Today we cover Part 2 of Artificial Intelligence Investing with Nathan Benaich of Playfair Capital. In this segment we address:
- Can you provide an overview of the funding landscape for AI in recent years and the major things that have changed?
- What are the primary sectors wherein AI-based startups are receiving the most funding?
- Can you describe where you see some of the most interesting applications both in the enterprise and consumer spaces?
- Who are the VC firms that are most active, investing the most capital in AI/Machine learning, both in the U.S. and outside?
- You conducted research on exits for AI vs. that of greater tech. Can you highlight some of the key learnings from your research?
- What, in your opinion, is most exciting about advances in AI?
- What, in your opinion, is the biggest threat regarding AI?
- What advice would you have to early-stage investors that have an interest in AI?
- Anything else that we didn’t address that you’d like to touch on for the listeners?
- Part 1 of the interview on AI
- Nathan on Twitter
- Nathan’s article on TechCrunch
- Playfair Capital
- DeepMind Mastering Atari Breakout
- Playfair on Twitter
1- The Pace of Progress
First, we started with a definition of AI, which in Nathan’s estimation is: The field of building computer systems that can understand and learn from observations without the need to be explicitly programmed. And that these systems can perform functions that are increasingly human-like. Nathan referred to AI as the umbrella term that includes all the disciplines and technologies required to build those systems.
Next we discussed the concepts that illustrate technological progress, contributing to the advancement of AI. Those two concepts were the Law of Accelerating Returns and Moore’s Law.
Law of accelerating returns: This was first discussed by Ray Kurzweil and says that as a society becomes more advanced, the rate at which it can progress increases. This is anything but linear. The rate at which technology has advanced in the past decade is much greater than the rate of advancement the decade before it, which is much greater than the decade before that. Here Nathan cited compute capacity and cost, which related to Moore’s Law; that maximum computing power doubles rougly every two years. Both of these ‘Laws’ have proven their merit as we measure progress and compute capacity over the past century. And they have significant implications on AI as it moves through different phases of development.
Those phases and types of AI discussed, included:
ANI, Artifical Narrow Intelligence: Intelligence around a very specific subtask. The intelligence system is narrow, focused on a specific problem and is unable to do anything else. Here is where Nathan cited the example of IBM DeepBlue which beat Garry Kasparov in chess. There are many examples of existing narrow intelligence systems.
AGI, Artificial General Intelligence: This is the goal of Google DeepMind. Intelligence from a non-human system is on-par with tasks that can be completed by humans. This could include reasoning, planning, communicating and even exhibiting emotion. And, this can be accomplished without the need for re-engineering, reporgramming or rebuilding. The experts from the science community generally believe that AGI will be reached within the next one to two decades.
ASI, Artifical Super Intelligence: As Nathan descirbed, this is essentially the point of singularity; the point at which AI is more intelligent than humans. This encompasses every field or activity that humans can accomplish and things beyond human capability. Creativity, wisdom, social skills, physical representation. An ASI system is one that is more preformant in every single task than the smartest humans on earth. And the experts predict this to be achieved by 2040.
2nd Wave: This is the Machine learning wave, where a raw training data set and a test data set is required; with one being tested against the other. Here Nathan talked about Supervised vs. Unsupervised approaches, the former providing examples upon which the data is tested against.
3rd Wave: Deep Learning. This wave requires massive data sets and significant processing powerWe also discussed the technology categories within AI including but not limited to:
-machine learning / deep learning
-natural language processing and semantic analysis
3- The VC AI Landscape
Nathan sees a future of human-machine collaboration instead of just machine replacement. Knowledge work, information retrieval, task automation, and business automation are all areas that he cited which are ripe for AI application. Especially as more SaaS services have become available that can acquire data and talk to each other.
Nathan mentioned that the majority of VC funding in AI started in 2013. Last year, $2B invested in AI… which is about 4% of all VC-backed technology and AI accounted for 1% of all technology exits. And the primary sectors receiving funding at this point includes business intelligence, predictive analytics, adtech, fintech. And Nathan also pointed out the health sector and predictive medicine. While most are focused on the threat of AI they may be overlooking the incredible benefits, not only to our efficiency, but to our well being.
Unfortunately the majority of the companies doing acquisitions in AI are not performing well in the public markets, which may restrict their ability to continue making acquisitions.
And upon wrapping up the interview, Nathan questioned if the current, broader VC category is well-suited to fund capital-intensive, long time-horizon technologies. It’s certainly a legitimate question as costs, risk-profiles, and return expectations have changed over the past couple decades.