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Excess Capacity Learning
We introduce a new framework for understanding how cognitive systems (e.g., humans) learn from experience, based on the concept of representational capacity—the relative amount of representational resources devoted to encoding past experiences. Most paradigms in cognitive science have operated under the assumption that these resources are constrained, forcing cognitive systems to compress rich and noisy experiences to effectively generalize to new situations. We leverage recent advances in computer science to outline the implications of learning with excess capacity, or applying even more representational resources than needed to perfectly memorize all the details of one’s past experiences. In particular, we review evidence suggesting that excess capacity systems can exhibit many of the characteristics of human learning, such as the simultaneous ability to memorize individual experiences and generalize knowledge to new situations. We define and differentiate between constrained (not enough), sufficient (just enough), and excess (more than enough to perfectly capture all the details of one’s past experiences) capacity. We derive empirical properties of learning in each of these capacity regimes, and compare these predictions to effects documented for human learning. We highlight the broad implications of this framework for advancing theoretical and empirical work across cognitive, clinical, and developmental psychology.
Thick practices for AI tools
This essay is the result of thoughts developed during the PIBBSS fellowship this summer. Thanks to Dusan and Maris for feedback on a draft of this essay. Thanks to Sahil and Niki for discussions that influenced the ideas of this essay. 1. Intro What if we could build AI tools...
Excess Capacity Learning
How do humans learn from experience? Traditionally, cognitive scientists have assumed that discovering generalizable patterns requires that humans compress rich and noisy experiences. However, recent computer science results suggest otherwise — systems can learn by ‘overfitting’ and expanding all the details of their experiences. We offer a new perspective on learning based on a cognitive system’s representational capacity, which can be constrained (forcing the system to compress details of past experiences), sufficient (to memorize past experiences), or excess (allowing the system to expand on the details of past experiences). This framework has implications for understanding learning across cognitive, clinical, and developmental contexts.
On Taste, Effort & Curiosity - again
When AI collapses how long it takes to ship, what’s left is judgment, experimentation, and knowing what not to build.
Weighted DVF: A Simple Model for Scoring and Prioritizing Ideas
Discover a powerful yet straightforward approach to evaluating new ideas by balancing desirability, viability, and feasibility. Learn how…
Why Information Grows
"Hidalgo has made a bold attempt to synthesize a large body of cutting-edge work into a readable, slender volume. This is the future of growth theory." -- F...
As We May Think
“Consider a future device ... in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.”
The Compendium
A compendium of insights about complexity structured as inter-linked cards by Alex Komoroske
Quotes
I have a theory, which has not let me down so far, that there is an inverse relationship between imagination and money. Because the more money and technology that is available to [create] a work, the less imagination there will be in it.
Quotes
I have a theory, which has not let me down so far, that there is an inverse relationship between imagination and money. Because the more money and technology that is available to [create] a work, the less imagination there will be in it.
Combinatorial Creativity
Combinatorial creativity recombines existing ideas to spark novel, valuable innovations in cognition, AI, and design.
Titans + MIRAS: Helping AI have long-term memory
Ali Behrouz, Student Researcher, Meisam Razaviyayn, Staff Researcher, and Vahab Mirrokni, VP and Google Fellow, Google Research
Compaction Is a Financial Strategy - The Phoenix Architecture
Why smaller codebases win in the AI era
How Infrastructure Works: Transforming our shared systems for a changing world
'Pulsing with wisdom and humanity... a masterpiece' ED YONG'A hopeful, lyrical - even beautiful - hymn to the systems of mutual aid we embed in our material world' CORY DOCTOROW'An extraordinary book' MARK MIODOWNIK'You won't see the world the same after reading this book!' AUSTIN KLEONEvery day, we are granted the power to travel at high speeds, fly, see in the dark, summon water from distant mountains and electricity from the sun. The systems that run our world are invisible to us until they fail.Infrastructure enables lives of astounding ease and freedom that would have been unimaginable just a century ago. These technological systems - the most complex and vast ever created by humans - have allowed us to work collectively for the public good. But these systems are now beginning to fail us.Engineering professor Deb Chachra takes readers on a fascinating tour of these essential utilities, revealing how they work, what it takes to keep them running, and just how much they shape our lives - but also the price they extract, who pays it and in what ways, as well as the threats to our infrastructure in a changing world.From Snowdonia's Electric Mountain to a solar plant in southern India, Chachra shows how we can rebuild our shared infrastructure to be not just functional but also equitable, resilient, and sustainable. We need to learn how to see these systems and to transform them, together, because the cost of not being able to rely on them is unthinkably high.
When it comes to our digital lives, too many people care about bigness and an imagined potential to achieve their own bigness, and too few care about connections and the potential to achieve meaningful connections.
Your Internet Doesn't Need to Be Big
www.aaronrosspowell.com