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Last Week in AI #44

Biased machines vs biased humans, the AI Index Report, drone racing and more!

Last Week in AI #44

Image credit: Tim Cook / New York Times

Mini Briefs

Biased Algorithms Are Easier to Fix Than Biased People

Sendhil Mullainathan makes the case that bias in algorithmic systems is easier to fix, than fixing bias in people. This analogy from the article succinctly makes the point:

It is much easier to fix a camera that does not register dark skin than to fix a photographer who fails to see dark skinned people.

As they correctly point out, fixing bias in algorithmic systems requires proper regulation, which does not exist yet. Kristian Lum (@KL Divergence) adds:

We aspire that algorithmic systems can be better than humans not just at scale and speed, but also in terms of fairness and bias. However, the fact that diversity and inclusion needs to improve to achieved this aspiration does not change. We need to have a diverse set of people at the table when discussing what bias even looks like in the context of an algorithmic system.

Introducing the AI Index 2019 Report

The AI Index Report for 2019 is out. It provides data about AI spanning multiple disciplines and industries.

The purpose of the project is to ground the discussion on AI in data, serving practitioners, industry leaders, policymakers and funders, the general public and the media that informs it.

This year, they have tripled the number of datasets included, and additionally created the Global AI Vibrancy Tool, an interactive tool that compares countriesā€™ global activities. As a means to track technical progress in the field, they have released the AI Index arXiv Monitor, that enables a better search experience on the arXiv preprint repository.

Advances & Business

Concerns & Hype

Analysis & Policy

Expert Opinions & Discussion within the field

Explainers

  • Towards Neuroscience-Grounded Artificial Intelligence - Why There Will be no Human-Level Artificial Intelligence Before Understanding Biological Intelligence First.

  • Model-Based Reinforcement Learning: Theory and Practice - Reinforcement learning systems can make decisions in one of two ways. In the model-based approach, a system uses a predictive model of the world to ask questions of the form ā€œwhat will happen if I do x?ā€ to choose the best x.

  • Why are so many AI systems named after Muppets? - One of the biggest trends in AI recently has been the creation of machine learning models that can generate the written word with unprecedented fluidity. These programs are game-changers, potentially supercharging computersā€™ ability to parse and produce language.

Awesome Videos

What facial recognition steals from us


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