Once again, the RSA conference is fast approaching, and that means it's time for the latest round of security buzzword bingo. To be sure, artificial intelligence (AI) and machine learning (ML) will be everywhere at the show. You can be certain that the halls at Moscone will be packed full of vendors pitching new security offerings that claim to use AI/ML, and there will be a glut of competitive messaging at all the other security shows, as some vendors seek to further confuse the marketplace. But the fact is, what they want to sell you are merely tools that use technology loosely based on the tenets of AI/ML, but are actually nothing more than repackaged offerings that rely on glorified signature-based security strategies. The technology the majority of these vendors are developing is basically the same type of stuff that emerged in the 1970's, and that companies were clamoring about back in the 1980's – versions of'expert systems' that have not proven to be very useful in most cases, and led to the long AI/ML winter from which we have only recently emerged.

The role of data that fuels artificial intelligence raises some ethical questions. Who owns the data collected from healthcare devices or the information gathered from a Fitbit? The technology industry is now trying to resolve the principles of collecting data and its democratization. "There's a real big gap, and I think probably part of what the industry has to do is not just build great new technologies, but sort of start to fill that gap with data education and literacy," said Dawn Nafus, Ph.D. (pictured), anthropologist at Intel and author of "Self-Tracking" and "Quantified: Biosensing Technologies in Everyday Life." While at the AI Intel Lounge in Austin, TX, during the South by Southwest event in Austin, TX, Nafus spoke with John Furrier (@furrier), host of theCUBE, SiliconANGLE Media's mobile live streaming studio, about establishing ethical guidelines within the parameters of AI used with personal data.

Recently, I asked a number of AI researchers this question. The responses i received vary considerably; it turns out there is not much agreement about the risks or implications. Non-experts are even more confused about AI and its attendant challenges. Part of the problem is that "artificial intelligence" is an ambiguous term. By AI one can mean a Roomba vacuum cleaner, a self-driving truck, or one of those death-dealing Terminator robots.

DeepMind, the Google-owned artificial intelligence company, is developing a new technology similar to blockchain for secure tracking of patient health data. In a blog post, London-based DeepMind said its new Verifiable Data Audit project could be the first steps toward a "a service that could give mathematical assurance about what is happening with each individual piece of personal data, without possibility of falsification or omission." The aim is to enable hospitals, and eventually patients, to gain real-time insight into where and how data is being used. "For example, an organization holding health data can't simply decide to start carrying out research on patient records being used to provide care, or repurpose a research dataset for some other unapproved use," according to DeepMind. "It's not just where the data is stored, it's what's being done with it that counts. We want to make that verifiable and auditable, in real-time, for the first time."

The U.S. state of California is easing its rules for autonomous car testing, by allowing testing of vehicles in which there is no human driver. The new rules have yet to be submitted for public consultation, with a final version expected by the end of the year, according to its Department of Motor Vehicles. "These rules expand our existing autonomous vehicle testing program to include testing vehicles where no driver is present," said DMV Director Jean Shiomoto. "This is the next step in eventually allowing driverless autonomous vehicles on California roadways." California took some heat earlier for seeking to stop testing of fully autonomous cars -- those in which a human is not physically on board.

CAPTCHA's are an irritating but necessary evil. The system that is used to verify whether or not a user is human has been around a while and it had to evolve because machines were getting better at reading the text than humans. With its latest iteration, Google says you'll no longer have to input anything at all. When the machines rise up, I'm jumping ship and selling out humanity so fast -- we suck, we deserve it. So colour me excited to see this cute ol' robot arm hilariously beat an'I'm not a robot' CAPTCHA (and then let go of the stylus in a perfect mic drop right after).

It's a total understatement to say that the growing presence of machine learning in games is big news. The technology promises to turn the industry topsy-turvy by changing our perception of what's possible in a gaming experience. "You can actually tailor game design and tailor levels to an individual's experience," George Dolbier, the CTO of Interactive Media at IBM, said at a talk at the Intel Buzz Workshop in Seattle last June. More interesting for us laymen, it's being used to create progressional curves that tailor themselves directly to each player according to their individual behavioral data. A simple example would be in a Tetris-like casual game where a player would first go through a few "seed" rounds of gameplay so that the machine learning element can first learn about how they play before it creates a customized roadmap that will ensure maximum engagement.

Medicine is a complex field. So complex that any given person can't know more than a fraction of what's going on. Keeping up with the latest discoveries is impossible. At the South by Southwest conference event in Austin, TX, a panel of experts came together to discuss the state of medical AI and how machine learning can benefit both patients and doctors. The discussion was moderated by Kay Eron, general manager of health and life sciences at Intel.

Natural Language Processing (NLP) has long been one of the holy grails of computer science. While we all know that computers are better than humans at making sense of highly structured information, there are still some important areas where humans are undeniably better than machines. Understanding language is one of those areas. For humans, understanding language is so natural we usually don't even have to make a conscious effort to do it. In reality, though, processing language and turning it into meaningful information is an extremely complex and difficult task.

Now seems like as good of a time as any to post some of the talks that I've done in the last year. I was originally going to give a talk I had done before (hence the same title), but I had some recent inspiration about some issues in the field that I wanted to share. This was a last minute whim, and after 24 hours of neither eating nor sleeping, I finished my slides with 30 minutes to spare. The talk went great and immediately afterwards, I even had the opportunity to do a TWiML podcast (thanks, Sam!). Both were surprisingly well-received, and writing the talk really helped me refine my thoughts on the field and what direction I wanted to take my work.