HappyLand

Startups of the Year 2023: Remote/Misc Winners.

Startups of the Year 2023: Remote/Misc Winners

Our world is still vindictive, but the all-reaching State of Utopia will have the strength that begets mercy.

Failure in a Modern Utopia

To the Labouring Classes of this Kingdom.


INTRODUCTION

That A.I. is able to beat humans in board games is common knowledge by now. How that is relevant to companies is less clear. Normally scientists could be of help here. Clearing up the mess by debunking the hype and showing how trustworthy the <a href="//gzht888.com/tagged/technology" target="_blank">technology</a> really is. But many of the good A.I. researchers are connected to big tech companies and these companies have little incentive to <a href="//gzht888.com/tagged/debunk" target="_blank">debunk</a> a hype when that negatively impacts their share holder value.

Superhuman artificial intelligence versus supercomputer humans

No selfrespecting artificial intelligence researcher would claim A.I. is going to take over the role of humans in this world any time soon. There are still many fundamental A.I. challenges that stop the rise of <a href="//gzht888.com/tagged/superhuman" target="_blank">superhuman</a> computers. These challenges will not disappear, not even in this data — or if you will “A.I. revolution” — that we are facing right now.

The Rise of Computational Sensemaking

Fairness and bias in machine <a href="//gzht888.com/tagged/learning" target="_blank">learning</a> is a blossoming line of research. Most of this work focuses on discrimination and inclusion. While this is an important line of research that I <a href="//gzht888.com/my-dearest-eliisabet-de724a86d105" target="_blank">wholeheartedly support</a>, I propose tech companies and platforms would also start working on “biased” algorithms that facilitate fair markets.

Design Fair Markets by Using Algorithms

It was early 2017 and it was only weeks after my start at the new institute for <a href="//www.jads.nl/" target="_blank">data science and entrepreneurship</a> that I began to lose hope. Tons of companies had shown their interest to collaborate in research or with students on company assignments, the interest was truly overwhelming. But all of them had <em>existing </em>datasets that <em>we </em>were supposed to turn into gold.

Data that you need is worth gold, not data that you already have

Bias in AI: a letter to my daughter

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