AI is not ready for use - Design and build models with less data, and more thought
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AI is not ready for use - Design and build models with less data, and more thought

Many people and companies perceive AI as the new electricity – a powerful and mature technology that is almost a given. And this view is not surprising, because algorithms are already transforming our lives. They help us to get from A to B, recommend us things to buy or watch, find fraudulent transactions, and predict how we vote, or even with whom we will fall in love. But can it really be commercialized ‘just like that’ to create smart innovative solutions for society?

My answer to this question is a firm and clear-cut “no”. In this talk I want to separate the hype from reality, and sketch what still needs to be done in the field of AI.

Tags: EXP

All talks:

Engineering machine learning applications AI is not ready for use - Design and build models with less data, and more thought Moral programming: ethics at the front door World modeling in automated driving Music enabled running Towards a vision on micro-credentialing and open badges How does your computer understand your facial emotions? Why bots need emotions Machine vision-driven manufacturing automation in the factory of the future Automated guided vehicles in the factory of the future Let’s work together: How your co-bot can become a trusted colleague Becoming the data science department of 400000 SMEs TamaCoachee: a virtual coachee in your pocket Robots with character: What we can learn from Disney to develop more intuitive products How smart industry? Let’s talk about griefbots Visualizing music with machine learning Creativity meets artificial intelligence SMILEI: Five steps to design emotional game mechanics Demystifying AI Cyber security in smart industry? Using cobots for testing of navigation systems Use bots to get insights! AI: a Call to action
Gerard Schouten Lector Fontys ICT
Gerard Schouten

About Gerard

Gerard Schouten is a professor at the School of ICT, Fontys University of Applied Science. He holds a master’s degree in physics from the Technical University of Eindhoven and has a PhD in the field of cognitive science. He is a self-made deep learning and machine learning practitioner and is experienced in large-scale software development in the healthcare industry. Currently, he leads a new educational program on applied data science at Fontys. His research interests cover a wide array of topics in the broad field of AI and Big Data.