
The AI Con
How to Fight Big Tech’s Hype and Create the Future We Want
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Narrateur(s):
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Jade Wheeler
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Auteur(s):
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Emily M. Bender
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Alex Hanna
À propos de cet audio
A smart, incisive look at the technologies sold as artificial intelligence, the drawbacks and pitfalls of technology sold under this banner, and why it’s crucial to recognize the many ways in which AI hype covers for a small set of power-hungry actors at work and in the world.
Is artificial intelligence going to take over the world? Have big tech scientists created an artificial lifeform that can think on its own? Is it going to put authors, artists, and others out of business? Are we about to enter an age where computers are better than humans at everything?
The answer to these questions, linguist Emily M. Bender and sociologist Alex Hanna make clear, is “no,” “they wish,” “LOL,” and “definitely not.” This kind of thinking is a symptom of a phenomenon known as “AI hype.” Hype looks and smells fishy: It twists words and helps the rich get richer by justifying data theft, motivating surveillance capitalism, and devaluing human creativity in order to replace meaningful work with jobs that treat people like machines. In The AI Con, Bender and Hanna offer a sharp, witty, and wide-ranging take-down of AI hype across its many forms.
Bender and Hanna show you how to spot AI hype, how to deconstruct it, and how to expose the power grabs it aims to hide. Armed with these tools, you will be prepared to push back against AI hype at work, as a consumer in the marketplace, as a skeptical newsreader, and as a citizen holding policymakers to account. Together, Bender and Hanna expose AI hype for what it is: a mask for Big Tech’s drive for profit, with little concern for who it affects.
©2025 Emily M. Bender and Alex Hanna (P)2025 HarperCollins PublishersCe que les auditeurs disent de The AI Con
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Au global
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Performance
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Histoire
- Jackson
- 2025-05-30
Doom and Gloom without Vision
I very much appreciate the topic this book is covering, as it is something I'm quite passionate about, and considering the all-star writers and contributors I was excited for the text.
This book fell disappointingly short of my expectations. It provides many, important examples of how this technology can be used without consideration, and how that can lead to negatives consequences. However, this is broadly all this work provides without going into more depth, description or vision on these issues. For example, it mentions of how this technology shouldn't be used in contexts of search of private repositories. But, it does not go into depth of how it can be solved such as fine-tunning on these custom datasets for indexing.
It covers many important issues in biases of these systems towards discrimination of margenizled classes. This could be a point of further discussion of how to ameliorate the problem through auditing of training datasets, or having user-informed development.
Overall these technologies are here to stay one way or another, and I hope we can learn from these visionaries on how to make this technology work for us.
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