In this episode of the A.I. Journey, Jeroen and Ron sit down with freelance Data Scientist and Enterprise A.I. evangelist, Longhow Lam to talk about Data Maturity within organizations, data scientists, and the dutch landscape as a whole.
Starting with a deep dive into what is Enterprise A.I. and the challenges that come with trying to align business with IT and then moving on to answering questions such as whether the dutch landscape has matured and what can we expect in the future.
In the second part of the podcast, the conversation shifts towards the topic of data maturity itself. What does it mean, when is an organization data mature, what is ML Ops and how is it connected to CI/CD practices? Tune in to learn this and more in the A.I. Journey podcast.
In the second episode of the A.I. Journey Podcast, Jeroen and Ron sit down with Pim Haselager, Associate Professor at the Donders Institute for Brain, Cognition & Behavior, to talk about all the burning questions relating to Explainable A.I.
The podcast starts with a deep dive into the topic of Explainable A.I., why should businesses care about it and how can they profit from understanding the concept.
Next to that, they take a more philosophical approach towards answering the question why we hold technology to a higher standard than humans and will we reach a moment when A.I. will be in a position to explain morality to us.
Finally, the conversation comes back to the more practical and pressing matter of which jobs will become redundant due to the advancements of technology and do we really need to worry about that ever happening.
This podcast takes off with Jeroen and Ron talking about how algorithms can become biased and they discuss this on the basis of the gender bias hiring example. How can you avoid black box algorithms and force the neural network to represent its decision making process?
Next, they touch upon the accuracy of face and emotion recognition and how this relates to the 'dream' of Artificial General Intelligence (AGI). Can machines actually point into places where humans didn't go yet? (Spoiler: AlphaGo Zero)
What can companies learn from this: who takes the responsibility to avoid bias and to have a balanced, unbiased data (training) set? Jeroen and Ron explain why Precision and Recall are better metrics (over accuracy) to check whether your algorithm or data set is unbiased or not. And how can recommendation engines combined with post-processing help avoid collaborative filtering.