Most forms of technology applications are well understood. Every computer programme can be deconstructed into the basic building blocks of code, and if it goes wrong, you can debug the software – often by simply stepping through the code line by line in order to find out where the problem lies. Artificial Intelligence, or AI, is different. With the latest AI large language models we can't predict exactly what it will output, but it will do a good job at writing an article or creating poetry. What makes them human-like is the lack of predictable outcomes – humans simply aren't predictable!

"Machine learning is really not dark magic, it's just another tool." As the leading professor involved in the development of Google Brain, and Google's Director of Augmented Intelligence Research, he worked on their release of their open-source library for machine learning, Tensorflow. "Our main hurdle is to get people educated on how this works in practice." He shared that practical advice last week at TQ in Amsterdam (disclaimer: TQ is part of TNW), alongside three startups that each have their own hands-on experience with machine learning as well. For years now, the decreasing costs of computation power and data storage have gone hand in hand with the explosive birth of tech startups.