Exponential Finance celebrates the incredible opportunity at the intersection of technology and finance. Apply here to join Singularity University, CNBC, and hundreds of the world's most forward-thinking financial leaders at Exponential Finance in June 2017. This is the year AI has hit the public consciousness hard. Whether calls for universal basic income in the face of an automation tsunami or alarms over the loss of privacy when everything you do can be monitored and analyzed, people are discussing and debating AI more than ever in an effort to quell their dystopian fears about humanity's future. Fueling the fire, it seems like every day someone is announcing a new AI beating another Turing test.

Google is ringing in 2014 with a spending spree, first dropping 3.2 billion to acquire Nest Technologies and now spending a reported 400 million (or more) on the UK-based artificial intelligence outfit DeepMind. It's no secret that Google has an interest in artificial intelligence; after all, technologies derived from AI research help fuel Google's core search and advertising businesses. AI also plays a key role in Google's mobile services, its autonomous cars, and its growing stable of robotics technologies. And with the addition of futurist Ray Kurzweil to its ranks in 2012, Google also has the grandfather of "strong AI" on board, a man who forecasts that intelligent machines may exist by midcentury. If all this sounds troubling, don't worry: Google's acquisition of DeepMind isn't about fusing a mechanical brain with faster-than-human robots and giving birth to the misanthropic Skynet computer network from the Terminator franchise.

We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition -- but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's MegaFace Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. "We're the first to suggest that face recs algorithms should be tested at'planet-scale,'" wrote the study's lead author, Ira Kemelmacher-Shlizerman, in an email to TechCrunch. "I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."

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.