Sharing takeaways from the Rise of AI conference, Berlin 2018
Building on the lessons we learnt from Tony Boobier’s book on AI & work, let’s hear more about the Rise of AI.
At the start of a month when I will be chairing and attending events, I was delighted to receive Francesco Corea‘s debrief. In this post, he shares his learning journey while attending the Rise of AI conference in Berlin.
Francesco has shared before his overview of the InsurTech sector, so it’s great to read these about wider AI applications. A number of Francesco’s lessons learnt echo warnings from Tony’s book. They also confirm the needed convergence of technologies, that I’ve shared before on blockchain.
So, over to Francesco, to share lessons from Berlin on the rise of AI & opportunities for us to respond…
Rise of AI conference in Berlin, where content is king
I have been out from the AI conference circuit for a while now, mainly because I find many of them quite repetitive and not very informative. Don’t get me wrong, there are many good conferences out there. But, very few where the content is placed before companies exhibitions or sales pitches.
And if there is one event in Continental Europe where “the content is king”, that is definitely the Rise of AI conference.
I happened to learn a thing or two at the event, which is what I am going to share below.
1) AI and Blockchain are a real deal
You might know by now that I am really keen to see and study how those two technologies interact. When you see Ben Goertzel on stage, everything gets a completely new sense, so expect some new thoughts soon.
In my view, one can have positive effects on the other and vice-versa. More specifically AI can make blockchain more energy-efficient, scalable, secure. Potentially more useful too (more details here. Blockchain can actually make AI more explainable, trustworthy, effective, secure and democratized. For more details on AI’s benefits to blockchain look here.
As I already stated, blockchain and AI are the two extreme sides of the technology spectrum. One fostering centralized intelligence on close data platforms. The other promoting decentralized applications in an open-data environment. But, if we find an intelligent way to make them working together, the total positive externalities could be amplified in a blink.
2) This AI technology wave is different
This point embeds different nuances: first of all, it is still not clear to everyone how to put in place an AI solution within an enterprise context. Working with AI means continuously experimenting. Pick a use case, start building, testing and iterating as quick as possible. Don’t look for the perfect mathematical (academic) solutions. (Have a look at the OODAloop, i.e., observe-orient-decide-act). Only then scale and impose a transformational shift to your organization.
Second, I have spoken and listened to investors and entrepreneurs all day long, and basically, no one mentioned IP protection of any kind. I have been thinking about this issue for startups for some time now. I keep finding evidence that patenting innovation belongs to the old-world of innovation. Meanwhile, open-source technologies dominate the AI scene (unless you are a very early-stage company — more on that here and here.
Finally, building AI simply for the sake of doing it is a waste of time. While using it for solving a real-world problem is extremely powerful (and well-seen by investors). Using Christian Nagel’s words, a partner at Earlybird Venture Capital, you need to move from a pure data-centric approach to an action-centric one and invest in team, category leadership, disruptive data acquisition strategies and business models (e.g., integrating models into customer processes or 3rd party systems, building infrastructure or platform to allow the construction of models and the value exchange between the parties, etc.).
3) AI is not all peaches and dandelions
There is a sane skepticism about how the press depicts AI and the future trajectory the field might take. I have heard the same thing over and over: “machines do not understand” (i.e., the Chinese room argument). They “do not have a human-like brain” (and likely never will). “Superintelligence is more a horror story” to be sure your son’s well-behaved, rather than a possible short-term scenario.
However, that does not rule out the possibility of having in the future a “general-purpose system of algorithms“. Supporting people in their daily activities. But, it asks for a good dose of realism. A new approach to solving the issues posed by several layers of non-easily-integrable technologies.
In this fashion, Peter Bentley suggested that in order to make a Strong AI:
1. We should not try to emulate human intelligence. Since true intelligence emerges through the need to solve complicated problems and it is general and adaptable
2. There is still a lot of uncertainty of which approach or algorithm will pay off. You need to consciously build new layers of the stack without losing the functionalities of the previous ones.
3. Testing is never enough, and increasing complexity demands for an increasing number of tests to build a safe AI.
With these principles in mind, you can try to overcome three main challenges to achieve a general AI. Chris Boos pointed these out:
* the transfer of knowledge (and the catastrophic forgetting problem)
* Moravec’s paradox (high-level reasoning requires little computation, while the opposite is true for low-level sensorimotor skills)
* Working with “small datasets” (one or zero-shot learning)
A few attempts exist to solve some of those problems. But, much more research is still required. To make them functionally attractive and scientifically accurate.
4) Germany is still a big AI player
Technical research is very strong in Germany. Whether this translates into an operating business model is a completely different story. But there are enough data points in the startups’ ecosystem to qualify Germany as one of the countries driving the field. Together with UK and France.
From a research standpoint, leading institutes are:
* German Research Center for AI
* Max-Planck Institutes
* The Fraunhofer Institute
* Cyber Valley
These are studying AI from the ground up (and the government itself has been highly supportive to numerous AI projects in the past). If you add to that a hundred early-stage AI companies, a strong industrial ecosystem (nurturing and investing in AI applications) & a few strong venture investors – you have the right ingredients for a very tasty AI meal.
Fabian has more thoughts and data on this topic here, if you want to check the German ecosystem. He also outlined the challenges and opportunities he encountered in the last 4–5 years investing in the space. In his own words:
> _- It is difficult to understand and evaluate Artificial Intelligence investments;_
> _- There is a lot of hype and a lack of substance with many teams;_
> _- Funding for AI startups is tougher and the seed stage takes longer;_
> _- The market potential for applied AI is huge;_
> _- Teams often have a high level of education and research background but lack entrepreneurial experience;_
> _- Once AI works, it is hard to replace._
In addition to that, don’t forget to download his just released Global AI report here.
Well, I really hope you enjoyed the conference as I did and that it was inspiring and provocative as it was for me. I hope to see you next year in Berlin!
How will you keep learning about the Rise of AI?
Many thanks to Francesco for sharing those interesting thoughts. It does sound like a tempting event, so perhaps I will aim to revisit Berlin next year. You can find the original version of this post and further interesting content from Francesco on his Medium presence.
If you have a story to share, we’d love to share debriefs that have helped other customer insight leaders. Let’s keep learning together and collaborating at events, as we prepare for the future of work.