Saved by Kasper Jordaens
AI is learning how to create itself
Self-Taught AI May Have a Lot in Common With the Human Brain
wired.com
Laura Pike Seeley added
In most areas of computing, programmers write code or train an AI model to achieve specific objectives, such as driving a car without crashing or generating humanlike text. Stanley creates systems that instead evolve, seeking novelty for its own sake. These systems sometimes discover stepping stones toward solutions that couldn’t be achieved via a ... See more
Maven Is a New Social Network That Eliminates Followers—and Hopefully Stress
Huge “foundation models” are turbo-charging AI progress
economist.com
Sterling Proffer added
The chief challenge, as Lee sees it, is how to make AI smarter without just throwing more data and computing power at it. His hope rests on the iterative tweaking of algorithms that improve the performance of AI at a “geometric pace.”
noemamag.com • Will AI Bring Plentitude or Further Imperil the Planet? | NOEMA
Leo Guinan added
This is the goal of subminds
The system had not been shown a single human game to learn from. But it was, nonetheless, learning by imitation. It was learning to imitate . . . itself.
Brian Christian • The Alignment Problem
about systems that learn from data without being explicitly programmed, and about how exactly—and what exactly—we are trying to teach them.
Brian Christian • The Alignment Problem
Instead, we're moving into Software 2.0 (a shift that Michael Taylor recently wrote about), where we describe a goal that we want to achieve and train a model to accomplish it. Rather than having a human write instructions for the computer to follow, training works by searching through a space of possible programs until we find one that works. In S
... See moreDan Shipper • Five New Thinking Styles for Working With Thinking Machines
Kaustubh Sule added
The Shift from Formal Theories to Engineering Outcomes in AI Development: In recent years, the development of artificial intelligence (AI) has transitioned from focusing primarily on formal theories and mathematical models to emphasizing practical engineering outcomes. This shift reflects a growing recognition of the importance of real-world applications, such as improving user experience and enhancing automation, over purely theoretical advancements. As a result, researchers and developers are increasingly prioritizing iterative design and testing processes to create more effective and user-friendly AI systems.