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I saw a cool version of the Monty Hall game here recently: https://monty.donk.systems. This is really cool! But I had an itch I wanted to scratch: rather than manually test out the probabilities, I wanted to run two games side-by-side: one where a switch happened and one where a switch didn't happen. Then, running those over and over, we should see the overall win percentages converge to 66.7% for switching and 33.3% for not switching. So I coded up a simulation here to scratch my itch! Controls # To control the simulation, you can start or stop here. You can also adjust the speed. Speed (between 1-30): Score # Cumulative stats as the games go on. Scenario Plays Wins Win Percent Switch 0 0 - No switch 0 0 - Switch simulation # If we switch cards after the first goat reveal. +---+ +---+ +---+ | | | | | | +---+ +---+ +---+ No switch simulation # If we don't switch. +---+ +---+ +---+ | | | | | | +---+ +---+ +---+ { playBtn.setAttribute('disabled', 'true'); setTimeout(() => { playBtn.removeAttribute('disabled'); }, 3000); started = !started; playBtn.innerHTML = started ? "Stop simulation" : "Start simulation"; play(true); play(false); }) let playCount = 0; let switchWinCount = 0; let nsPlayCount = 0; let noSwitchWinCount = 0; const playCountDisplay = document.querySelector('#plays'); const winCountDisplay = document.querySelector('#wins'); const winPctDisplay = document.querySelector('#win-pct'); const nsPlayCountDisplay = document.querySelector('#ns-plays'); const nsWinCountDisplay = document.querySelector('#ns-wins'); const nsWinPctDisplay = document.querySelector('#ns-win-pct'); const getRandomIndex = () => { return Math.floor(Math.random() * 3); } const boardDoors = (isSwitch) => document.querySelectorAll(`#${isSwitch ? '' : 'no-'}switch-board span.card`); const boardGuesses = (isSwitch) => document.querySelectorAll(`#${isSwitch ? '' : 'no-'}switch-board span.guess`); const clearBoard = (isSwitch) => { for (let i = 0; i { speed = parseInt(e.target.value); }) const delay = (seconds) => { return new Promise(res => { setTimeout(res, seconds * 1000 / speed) }); } const doorIndices = [0, 1, 2]; const setStatus = (isSwitch, text) => { const statusArea = document.querySelector(`#${isSwitch ? '' : 'no-'}switch-board section`); statusArea.innerHTML = text; } async function play(isSwitch) { if (!started) return; setStatus(isSwitch, " "); clearBoard(isSwitch); // Start with all goats const doors = new Array(3).fill("G"); // Add prize randomly const winIndex = getRandomIndex(); doors[winIndex] = "C"; await delay(0.5); setStatus(isSwitch, "Initial guess") await delay(0.5); // Select random spot for guess let guessIndex = getRandomIndex(); boardGuesses(isSwitch)[guessIndex].innerHTML = "✔" await delay(0.5); setStatus(isSwitch, "Revealing a goat") await delay(0.5); // Reveal a goat const doorsThatCanBeRevealed = doorIndices.filter(el => { return el !== guessIndex && el !== winIndex }) const revealIndex = Math.floor(Math.random() * doorsThatCanBeRevealed.length); const doorToReveal = doorsThatCanBeRevealed[revealIndex]; boardDoors(isSwitch)[doorToReveal].innerHTML = "G" await delay(0.5); setStatus(isSwitch, isSwitch ? "Switching choice" : "Not switching choice"); await delay(0.5); if (isSwitch) { boardGuesses(isSwitch)[guessIndex].innerHTML = " "; guessIndex = doorIndices.filter(el => { return el !== guessIndex && el !== doorToReveal })[0]; boardGuesses(isSwitch)[guessIndex].innerHTML = "✔" } await delay(0.5); setStatus(isSwitch, "Reveal") await delay(0.5); // Reveal for (let i = 0; i
I'm kind of writing this to "past" me, who I assume is "current" you for a number of folks out there. For the rest of you, this might just sound like ramblings of an old fogey super late to the party. Yes, AI is over-hyped. LLMs will not solve every problem under the sun but, like with any hot new tech, companies are going to say it will solve every problem out there, especially problems in the domain space of the company. Startups who used to be "uber for farmers" are now "AI-powered uber for farmers." You can't get away from it. It's exhausting. I let the hype exhaustion get the best of me for a while and eschewed the tech entirely. Well, I was wrong to do so. This became clear when my company bought Cursor licenses for all software developers in the company and strongly encouraged us to use it. I reluctantly started experimenting. The first thing I noticed is that LLM-powered autocomplete was wildly accurate. It seemed like it "knew" what I wanted to do next at every turn. Due to my discomfort with AI, I just stuck with autocomplete for a while. And, honestly, if I stuck with just using autocomplete it would still have been a massive level up. I remember having a few false starts with the agent panel in Cursor. I felt totally out of control when it was making changes to all sorts of files when I asked it a simple question. I have since figured out how to ask more directed questions, provide constraints, and supply markdown files in the codebase with general instructions. I now find the agent panel really helpful. I use it to help understand parts of a codebase, scaffold entirely new services or unit tests, and track down bugs. As a former skeptic, I am a wildly more productive developer with AI tooling. I let my aversion to the hype train cause me to miss out on those productivity gains for too long. I hope you don't make the same mistake.
Generative AI will probably make blogs better. Have you ever searched for something on Google and found the first one, two, or three blog posts to be utter nonsense? That's because these blog posts have been optimized not for human consumption, but rather to entertain the search engine ranking algorithms. People have figured out the right buzzwords to include in headings, how to game backlinks, and research keywords to write up blog posts about things they know nothing about. Pleasing these bots means raking in the views—and ad revenue (or product referrals, sales leads, etc.). Search Engine Optimization (SEO) may have been the single worst thing that happened to the web. Every year it seems like search results get worse than the previous. The streets of the internet are littered with SEO junk. But now, we may have an escape from this SEO hellscape: generative AI! Think about it: if AI-generated search results (or even direct use of AI chat interfaces) subsumes web search as a primary way to look up information, there will be no more motivation to crank out SEO-driven content. These kinds of articles will fade into obscurity as the only purpose for their existence (monetization) is gone. Perhaps we will be left with the blogosphere of old with webrings and RSS (not that these things went away but they're certainly not mainstream anymore). This, anyways, is my hope. No more blogging to entertain the robots. Just writing stuff you want to write and share with other like-minded folks online.
Here we go again: I'm so tired of crypto web3 LLMs. I'm positive there are wonderful applications for LLMs. The ChatGPT web UI seems great for summarizing information from various online sources (as long as you're willing to verify the things that you learn). But a lot fo the "AI businesses" coming out right now are just lightweight wrappers around ChatGPT. It's lazy and unhelpful. Probably the worst offenders are in the content marketing space. We didn't know how lucky we were back in the "This one weird trick for saving money" days. Now, rather than a human writing that junk, we have every article sounding like the writing voice equivalent of the dad from Cocomelon. Here's an approximate technical diagram of how these businesses work: Part 1 is what I like to call the "bilking process." Basically, you put up a flashy landing page promising content generation in exchange for a monthly subscription fee (or discounted annual fee, of course!). No more paying pesky writers! Once the husk of a company has secured the bag, part 2, the "bullshit process," kicks in. Customers provide their niches and the service happily passes queries over to the ChatGPT (or similar) API. Customers are rewarded with stinky garbage articles that sound like they're being narrated by HAL on Prozac in return. Success! I suppose we should have expected as much. With every new tech trend comes a deluge of tech investors trying to find the next great thing. And when this happens, it's a gold rush every time. I will say I'm more optimistic about "AI" (aka machine learning, aka statistics). There are going to be some pretty cool applications of this tech eventually—but your ChatGPT wrapper ain't it.
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This blog has moved to https://blog.caseyhandmer.com/?moved. All the old posts are there, with interactive versions of many of the charts. The RSS feed is now https://blog.caseyhandmer.com/feed.xml. This site stays up for old links but won’t get new posts.
There’s a famous (but unattributable) quip that goes something like this: If you are not a liberal at 25, you have no heart. If you are not a conservative at 35, you have no brain. In Chapter 12 of Probably Overthinking It, I used data from the General Social Survey (GSS) to show that the quip is half right. Older people hold more conservative views. But as people get older, their views don’t become more conservative; on average, they become... Read More Read More The post Do People Get More Conservative With Age? appeared first on Probably Overthinking It.
Like some of you, we have several kitchen appliances with integrated digital timers.
In this conversation, Blaise Agüera y Arcas and Baratunde Thurston explored what a story of symbiosis and interdependence might teach us about living alongside AI. Agüera y Arcas began by calling individual human intelligence a myth: we’ve always been a collective intelligence, entwined with our technologies. He traced this back to symbiogenesis. Life has grown more complex not by splitting apart but by merging, as separate organisms fuse into increasingly complex new forms of life. These mergers speed up change by opening new possibilities. Thurston agreed that union is in our nature but framed the current technological acceleration as a speed problem. We need time and new stories to adjust because, as he reminded us, “when you invent the ship, you invent the shipwreck.” Caring for one another through acceleration must be our prime directive, he said. The two then turned to how we relate to AI. Responding to Joanna Bryson’s argument that robots should be our slaves, Thurston said the master is also in chains — owning another dehumanizes the owner. As relational beings, how we treat AI will shape how we treat each other. Thurston called for both an expansion and a humbling of how we define ourselves: “We’ve overdone independence,” he said, “interdependence makes us richer.” Agüera y Arcas agreed and pushed us to drop a zero-sum definition of human intelligence, where every AI advance diminishes humanity’s value. As technology advances, we trade some individual skills for new collective capabilities, Agüera y Arcas acknowledged. But over the long arc of history, he believes, that exchange pays off in collective flourishing. Thurston didn’t deny this, but he closed with an urgent question: who controls the loss of capabilities and agency?
