Join the SPRING team as we interview the leading minds in the intelligent systems landscape. Through SPRING, participants have the chance to attend and ask questions in live interviews with diverse industry thought leaders. Below, you will find recordings of these interviews and links to other interviews curated from around the web. Have a suggestion for who we should interview next? Let us know!
Upcoming SPRING interviews (via webinar):
How Lenovo is Preparing for the Future of Intelligent Systems
Chris Verne, Executive Director of Lenovo’s Data Center Group
In the last decade, cloud computing has transformed the way organizations think about the computer architecture supporting their business operations. Now, intelligent systems combined with cloud computing and data proliferation are again likely to reshape the competitive landscape and require companies to continue to innovate to stay competitive. In this webinar, RTI’s Jim Redden and Chris Verne, the executive director of the Systems Technology Innovation Center at Lenovo, will discuss the ways in which Lenovo is adapting their business to succeed in the intelligent future. Join us as we discuss Lenovo’s views on the ways that emerging trends in computer infrastructure are going to enable new business applications for intelligent systems and for your organization.
- When: September 25, 10 – 11am ET
Archived SPRING interviews:
Blockchain and distributed ledgers are a group of emerging technologies with vast implications for the way that we share and exchange both value and information. The technology applies to a wide variety of verticals but provides uneven business value across sectors and use-cases. The value proposition for blockchain is not yet well understood and is rarely quantified -yet most conferences still present an aspirational blockchain future without addressing the real questions about when, how, and why the technology may disrupt an industry. Jason Cross, Co-founder and Chief Strategy Officer of Rymedi, spoke with RTI’s Jim Redden about the current state of the technology, possible applications, and the ways that the co-evolution of blockchain and intelligent systems might shape the future.
Alan Blatecky is a visiting fellow at RTI International and the former director of the Office of Cyberinfrastructure at the National Science Foundation. At RTI, he advises on advanced data technologies and cyberinfrastructure capabilities. In this small group conversation with RTI’s Jim Redden and the SPRING Pioneers, Alan touches on a range of topics from cybersecurity to ethics to supply and demand for talent trained in AI technologies.
Dr. Brian M. Sadler, Senior Scientist and Fellow at the U.S. Army Research Laboratory, serves as the Army’s principal scientific leader for basic and applied research in intelligent systems. Dr. Sadler has contributed to several U.S. Army Research Lab (ARL) Collaborative Technology Alliances, or CTAs, most recently as a technical area lead in the Micro Autonomous Systems and Technology CTA since its inception. He has been the principle investigator for a variety of internal ARL and collaborative R&D projects, worked closely with DARPA, and mentored many ARL S&E’s.
In this conversation, Dr. Sadler discusses the maturation of technologies that has led to the rise in the hype over AI. He also dives into AI applications for the U.S. Army, including the use of autonomous agents and distributed collaborative intelligence.
Dr. Nita Farahany is a professor of law and philosophy at Duke University and a leading scholar on ethical, legal, and social implications of biosciences and emerging technologies.
Dr. Farahany’s work explores the ethical and legal implications of artificial intelligence (AI), consumer-based neurotechnology, and augmented and virtual reality. Her research approaches ethics from a practical standpoint: how research can inform policy and define laws in light of the rapid advances in science and technology. In this wide-ranging conversation, Dr. Farahany discusses a range of ethical and regulatory issues including data privacy, algorithm bias, the emerging implications of the General Data Protection Regulation (GDPR), and more.
Sam Adams is a distinguished engineer and master inventor at IBM Research. He has been working on AI with IBM for over 20 years and has been involved in the development and evolution of AI systems for over 35 years. He has lived through previous AI hype cycles. In this interview, he discusses the future shift from “narrow” single-task AI to “broad” AI systems, the capabilities of which extend beyond narrow tasks. He also describes several notable advances in hardware (neuron on a chip), internet of things (IoT) sensing (wifi tomography), and other important intelligent-systems trends.
Although artificial intelligence (AI) as a scientific discipline dates to 1956 and the Dartmouth Summer Research Project, recent advances have brought intelligent systems to the center of the conversation. What has changed to shift these technologies into focus? Raj Minhas, director of the AI lab at the Palo Alto Research Center, helps us separate what is real from what is hype and covers important topics including the future of the human machine interface, the need to open the “black box” of AI, and emerging trends shaping the future of intelligent systems.
Other Thought Leader Interviews
One of the benefits of the explosion of interest in intelligent systems has been an explosion in thought-leader interviews available online. Titans of industry and academia like Yann LeCun (Facebook), Andrew Ng (Stanford University, Baidu), and Geoffrey Hinton (University of Toronto) have publicly discussed their views on intelligent systems trends and futures. The following are some of our favorite interviews with these tech visionaries.
Andrew Ng is a former chief scientist at Baidu, where he led the company’s AI Group. He is an adjunct professor at Stanford University. He is widely recognized as one of the preeminent thought leaders in the field of AI. Andrew Ng coined the phrase “AI is the new electricity,” highlighting its future ubiquity and power to transform nearly every industry.
The State of Artificial Intelligence (December 2017)
In this 30-minute talk at the Massachusetts Institute of Technology (MIT), Ng discusses business use cases for current AI technology, examines the workforce implications of this transformation, and peers into the future to examine the potential for new applications as technology progresses.
How Do AI Companies Think about Data Strategy and Competition?
In this 2-minute video, Ng shares how AI companies think about data strategy and competition. He describes how startups can enter the market with a product that is just “good enough” and then use this early product to generate data that improve the product. This “virtuous data feedback loop” makes AI startups particularly dangerous disruptors. In The Innovator’s Dilemma, Christensen explains how disruptors often enter the market with an inferior product at a lower price point. AI startups may enter with an inferior product, but the “virtuous data loop” means that many of those products will not stay inferior for long.
Yann LeCun is the director of AI research at Facebook and a professor of data science, computer science, neural science, and electrical engineering at New York University. He is a pioneer in the field of deep learning and has been working in the field of deep learning for decades.
Medalist Keynote – The Power and Limits of Deep Learning (IRI Annual Summit – IRI members only)
In this talk, LeCun explains deep learning: its current capabilities, limitations, and future possibilities.
The Power and Limits of Deep Learning (MIT Conference – publicly accessible)
Deep learning has caused revolutions in computer perception and natural language understanding, the enabling of new applications such as autonomous driving, radiology screening, real-time language translation, and dialog systems. Good predictive world models are an essential component of intelligent behavior: With them, one can predict outcomes and plan courses of actions. One could argue that good predictive models are the basis of common sense, allowing us to fill in missing information: predict the future from the past and present, the past from the present, or the state of the world from noisy precepts. LeCun reviews some principles and methods for predictive learning, and gives examples of applications in virtual assistants and creative tools.
Fei-Fei Li leads the AI and machine learning (ML) research and development efforts at Google Cloud. Her responsibility includes overseeing basic science AI research, all Google Cloud’s AI/ML products and engineering efforts, university relations, and Google AI’s China Center.
Li is the director of the Stanford Vision Lab, which focuses on connecting computer vision and human vision; the director of the Stanford Artificial Intelligence Lab (SAIL), which was founded in the early 1960s; and the director of the new SAIL-Toyota Center for AI Research, which brings together researchers in visual computing, machine learning, robotics, human-computer interactions, intelligent systems, decision making, natural language processing, dynamic modeling, and design to develop “human-centered AI” for intelligent vehicles.
In this conversation with A16Z’s Frank Chen and Sonal Chokshi, Fei-Fei Li discuss Who has the advantage in AI — big companies, startups, or academia. In addition, they cover questions like “why now for AI?”; “is deep learning ‘it’?”; and “what comes next?” Li explains what might happen as AI moves from its in vitro phase to its in vivo phase. Beyond ethical considerations, Li argues that we need to inject a stronger humanistic thinking element to design and develop algorithms and AI that can cohabitate with people and in social spaces.
Geoffrey Hinton is the father of ‘deep learning,’ the technique behind the current excitement about AI. ‘In 30 years, we’re going to look back and say Geoff is Einstein—of AI, deep learning, the thing that we’re calling AI,’ Jacobs says. Of the researchers at the top of the field of deep learning, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. In fact, nearly every achievement in the last decade of AI—in translation, speech recognition, image recognition, and game playing—traces in some way back to Hinton’s work.” (MIT Tech Review)
The Neural Network Revolution
In this 45-minute talk from January 2018, Hinton describes artificial neural networks work, discusses their current capabilities, and explores the future possibilities for this powerful technology.
Andrew Ng interviews Geoffrey Hinton
In this 40-minute interview, Hinton gives us a unique look into his career journey and how he became known as the Father of Deep Learning. He discusses his current research activities, including some of his work with Google Brain, and describes some of his biggest lessons learned from more than 30 years in the field. Hinton says that General Adversarial Networks could lead to some near-term “big breakthroughs.” He compares the coming transformation from AI to the previous industrial revolution in terms of impact and scale.
Hinton answers the following questions:
- How has your work advanced the frontiers of knowledge in AI?
- How can such advances improve the world and benefit humanity?
- Will AI create mass unemployment?
- Will machines be more intelligent than humans?
- Will machines ever have emotions?
- Are you concerned about the future of science
Rodney Brooks is the Panasonic Professor of Robotics (emeritus) at MIT. He is a robotics entrepreneur and founder, chairman, and chief technology officer (CTO) of Rethink Robotics (formerly Heartland Robotics). He is also a founder, former board member (1990–2011) and former CTO (1990–2008) of iRobot Corp. Dr. Brooks is the former director (1997–2007) of the MIT Artificial Intelliigence Laboratory and then the MIT Computer Science & Artificial Intelligence Laboratory.
Robotics and AI: Their Present and Future
In this 1-hour interview with Rodney Brooks, podcast host Rob Reid inquires about the future of robotics, human–machine interaction, and much more. Brook’s background in robotics gives him a unique perspective on the rapid acceleration of robotics and intelligent system technologies. He describes a near future in which robots are “released from the cages” that have traditionally separated humans and machines in industrial spaces. As a critical observer of the intelligent systems landscape, Brooks offers a well-informed and thought-provoking point of view on the trends impacting the future of these systems. Brooks also wrote a great long-form blog post pointing out common mistakes that are made when predicting the future of AI.
Frank Chen is a partner at the venture firm Andreessen Horowitz (A16Z), a firm that specializes in high-growth software startups. Chen and the A16Z team have a front row seat as the intelligent systems startup landscape and invest in many startups that apply AI to transform industries.
The Promise of AI
In this 45-minute talk, Chen explores the categories that “AI will make cheap,” including transportation, perception, content generation, and optimization of complex systems. He also highlights several startups that are building for a future that looks very different than today and points to key enabling technologies like Generative Adversarial Networks that pose significant promise for intelligent systems.
John Launchbury is an American and British computer scientist who is currently chief scientist at Galois, Inc., and the former director of the Defense Advanced Research Projects Agency’s (DARPA’s) Information Innovation Office, where he oversaw nation-wide scientific and engineering research in cybersecurity, data analysis, and AI.
A DARPA Perspective on Artificial Intelligence
What’s the ground truth on AI? In this video, Launchbury attempts to demystify AI—what it can do, what it can’t do, and where it is headed. Through a discussion of the “three waves of AI” and the capabilities required for AI to reach its full potential, Launchbury provides analytical context to help understand AI’s rolein the past, the present, and the future.
Amy Webb is a quantitative futurist who is a professor of strategic foresight at the NYU Stern School of Business and the founder of the Future Today Institute, a leading foresight and strategy firm that helps leaders and their organizations prepare for complex futures. She is the author of The Signals Are Talking, an award-winning book about how to identify emerging trends early and use strategic foresight to manage risk and opportunity.
What’s Next for AI from Wall Street Journal – The Future of Everything Podcast
In this 20-minute interview, Webb highlights what she sees as some alarming trends across the AI ecosystem. From data privacy and ethics to the country-scale “AI arms race” happening in China, the EU, and the US, Webb points out several alarming indicators that portend a future that few are currently envisioning.
Dr. Adam Coates spent 12 years at Stanford University studying AI before accepting a position as director of Baidu’s Silicon-Valley based AI lab. He is currently operating partner at Khosla Ventures.
In this interview, Coates discusses consumer AI applications, what he’s excited about, and what he thinks may be more ‘hype’ than reality. He talks about applications that could potentially influence billions of mobile-phone and computer users worldwide. If you’re interested in the developments of speech recognition and natural language processing, this is an episode you won’t want to miss.
Still Want More?
In truth, we have only scratched the surface of available thought-leader interviews. The growth in the popularity of podcasting as a new content platform means that there are lots of high quality interviews available for free. The following are our three favorite aggregators of additional intelligent systems content:
The team at the investment firm a16z produces a lot of great content about the future of AI, ML, and deep learning. The complete list of their AI, machine learning, and deep learning content includes original podcasts, videos, and blog posts. They have also curated their own list of resources from around the web, which is very good. One of our favorite a16z podcast episodes is summarized below:
AI, from ‘Toy’ Problems to Practical Application With Scott Clark (SIGOPT), Joseph Spisak (Amazon Web Services), Martin Casado(a16z), and Sonal Chokshi (a16z)
When you have “a really hot, frothy space” like AI, even the most basic questions—like what it’s good for and how to make sure your data is in shape—aren’t answered. So, what does this mean for organizations going from so-called toy problems in research and development to real business results tied to KPIs and returns on investment? In this episode of the a16z podcast, participants share their thoughts on what’s happening and what’s needed for AI in practice, given their vantage points working with both large companies and AI startups.
In this podcast series, host Byron Reese interviews more than 50 thought leaders in various fields of artificial intelligence. It’s a great resource for in-depth conversations with some of the pioneers and technical experts in the field of AI. If you are looking for a good place to start with GigaOm, we recommend the interview with Steve Pratt, the Chief Executive Officer at Noodle AI, an enterprise artificial intelligence company.
Robbie Allen, speaker at the upcoming SPRINGBOARD conference, has created a curated list of AI/ML resources:
It seems fitting to end with Robbie’s list. If you’ve made it this far and are still looking for more, Robbie’s list will keep you busy for weeks!
Know of a great publicly available thought leader interview we missed? Let us know!