Archives for machine learning

Fraiday #5: Let’s Get Practical!

Fraiday is the monthly meet-up for professionals who are AI-curious. We have a simple format: involving beers, a theme, lively debate and a group picture.

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Photos courtesy of Mike Breeuwer. Click the photo for the whole album!

The 5th edition of Fraiday was all about practical application of AI. Bart Vredebrecht from Aiir Innovations and our very own Michiel Berger were there to educate participants on all the ins and outs of AI projects.

For a change of scenery, this edition of Fraiday was at Freedomlab. Upon entering it was immediately clear that it is a place that defies definition. Co working-space, maker-space and innovation hub are all lables that sort of fit the bill at this “Institute for Redefinition” that is all about improving the way we life, work and relate to one another.

Bart gave us an overview of the triumphs and pitfalls that a real life AI project entails. He is the CEO of Aiir Innovations, an AI company that helps airlines with inspection of aircraft engines. He highlighted that a team of human + AI will outperform either of them alone.

After this introductory talk the group split into two breakout sessions. One group, meant for people who are already implementing AI, went with Michiel and discussed practical issues relating to that. The other group, which consisted of the AI curious, was introduced to the Machine Learning Canvas by Bart. The Machine Learning Canvas, which is based on the Business Model Canvas,

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Photos courtesy of Mike Breeuwer. Click the photo for the whole album!

After all that learning the group needed some nourishment, which duly arrived. The post-event discussions were held under the enjoyment of pizza and drinks. If you’d like to see the pics that were made during the day. Click here for the Facebook album.

At the next Fraiday, on 29 September, we’ll find out about all the different ways that AI can be used for good in non-commercial settings. Curious about the possibilities? Join us!

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AI with a conscience?

Three interesting articles on how A.I. shouldn’t be a black box when it comes to scientific value or ethical and moral values.

1. Technology Review writes about how Algorithmic systems have a way of making mistakes or leading to undesired consequences. They offer five principles to help technologists deal with that. Because despite the potential for efficiency gains, algorithms fed by big data can also amplify structural discrimination, produce errors that deny services to individuals, or even seduce an electorate into a false sense of security. Indeed, there is growing awareness that the public should be wary of the societal risks posed by over-reliance on these systems and work to hold them accountable. Read More

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