Predictive Analytics Offers Customized Solutions to Complex Problems – A Conversation with IBM’s Swami Chandrasekaran

1 - DARPA Challenge Tests AI as Cybersecurity Defenders

In August, seven teams will compete in DARPA's Cyber Grand Challenge, pitting autonomous AI systems against threats and attacks from other network servers and patching up proprietary software errors in the process. The U.S. military agency hopes to promote programs that can work side by side with humans examining crash reports and assessing network vulnerabilities. Network protection can be implemented in the form of either 'binary armor' or through the finding and correcting of software malfunctions. In addition to a $2 million prize, the winning team will compete against human hackers at the DEF CON 2016 conference taking place in Las Vegas at the same time, marking the first time that a completely automated system will go 'head to head' with human experts.

Episode Summary: A lot of companies in the San Francisco Bay area make the claim that they can do something great with data; many fewer are at a degree of scale to make this vision possible. Today we speak with Nicholas Clark, CEO of DoubleDutch, a company now powering thousands of events nationally and implementing machine learning into their operations, including predicting business results from actual attendees. DoubleDutch is at the beginning of its journey with predictive analytics, having to make hard choices around what sort of information and thought processes they need in order to use machine learning and remain profitable. Nicholas gives his perspective on these decisions, as well as how he thinks DoubleDutch’s efforts will impact the conference/event industry at scale.

Episode Summary: How do you know if you’ve made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on best hiring decisions. In this episode, Pasha Roberts, chief scientist at Talent Analytics, tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.

Episode Summary: Few astrophysicists are as decorated as Martin Rees, Baron Rees of Ludlow, who was a primary contributor to the big-bang theory and named to the honorary position of UK's astronomer royal in 1995. His work has explored the intersections of science and philosophy,  as well as human beings’ contextual place in the universe. In his book "Our Final Century", published in 2003, Rees warned about the dangers of uncontrolled scientific advance, and argued that human beings have a 50 percent chance of surviving past the year 2100 as a direct result. In this episode, I asked him why he considers AI to be among one of the foremost existential risks that society should consider, as well as his thoughts around how we might best regulate AI and other emerging technologies in the nearer term.

Episode Summary: When we think about applying AI and data science to different areas of business, we often think about those domains that offer a wide swath of quantitative metrics that we can feed a machine, like marketing or finance. Human resources (HR) normally doesn’t fit the bill. How we hired someone, how we felt about them when we hired them, how they perform qualitatively, these are things that are often difficult to discern in team dynamics. That being said, big teams like Google are applying machine learning (ML) to some of their HR choices, and our guest today believes more companies will be doing the same in future. CEO of Humanyze Ben Waber applies ML  to HR decision-making, helping people get better employees and better performance by measuring and improving using data science in new ways.