26 February 2019 Nordea Markets and Nordea Corporate & Investment Banking Bringing AI into operational business applications We interview Luka Crnkovic-Friis, co-founder and CEO of AI platform provider Peltarion, about where AI is today, common misconceptions about the technology and what management teams should think about organisationally and technologically to keep up to speed. He explains how the Peltarion platform is able to put the power of machine learning in the hands of non-experts to build operational AI systems that have been used for everything from cancer diagnosis and house price prediction to music categorisation and optimised pulp production. He also discusses Peltarion's ambition to educate both managers and citizens in general about AI. HS: How would you describe AI today – how far advanced is it? Where do you see the biggest limitations and opportunities for using AI/ML in business application? LCF: The concept of AI is first of all a broad term and as it grows in popularity it is becoming even broader. If we zoom in on the past ten years, the greatest leap forward has been seen in so-called deep learning, which is a reinvention of an old technique called neural networks, which in a sense replicate how the brain works. Neural networks tend to perform better the bigger they are and the more data they are trained with, and today we have way more data available than previously, along with higher levels of computing power where modern GPUs (graphics processing units) can train these systems 50-100x faster than ordinary CPUs can – and thus we can do much more with AI. The basic techniques are in themselves not new, but we have more resources now, and we can now do in a week what used to take a year. During the industrial revolution, we automated physical power. Now we are automating intellectual power; ie we are in a cognitive revolution. This will likely affect sectors across the board. We have never seen such fast adoption of new technology as we are seeing now. It took 150 years for the steam engine to develop from the invention of its basic principle to fully operational machines. Transistors took about 60 years to reach large-scale use, whereas computers and the internet took about 40 years. This puts a strain on businesses. We are in the first generation where business leaders are experiencing two disruptive events during their careers – first with digitalisation and the internet, and now with AI. We are seeing a cognitive revolution that affects all sectors, but current AI frameworks solve very narrow tasks In terms of limitations, current AI systems are rather narrow; general intelligence is an advancement that is still far in the future. Current AI frameworks can be trained to perform narrow tasks relatively well – predicting weather, composing music or predicting stock market developments. This does not delimit its impact in any way. For example, to get people from point A to point B, you only have to excel in so many small tasks; you do not have to be interested in poetry, have a mortgage or love your children. It is the task of getting from A to B that is subject to automation – not humans on a general level. Organisations must focus on data to succeed with AI/ML Challenges come from transformation and depend on where in the digitalisation process companies are. A data-enabled company has data at its core, around which it builds layers of first algorithms and then processes – encompassed by the people of the organisation. However, a more traditional company usually puts people at the core, tries to build a layer of culture and values and then tools around that, while data is an object that is distant from the thoughts of the organisation. The first type of business, the "Googles" and "Amazons" of this world, should be able to utilise AI and machine learning (ML) quite easily, as data is at their core. For the more traditional type of company, it quickly becomes difficult to utilise data-driven processes. Most companies are somewhere in between these two extremes, and the closer to a traditional organisation that a company is, the more challenging it will be to succeed in an emerging AI/ML environment. 10
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