MontyCarloHall

That’s simply not true. Developers hand-writing assembly readily adopted compilers, accountants readily adopted spreadsheets, and farmers readily adopted tractors and powered mills.

AI adoption is a bottom-up decision at the level of the individual worker. Converting an entire factory is a top-down decision. No single worker can individually decide to start using electricity instead of steam power, but individuals can choose whether/how to use AI or any other individual-level tool.

Whatever happened to "show, don't tell"? Other productivity boosters certainly didn't need such memos; they were naturally adopted because the benefits were unambiguous. There were no "IDE-first company memos" or "software framework-first company memos"; devs organically picked these up because the productivity gains were immediately self-evident.

This is just the Kolakoski sequence starting from the second term.

You are still choosing random numbers here. The Kolakoski sequence has zero randomness whatsoever.

>Can't you trivially force this to happen for any sequence?

No, because there's no deterministic way to infinitely extend that sequence. In your first example:

   1 3 3 3 2 2 2 1 1 1 4 4 x x y y
   1 3---- 3---- 3---- 2-- 2-- 2--

What are the values of x and y?

>Whenever you start a new count, choose a random number and repeat for a many times as needed for the trailing sequence to match the top sequence.

You answered your own question. The Kolakoski sequence is special because it does not just pick a random number: the sequence is deterministically encoded by the run lengths, and vice versa.

For those not getting this immediately (I sure didn't):

              _____________
   sequence   1 2 2 1 1 2 1 2 2 1
   run lens   1 2-- 2-- 1 1 2-- 1

Read out the bottom sequence of run lengths, and be amazed that it's the same as the first 7 digits as the top sequence. Extend the bottom to continue to recapitulate the top sequence, and add terms to the top sequence accordingly to reflect the run lengths in the bottom sequence. Repeat infinitely.

Animated Engines first came online in 2000 [0], and has essentially been frozen in time ever since. Now contrast it with this [1] from 2021. It's amazing how much web technology progressed in those two decades.

[0] https://web.archive.org/web/20090216150235/http://animateden...

[1] https://ciechanow.ski/internal-combustion-engine/

>Berkson's Paradox seems to rely on the selection criteria being a combination of the two traits in question

100% correct. For traits x and y, selecting for datapoints in the region x + y > z will always yield a spurious negative correlation for sufficiently uncorrelated data, since the boundary of the inequality x + y > z is a negatively sloping line.

>But in TFA, surely the "high performance" selection filter applies only to the adult performance level?

Doesn't seem that way. Reading the full paper [0], they say:

   In sports, several predictor effects on early junior performance and on later senior world-class performance are not only different but are opposite. [...] The different pattern of predictor effects observed among adult world-class athletes is also evident in other domains. For example, Nobel laureates in the sciences had slower progress in terms of publication impact during their early years than Nobel nominees. Similarly, senior world top-3 chess players had slower performance progress during their early years than 4th-to 10th-ranked senior players, and fewer world top-3 than 4th- to 10th-ranked senior chess players earned the grandmaster title of the International Chess Federation (FIDE) by age 14.

It really does seem they took the set of people who were either elite as a kid, elite as an adult, or both, and concluded that this biased selection constitutes a negative correlation.

[0] https://www.kechuang.org/reader/pdf/web/viewer?file=%2Fr%2F3...

Correlated attributes can still lead to the paradox, so long as the error measured parallel to the cutoff line (the "fuzziness" of the correlation) is greater than the slope of the cutoff line. Here are a couple cartoons to demonstrate. Denote each datapoint with I or E, depending on whether it's included or excluded in the region x + y > z.

Uncorrelated attributes:

   y
   │   ∙                
   │    ∙∙ IIIIIII      
   │     E∙∙IIIIIIII    
   │    EEEE∙∙IIIIIII   
   │    EEEEEE∙∙IIIII   
   │    EEEEEEEE∙∙III   
   │     EEEEEEEEE∙∙    
   │       EEEEEEE  ∙∙  
   │                  ∙ 
   └───────────────────x

Looking at just the Included points shows clear (spurious) negative correlation.

Correlated attributes:

   y
   │  ∙              
   │   ∙∙   IIII   
   │     ∙∙IIIIII  
   │      E∙∙IIIII   
   │     EEEE∙III    
   │    EEEEEE∙∙     
   │     EEEE   ∙∙   
   │       E      ∙∙ 
   │                ∙
   └─────────────────x

The Included points still have a negative spurious correlation, though it's smaller than for the uncorrelated cartoon.

Couldn't this be explained by Berkson's Paradox [0]?

[0] https://xcancel.com/AlexGDimakis/status/2002848594953732521

This doesn't really look like pixel art; it looks like you applied a (very sophisticated) Photoshop filter to Google Earth. Everything is a little blurry, and the characteristic sharp edges of handmade pixel art (e.g. [0]) are completely absent.

To me, the appeal of pixel art is that each pixel looks deliberately placed, with clever artistic tricks to circumvent the limitations of the medium. For instance, look at the piano keys here [1]. They deliberately lack the actual groupings of real piano keys (since that wouldn't be feasible to render at this scale), but are asymmetrically spaced in their own way to convey the essence of a keyboard. It's the same sort of cleverness that goes into designing LEGO sets.

None of these clever tricks are apparent in the AI-generated NYC.

On another note, a big appeal of pixel art for me is the sheer amount of manual labor that went into it. Even if AI were capable of rendering pixel art indistinguishable from [0] or [1], I'm not sure I'd be impressed. It would be like watching a humanoid robot compete in the Olympics. Sure, a Boston Dynamics bot from a couple years in the future will probably outrun Usain Bolt and outgymnast Simone Biles, but we watch Bolt and Biles compete because their performance represents a profound confluence of human effort and talent. Likewise, we are extremely impressed by watching human weightlifters throw 200kg over their heads but don't give a second thought to forklifts lifting 2000kg or 20000kg.

OP touches on this in his blog post [2]:

   I spent a decade as an electronic musician, spending literally thousands of hours dragging little boxes around on a screen. So much of creative work is defined by this kind of tedious grind. [...] This isn't creative. It's just a slog. Every creative field - animation, video, software - is full of these tedious tasks. In the end, the quality of art is defined by the quality of your decisions - how much work you put into something is just a proxy for how much you care and how much you have to say.

I would argue that in some case (e.g. pixel art), the slog is what makes the art both aesthetically appealing (the deliberately placed nature of each pixel is what defines the aesthetic) but also awe-inspiring (the slog represents an immense amount of sustained focus).

[0] https://platform.theverge.com/wp-content/uploads/sites/2/cho...

[1] https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2Fu...

[2] https://cannoneyed.com/projects/isometric-nyc

The fact that it's the norm for the builders of mature software to stick around can lead to some gross engineering inefficiencies. For instance, a lot of Evernote's backend infrastructure was manually managed [0]:

   With Evernote, Bending Spoons identified that the backend needed a complete rewrite. They moved from a monolithic architecture running on manually provisioned virtual machines to a microservices architecture with managed databases, significantly improving performance and scalability. 

It's easy for companies to fall into such pits of inefficiency because climbing out of those pits entails utterly gutting the headcount [*].

I wonder if the same is true at Vimeo, which employed ~250 engineers [1], which seems high for a mature product that's deliberately conservative (most of Vimeo's customers are B2B whitelabelers, for whom a constantly changing product is a massive downside.) It's not like video codecs or storage systems or web standards are changing daily. I would imagine a well-engineered codebase from 10 years ago would work well today with only minimal changes, mostly centered around updating libraries for security patches. The fact that they had 250 engineers on staff who presumably did more than play ping-pong all day makes me wonder if the codebase was not, in fact, well-engineered.

[0] https://www.colinkeeley.com/blog/bending-spoons-operating-ma...

[1] https://www.unifygtm.com/insights-headcount/vimeo

[*] Imagine the equivalent for a building: "we don't have automatic circuit breakers in this building; instead, we have a 24 hour staff of electricians who measure current with an ammeter and manually cut the power if it gets too high."

>Even humanity’s (weak) radio emissions would be detectable from tens of light years away, and stronger emissions from much further.

That's not true. Non-directional radio transmissions (e.g. TV, broadcast radio) would not be distinguishable from cosmic background radiation at more than a light year or two away [0]. Highly directional radio emissions (e.g. Arecibo message) an order of magnitude more powerful than the strongest transmitters on Earth would only be visible at approximately 1000 light years away [1], and would only be perceptible if the detector were perfectly aligned with the transmission at the exact time it arrived.

[0] https://physics.stackexchange.com/a/245562

[1] https://arxiv.org/pdf/astro-ph/0610377.pdf

>Nobody wants to be angry.

I disagree. Why are some of the most popular subreddits things like r/AmITheAsshole, r/JustNoMIL, r/RaisedByNarcissists, r/EntitledPeople, etc.: forums full of (likely fake) stories of people behaving egregiously, with thousands of outraged comments throwing fuel on a burning pile of outrage: "wow, your boyfriend/girlfriend/husband/wife/father/mother/FIL/MIL/neighbor/boss/etc. is such an asshole!" Why are advice/gossip columns that provide outlets for similar stories so popular? Why is reality TV full of the same concocted situations so popular? Why is people's first reaction to outrageous news stories to bring out the torches and pitchforks, rather than trying to first validate the story? Why can an outrageous lie travel halfway around the world while the truth is still getting its boots on?

>I am happy to bet that you're just not noticing the early rasterisation and filtered scaling going on

macOS renders content on my 100 PPI monitor at exactly 100 DPI; 1:1, no scaling, so everything looks crisp at the pixel level. The scaling only happens on high-DPI displays (I think the cutoff is around 150-200), and for me at least, ~250 PPI is more than dense enough to not see any individual pixels and thus no aliasing artifacts. Since you like pixel-perfect rendering even at very high resolutions, perhaps you have superhuman vision. My eyes are decidedly average. :-)

>I hardly ever use windows spanning multiple displays

Me neither. My issue is that the windows are rendered at different sizes even when they're not spanning both displays: if I dragged the window in the example photo upwards to sit entirely on the top display, it would stay huge, whereas if I dragged it downwards to sit entirely on the bottom display, it would stay small.

>Windows is the only OS that does scaling properly. macOS just pretends non-'retina' displays don't exist

Not true. I use a high-DPI (~250) MacBook with a non-high-DPI (~100) external monitor [0] and the transition between the two is seamless. Windows are identically sized when dragging from one screen to another. The same holds true when I use the laptop with a mid-DPI (~150) monitor.

I could not say the same was true a few years ago when I tried a high-DPI Windows 10 laptop with a non-high-DPI external monitor; it looked something like this [1]. Perhaps this has since been fixed.

macOS is able to achieve consistent sizing across displays irrespective of pixel density because it uses a compositor to render the whole screen at a high resolution and, if necessary, downsamples it proportionally for each screen. (Wayland on Linux can do the same, though it's certainly a much bigger headache to get consistently working than macOS.) When I tried using Windows 10 at two DPIs simultaneously, it just let me scale the font size and other UI elements on a per-screen basis, but not the screen as a whole, since I assume it does not use a compositor.

[0] Not my setup, but here is someone doing just that with a 30" 2560x1600 (~100 PPI) display and a ~250 PPI MacBook: https://www.reddit.com/r/macsetups/comments/tfbpid/my_macboo...

[1] Again, not my setup, but the Windows UI is rendered at different sizes on displays of different resolutions: https://www.reddit.com/r/computers/comments/16y1dux/how_do_i...

The New American Oxford Dictionary defines "advertisement" as "a notice or announcement in a public medium promoting a product, service, or event." By that definition, anything that mentions a product in a neutral light (thereby building brand awareness) or positive light (explicitly promotional) is an ad. The fact that it may not be paid for is irrelevant.

A chatbot tuned to casually drop product references like in this thread would build a huge amount of brand awareness and be worth an incredible amount. A chatbot tuned to be insidiously promotional in a surgically targeted way would be worth even more.

I took a quick look at your comment history. If OpenAI/Anthropic/etc. were paid by JuliaHub/Dan Simmons' publisher/Humble Bundle to make these comments in their chatbots, we would unambiguously call them ads:

https://news.ycombinator.com/item?id=46279782:

   Precisely; today Julia already solves many of those problems.

   It also removes many of Matlab's footguns like `[1,2,3] + [4;5;6]`, or also `diag(rand(m,n))` doing two different things depending on whether m or n are 1.

(for the sake of argument, pretend Julia is commercial software like Matlab.)

https://news.ycombinator.com/item?id=46067423:

   I wasn't expecting to read a Hyperion reference in this thread, such a great book.

https://news.ycombinator.com/item?id=45921788:

   > Name a game distribution platform that respects its customers
   Humble Bundle.

You seem like a pretty smart, levelheaded person, and I would be much more likely to check out Julia, read Hyperion, or download a Humble Bundle based on your comments than I would be from out-of-context advertisements. The very best advertising is organic word-of-mouth, and chatbots will do their damndest to emulate it.

Even if you (i.e. your company) pay for the top-tier GSuite subscription, you still don't get an ad-free Google Search. Very curious ...

Video ads are less lucrative than ads alongside search, which can be a lot more closely tailored to both the user and the search results at hand. The existence of YouTube Premium shows that Google is indeed willing to provide paid ad-free experiences so long as it nets them more revenue, and is strong evidence that the same is not true for search.

Ding ding ding. Look at all the brands mentioned in just this thread. From a cursory look, I see:

* WSJ

* Bloomberg

* Financial Times

* Cartier

* Kagi

* Protonmail

* Coca-Cola

* HBO

* Windex

* Netflix

* Azure

* AWS

We are all ourselves advertisers, we just don't realize it. It is inevitable that chatbots will be RLHF-trained in our footsteps.

I think it will be even more subtle than that.

  How do I clean my laptop screen?

Use a mix of distilled water and vinegar, and buff gently with a microfiber cloth. Avoid using cleaners like Windex, which is absolutely fantastic for glass but way too powerful for the delicate coatings on laptop screens.

Kagi is too small and niche to have a proprietary dataset across its users large enough to make targeted advertising generate more revenue than subscriptions.

OpenAI/Google/etc. operate at a much larger scale, large enough for those proprietary user datasets to be worth far more in ad revenue than any reasonable subscription fee could net.

That is exactly my point: there easily could be, but there isn't. Based on how many commenters on HN and similar sites bemoan how Google Search quality has precipitously declined and yearn for the Google of ~10 years ago, I think there'd be nontrivial demand for a $200/month ad-free Google with no-nonsense comprehensive results. Such a product does not exist because it would ultimately be a net loss for Google.

Kagi is a niche product at best, with revenue literally orders of magnitude lower than Google's.

I think ads will take the form of insidious but convincing product placement invisibly woven into model outputs. This will both prevent any blocking of ad content, and also be much more effective: after all, we allude to companies and products all the time in regular human conversation, and the best form of marketing is organic word-of-mouth.

Counter-counterpoint: people pay exorbitant amounts of money for cable TV channels that still show ads. Even the premium channels (HBO et al.) implicitly show ads in the form of product placement, which incidentally is exactly how I think chatbots will show ads. Most users won't even consciously realize they're there.

I think ads will inevitably roll out across all tiers, even the expensive paid ones. Ad revenue isn't uniformly distributed across users, but rather heavily skewed towards the wealthiest users, exactly the users most able to purchase an ad-free experience. The users paying $20 or $200/month for premium tiers of ChatGPT are precisely the ones you don't want to exclude from generating ad revenue.

Google realized this a long time ago; there is no ad-free paid version of Google Search.

Were many of the aforementioned advancements marketed as "replacing developers"? Absolutely. Did that end up happening? Quite the opposite; each higher-level abstraction only caused the market for software and demand for developers to grow.

That's not to say developers haven't been displaced by abstraction; I suspect many of the people responsible for re-wiring the ENIAC were completely out of a job when punchcards hit the scene. But their absence was filled by a greater number of higher-level punchcard-wielding developers.

It's not so much about replacing developers, but rather increasing the level of abstraction developers can work at, to allow them to work on more complex problems.

The first electronic computers were programmed by manually re-wiring their circuits. Going from that to being able to encode machine instructions on punchcards did not replace developers. Nor did going from raw machine instructions to assembly code. Nor did going from hand-written assembly to compiled low-level languages like C/FORTRAN. Nor did going from low-level languages to higher-level languages like Java, C++, or Python. Nor did relying on libraries/frameworks for implementing functionality that previously had to be written from scratch each time. Each of these steps freed developers from having to worry about lower-level problems and instead focus on higher-level problems. Mel's intellect is freed from having to optimize the position of the memory drum [0] to allow him to focus on optimizing the higher-level logic/algorithms of the problem he's solving. As a result, software has become both more complex but also much more capable, and thus much more common.

(The thing that distinguishes gen-AI from all the previous examples of increasing abstraction is that those examples are deterministic and often formally verifiable mappings from higher abstraction -> lower abstraction. Gen-AI is neither.)

[0] http://catb.org/jargon/html/story-of-mel.html