Satalia CEO Daniel Hulme on the transformative power of machine learning & adaptive systems

Daniel Hulme sees artificial intelligence from two perspectives, as both an academic at University College London, and the CEO of AI consultancy Satalia. At Think 2018, he gave an overview of the fundamentals of this exciting new technology, and explored what it means for the future of innovation.

According to Daniel Hulme, one of the biggest challenges in computer science is taking the huge amount of data generated by the modern world, and figuring out what it means. To become useful, it needs to be married to context - an often complex and time-consuming process. Only then can the resulting information be analysed for insights.

Daniel Hulme, AI and the future of business

Fortunately, machine learning is already revolutionising these tasks, enabling data processing and analysis that would be impossible with human resources alone. Able to solve problems with billions of moving parts, advanced machine learning is already revolutionising everything from supply chain management, to public transit, to marketing attribution. Identifying patterns in datasets too large for people to comprehend, let alone analyse, these systems are transforming raw data into knowledge on an unprecedented scale.

However, such impressive feats shouldn’t be mistaken for true artificial intelligence. Borrowing a definition of AI from academia, Hulme says that true artificial intelligence will display ‘goal-directed adaptive behaviour’. In other words, the truly intelligent systems of the future will be able to adapt their own processes in real time to optimise for a strategic goal. Until this next level of sophistication is achieved, Hulme expects to see a new category of practitioners emerge over the next few years, with ‘decision scientists’ specialising in taking the patterns identified by machine learning, and applying the context that connects them to real-world behaviours.

If you want to know more, find out how AI and ML can create relevant reach at scale.

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