What the ancient Greeks can teach us about investing in AI

10 October 2016

Since Icarus and his dad looked at the birds in the sky to inspire their escape from Crete, humans have attempted to mimic the natural world. Two thousand years later, we have engineered ways to swim, fly and travel faster than nature, but are we finally ready to build a machine that can think faster than anything in nature?

Anyone who saw the news of Google’s recent victory over the world champion of the fiendishly complex strategy game “Go” would be right in thinking that we are suddenly making significant progress in this goal. A combination of vast amounts of data and Deep Learning techniques have created a perfect storm for the exponential growth in progress from the fragile neural networks operating in labs just a few years ago, to the real world systems which have already begun to infiltrate our daily lives. Every time you use a search engine or the voice control technology on your phone, you are benefitting from the explosion in Artificial Intelligence (AI).

Of course, the challenge of taking any product from controlled environment (such as in a lab, or a rule-based game like Go) into the real world is roughly proportional to the complexity of the interface. Whilst optimising a bucket to successfully operate in the real world is relatively straightforward, the list of testable attributes of a complex machine, let alone an infinitely complex structure like the brain, quickly becomes overwhelming. And, although AI systems can continuously learn and self-correct, the initial learning phase will have to be careful managed to minimise damage along the way. The consequences of not doing this can range from embarrassing to even potentially fatal.

Broadly, there are two ways that an AI system can improve through feedback and learning: “supervised” (seeing defined rules applied to real world examples) and “unsupervised” (deducing rules based on observed inputs and associated results). IBM’s AI approach seems to be focused around the former, seeking to solve business problems by studying “case histories”, whilst Google AI uses the latter, learning based on huge volumes of data and associated outcomes. The potential for any system that combines both of these is difficult to imagine, but there is an even higher level: instead of human versus machine, consider human plus machine. We watch with interest the situations where humans and AI are working together, for example, in the worlds of medicine (diagnostic imaging) and business (such as Deep Knowledge Ventures, who have appointed a machine learning program to their board of directors).

Such technology is already making its mark in Professional Services in AI tools such as ROSS and Kira, which are being used to perform advanced analysis at a fraction of the traditional cost. It is only a matter of time before M&A players start using AI: to improve target identification; to increase the depth and speed of diligence analysis; to mine post-deal opportunities.

And of course the AI industry itself continues to attract interest from investors – while Tech as a sector flat-lined in 2015, deal activity in AI continued to grow and has quadrupled between 2010 and 2015. However, whilst AI will no doubt take us into a new realm of capability, when investing, as in the case of Icarus, we need to ensure that we do not allow hubris to overtake us.



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