AI Podcast Interviews Articles and Reports
Our podcast interviews feature the best and brightest executives and researchers in artificial intelligence today, and each episode highlights current and near-term AI use-cases of value for business leaders. Explore our full list of AI podcast episodes below:
Episode Summary: When it comes to finding an expert who knows how to hire machine learning talent, Parshu Kulkarni may just be the guy to ask. Not only is Kulkarni one of a small subsegment of the global population with an advanced degree in data science who has also been hired to work in tech companies like eBay, but he's been on the unique side hiring of ML and AI talent.
Today, Kulkarni works full-time as Head of Data Science at Hired, Inc., a giant platform for hiring top talent in tech and other areas. In this episode, Kulkarni provides insight into what executives with experience in data science look for in potential hires, alongside what businesspeople get wrong about machine learning. He also gives his insights on the supply-and-demand landscape for data scientists now and in the future. Kulkarni's is an interesting interview for anyone looking to hire or be hired as a data scientist in the ML and AI space.
Episode Summary: In marketing, there are lots of applications in AI and machine learning (ML), from recommendation engines to predictive analytics and beyond. At the company Albert, there are even more ambitious projects underway - like automating the process of marketing altogether by having a machine run and generate ads, or test and spend the marketing budget of a company. Or Shani, CEO of Adgorithms, focuses on the quantitative aspects and optimization of marketing, using algorithms to improve advertising processes. In this interview, Shani talks about how Adgorithms' smart marketing platform "Albert" meshes with humans in marketing, and also discusses how these roles might change over the next 5 to 10 years as we move towards an ever more automated marketplace.
Episode Summary: Not all knowledge work can be crunched by a program, but there are some hard-to-automate business processes that a select few entities are making an attempt to automate now. Boston-based Rage Frameworks, Inc. is one such company, and in this episode we speak with Senior Vice President (SVP) Joy Dasgupta about specific applications of automation technologies applied to white collar environments. Rage Frameworks has developed intelligent machines that have been able to take over process that, prior to the emergence of AI and automation technologies, would have required thousands of people to accomplish. These developments are a microcosm of what is to come, and the process is not without its ethical considerations (as discussed in a previous interview with Yoshua Bengio). But Dasgupta's insights provide a concrete glimpse into how these processes are being automated in the knowledge workplace today and what that might mean or look like decades from now.
Episode Summary: This week we speak with CEO and Founder of Nexar Inc., Eran Shir, whose company has created a dashboard app that allows drivers to mount a smartphone, which then collects visual information and other data, such as speed from your accelerometer, in order to help detect and prevent accidents.
The app also serves as a way to reconstruct what happens in a collision - a unique solution in a big and untapped market. In this episode, Shir gives his vision of a world where the roads are filled with cyborgs, rather than autonomous robots, i.e. people augmented with new sensory information that trigger notifications, warnings or prompts for safer driving behavior, amongst a network of cloud-connected cars. He also touches on what the transition might look like in response to the question - when will autonomous cars be mainstream?
Episode Summary: In this episode, we speak with Senior Editor for The Economist in digital and data products and Co-author of "Big Data: A Revolution that Will Transform How We Work, Live and Think", Kenneth Cukier, who speaks on the technologies that underlie big data and make it what it is today. Cukier addresses common misconceptions about machine learning and dives into how companies can catch up with this technology by thinking through, assessing ROI, and making sense of the dynamics of data assets for business. Listen for Cukier's apt analogy in comparing machine learning technology to the dynamics of computing from decades ago.
Episode Summary: What are executives missing the boat on and what do they need to think about when it comes to AI and machine learning? This week, we speak with John Straw, who has had a number of businesses in the UK and US and is currently a senior advisor to McKinsey & Co.
John works with a lot of executive teams in finding new applications for AI and machine learning and pinpointing ROI for those technologies in industry. This week, Straw shares his insights on how to solve business problems with machine learning. Straw also touches on aspects he believes are most commonly overlooked when executives learn machine learning, specifically in finding applications that can keep them up to speed with their competitors in the field.
Episode Summary: In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with, so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning. Artificial intelligence in stock trading certainly isn't a new phenomena, but access to it's capabilities has historically been rather limited to large firms.
This week, we’re joined by CEO and Co-founder of Kavout Alex Lu, whose company offers AI trading applications for enterprises and individuals. Lu speaks today about the kinds of patterns that traders now have access to in finance, and he gives examples of ways Kavout and other institutions are using artificial intelligence in stock trading to build better and more personalized products and services.
Episode Summary: Market research and trends is important when discussing AI and business, but it's also worthwhile to contemplate the ethical and social implications further down the line. How will countries deal with potential unemployment problems? How might countries collaborate to hedge against the risks that AI poses to the future of work and other economic facets? A relatively small group is helping people do just that i.e. getting organizations and countries to think through how they could hedge against the grander risks inherent in a world powered by AI.
In this episode, we speak with Jerome Glenn, head of the Millennium Project, a global participatory think tank with 60 Nodes around the world that that focuses on research implementing the organizational means, operational priorities, and financing structures necessary to address 15 Global Challenges. Glenn talks about how he gets principalities of the world to bring their big industrial players and the public to talk through possible scenarios that are 30, 40, even 50 years in the future, and about ways we might potentially hedge against risks and make the most of the upsides of AI in a global economy. If you enjoyed listening to our recent podcast with OpenAI's Ilya Sutskever on preparing for the future of AI, then you may find Glenn's ideas and the mission of The Millennium Project to be an interesting and useful perspective on this issue as well.