My learning journey: AI Europe

Takeaways from AI Europe Conference (London, Dec. 2016)

I. The AI French ecosystem is pretty robust.

There were several French companies and researchers, and it seemed obvious that France has a good understanding of AI overall. Actually, a deeper analysis confirms that: France is one of top ten countries investing and developing AI solutions, right after the ‘usual suspects’, i.e., US, China, UK, Canada, and India.

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Countries breakdown of AI companies
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Top 15 cities in the world with AI startups concentrations

II. The Investing Landscape is Changing

David Kelnar (MMC Ventures) presented some of his insights as a venture capitalist on what it means to invest in AI companies in the UK (read also his incredibly smart pieces here and here). I believe some of his conclusions can be generalized, and I would like to start from those and add my considerations on the AI market:

  • Many AI applications are not mature yet. I said this because many AI startups prefer to target other businesses (B2B) rather than final customers (B2C). David attributes this to the data obstacle small companies face which is undoubtedly correct, but there are also two other trends that are taking shape: startups are entering specific applications with i) either the goal of being acquired (hopefully within 3–5 years), or ii) to disrupt sectors where big tech companies are not present (e.g., finance, healthcare, insurance, etc.). Direct competition is no more in any founder’s long-term vision.
  • AI is (really?) booming. There is no doubt AI is the next big thing and both the entrepreneurial activity and investments flows are increasing everyday.
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Search trends for robotics and other fields artificial intelligence alike (created with the CBInsights Trends tool)

The ‘unfair’ rebranding of companies as AI-driven is one of major threat I personally see for the technological development of new solutions for all of us.

David as well found that in the British AI companies get ‘typically 20% to 60% larger than average capital infusions’, which I don’t believe to be always justified.

III. It is not clear where AI is going (yet)

The market is huge and still too complicated to be addressed by a single player or business model. There are scattered clusters of knowledge that have been built in either geographical locations or within industry verticals, but this is not enough to understand what the landscape will look like in a decade.

  • Fail-fast-and-first. The trial-and-error feedback loop is the only way to train a neural network, and it is the only way as well to turn your organization into an AI-driven company too. Start with small data, supervised learning models, and use the cloud. Go for the low-hanging fruit.
  • ‘Pay for Success’ VS ‘Pray for Success’. This sentence is an outcome of the conference, and it suggests to build incrementally and steadily rather than waiting for the big deployment all-in-once. Find the right project, experiment and deploy fast. Cognitive Scale uses the 10–10–10 rule, i.e., identify a use case in 10 hours; build reference app in 10 hours; and finally, go live in 10 weeks.
  • It is an ecosystem matter. No matter how hard you try, none of us is so smart to crack human brain, consciousness, and all those matters by himself. Get yourself wisely as many partners as you can, and become part of the AI ecosystem.

Written by

Research Lead @Balderton. Formerly @Anthemis @UCLA. All opinions are my own.

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