Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off  is in finance and hedge funds. In this episode, we speak with Richard Craib, founder of Numerai, about the company's model for pooling data science talent, using "anonymous" models to train financial data, and competing against one another, in which winners are rewarded in bitcoin to exchange through virtual markets. Craib speaks about his overarching vision for the company, and also delves into the past, present, and future of AI applications in finance.

What does the world look like when we can replicate human expertise in an assistant? Are we close to developing human-level chatbots that we can ask about law or medical conditions? We dive into this topic with Founder and CEO of exClone Dr. Riza Berkan, whose personal assistant and chat-bot company is leveraging day-to-day human conversational templates in machine learning technology in order to better approach the tough task of replicating human expertise through a machine. Berkan talks about the edge layer of his company's “secret sauce”, and touches on the future applications of what might manifest in this field in 5 to 10 years in medical and other consumer applications.

In one of our most recent consensus, we took a close look at future trends in artificial intelligence consumer applications, but it's also interesting to see what’s happening now in businesses. Chris Nicholson is the CEO of Skymind.io, which offers deep learning applications that integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current trends that he sees across industries and best practices for implementing AI solutions to gain consistent return on investment.

We've interviewed a number of guests on TechEmergence, but very few who have had a serious part of their career in selling automobiles. But Michael Perry did just that for 5 years before founding Kit, his third startup - an AI application that works in marketing for small businesses and was acquired by Shopify in April 2016. In this episode, Perry speaks about how Kit and Shopify leverage AI on a daily basis, and how a “non-tech” person with no formal background in AI or data science can build a team  for an AI project. 

Martin Ford started off as a software entrepreneur in Silicon Valley, but became better known for his speaking and writing on robotics' and automation's influence on the job market after writing his best-selling book, Rise of the Robots: Technology and the Threat of a Jobless Future. In this episode, Martin talks about why he believes 'white collar' jobs (as opposed to blue) are at a higher risk for automation, and gives his predictions on how automation and robotics will impact the job market over the next 5 to 10 years.

As Senior Director and World Wide Head of the Cognitive Innovation Group at Nuance Communications, Mark Hanson works on bringing Nuance lab innovations to business applications, with the guiding goals of  improving customer experience and business efficiency. In this episode, Hanson speaks about natural language processing (NLP), where he believes this technology is headed in the future and where it's driving value now, and how companies are applying NLP in Silicon Valley and elsewhere.

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.

Big data is often a buzzword, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information  - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, Chief Analytics Officer and Economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

When Google’s DeepMind won against one of the best modern Go champions, is used multiple AI approaches and exposed gaps in some individual strategies. This even has shed more light on AI, but also on the utility in combining approaches to AI for individual problems. Data security is one of these problem areas where multiple AI approaches is being used to make our information safer. Dr. Sal Stolfo has been a professor at Columbia in Computer Science since 1972 and is now also the CEO of Allure Security, with a focus on engineering network intrusion detection solutions using AI applications. In this episode, Stolfo talks about the various styles of AI and statical methods that have been and are being used to detect malicious activity, as well as how he believes the future of security is going to have to adapt as increasing amounts of data become available.

It isn’t by chance that birds fly in flocks and fish swim in schools -  they’re actually smarter when they act in a group. Could it be possible to extend that collective intelligence to human beings, and even AI? Louis Rosenberg is a PhD from Stanford, previously founder of Immersion and who now runs Unanimous AI, a company focusing on harnessing swarm intelligence with human beings. In this episode, Rosenberg speaks about how this collective-intelligence approach has been applied to human beings in terms of garnering improvements in a range of predictions, and he also touches on what this type of swarm intelligence might mean when we talk about multiple AI’s in the future.