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Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).

Episode Summary: In this week's episode, we feature an in-person interview from Facebook's headquarters with Hussein Mehanna, director of engineering of the Core Machine Learning group. Mehanna and I talk in-depth about the topic of personalization, touching on the pros and cons, how it works at Facebook, and how his team is working to overcome technological barriers to implement personalization in a way that improves the customer experience. This interview was recorded in their famous "Building 20," which houses thousands of Facebook artificial intelligence employees and developers.

1 - How Locus Robotics Plans to Build a Successor to Amazon's Kiva Robots

Quiet Logistics' spin-off company Locus Robotics is building warehouse robots to help replace the former Kiva Robots once used by its parent company. Before being bought by Amazon in 2012, Kiva Robots supplied all of Quiet Logistics' robots, which were distributed to its clients to support warehouse operations. Instead of going to a new supplier, Locus Robotics was formed to design and build its own lines of warehouse fulfillment robots, which work in collaboration with humans to fulfill orders. When asked what makes the company's robots stand out amongst competitors, Locus Robotics' Chairman and Founder Bruce Welty said:
"My impression of everyone else who has entered this space is that they’re brilliant roboticists who have come into a market that they know nothing about. They don’t really, truly understand the business problem...We’re way down that path: because we know how warehouses work..."
Welty believes these robots will work alongside human workers for the foreseeable future, a minimum of five to seven years, citing their current limitation in working in the unpredictable situations that often occur in warehouses.

Episode Summary: When one thinks through important industry apps of AI, law or legal apps are not usually the first to jump to mind, but there’s certainly a need. Richard Downe PhD is vice president of Data Science at Casetext, a startup working on improving search and natural language processing and democratizing legal information. In this episode, he speaks about the current bottlenecks for people trying to get more out of of legal case documents, as well as some of the apps on which the Casetext team is working, to make these processes easier and to gain strategic advantage in this industry.

Bots are where the web was in 1994. The arena is still wide open, and we don’t know what’s going to work and what’s not, or areas where the overhype is most prevalent. The rise of the chat bots domain is still filled with unknowns, but there’s a tremendous amount of money to be invested and made in this industry, along with big wins and big losses, especially during this training-wheels period.

In the hundreds of researcher and executive interviews we've been fortunate enough to conduct in the last three years, few artificial intelligence applications are brought up more than marketing and advertising. During talks with execs and researchers from companies ranging from Facebook to Baidu, and IBM to AT&T, marketing has been a perennial theme in conversations of AI's hottest applications.

Episode Summary: Learning about the research behind machine learning is always fun, but so is learning about the real-world applications. In today’s episode, we’re joined by the CEO and Founder of Wrike, Andrew Filev. Filev speaks about where Wrike is currently applying machine learning and AI in their fast-growing, data-driven company. He shares his insights as to why he thinks marketing might be the most ripe for disruption by AI, and also discusses how most companies in any industry can prepare to take advantage of machine learning.

1 - IBM Scientists Imitate the Functionality of Neurons with a Phase-Change Device

Episode Summary: Today we have a guest who has interviewed more futurists than anyone else I know. While at Emerj a lot of our interviews focus on executives in AI, Nikola Danaylov has had the pleasure of interviewing some of the finest futurists and forward-thinking minds in the world, including Ray Kurzweil, Verner Vinge, Marvin Minsky, and many others. We speak today about the trends he’s seen aggregated (if any) amongst futurists, and about how technology may be dragging us farther into a transhuman future, whether that be closer to a utopia or a dystopia.

AI investments have become more and more prevalent over the last 5 years, and articles in the Financial Times, TechCrunch, WIRED, and elsewhere tout an era of heightened interest in machine learning and artificial intelligence-oriented companies.