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o, alternative pathways to building Type-2 reasoning-capable AI systems, likely using neurosymbolic approaches, have become much more attractive. People like Gary Marcus have argued for neurosymbolic approaches for decades. Such approaches combine the pattern recognition of neural nets, like LLMs, with symbolic reasoning’s logic and rules. Vinod Kh... See more

Azeem Azhar • 🧠 AI’s $100bn question: The scaling ceiling

Searching for Outliers

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on customer-oriented analytical applications involving personalization, ad and e-mail targeting, and search engine optimization.

Thomas H. Davenport • Big Data at Work: Dispelling the Myths, Uncovering the Opportunities

predicting influence on behavior.

Eric Siegel • Predictive Analytics

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Nate Silver • The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

In his April 2015 letter to shareholders, Amazon founder/CEO Jeff Bezos listed the four requirements for a “dreamy business”: “Customers love it, it can grow to very large size, it has strong returns on capital, and it’s durable in time—with the potential to endure for decades.” We want Prime to be such a good value, you'd be irresponsible not to b

Choice architects (such as Carolyn the cafeteria director) have many opportunities to choose defaults, and they can do so in ways that are self-serving or welfare enhancing.

Cass R. Sunstein • Nudge: The Final Edition

These things aren’t exactly easy—many people get them wrong. But they aren’t hard either, and by doing them you may be able to make predictions 80 percent as reliable as those of the world’s foremost expert. Sometimes, however, it is not so much how good your predictions are in an absolute sense that matters but how good they are relative to the co

Nate Silver • The Signal and the Noise: Why So Many Predictions Fail-but Some Don't

Many of your everyday clicks contribute to the web’s constant testing of how to improve overall persuasiveness.