Pointer: What Current & Future Engineering Leaders Read

Testifying At the Senate About A.I. - Selected Content On The Internet

- Stephan Wolfram

tl;dr: Over the summer Stephan was asked by congress whether "algorithmic transparency" is a policy option with regard to regulating "persuasive internet platforms." He discusses the complexities involved and a couple of conceptual options.

featured in #166

Responsible AI: Putting Our Principles Into Action

- Jeff Dean

Google
AI

tl;dr: An outline of what Google is doing to educate and train its employees on the ethics of AI, including research papers and internal tooling.

featured in #147

Notes on AI Bias

- Ben Evans

AI

tl;dr: "Machine learning finds patterns in data. ‘AI Bias’ means that it might find the wrong patterns". Article runs through examples and scenarios of bias, along with methods to troubleshoot and manage such issues.

featured in #138

The Bitter Lesson

- Richard S. Sutton

AI

tl;dr: AI Research is conducted with the assumption that computational power is constant so researchers seek human knowledge as leverage. However, due to Moore's Law, computational power is the defining factor in the long run.

featured in #133

A Radical New Neural Network Design Could Overcome Big Challenges In AI

- Karen Hao