral/negative).

Adam Kassam is the chief resident physician in the department of Physical Medicine & Rehabilitation at Western University in London, Ont.. Naila Kassam is a family physician and adjunct professor in the department of Family Medicine at Western University. By honing the incredible potential of neural nets -- the assembly of computer networks -- in a way that mirrors the architecture of the human brain, Dr. Hinton has unlocked the learning power of machines. In so doing, he has become the darling of a burgeoning area of computing that has the ability to completely revolutionize the practice of medicine and the delivery of health care. Geoffrey Hinton of the University of Toronto is among the foremost researchers in the field of machine-learning and artificial intelligence. As AI begins to take off, will the health-care industry be receptive to technological disruption despite its notorious aversion to change? What is clear is that it will be crucial for the medical community to use its clinical expertise to help leverage technology and AI to improve the delivery of health care.

Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training. Our experiments show that it is crucial to balance every manner of regularization for each dataset and architecture. Weight decay is used as a sample regularizer to show how its optimal value is tightly coupled with the learning rates and momentums.