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📚 Halfway through Hilary Mantel’s Wolf Hall for my book club. So far, I’d highly recommend. There’s a miniseries adaptation too.
🇮🇹 Also, if you’re in the market for an Italian summer home, check out this steal I found.
Highlights in this issue:
Venezuela gearing up to invade Guyana for its oil?
conflict escalating on the Lebanon-Israel border
philosophical attitudes around having kids
Good reading,
—TK
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Here’s a link to my 2024 News Journal where I'm collecting the headlines that catch my interest each day so that when we look back at that at the end of the year, we'll see when things happened, what kind of patterns emerged, without the problem of hindsight bias.
🍼 Anastasia Berg and Rachel Wiseman have an op-ed in the NYT introducing their book What Are Children For?, which examines the attitudes and philosophical thoughts around having children in liberal middle/upper class circles.
There is nothing inherently unprogressive about embracing the prospect of children. Even Simone de Beauvoir, the philosopher who was among the first to critique reproduction and family as instruments for the oppression of women, acknowledged that shaping the character and intellect of another human being was “the most delicate and the most serious undertaking of all”… our fellow progressives need to stop thinking of having children as a conservative hobbyhorse and reclaim it for what it is: a fundamental human concern…
One can’t help noting the irony: In permitting the conservative movement to alienate them from the question of whether they want to have and raise children, these liberals and progressives are allowing the right to shape their reproductive agendas in yet another way.
Jay Caspian Kang also has a thoughtful review of the book in The New Yorker.
🤖📍 We all know ChatGPT can do some powerful things. My friend and AI researcher Keyon Vafa, recently published a paper (Twitter thread) that examines whether this means that transformers (the architecture behind LLMs like ChatGPT or Google’s Gemini) actually have accurate understandings and representations of the real world. For example, if it can tell you how to accurately navigate a city, it must have internally something like a map of that city, right?
I asked Keyon for his thoughts:
Some people think that LLMs are successful because transformers implicitly learn "world models". This paper tried to test that by training a transformer on turn-by-turn directions of taxi rides in NYC and seeing if we could recover a map of the city.
The model does really well. It can generate the shortest route between unseen locations and it makes legal turns almost all the time. But the model's implied map of NYC is incoherent, which implies fragility. When we add detours to the map, the model's capabilities break down.
The paper shows that LLMs can still do amazing things with incoherent world models. But it makes them fragile for other tasks.1
🕊🦠 The third human case of H5N1 bird flu was reported this month after a Michigan farmworker contracted it from contact with dairy cattle, where the virus has been found in herds across nine states.
The first two cases were relatively mild, involving symptoms like eye irritation, or conjunctivitis. However, the most recent case has shown more concerning signs, including coughing. The emergence of respiratory symptoms is disconcerting because it indicates a potential shift in how the virus affects humans. Coughing can spread viruses more easily than eye irritation can.
🌍🌡 GLOBAL HOTSPOTS
🇻🇪🇬🇾 I wrote about the Venezuela-Guyana border conflict in December and followed up in February about Venezuela’s troop movements to the border. The WSJ has a good 6-minute explainer video if you want a quick refresher.
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