In what feels like a troll but is in all likelihood completely serious, some parents are deciding to have their children fully immersed in AI LLMs:

We’re declaring bankruptcy on humans. Bring on the AI. In addition to integrating AI into as many facets of our lives as possible (our health, our work, our entertainment, and our personal lives), we’re designing an AI-integrated childhood for our kids—all while feeling like we’re helping them dodge a major bullet.

Did CS Lewis suspect, when he wrote The Abolition of Man, that the anti-human sentiment would be expressed as freely and overtly as the first sentence of this intellectually bankrupt paragraph? A paragraph that would be horrifying even if the AI it touts was actual intelligence, an AGI, but what these families are actually immersing themselves in is industrial-grade bullshit. As useful as bullshit can be — I hear it makes for great fertilizer! — one should not drink it as one would do Kool-Aid.

📚 Finished reading: In the Beginning… Was the Command Line by Neal Stephenson, almost thirty years old and more relevant than ever. Download it for free here, and if you think you don’t have time for all 65 pages Chapter 12 about the Hole Hawg should motivate you to read the entire essay.

When I wrote about formalizing AI “peer” review I meant it as a tongue-in-cheek comment on the shoddy human peer review we are getting anyway. “Wittgenstein’s ruler: Unless you have confidence in the ruler’s reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler. The less you trust a ruler’s reliability (in probability called the prior), the more information you are getting about the ruler and the less about the table.”, Nassim Taleb in Fooled by Randomness. Peer reviewers are the ruler, the articles are the table, and there is zero trust in the ruler’s reliability. It was also (1) a bet that the median AI review would soon be better than the median human review (and remember, the median journal article is not submitted to Nature or Cell but to a journal that’s teetering on being predatory), and (2) a prediction that the median journal is already getting “peer” reviews mostly or totally “written” by LLMs.

Things have progressed since January on both of these fronts. In a textbook example of the left hand not knowing what the right hand is doing, some journals are (unintentionally?) steering their reviewers towards using AI while at the same time prohibiting AI from being used. And some unscrupulous authors are using hidden prompts to steer LLM review their way (↬Andrew Gelman). On the other hand, I have just spent around 4 hours reviewing a paper without using any AI help whatsoever, and it was fun. More generally, despite occasionally writing about how useful LLMs can be, my use of ChatGPT has significantly decreased since I fawned over deep research.

Maybe I should be using it more. Doc Searls just wrote about LLM-driven “Education 3.0”, with some help from a sycophantic ChatGPT which framed eduction 1.0 as “deeply human, slow, and intimate” (think ancient Greeks, the Socratic method and the medieval Universities), 2.0 as “mechanized, fast, and impersonal” (from the industrial revolution until now), and 3.0 as “fast and personal”. Should I then just let my kids use LLMs whenever, unsupervised, like Neal Stephenson’s Primer (“an interactive book that will adapt as the user grows and learns”)? But then would I want my kids hanging out with a professional bullshitter? Helen Beetham has a completely contrarian stance — that AI is the opposite of education — and her argument is more salient, at least if we take AI to mean only LLMs. Hope lies eternal that somebody somewhere is developing actual artificial intelligence which could one day lead to such wonderful things as the “Young Lady’s Illustrated Primer”.

Note the emphasis on speed in the framing of Education 3.0. ⊕ I am less concerned about LLM bullshit outside of education, in a professional setting, since part of becoming a professional is learning how to identify bullshitters in your area of expertise. But bullshit is an obstacle to learning: this is why during medical school in Serbia I opted for reading textbooks in English rather than inept translations to Serbian made by professors with an aptitude for bulshitting around ambiguity. This is, I suppose, the key reason why we need LLMs there in the first place for there is nothing stopping a motivated learner from browsing wikipedia, reading any number of freely available masterworks online, watching university lectures on YouTube, and interacting with professionals and fellow learners via email, social networks, Reddit and what not. But you need to be motivated either way: to be able to wait and learn without immediate feedback in a world without LLMs, or to be able to wade through hallucinations and bullshit that LLMs can generate immediately. Education faces a bootstrapping problem here, for how can you recognize LLM hallucinations in a field you yourself are just learning?

The through-line for all this is motivation. If you review papers in order to check a career development box, to get O1 visa/EB1 green card status, and/or get brownie points from a journal I suspect you would see it as a waste of time and take any possible shortcut. But if you review papers because of a sense of duty, for fun, or to satisfy a sadistic streak — perhaps all three! — why would you want to deprive yourself of the work? Education is the same: if you are learning for the sake of learning, why would you want to speed it up? Do you also listen to podcasts and watch YouTube lectures at 2x? Of course, many people are not into scientia gratia scientiae and are doing it to get somewhere or become something, in which case Education 2.0 should be right up their alley, along with the playback speed throttle.

📚 Finished reading: Thinking With Tinderbox by Mark Bernstein, after starting two months ago. It is broader in scope and less Tinderbox-specific than The Tinderbox Way, his first book about, well, Tinderbox, a lovingly crafted “tool for thinking” that I have been using off and on for the last seven years. This is for the best: The Tinderbox Way was meant to convert the technical language from the official code reference into something us muggles can use, which is a job that ChatGPT can do much better and using the latest version of the app. Thinking With Tinderbox is more strategic than tactical, elaborating on why anyone whose primary job is not programming would want to dabble in code in the first place.

Two unrelated articles about AI greeted me from the feed reader this morning:

• Context, Memory, and Voice by Michael Lopp
• Emerson, AI, and The Force by Neal Stephenson

Both are worth reading, and Stephenson’s in particular may lead you down some nice rabbit holes owing to his profuse linking.

Cal Newport’s latest article about common sense in parenting closes with this punchline:

If you’re uncomfortable with the potential impact these devices may have on your kids, you don’t have to wait for the scientific community to reach a conclusion about depression rates in South Korea before you take action.

But does anyone — Georgetown math professors notwithstanding — make decisions this way, neatly compartmentalizing “the science” from their moral intuition? Or is there a mutually reinforcing interaction between the two, with our intuition exposing us to the confirmatory facts?

Apparently, I blinked and missed some extraordinarily good games on iOS that came out in recently in the last few years almost a decade ago. Fortunately my kids were there to educate me, yes, including the 6-year-old:

• Rodeo Stampede, nine years old and with ads but man what fun. Echoes of YMBAB and Crossy Road.
• Geometry Dash, which I could and should just watch my kids play because my reflexes and sense of rhythm aren’t enough for level 1 let alone this sorcery. Both the in-game music and the name reminded me of the first game I ever bought on Steam, which was of course Geometry Wars: Retro Evolved.
• Snake.io+, which brought memories of my old Nokia pouring down. But now it is a bit too competitive for my taste and I could only watch in awe as progeny raked up points two orders of magnitude higher than the runner-up.

All this reminded me of a conversation Tyler Cowen had with the YouTuber Any Austin who said that every medium reached its peak — with which I wholeheartedly agreed ⊕ And quite clearly we had reached Peak Movie in the late 1960s and the early 1970s, going downhill ever since Jaws graced the screen. — and that Peak Gaming was Pac-Man and Space Invaders — to which I could only say Huh?!

But then the more I thought about it the more I realized that he was basically correct. Well, in generalities if not in the specifics, as the Peak Single Player Video Game ⊕ Let’s not put in multiplayer games there, as they should be compared to card games, board games and sports was clearly Tetris.

I only half-kid. Show Tetris to a 10-year-old and she will immediately get it, spend a half-dozen hours on it the same day, and then dream about the figures. There are a few other gaming prototypes — and yes, Space Invaders and Pac-Man are both examples — but everything since then could be interpreted as a variation on a theme, adding whiz-bang graphics and sound effects to sugar-coat a basic mechanic. In an alternative universe where I have a PhD in ludology I would have been able to name a few more prototypes and family trees, digging into the core mechanic of each AAA title to get to its Space Invaders nugget; and if there are any blogs where this is actually done please point me to them, I would love to subscribe!

Twelve years ago I made a single $50 payment for continued development of MailMate. This has been one of the best software purchases I have ever made, so I didn’t hesitate for a moment when the developer asked for continued support at $40 per year. I consider it an enshittification avoidance fee.

Microsoft claims their new medical tool is “four times more successful than human doctors at diagnosing complex ailments”. Unsurprisingly, what they meant by “diagnosing a disease” was the thinking-hard part, not the inputs part:

To test its capabilities, “MAI-DxO” was fed 304 studies from the New England Journal of Medicine (NEJM) that describe how some of the most complicated cases were solved by doctors. 

This allowed researchers to test if the programme could figure out the correct diagnosis and relay its decision-making process, using a new technique called “chain of debate”, which makes AI reasoning models give a step-by-step account of how they solve problems.

If and when deployed, how likely is it that these algorithms will get a query comparable to a New England Journal of Medicine case study? Most doctors don’t reach those levels of perception and synthesis, let alone the general public.

📚 Finished reading: A Thousand Brains by Jeff Hawkins, having no idea how it ended up in my Kindle library. I am glad to have opened it, as I now have some semblance of a framework for how this thing we call intelligence might work. Note that the newest developments in neuroscience are just a starting point, as most of the book deals with their implications for AI and the future of humanity. If that sounds like overreach, know that by the end of the book it is. Still, these wafer-thin speculations don’t detract from the book’s meatier parts.

Confirmation bias alert: the framework repeats almost word for word the thought I had a while back — and more recently — about AI, that true general intelligence needs to be able to interact with its environment. So I may be blind to some obvious deficiencies in the argument. But then again, great minds, etc.