Posts tagged AI
AI and the evolution of Software

The conversation around AI has settled into a predictable cycle: the announcement of a reality-altering feature from a new model, followed by a scientific study reminding us that AI is neither truly intelligent nor capable of reasoning, and may, in fact, be making us dumber. I should be upfront: I think AI models are great. I use them as much as I can, I try to learn with them, and I believe they will fundamentally transform how we work. In this essay, I’ll explain why.

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Things to think about #10 - Ukraine, the Endgame, and Coding with AI

Glenn Loury and John McWhorter are at their best when they disagree, and I enjoyed their discussion about the disastrous exchange between Trump, Vance, and Zelensky at the White House. Both agree that the U.S. is right to push for a negotiated settlement, which involves pressuring Ukraine to acknowledge its precarious position. However, they diverge on how this pressure was communicated and its potential repercussions. Glenn argues that Trump and the vice president rightly prioritized American interests by applying pressure on Zelensky, while John takes the opposite stance, framing his argument within a broader critique of the U.S. president and his administration.

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Things to think about #4

The Economist’s Free Exchange column drops in on the question of an economic motherhood penalty from childbirth. It is nice to see that the Economist correctly distinguishes between two distinct economic motherhood penalties, both of which can be traced to the interplay between evolutionary forces and modernity, where the latter in this case is defined as an environment with rapidly increasing returns to investment in human capital and education. The first, between fathers and mothers, emerge because the cost of child-rearing especially in the early part of a child’s life overwhelmingly falls on the mother, a conclusion which follows from Trivers (1972). This is true in terms of the cost during pregnancy and immediately after too. It is also true before we consider the possibility that the resource allocation trade-off for many women shifts in the wake of motherhood. The second motherhood penalty occurs between women. Put simply, in an economic structure where childless women have the ability to devote all their resources to somatic investment and take advantage of the above-mentioned increasing returns to human capital investment, the wage and wealth divergence between women who have many children and those who have none will widen significantly, at least in theory. For more on this, I cover the theory in more detail in my essay on fertility and sexual selection; see here.

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The BIS gets it wrong on AI/LLM and feminism & reproduction

The BIS has a Bulletin out on the usefulness of AI and large language models. They’re not terribly impressed.

When posed with a logical puzzle that demands reasoning about the knowledge of others and about counterfactuals, large language models (LLMs) display a distinctive and revealing pattern of failure. 

The LLM performs flawlessly when presented with the original wording of the puzzle available on the internet but performs poorly when incidental details are changed, suggestive of a lack of true understanding of the underlying logic. 

Our findings do not detract from the considerable progress in central bank applications of machine learning to data management, macro analysis and regulation/supervision. They do, however, suggest that caution should be exercised in deploying LLMs in contexts that demand rigorous reasoning in economic analysis.

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