This post outlines the European machine intelligence landscape, which, until recently, has been under-appreciated in its contribution to the innovation and commercialisation of machine intelligence technologies.
[email protected] big fans of landscapes. That’s why we’ve created a machine intelligence landscape focused entirely on Europe .
Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO. London is Europe’s startup centre, mixing capital, proximity to markets, and world-class research hubs. Not to mention that we are proudly European — British, French, and German — with our associated partialities.
We have focused on startups. This is where our hearts lie. We plan to look at corporates, research institutions, and VCs at a later date.
We acknowledge that there are many ways of slicing & dicing the startup landscape. Companies don’t neatly fit particular criteria, and they inevitably change direction or emphasis over time. We tried to fit our startups into one of the already published taxonomies  and ended up scratching our heads with what to do with some of them. These are the categories that we ended up with:
These are the companies whose business is machine intelligence itself. Whether building engines specifically for vision, language, speech or general optimisation problems, or working on algorithms and techniques intended to have very broad applicability (Artificial General Intelligence), these companies invest in proprietary methods and expertise. Google Deepmind is the most prominent example in this category, having applied its methods to such seemingly disparate tasks as mastering the millennia-old Chinese game Go, cutting data centre energy use, and synthesising natural human speech.
These companies have sprung up to address a skills gap, providing the training and education necessary to meet the demand for specialist roles in machine intelligence.
With one robotics expert and one unreformed(-ish) hardware investor on our team, of course we’re interested in physical systems using machine intelligence. These might be sensor networks capable of dynamically optimising resources in the home or city, intelligent manufacturing systems, robots in the factory, home or service sector, and let us not forget autonomous vehicles. The latter category is remarkably populous in Europe — MobilEye, Dibotics, Oxbotica, AdasWorks, Dyson, FiveAI, Innoviz Technologies, and Starship Technologies — and may reflect academic and industrial expertise here.
Machine intelligence offers a real chance for natural interaction with the machines and computers that facilitate our everyday lives. Machines of the future will be able to “perceive” us in multiple ways, and they may also have personalities and emotions of their own.
Such interaction necessitates natural language understanding that is context specific, in speech or text form, another topic close to our hearts due to our team’s background, and one that is still far from ‘solved’. The popularity of chatbots is one nod in this direction.
This category also covers the myriad ways in which companies are using smart learning techniques to build intuitive user-experiences (from Swiftkey’s predictive keyboard to Weave’s explainable contextual assistance), and providing machines with the ability to respond to visual or physical cues.
Deep generative models are already emulating human creativity in some fairly narrow domains. They are being used by Jukedeck and Melodrive to compose music, and by DeepArt and Prisma to create art. These creations have triggered conversations about the human creative process.
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