TU Wien Informatics will be welcoming Toby Walsh, AI expert and "rock star" of Australia's digital revolution, for the Gödel Lecture 2022. Artificial intelligence is an essential part of our lives – for better or worse. It can be used to influence what we buy, who gets shortlisted for a job, and even how we vote. Without AI, medical technology wouldn't have come so far, we'd still be getting lost on backroads in our GPS-free cars, and smartphones wouldn't be so, well, smart. But as we continue to build more intelligent and autonomous machines, what impact will this have on humanity and the planet?

The Defense Department's recent efforts to raise its artificial intelligence game have revealed a few obstacles. There are no cohesive goals across the military branches, and there is no way of knowing whether each service has enough people with the right skills. DOD should work with the services to establish AI-specific goals for cultivating technical talent, make it easier for all personnel to learn about AI and put it to use, and enable AI "rock stars" to succeed. It is currently impossible for the DOD to assess its AI posture, let alone assert leadership in AI. That's because posture assessment requires measurement.

AI is the newest rock star on the technology scene. But just because artificial intelligence (AI) is the hottest new thing, that doesn't mean it can survive the inherent data challenges that come with it. These challenges include data accessibility, selection, timeliness and trust. So just as "video killed the radio star," data is threatening to kill the AI star. According to a recent Infosys survey, 49 percent of organizations reported that they will not be able to deploy AI because of data challenges.

At Google, the notion of "20% time" allows employees to focus on side-projects that can evolve into new passions or products at the company. Many Google products, from Gmail to Maps to AdSense, came from this concept of "20% time." Some people quip that that the other name for "20% time" might be, "Saturday." But joking aside, at Google there is an institutional investment allowing employees to pursue their curiosities and peripheral interests, a way of providing a check and balance to the overbearing manager who asks for a very narrow focus. "I'll give you 100% of my 80% time," you can reply.

Governments, corporations and even technology vendors are all grappling with major opportunities and challenges emerging as artificial intelligence moves from the lab into the boardroom. With any game-changing innovation the biggest challenge is often the scarcity of talent, not least in the field of AI where many of the advances emanated from academia. Often pioneers of this latest wave of disruption are PhD students from top global universities who see the opportunity to take a theory from the classroom and create a product or solution for the world. AI and deep learning, automation, predictive analytics, quantum computing and nano technology have all in some form started life in a lab or classroom - not in a traditional software development environment. The net result is that the new'rock stars' (well-paid technical talent) as they are so commonly called in Silicon Valley, are the PhD Research Scientists who are even fewer in number today than their predecessor software engineers.

Throughout history, different eras have begotten different heroes of productivity in industry. In the 80s, the stock broker was the rock star of the business world. In the late 90s and 2000s, it was the computer programmer. For the last decade or so, it's been the data scientist. As the work of data scientists and engineers creates the Automation Age, the next industrial rock star will be the customer service specialist.

Tony Fadell is at the Grove, a spectacularly beautiful country estate outside of London. The event is Founders Forum: the ultra exclusive invite-only tech conference. Prince William is in the house. The guest list is lousy with knights and lesser officers of the Most Excellent Order of the British Empire. Marissa Mayer, the now ex-CEO of Yahoo, and Biz Stone, recently returned to Twitter, are mingling with the other hundred or so invitees.

Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.

Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.