The Crooked Timber of AI

Terence Tao – Kepler, Newton, and the true nature of mathematical discovery

“And what those stories teach us about how AI will revolutionize math”

AI FOR EPISTEMICS & COORDINATION

Civilization and technology have radically improved the human condition. Nonetheless, the world sometimes goes in directions which essentially nobody would prefer — e.g., nuclear arms races, unexpected financial crashes, predatory marketing, or ubiquitous political misinformation.

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

The fall of the theorem economy

How AI could destroy mathematics and barely touch it

Artificial intelligence and illusions of understanding in scientific research

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

AI is not superhuman

What metaphor should drive the field of AI research?

What we can’t measure about AI – yet | Aeon Essays

The costs of transformative innovations are immediately clear: it’s the longterm gains that are hardest to understand

AI, Ethics, and Society — Home

The Scaling Era: An Oral History of AI, 2019–2025

An inside view of the AI revolution, from the people an…

Field Theory: AI as Social Science Question, Object & Tool

Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.

Separating AI’s Technological Problems From its Capitalism Problems

Nathan E. Sanders and Bruce Schneier say integrating a technology as disruptive as AI responsibly requires deep structural reforms.

We're not taking the fact-checking powers of AI seriously enough. It's past time to start.

Some notes on the Nobel Prize hallucination that wasn't

Knowledge Collapse

AI companies are racing to mechanize mathematics. Where does that leave human understanding?

The Scaling Era: An Oral History of AI, 2019–2025

An inside view of the AI revolution, from the people and companies making it happen.

AI That Evolves in the Wild | Edge.org

I’m interested not in domesticated AI—the stuff that people are trying to sell. I'm interested in wild AI—AI that evolves in the wild. I’m a naturalist, so that’s the interesting thing to me. Thirty-four years ago there was a meeting just like this in which Stanislaw Ulam said to everybody in the room—they’re all mathematicians—"What makes you so sure that mathematical logic corresponds to the way we think?" It’s a higher-level symptom. It’s not how the brain works. All those guys knew fully well that the brain was not fundamentally logical.