Many believe that artificial intelligence is set to reshape global economies and society, with AI expected to double economic growth. But what are the opportunities and challenges to investing in the current world of AI?
In 2016, the AI market was worth just $644 million, according to Tractica. This year that amount is due to almost double and continue to grow exponentially, with predictions showing it to reach $36.8 billion by 2025. With so many pioneering companies and world-leaders focusing their attention and funding on artificial intelligence fields such as deep learning and machine intelligence, the momentum for progress is well underway.
At the Machine Intelligence Summit on 28-29 June, Julius Rüssmann, Analyst at Earlybird Venture Capital, will share expertise on a panel exploring the challenges and opportunities of investing in AI. Julius believes investors themselves will be disrupted by the AI revolution. Could future investments in AI be decided by other artificially intelligent systems instead of humans? I spoke to him ahead of the summit to learn more.
What are in your opinion the most promising machine intelligence sectors to invest in?
Broadly defined, the service and manufacturing industries will probably benefit the most from (an increased level) of Machine Intelligence. By that I refer to the idea that large parts of the service industry, today still based on human work, can be heavily digitized, automatized and even improved through intelligent software applications. Especially, when considering the fact that the complexity inherent to each and every service inquiry increases, exponentially as data growth, machine intelligence is critical to ensure smoothly running system.
Besides primarily consumer focused service applications, the manufacturing industry will not remain the same. As machine intelligence develops, the sharp line between human-based work and robotics vanishes (think of collaborative robotics) until robotics will effectively overtake the bulk share of manufacturing processes and frees up billions of hours every day that have been had allocated to human work beforehand (problems that will arise).
One good example of Machine Intelligence that will redefine industries and humans alike is enriched or contextual computer vision. If it would be possible for software to contextually accurately understand video content, that would change a lot (autonomous driving, health care and so forth).
Besides industries and application fields, we deem Deep Reinforcement Learning as well as Neural Networks to be critical to facilitate further use-cases of Machine Intelligence.
What are the characteristics you are looking for in a startup prior to investing?
First and foremost, we look at the people behind the startup. Why is that? Because we think that complementary skills and solid commitment are in every successful company the kea driver. Starting a company, especially in the field of technology, always implies that there will be tough times and complex problems to be solved. In those situations it is almost irrelevant, how attractive you business model or the targeted market is. It is all about the team, to steer and pivot the company in the right direction (again). Besides outstanding people, we like to see early product (prototypes) to see credibility on execution, management and skills. In most cases that turn out to be successful you will see some sort of market adaption or commercial traction already early on, as customers and markets grave for such a solution and are open to use the new offering (think of solving a real problem.
How will venture capitalists be impacted by machine intelligence in the next 5 years?
Venture capitalists (VCs) will face a two-sided effect. First of all deal flow will significantly increase in the area of Machine Intelligence-based companies that are able to produce and deliver a solid value proposition to the market and are truly disruptive to specific industries. Today we still see a lot of evolutionary Machine Intelligence applications compared to revolutionary business models, many cases we see are rather a MI feature set, but not a standalone business case or company – this will change.
VCs will be threatened and potentially even disrupted themselves by MI. In essence, VC is also just a service industry (we service our portfolios and LP’s) and evidence clearly suggests that advanced MI will reach better (investment) decisions than humans do. However, the question remains how long this development will take; and yet, there is no clear evidence that MI will be capable of assessing or completely understanding humans. Considering, what has been stated in the beginning, namely that team are key, it’s not clear whether MI will necessarily reach better decision.
What are the dangers of no distinguishing between hype and reality in AI?
As explained above, AI/ML/MI are today quite advanced but not ultimately “ready” yet. This means that a lot of application fields (e.g. customer service industry) can clearly benefit from the introduction of smart algorithms (get more efficient, partially replace humans, better results, faster etc.), but they are not yet ripe for disrupting those industries effectively. So the danger is, from a VC’s or Founders perspective, to overestimate the capabilities of the algorithm, or to underestimate the importance of human-based decisions and verification. Effectively, there is lot of (technical) work still to be done and markets are only about to open up or be created for AI application. Time to market is critical for building up good investment cases.
Do VCs have a role in progressing the fields involved with AI?
VC’s, as with every other technology field, are responsible in finding and funding the leading teams/brains/companies in the respective field. This work is critical to contribute to the technology’s further development, to facilitate market adoption, to help identify viable business models and so forth (offering capital to outstanding entrepreneurs will improve the economy and the startup ecosystem in any ways). By finding and funding good technology companies in the AI field, VC’s also help to steer public attention to this area and to help create flagship project that then attract more brain-power and top-talent. As state before, it also in the responsibility of VC’s to be critical in their decision process also in order to prevent over-hype and bubble effects. It is sort of an educational responsibility that VC’s have for the tech ecosystem, the economy and the society.
| Julius Rüssmann will be speaking at the Machine Intelligence Summit, taking place alongside the Machine Intelligence in Autonomous Vehicles Summit in Amsterdam on 28-29 June. Meet with and learn from leading experts about how AI will impact transport, manufacturing, healthcare, retail and more. Other confirmed speakers include Roland Vollgraf, Research Lead, Zalando Research; Alexandros Karatzoglou, Scientific Director, Télefonica; Sven Behnke, Head of Autonomous Intelligent Systems Group, University of Bonn; Damian Borth, Director of the Deep Learning Competence Center, DFKI; Daniel Gebler, CTO, Picnic; and Adam Grzywaczewski, Deep Learning Solution Architect, NVIDIA. View more speakers and topics here. Tickets are limited for this event. Register to attend now. |
Opinions expressed in this interview may not represent the views of RE•WORK. As a result some opinions may even go against the views of RE•WORK but are posted in order to encourage debate and well-rounded knowledge sharing, and to allow alternate views to be presented to our community.
