The U.S. Defense Department's research and development arm is offering to fund projects that will simplify the massively complex task of building models for machine learning applications. Models are a fundamental part of machine learning. Similar to algorithms, they help teach computers to, say, identify a cat in a photo, forecast weather from historical data or sort spam from legitimate email. But writing the models takes time and requires many skills. Typically, data scientists, subject matter experts and software engineers all have to come together to develop the model. When New York University researchers wanted to model block-by-block traffic flow data for the city, it took 60 person-months of work by data scientists to prepare the data for use and an additional 30 person-months to develop the model.

The post-mortem activity is just as messy as the outage itself. Members of multiple teams gather in a "war room", go through multiple product consoles and logs, and try to identify the cause of the incident. This costly, manual process often results in finger-pointing and passing the buck rather than getting real answers. By using advanced analytics based on the latest machine learning techniques, Perspica gives you a definitive post mortem analysis with actionable recommendations, dramatically reducing your mean time to repair.

There's been much talk about how artificial intelligence will benefit society, but what about the potential impacts that AI has when the system is poorly designed and creates problems? This is a question several researchers and OpenAI, a non-profit artificial intelligence research company, tackled in a recent paper. The paper was written by researchers from Google Brain, Stanford University and the University of California, Berkeley, as well as John Schulman, research scientist at OpenAI. It's titled Concrete Problems in AI Safety, and it looks at research problems around ensuring that modern machine learning systems operate as intended. Researchers have started to focus on safety research in the machine learning community, including a recent paper from DeepMind and the Future of Humanity Institute that looked at how to make sure that human interventions during the learning process would not induce a bias toward undesirable behaviors in machine learning robots. But, according to a blog post by OpenAI, many machine learning researchers are wondering just how much safety research can be done today.

Which kinds of knowledge workers are at high risk of job loss thanks to smart machines? Usually we don't love getting that question, because the answer isn't the simple one interviewers are seeking. Many jobs include tasks that can and will be automated, but by the same token, almost all jobs have major elements that -- for the foreseeable future -- won't be possible for computers to handle. Our advice therefore can't boil down to a clear "avoid careers in a, b, and c" or "apply for jobs x, y, or z." And yet, we have to admit that there are some knowledge-work jobs that will simply succumb to the rise of the robots.

This AI-created short is proof that original sci-fi films still exist, assuming you are okay with something as comprehensible as Terrence Malick's works. Sunspring, a short film that debuted on Ars Technica, was written entirely by an artificially intelligent system named Benjamin. It looks and feels like a sci-fi thriller, but makes absolutely no sense. The dialogue is so incomprehensible that, when paired with the film's delightfully talented and dedicated actors, provides more comedy than plot. But hidden behind the laughter is a deeply disturbing thriller with images of suicide, the coughing up of body parts, and loss.

The Cannes Lions International Festival of Creativity is a great place to check out what's going on right now in creative industries all over the world--but what can it also tell us about the future? In his presentation at this year's event, Kevin Kelly, founding executive editor of Wired, talked about the forces of change that will shape all of our lives over the next 20 years. The two topics he focused on are both critical areas that brands are looking towards more than ever: Virtual Reality and AI. According to Kelly, who tested some of the earliest VR tech in the 1980s as a journalist, it's still going to be a number of years before VR is widely available, a staple in the home and consumer-friendly. "In the short term it's overhyped, but in the long term it's underhyped," he observed.

DeepMind, the artificial intelligence research lab acquired by Google for a reported 400 million in 2014, has called on the UK government to consider funding degree courses that focus on machine learning, which is a subfield of AI. The company -- cofounded by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011 -- said the government needs to support the next generation of machine learning experts if it wants the UK to cement its position as a world leader in AI. Writing in evidence submitted to a parliamentary inquiry into robotics and AI last month, DeepMind said: "The government should consider funding for machine learning masters and PhD programmes at British universities, to encourage more research in the field and nurture the next generation of scientists who will help preserve the UK's preeminent position." The company added: "This funding could also include direct support for modules within programmes that train machine learning researchers in the ethics of data science and increasingly autonomous decision-making, to ensure that the pursuit of beneficial outcomes is embedded in the science of machine learning at every level." Machine learning masters degrees and PhDs can cost individuals upwards of 10,000 at the top universities.

You, client! may not be science fiction for much longer. On both sides of the Atlantic and elsewhere, British authors Richard and Daniel Susskind and others predict Artificial Intelligence is on the cusp of changing the legal profession more than any other technology. We've already seen the transformation triggered by word-processing, the Internet and e-mail, but the high hourly rates of legal professionals and the exorbitant expense of court time demand more reform. The B.C. government has been an early adopter of software solutions and the province already has a handful of dispute-resolution and legal platforms intended to make access to legal services and justice easier and cheaper. The next development, however, is heralded by the arrival of "digital legal advisers" -- the progeny of Deep Blue, which destroyed the chess hegemony of humanity, and Watson, which ruined Jeopardy!

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Iyad Rahwan was the first person I heard use the term society-in-the-loop machine learning. He was describing his work which was just published in Science, on polling the public through an online test to find out how they felt about various decisions people would want a self-driving car to make - a modern version of what philosophers call "The Trolley Problem." The idea was that by understanding the priorities and values of the public, we could train machines to behave in ways that the society would consider ethical. We might also make a system to allow people to interact with the Artificial Intelligence (AI) and test the ethics by asking questions or watching it behave. Society-in-the-loop is a scaled up version of human-in-the-loop machine learning - something that Karthik Dinakar at the Media Lab has been working on and is emerging as an important part of AI research.