The A.I. Journey

In this episode of the A.I. Journey, Ron is sitting down with Raoul Grouls, a Senior Data Scientist at Anchormen and Rens van de Schoot, Professor of Statistics at Utrecht University with Small Data Sets, a member of the Royal Netherlands Academy of Arts & Sciences and the creator of ASReview - a revolutionary tool in the academic circles that reviews literature using Machine Learning, Deep Learning, and Active Learning.

A systematic review is a complex piece of research that aims to identify, select and synthesize all research published on a particular question or topic. It is a tedious task that researchers face frequently but a vital step of the scientific process. A recent example for the need for speed and accuracy is the Covid-19 outbreak. In order to develop a vaccine quickly, scientists needed to first review all available data in the field and pinpoint the relevant and valuable information, which could mean going through thousands of papers on a topic. ASReview started as a pet project to help Rens in his own research and has now grown to be the biggest open source project at his University, being downloaded hundreds of thousands of times and used in almost every scientific field.

Next to that, they also discuss the underlying principle used to develop this tool - Active Learning, as well as the development of Academia and how Machine Learning & A.I. is changing the game.

MRO (Maintenance, Repair & Overhaul) Optimization and spare-parts management has been left forgotten for the most part by the Manufacturing industry for a long time. In the face of growing complexity, conflicting departmental interests, and inadequate ERP systems, companies have turned their back on this issue. But today's growing digital maturity, regulatory push, and opportunities brought by machine learning & A.I. have reignited organizational interest in finally tackling their MRO challenges head on.

In this episode, we sit down with Laurent Chavagne, current Head of Procurement at IESA and MRO consultant with more than 25 years of experience, to talk about how the world of Supply Chain and Procurement has evolved in the last decades and where it is headed due to current and future developments.

We deep-dive into the main challenges that Supply Chain and Procurement departments face and what benefits they can reap by optimizing MRO and reducing complexity. As well as why ERP systems lag behind Data & A.I. solutions when trying to solve these issues. If you want to learn how you can reduce complexity in your Procurement or Supply Chain departments with the use of data and machine learning, then tune in to this episode of the A.I. Journey.

Big Data has come a long way from a corporate buzz word to the cultural and technological behemoth of nowadays. Today, most organizations understand that if they manage their data properly, they can apply analytics and machine learning to get valuable insight out of it. But back in 2012, Anchormen was one of the only companies in the Netherlands to develop big data projects at that scale. Since then, a lot has changed and even more can be expected in the near future.

In this episode, Jeroen and Ron have invited Corné Versloot, one of Anchormen's lead Big Data Architects and Consultants to reminisce of the early baby steps in the industry, as well as talk about the future of platforms such as our Data Hub. During the podcast they also discuss what a typical use case looks like, what is the process, and what it means for a company and its employees.

If you want to learn how we accelerate every data strategy and give business insights and meaningful results in the shortest period of time, then tune in to this episode of the A.I. Journey.

With a background (in) and passion for Physics, and an impressive career at companies such as KPN, the Justice Department, and for more than 20 years consulting at Quint, Jan Heuthorst joins this podcast episode to share his experience in Digital Transformation.  

Together with Jeroen and Ron, they deep-dive into the processes, challenges, and critical factors that organizations encounter on their path to Digital Transformation. As well as answer questions such as where does data and A.I. fit in that picture, and how to ensure long-lasting, positive change.  

Visit https://anchormen.nl/ to learn more.

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.