This post is aimed at artists and other creative people who are interested in a survey of recent developments in machine learning research that intersect with art and culture. If you've been following ML research recently, you might find some of the experiments interesting but will want to skip most of the explanations. The first AI that left me speechless was a chatbot named MegaHAL. It turns out MegaHAL was basically sleight of hand, picking a single word from your input and using a technique called Markov chains to iteratively guess the most likely words that would precede and follow based on a large corpus of example text (not unlike some Dada word games). But reading these transcripts in high school had a big effect on how I saw computers, and my interest in AI even affected where I applied to college.

If the movies have taught us anything, artificial intelligence created by the government bad. The Obama administration is aware of those concerns, which is why a new report questions how government-backed AI could impact society and public policy. It calls for long-term investments in AI research, as well as investigations into the ethics and security implications of the technology. "Advances in AI technology hold incredible potential to help America stay on the cutting edge of innovation," the White House said in a blog post. At Walter Reed Medical Center, for example, the Department of Veteran Affairs is using AI to better predict medical complications and improve treatment.

Drive.ai is a Silicon Valley startup working on a kit to retrofit your ride If Drive.ai is a success, your first self-driving car might already be parked in the driveway. The Silicon Valley start-up, founded recently by a team of former Stanford University Artificial Intelligence Lab products, is working on a software kit that can be used to retrofit existing vehicles. "We started Drive.ai because we believe there's a real opportunity to make our roads, our commutes, and our families safer," the company announced in a statement on its blog, citing a statistic that more than one million people die each year worldwide in automobile accidents caused by human error. At its foundation, Drive.ai is looking to use deep learning -- which its founders consider the most effective form of artificial intelligence ever developed -- to key a breakthrough in a field that giant companies such as Google and General Motors have been trying to master for years. "Unlike other forms of AI, which involve programming many sets of rules, a deep learning algorithm learns more like a human brain. You provide examples, tagged and labeled by an expert, and the system starts to learn for itself -- creating its own rules."

IBM Corp.'s Watson technology defeated two Jeopardy champions in a famous man-against-machine TV showdown in 2011, and now IBM is counting on Watson to help its U.S. employees fight cancer. IBM, developer of Watson, a supercomputer that combines artificial intelligence and advanced analytical software in a format that turns a computer into a "question and answer" machine, announced that as a new benefit for its U.S. workforce certain employees with cancer or undergoing a diagnosis for cancer will have access to Watson for insight into better types of treatment. IBM's cancers-stricken U.S. employees will have access to Watson and an oncology collaboration with Best Doctors, which provides diagnosis and treatment plan reviews using a network of physician specialists, beginning in January. The IBM benefit uses the artificial intelligence capability of Watson to provide employees and their doctors with evidence-based treatment recommendations related to breast, lung, colorectal and gastric cancers, IBM says. With the patient's permission, Best Doctors will collect medical records and feed relevant data into Watson, IBM says.

The oncoming storm of Artificial Intelligence as popularized by sci-fi films and to some extent over-inflated expectations of machines that can learn to mimic humans has created an early worry of its impact on jobs, society and us in general. The announcement of a UK commission to look at ethical, legal and societal impact needs to be unpacked in terms of what the state of current A.I. is today and what is causing this early alarm bell to be taken seriously. Why are we raging against the machine? This is perhaps in two areas, the level of sensors and data collection is now rapidly increasing from mobile phones, security cameras to devices in the home like Amazon Echo and Google Home starting to record huge amounts of data about our behavior and choices. On one level this is a concern for privacy and identity that machine algorithms are in use already that analyze this sea of data and can start to manipulate and influence our outcomes and expectations. This is part of the source of the commission focus.

Fisher plays a recently divorced mother of two teens and out-of-work art critic determined to cook a traditional festive dinner with all the trimmings in her sunny Southern California home for her smartphone-addicted friends and extended family. But taming the turkey proves to be the least of her challenges when her neighborhood's cell reception suddenly goes dead, which proceeds to bring out the worst in some already less than exemplary behavior from her preoccupied houseguests. Unfortunately many viewers will have experienced their own connectivity issues long before those characters do. Although there's a genuinely cozy rapport between Fisher and Stevens, the other cast members, including Daphne Zuniga, Nick Court, Natasha Gregson Wagner and Michael Muhney, have a tougher time trying to make all the overwritten, self-consciously quirky dialogue believably their own. Filmmaker Russell Brown clearly had something pertinent he wished to say about our plugged-in, tuned-out obsession with the Internet and was obviously going for a Luis Buñuel-Robert Altman style of social commentary here.

Humanity may still be years if not decades away from producing sentient artificial intelligence. But with the rise of machine-learning services in our smartphones and other devices, one type of narrow, specialized AI has become all the rage. And the research on this branch of AI is only accelerating. In fact, as more industries and policymakers awaken to the benefits of machine learning, two countries appear to be pulling away in the research race. The results will probably have significant implications for the future of AI.

Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like Data Mining, Predictive Analytics, and Advanced Analytics are considered too geeky or old for industry marketers and headline writers. The term Cognitive Computing seemed to be poised to win, but IBM's strong association with the term may have backfired -- journalists and analysts want to use language that is independent of any particular company.

Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.

At Data By The Bay in May, we saw a great talk by Netflix's Justin Basilico: Recommendations for Building Machine Learning Software. Justin describes some principles for effectively developing machine learning algorithms and integrating them into software products. We found ourselves nodding violently in agreement, and we wanted to recapitulate a few of his points that resonated most strongly with us, based on our experience working with data science teams in other organizations. Justin emphasizes that "developing models is iterative" and experimentation is important. He also suggests "avoiding dual implementations" so it's easy to use a model in production once it's been built, without a re-implementation step.