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It’s not the code we write to develop AI that determines their value system, it’s the information we feed them.

Mo Gawdat • Scary Smart: Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World

The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace many college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be unleashed,... See more

Leopold Aschenbrenner • SITUATIONAL AWARENESS: The Decade Ahead

Embed good management of people into the software.

James Currier • Status, Wealth, & Power: Network Effects Demand A New Social Contract

But a human brain is a flawed lens that can understand its own flaws—its systematic errors, its biases—and apply second-order corrections to them.

Eliezer Yudkowsky • Rationality

ozanvarol.com/genius

Ozan Varol • Awaken Your Genius: Escape Conformity, Ignite Creativity and Become Extraordinary

Balaji Srinivasan, the tech entrepreneur and crypto advocate, is a classic example of a person who is high in generativeness. He tweets his thoughts just about every single day on a wide variety of topics, ranging from media to crypto to the pandemic. A lot of it is speculative or maybe even wrong, but when he has a hit it is truly important.

Daniel Gross • Talent: How to Identify Energizers, Creatives, and Winners Around the World

Unplugging the hardwired external rewards may be a necessary part of building truly general AI: because life, unlike an Atari game, emphatically does not come pre-labeled with real-time feedback on how good or bad each of our actions

Brian Christian • The Alignment Problem

Second, the researchers posted this generalized challenge to Topcoder, an online platform for computationally intensive problems. At

Andrew McAfee, Erik Brynjolfsson • Machine, Platform, Crowd: Harnessing Our Digital Future

The two most important problems (at least how I am thinking about them currently), are:

Finding a rigorous scientific framework for how different agent skills, personalities, and instructions combine to be most capable for different problems (think of this as social management science for AI agents) .

Figuring out how you formally validate and verify... See more