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My learning journey: Rise of AI

Takeaways from the Rise of AI conference (Berlin, May 2017)

Last week I attended the Rise of AI conference in Berlin. I have already mentioned the conference time ago in my article about AI investors cause it is organized by Fabian Westerheide who manages Asgard VC.

It is one of the few well-organized conferences in continental Europe and it had a slightly different format with respect to other conferences I have been to. In addition to several standard talks, the one day event was spaced out by an AI demo section (1 hour, 6 startups, 10 minutes each one to showcase their products) as well as an area with a dozen of tables organized by topic where you could stop and have a chat with other people interested in the same AI nuance.

Another good extra point was that I have seen a lot of new faces with respect to other events in London, Paris, or elsewhere I have recently been to. Different speakers from the field, new people to talk to, and a completely new ecosystem to explore and connect with. Really good.

Kudos to Fabian and Veronika then for the organization and the location. As usual now, I will try to summarize my three personal re-elaborated takeaways from the conference.

I. AI and The Curious Case of Benjamin Button

Image Credit: Vectortone/Shutterstock

In spite of all the current hype, AI is not a new field of study, but it has its ground in the fifties. If we exclude the pure philosophical reasoning path that goes from the Ancient Greek to Hobbes, Leibniz, and Pascal, AI as we know it has been officially started in 1956 at Dartmouth College, where the most eminent experts gathered to brainstorm on intelligence simulation.

It went then through two main ‘winter’ periods, in which investments and interested drastically declined, and back in the nineties it looked like pursuing the creation of an artificial intelligence system was a public shame and a waste of energy. However, as in the movie “The Curious Case of Benjamin Button”, the more time it passed the more AI became actual and relevant.

Luckily enough, in 1993 this period ended with the MIT Cog project to build a humanoid robot, and with the Dynamic Analysis and Replanning Tool (DART) — that paid back the US government of the entire funding since 1950 — and when in 1997 DeepBlue defeated Kasparov at chess, it was clear that AI was back to the top.

In the last two decades, much has been done in academic research, but AI has been only recently recognized as a paradigm shift. There are of course a series of causes that might bring us to understand why we are investing so much into AI nowadays, but there is a specific event we think it is responsible for the last five-years trend.

If we look at the following figure, we notice that regardless all the developments achieved, AI was not widely recognized until the end of 2012. The figure has been indeed created using CBInsights Trends, which basically plots the trends for specific words or themes (in this case, Artificial Intelligence and Machine Learning).

Artificial intelligence trend for the period 2012–2016.

More in details, I drew a line on a specific date I thought to be the real trigger of this new AI optimistic wave, i.e., Dec. 4th 2012. That Tuesday, a group of researchers presented at the Neural Information Processing Systems (NIPS) conference detailed information about their convolutional neural networks that granted them the first place in the ImageNet Classification competition few weeks before. Their work improved the classification algorithm from 72% to 85% and set the use of neural networks as fundamental for artificial intelligence.

In less than two years, advancements in the field brought classification in the ImageNet contest to reach an accuracy of 96%, slightly higher than the human one (about 95%).

The picture shows also three major growth trends in AI development (the broken dotted line), outlined by three major events:

i) The 3-years-old DeepMind being acquired by Google in Jan. 2014;

ii) The open letter of the Future of Life Institute signed by more than 8,000 people and the study on reinforcement learning released by Deepmind (Mnih et al., 2015) in Feb. 2015;

iii) The paper published in Nature on Jan. 2016 by DeepMind scientists on neural networks followed by the impressive victory of AlphaGo over Lee Sedol in March 2016 (followed by a list of other impressive achievements — check out the article of Ed Newton-Rex).

II. What impact AI is having on being human?

Image Credit: posteriori/Shutterstock

It has been mentioned more than once that AI is having a profound effect on how we live our life as well as on how we think and act.

Some of the speakers stressed that AI is somehow making us more human and that eventually, it will free us all from labor-intensive boring activities we don’t want to do, giving us back the time to explore what makes us humans (e.g., creativity, personal relationships, etc.).

Although I think it might be true, I also consider the matter a bit more complicated than that. Indeed, AI is not simply turning us in more human, but also in better robots — and we are doing the same for AI as well (think about the chabot Tay, for example, or about emotional robots).

I call this paradigm the Paradigm 37–78”. I named it so after the events of March 2016, in which AlphaGo defeated Lee Sedol in the Go game. In the move 37, AlphaGo surprised Lee Sedol with a move that no human would have ever tried or seen coming, and thus it won the second game. Lee Sedol rethought about that game, getting used to that kind of move and building the habit of thinking with a new perspective. He started realizing (and trusting) that the move made by the machine was indeed superb, and in game four he surprised in turn AlphaGo at Move 78 with something that the machine would not expect any human to do.

The Paradigm 37–78 is a way to acknowledge that the humans and AI have a symbiotic relationship which shapes them both contemporaneously.

III. Germany is becoming a hot hub for AI startups

It is clear that a few hubs will be the major centers in Europe for AI and data technologies. Paris is going to likely be one of those, and Berlin is, of course, the other one.

Fabian himself has written a detailed post some time ago on the AI German landscape (you can check it here), and I’d like to summarize as follows a few conclusions based also on his analysis:

Fabian Westerheide’s analysis of the German AI landscape: see the full post.
  • Most founders solve well-known problems with AI. For some reason which I can’t really explain, it also looks German entrepreneurs prefer to solve issues related to front-office or customer centric activities (Customer support, customer communication, marketing, etc.). Fintech, on the other hand, is quite overlooked, which I honestly didn’t expect;
  • Berlin is the AI capital of the country. Even though Munich is trying to keep up, almost everything is happening in Berlin;
  • There are a few specialized investors and accelerator programs for startups focusing on AI applications, which nurture the ecosystem and foster the creation of AI companies;
  • Germany is a good place to build an AI startup: engineers and data experts are much more affordable with respect to other hubs (and usually the quality is high), cost of living and administrative costs are limited, and there is already a generation of second-time tech entrepreneurs.

Waiting for the next data event…many more conferences coming soon, so stay tuned!