This kind of question has been on IQ tests for years and the answer, it turns out, depends a good deal on which era you live in. Back in the down-home, cotton-spinning, 19th century you'd be right in thinking you use dogs to hunt rabbits. Shift forward to the glass-fronted skyscrapers of the 20th century Information Age and there was a new right answer; they are both mammals. But, as humans leapfrog into the 21st century of Artificial Intelligence (AI), robots and machine learning, is there an even newer answer? And if so, what is it?

Episode Summary: There's been lot of hype around AI and ML in business over the past five years. Even among investors exist a lot of misconceptions about using ML in a business context, and how to get up to speed on and learn machine learning as it applies to utility in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, whom I met through an investment banking friend in Boston. BootstrapLabs invests in Bay area companies, and Levy also travels around the world speaking about investing in AI companies and raising funds for new ventures. In this episode, Levy gives his perspective on what investors and executives get wrong about ML and and AI, and discusses how they can get up to speed and leverage the applications for these technologies and related expertise to really make a difference (i.e.

Artificial Intelligence is a key topic at this year's World Economic Forum Annual Meeting. Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that"the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines.

On Friday, June 24, 2016, the world watched in horror as Britain voted to commit economic suicide as a nation. On November 8, 2016, America will vote. Will it also commit economic and political suicide? Increasing inequality is building up great stress in the world economic system. The disenfranchised masses are expressing their anger, including in irrational ways such as the Brexit vote.

Rather than focus on attack signatures, these AI solutions look for anomalous network behavior, flagging when a machine goes rogue or if user activity or traffic patterns appear unusual. "A really simple example is someone with high privilege who attempts to get onto a system at a time of day or night that they never normally log in and potentially from a geolocation or a machine that they don't log in from," said Kelley. Another example would be a "really rapid transfer of a lot of data," especially if that data consists of the "corporate crown jewels." Such red-flags allow admins to quickly catch high-priority malware infections and network compromises before they can cause irreparable damage. IBM calls this kind of machine learning "cognitive with a little'c'" – which the company was already practicing prior to Watson.

Qualitative reasoning formalisms are an active research topic in artificial intelligence. In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of omega-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms.

Elon Musk believes that artificial intelligence will create a cyborg race. During the World Government Summit in Dubai, the SpaceX founder explains that deep artificial intelligence - the kind that is much smarter than the smartest man in the world - can be a dangerous thing. During his talk, Elon Musk believes that robot automation and artificial intelligence will be taking over 15 percent of the global workforce. The SpaceX, Tesla and Hyperloop authority believes that humans are becoming more and more dependent on technology. He sees a world where human beings are already cyborgs.

How would you deal with the likelihood that robots and automation will likely lead to many people losing their jobs? For Microsoft co-founder Bill Gates, the answer is straightforward: tax the robots. In an interview with Quartz, Gates argues that taxing worker robots would offset job losses by funding training for positions where humans are still needed, such as child and senior care. It could even slow automation to a more manageable rate, if necessary. Gates is well aware of potential pitfalls -- he knows that taxation could ultimately slow innovation by making worker robots prohibitively expensive. However, he's convinced that governments should be "figuring [policy] out" so that they're ready when there's a sudden glut of unemployed workers.

UK-based security technology company Sophos (OTC:SPHHF) (LSE: SOPH) has announced an agreement to acquire Invincea's software business for up to $120 million in total consideration. Invincea has developed security software that utilizes machine learning and behavioral modeling to quickly detect and react to endpoint, perimeter threats and malware attacks. The technology is a necessary upgrade to the Sophos Central product line and also provides middle market focused Sophos with a foothold in the small business market through the addition of Invincea's customer base. Fairfax, Virginia-based Invincea was founded in 2009 by CEO Anup Ghosh to focus on endpoint threat detection, prevention, and analysis. The company provides security software to more than 25,000 small and medium enterprise customers through its antivirus'X' system that'combines machine learning and behavioral monitoring to eliminate endpoint security blind spots without sacrificing usability.'

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.