Global AI Index: All Four Nordics in Top 25
Scandinavia is recovering from the harsh AI Winter of the 1990’s. British Tortoise AI index ranks Finland a global #11 plus a “rising star,” as a result of Finland’s ongoing focus on skills development, driven by both government strategy and an excellent ecosystem of academia, AI-accelerators, private AI labs, tech startups, the Tampere AI Hub, a nationwide network of AI centers being built, etc. Sweden placed #15, Denmark 16, and Norway 25.
Based on 143 indicators, The AI Index benchmarks 62 countries on their level of AI investment, innovation and implementation. Although almost a blank space on the AI map of the past, the Nordics are picking up. In the early years of AI, quite a few within the AI & ML community still supposed that professor Kohonen was an American. Now, the Index ranks Finland a global 5 in government investment strategy and 9 in innovation/research.
According to a brief by four Nordic schools of economics, quoting Statista, there were 443 AI startups across Sweden-Finland-Denmark in April 2020. One of Scandinavia’s most prominent private sponsors of AI R&D is the Wallenberg Foundation, who financed Sweden’s by far fastest supercomputer Berzelius, built for AI (a donation of 300 million SEK = 29.5 million EUR to this project). Running since March 2021, Berzelius boosts R&D collaborations between academia and industry, into AI/ML, mathematics, and life sciences.
The latter is more intertwined with AI and data science than laymen tend to believe (nonetheless, even Berzelius-the-chemist began his professional life working as a doctor and summer apprentice at a pharmacy). Now that medtech and biotech are stepping into the shoes of a major AI driver, the Nordics can leverage their fairly clean clinical data from both mass screenings and individual treatments spanning decades, way into the past, due to half a century of longevity and thus smaller drop-off from long clinical studies. Wherever available, long time series offer a gold mine as training-data sets in ML within life sciences.
Bilinguality in terminology
Having said that, let’s keep in mind it’s a two-way street between two communities (medical & pharma, and AI & IT architecture) including some tricky “jargon bilinguality” on both sides, as is often the case in other domains too. Researchers, financial analysts, regional government authorities, power-grid planners, rail operators, and on it goes, are generally proficient in (and thus biased toward) classical statistics and its lingo whereas the straggly roots of appliedAI-ish, aged 65+, span across business rules, data/computer science, symbolic reasoning and object orientation, “subsymbolic” representations, explainability, knowledge management and elicitation, informatics, logic, set theory, search algorithms, and a dozen more.
Although much of the machinery underlying AI/Machine Learning/neural networks uses statistics-based methods, it does so in an own non-classical way. During the first Covid wave in 2020, California governor Gavin Newsom told CBC News: ”It’s not a gross exaggeration when I say this – the old modeling is literally pen–to-paper in some cases. And then you put it into some modest little computer program and it spits a piece of paper out. I mean, this is a whole other level of sophistication and data collection (…) We are literally seeing into the future and predicting in real time, based on constant update of information where patterns are starting to occur before they become headlines”. Harvard Medical School News & Research, March 2021, sees adaptive learning as a key source of value added by ML systems, “(…) they rely on adaptive learning: with each exposure to new data, the algorithm gets better (…) offering doctors accumulated knowledge from billions of medical decisions, billions of patient cases, and billions of outcomes”.
The roles-section of the “two-way street” is no less tricky than terminology gaps. It depends on the domain to be supported, line of business, number of employees, organization and enterprise architecture, degree of organizational learning, skills of the staff, collaboration culture (hierarchical or horizontal, formal or informal/lean/agile) etc. In most contexts, the key roles to be upgraded in the first place are architect roles: enterprise, data, IT, where “the spider in the web” is part of the job description; AI boosts both the business and the architects’ daily work.
(From Informator course Agile Architechture Fundamentals )
Summing up: Life sciences are a key driver behind Scandinavia’s (regrettably late but impressively fast) AI revival, but no part of the world has yet invented a vaccine against any kind of corporate friction during major innovation waves. Architects shall stay up-to-date on AI, prepared for the transition, and able to guide others through it, be technical roles or non-technical.
(on demand: Modular Product Line Architecture )Nyckelord: machine learning, science