At this year's SXSW the theme for our conference tech coverage is "AI for good." Technologists will be discussing how they are using new developments in artificial intelligence to solve complex, long-standing social problems, including crimes against children. Federico Gomez Suarez (pictured), senior technical program manager at Microsoft, spoke to John Furrier (@furrier), host of theCUBE, SiliconANGLE Media's mobile live streaming studio, during SXSW about his involvement in Thorn, a special outfit that works with law enforcement to rescue traffic or exploited children. "The fact that we're making a difference in those lives is extremely encouraging," Suarez said. He credits Microsoft's Hack for Good program for paving his avenue to Thorn.

As business opportunities for artificial intelligence multiply, how can industry leaders design the principles and technical standards into their products that benefit society as a whole?

For Silicon Valley, the headline was sweet nectar: Google DeepMind, the world's hottest artificial intelligence lab, embraces the blockchain, the endlessly fascinating idea at the heart of the bitcoin digital currency. The lab's re-imagining of the blockchain has very little to do with AI--or the blockchain, for that matter. If you want AI crossed with the blockchain, try wrapping your head around Numerai, the world's strangest hedge fund. To DeepMind's credit, its new project depends less on trendy ideas than an apparent desire to solve a real problem in the real world--one that involves the most private and personal information. DeepMind is building an auditing system for healthcare data. That may not sound sexy, but it matters.

The Indus script has long challenged epigraphists because of the difficulty in reading and classifying text and symbols on the artefacts. Now, a Chennai-based team of scientists has built a programme which eases the process. Ronojoy Adhikari of The Institute of Mathematical Sciences and Satish Palaniappan, who is at Sri Sivasubramaniya Nadar College of Engineering, have developed a "deep-learning" algorithm that can read the Indus script from images of artefacts such as a seal or pottery that contain Indus writing. Scanning the image, the algorithm smartly "recognises" the region of the image that contains the script, breaks it up into individual graphemes (the term in linguistics for the smallest unit of the script) and finally identifies these using data from a standard corpus. In linguistics the term corpus is used to describe a large collection of texts which, among other things, are used to carry out statistical analyses of languages.

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Machine learning (ML) technologies and solutions are expected to become a prominent feature of the information security landscape, as both attackers and defenders turn to artificial intelligence to achieve their goals. "The advent of machine learning in security comes alongside the increased capability for collecting and analyzing massive datasets on user behavior, client characteristics, network communications, and more. As we have already witnessed in many other technological domains, I think machine learning will become the main driver for innovation in information security in the coming decade," says security researcher Clarence Chio. Alongside Anto Joseph, a security engineer at Intel, Chio is scheduled to give Hack In The Box attendees a quick and practical introduction to the world of machine learning in April. But, he says in advance, machine learning is no silver bullet.

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."