Artificial intelligence can often be just as unintentionally prejudiced as its human creators, with potentially disastrous consequences. The US government thinks educating future programmers on AI ethics will help solve our computers' fairness problem. The White House released its report on the future of artificial intelligence research in the US on Wednesday, and it contains a slew of recommendations. In a section on fairness, the report notes what numerous AI researchers have already pointed out: biased data results in a biased machine. For example, artificial intelligence is being used by law enforcement across North America to identify convicts at risk of re-offending and high-risk areas for crime.

We have seen the likes of Google, Facebook, Amazon and many more come out in open and acknowledge the impact machine learning and deep learning had on their business. Last week, I published top videos on deep learning from 2016. I was blown away by the response. I could understand the response to some degree – I found these videos extremely helpful. So, I decided to do a similar article on top videos on machine learning from 2016.

Jeremy P. Howard, @JeremyPHoward, is a leading Machine Learning and Deep learning researcher and entrepreneur. His current startup is fast.ai Previously, he was CEO and founder of Enlitic, Kaggle President, and #1 ranked Kaggle competitor. Jeremy initiatives attracts a lot of attention in the industry, so I was very interested to learn from him about his latest project, a first Deep Learning for coders MOOC at course.fast.ai. The course is totally free and includes no advertising - Jeremy created it purely as a service to the community.

It came from beyond the Large Magellanic Cloud. The signal, a mere 20 milliseconds long, captured the moment when two black holes slammed together – a cataclysm that sent ripples through spacetime and onwards to Earth, where they made instruments chirp and scientists cheer. "We have detected gravitational waves," said David Reitze of the Laser Interferometer Gravitational-Wave Observatory (Ligo). The announcement ranked as the physics discovery of the year, confirming Einstein's century-old theory of gravity and putting the Ligo team on course for a Nobel. But the real excitement is yet to come.

Hearing that Bob Dylan just won the Nobel Prize for Literature, how could I not begin this talk with his famous line, "Something is happening here, but you don't know what it is, do you, Mr. Jones?" The future is full of amazing things. On my way here, I spoke out loud to a $200 device in my kitchen, and asked it to call a Lyft to take me to the airport. And in a few years, that car might well be driving itself. Someone seeing this for the first time would have every excuse to say "WTF?"

Data driven recruitment has a significant, positive impact on talent management strategies and business performance. As technology becomes more sophisticated, AI is playing an increasingly essential role in decisions made around hiring and is used by brands such as Facebook as an integral part of the screening and assessment of candidates. This article examines its ongoing effect on the jobs market and the ways in which HR can harness its advantages to better understand, improve and predict hiring needs and potential problems. AI is broadly defined as'machines which perform tasks which humans are capable of performing'. It has been traditionally been regarded as a threat to jobs, with the most drastic predictions suggesting that unemployment rates will reach 50% within 30 years, but perceptions and predictions are changing.

Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks. In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks – on which business functions have the greatest potential for automation today, and in the coming five years ahead. Together with San Fransisco-based venture firm BootstrapLabs, we designed a simple survey that was handed out during their "Autonomous Corporation" event in November 2016. It is interesting to note all three groups of respondents considered business intelligence to be the business function with the most current automation potential.

Advanced analytics and other emerging technologies are revolutionizing the way governments and public service agencies are trying to address citizen demands, helping to overcome persistent challenges such as regulatory compliance, outdated legacy IT infrastructures and organizational cultures, according to a new research report from Accenture. The report, Emerging Technologies in Public Service, examines the adoption of emerging technologies across agencies with the most direct interaction with citizens or the greatest responsibility for citizen-facing services: health and social services, policing/justice, revenue, border services, pension / social security and administration. As part of the report, Accenture surveyed nearly 800 public service technology professionals across nine countries to identify emerging technologies being implemented or piloted. These technologies include advanced analytics/ predictive modeling, the Internet of Things, intelligent process automation, video analytics, biometrics/ identity analytics, machine learning, and natural language processing/ generation. The survey found that while more than two-thirds (70 percent) of public sector agencies are evaluating the potential of emerging technologies, only a small percentage (25 percent) is moving beyond the pilot phase to full implementation.

Virtually everywhere you look, Bay Area tech businesses are running into walls. Smartphones were revolutionary and lucrative, but the U.S. market is saturated, and Apple's iPhone sales have fallen for three quarters. The "app economy" has matured, with more people using existing apps than downloading new ones. And Facebook, which has filled users' news feeds with so many ads it can barely add more, is predicting its revenue growth will slump next year. Silicon Valley needs its next big thing, a focus for the concentrated brain power and innovation infrastructure that have made this region the world leader in transformative technology.

Regression is a modelling technique for predicting the values of an outcome variable from one or more explanatory variables. Logistic Regression is a specific approach for describing a binary outcome variable (for example yes/no). Let's assume you are own a new boutique shop. You have a list of potential clients you are thinking of inviting to a special event with the aim of maximizing the number of sales – who should you invite? Data on previous events you have run is a great starting point here, allowing you to predict an individual's likelihood of buying given the information you have on them.