Stay ConnectedThe AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI.
Each week, Emerj research staff and journalists interview top AI executives from Fortune 2000 firms and unicorn startups – uncovering trends, use-cases, and best practices for practical AI adoption.
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Copyright: © Daniel Faggella, 2014
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 grasp and understand leveraging related technologies in industry. Recently, I talked with Benjamin Levy of BootstrapLabs in San Francisco, who 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 on the applications for these technologies and leverage them and related expertise to really make a difference (i.e. increased ROI) in their businesses.
Uday Veeramachaneni is taking a new approach to machine learning in infosecurity, AKA infosec. Traditionally, infosec has approached predicting attacks in two ways: through a system of hand-designed rules, and through anomaly detection, a technique that detects statistical outliers in the data. The problem with these approaches, Veermachaneni says, is that the signal-to-noise ratio is too low. In this episode, Veermachaneni discusses how his company, PatternEx, is using machine learning to provide more accurate attack prediction. He also discusses the cooperative role of man and machine in building robust AI applications in data security and walks us through a common security attack scenario.
When it comes to finding an expert on interviewing and finding machine learning (ML) 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, he provide an interesting distinction between what individuals with experience in data science look for in potential hires versus those who do not have the tech background tend to look for, and also dives into the supply-and-demand landscape for data scientists now and in the future—an interesting interview for anyone looking to hire or be hired in the ML and AI space.
In marketing, there are lots of applications in AI and machine learning (ML), from recommendation engines to predictive analytics and beyond. At the company Adgorithms, 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 online advertising, using algorithms to improve advertising processes. In this interview, Shani talks about how Adgorithms' smart marketing platform "Albert" meshes with humans' role in marketing, and also discusses how these roles might change over the next 5 to 10 years as we move towards ever more automated marketing processes.
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.
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?
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 big data. Listen for Cukier's apt analogy in comparing machine learning technology to the dynamics of computing from decades ago.
What are executives missing the boat on and what do they need to think about when it comes to AI and ML? This week, we speak with John Straw, who has had a number of businesses in the UK and US, currently a senior advisor to McKinsey & Co., and who works with a lot of executive teams in terms of finding new applications for AI and finding ROI for those technologies in industry. We speak this week about how executives can get up to speed, what degree of knowledge and in what way they should learn it so they can find opportunities in their own companies. Straw also touches on what he sees as the biggest areas of oversight, in terms of preventing companies from finding those applications that can keep them up to speed with competitors and the big technology players.
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. 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.
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, an initiative that focuses on research implementing the organizational means, operational priorities, and financing structures necessary to achieve the Millennium Development Goals or (MDGs). 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.
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