Something quite concerning is happening right now that threatens to cause a monumental rift in the American public... and ultimately the world at large. Contrary to what you might be thinking, I'm not referring to our current election cycle, or anything else having to do with the common, petty politics of today. What I'm talking about is something far more consequential than any modern political squabble. It's something that promises to shake the foundations of society in a way we have never seen before. It is an event that virtually everyone in my field of study agrees is coming.

In his first trip to Japan as Apple CEO, Tim Cook painted a grand future for the artificial intelligence that will soon be developed in the company's Yokohama facility, telling Nikkei it will be "horizontal in nature, running across all products." Cook said new AI developments will transform the way people use their phones "in ways that most people don't even think about," helping with everything from increasing battery life in iPhones, to locating cars in a crowded parking lot. Cook sees an "incredible future ahead" as the smartphone industry matures, and other smart devices expand into new business and medical uses. In addition to Apple's opening its new Yokohama research and development center later this year, the company has partnered with IBM and Japan Post Holdings to develop iPad-based health care services for the elderly, with Cook pointing out that Japan's rapidly aging population put the country in the "best position to lead" in that area of innovation. The company has also strengthened its ties to Japan by including the country's Felica mobile payment standard in the iPhone 7, paving the way for expanding Apple Pay into Japan.

If you are modeling binomial data; ie a numerator consisting of the number of 1/0 successes you have for a given pattern of covariates, and a denominator that gives the value of the total number of observations having that covariate pattern (a specific profile of predictor values; eg age 23, married 1, working 0), a logistic regresson is generally appropriate. But when the mean values of the numerators are less than 10% of the mean values of the denominator, it is likely that a Poisson model is preferred. The otherwise logistic numerator is the count response variable (dependent variable) and the natural log of the denominator is the offset. Generally the Poisson model will fit the data better. Logistic models are not indended for rare occurrences.

Machine Learning is considered as the practical face of Artificial Intelligence. In another word, it's a type of AI that provides computers with the ability to learn without being explicitly programmed. As the AI market is booming, let's take a look at where these geniuses are the most sought after in the UK. London is breaking the chart with a total of 856 Machine Learning-related jobs advertised at the moment, which is more than half the total amount of Machine Learning jobs in the whole of the UK. Eastern England and South East England follow the capital, both respectively looking to hire 142 and 133 Machine Learning specialists over the same period.

This week in San Diego, Singularity University hosted its annual Exponential Medicine conference. The conference aims to connect the dots between healthcare disciplines and cutting-edge tech by convening medical practitioners, technologists, entrepreneurs, and over 80 expert speakers from the field. It's easy to say "healthcare is broken" and call it a day, but a quote from brilliant thinker Maria Popova reminds us of the power of optimism to create change: There's still considerable work to be done to create more effective healthcare systems in the US and worldwide. That said, at the conference we learned about incredible progress we can both celebrate and focus on moving forward. Healthcare as we've known it for decades is evolving.

I've been trying to think of a way to describe how big Machine Learning is, and I think I finally have a decent one: Because Statistics is the primary mechanism we've had for decades to learn about the world. That's what Machine Learning is (ML can be considered a subset of statistics) except its method of doing it is far more powerful. Most importantly, machine learning can…well, learn. It improves as it gets more data. With traditional Statistics you can potentially extract additional insights with more (and better) data, but the model for doing the analysis itself doesn't improve.

Making computers more similar to the human brain is most probably one of the most major challenges facing us in the 21st century. We expect computers to begin talking, comprehend and provide solutions to problems of all kinds. There is now a rising demand for computers to be able to see and identify images. After being blind for too long, now our smartest computers can finally begin to see their outside world. Deep learning is making this truly revolutionary advance very possible indeed.

Human-level intelligence is familiar in biological hardware – you're using it now. Science and technology seem to be converging, from several directions, on the possibility of similar intelligence in non-biological systems. It is difficult to predict when this might happen, but most artificial intelligence (AI) specialists estimate that it is more likely than not within this century. Freed of biological constraints, such as a brain that needs to fit through a human birth canal (and that runs on the power of a mere 20W lightbulb), non-biological machines might be much more intelligent than we are. What would this mean for us?

The future of work in America is uncertain. What we know is that things are going to change. Technology will upend countless careers, workers across fields will be displaced, and it's not entirely clear how many jobs will be replaced. When driverless trucks are manufactured at scale, which will happen far sooner than many realize (as soon as five years), America's 3.5 million truck drivers will be suddenly dispensable. That doesn't mean that the profession of truck driving will disappear overnight, but it will shrink considerably.

When the movie The Terminator was released in 1984, the notion of computers becoming self-aware seemed so futuristic that it was almost difficult to fathom. But just 22 years later, computers are rapidly gaining the ability to autonomously learn, predict, and adapt through the analysis of massive datasets. And luckily for us, the result is not a nuclear holocaust as the movie predicted, but new levels of data-driven innovation and opportunities for competitive advantage for a variety of enterprises and industries. Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we're getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute.