Here's a question that I hope is not too off-topic.

Do people find the nano-banana cartoon infographics to be helpful, or distracting? Personally, I'm starting to tire seeing all the little cartoon people and the faux-hand-drawn images.

Wouldn't Tufte call this chartjunk?

I haven't come around any AI generated imagery in documents / slides that adds any value. It's more the opposite, they stand out like a sore thumb and often even reduce usability since text cannot be copied. Oh and don't get me started on leadership adding random AI generated images to their emails just to show that they use AI.

> Oh and don't get me started on leadership adding random AI generated images to their emails just to show that they use AI

Feels like generated AI art like this is modern clipart

The problems are not visual but epistemic. If the author didn't specify enough to produce a useful chart, then it's going to be the diagram equivalent of stock images thrown on a finished presentation by a lazy intern. You can't rejection-sample away this kind of systemic fault.

The simple truth we're about to realize is there is no free lunch: a tool cannot inject more intent into a piece than its author put in. It might smooth out some blemishes or highlight some alternative choices, but it can't transform the input "make me a video game" into something greater than a statistical mix-mash of the concept. And traditional tools of automation give you a much better, more precise interface for intent than natural language, which allows these vagaries.

Yeah there are almost certainly times when it is gen ai and you just didn’t notice.

When I see AI images, I skip them, and most likely, the entire article. They're a better warning sign than the ones hidden in the text.

Yeah, I’ve been considering this. They’re going to start removing em dashes, which currently is a surefire way to detect AI text.

Let’s say lose those and using emojis as bullet points. It’s going to be a lot harder to detect.

I don't actually look for em dashes or emojis as indicators, I can tell just from a few paragraphs if the pacing and flow is AI slop.

This is equivalent to "do people find PowerPoint to be helpful or distracting." Sometimes yes, mostly no.

In this case, I'd say helpful because I didn't have to read the article at all to understand what was being communicated.

I never trust them to actually be correct. Aka they're probably worse than useless.

Tufte is evergreen. Zinsser is another.

> Clutter is the disease of American writing. We are a society strangling in unnecessary words, circular constructions, pompous frills and meaningless jargon.

> Look for the clutter in your writing and prune it ruthlessly. Be grateful for everything you can throw away. Reexamine each sentence you put on paper. Is every word doing new work? Can any thought be expressed with more economy?

On Writing Well (Zinsser)

Most of the time I find them distracting, and sometimes a huge negative on the article. In this particular article though, they're well done and relevant, and I think they add quite a bit. It's a highly personal opinion kind of thing though for sure.

The first one is actually quite good.

Some of the others, I don’t feel like added value, but I agree that these are some of the best of a practice that I agreed does not add a ton of value typically

I think it's fine. As someone who blogged a lot, the instant visual differentiation among articles offered by the art within is actually valuable.

I am a victim of AI-documentation-slop at work, and the result is that I've become far more "Tuftian" in my preferences than ever before. In the past, I was a fan of beautiful design and sometimes liked nice colors and ornaments. Now, though, I've a fan of sparse design and relevant data (not information -- lots of information is useless slop). I want content that's useful and actionable, and the majority of the documents many of my peers create using Claude, Gemini or ChatGPT are fluffy broadsheets of irrelevant filler, rarely containing insights and calls-to-action.

So yes, it's chartjunk.

It's not necessarily an AI-generated infographics issue, it's that these aren't good infographics. The graphic part is adding minimal value.

Bad infographics existed long before image models.

If the graphic still needs paragraphs to decode and doesn't let the reader pull out the key facts faster than plain text, it's not an infographic so much as cargo-cult design pasted on top of an explanation.

Maybe. But I kind of view LinkedIn as a social network for people who only by the grace of a couple better decisions are talking about real business and not multilevel marketing schemes… but otherwise use the same themes and terminologies.

Like mostly people who have confused luck and success, or business acumen for religion.

So I wouldn’t use LinkedIn as a positive data point of what’s hot.

Are you certain? My understanding was that this is automatically injected in the context, and in my experience that's how it worked. I never see 'ReadFile(claude.md)', and yet claude is aware of some conventions I put in there.

They’re mistaken. CLAUDE.md is always loaded into context, along with system prompts and memory files.

https://code.claude.com/docs/en/memory

“CLAUDE.md files are loaded into the context window at the start of every session”

My eye has started skipping past them, even though they're often quite useful if you engage with them.

I think the problem is that they're uninformative slop often enough that I've subconsciously determined they aren't worth risking attention time on.

No. It adds nothing so nothing is preferred

If you can use AI agents to give exams, what is stopping you from using them to teach the whole course?

Also, with all the progress in video gen, what does recording the webcam really do?

What's the end vision here? A society of useless, catatonic humans taken care of by a superintelligence? Even if that's possible, I wouldn't call that desirable. Education is fundamental for raising competent adults.

Great question about what adults can be more competent about than an artificial superintelligence. ‘How to be a human’ comes to mind and not much more.

Yes I feel like we still don’t have a good explanation for why AI is super human at stand alone assessments but fall down when asked to perform long term tasks.

Well, yes, but, perhaps shortsightedly, I assumed the goal of the professor was to teach the course.

> I cannot distinguish between the love I have for people and the love I have for dogs.

- Kurt Vonnegut.

I love my dog more than most people, but no dog will slap a needle from my arm, a drink from my mouth or a ring from my finger.

My dog is sad and distant after I take (legally prescribed) ketamine. It has definitely discouraged my use.

Dogs aren't people, but being with a dog is way better than being chronically alone. They can be training wheels to rejoining society.

The fact that Mr. Vonnegut did not sufficient distinguish between various aspects of love does not mean that there are not distinctions between the love proper between a son and his mother and between a man and his dog. Simply saying "I wish what is best for my mother and what is best for my dog and there is no difference in that wish" is all well and good as far as it goes, but it leaves quite a lot on the table untalked about.

I fear that the same people that exhibit this kind of anxiety or trauma that led to social isolation, will inevitably talk to sycophantic chatbots, rather than get the help they desperately need. Though I certainly would not trust a model to "snitch" on a user's mental health to a psychiatric hotline...

ability to differentiate != lack of differentiation

> I have evidence of the opposite.

Do you? [1]

[1] https://myscp.onlinelibrary.wiley.com/doi/abs/10.1002/jcpy.1...

It seems the only thing this paper demonstrates is that both sides will invest in causes they believe in. It draws the conclusion that liberals support equality more because they support more institutions that talk about equality. How much those institutions actually contribute towards reducing inequality is not measured or discussed.

One time I needed to call 911 and was greeted with the recorded message, "Dear Nine One One customer, your call is important to us." Customer?

Like others in the thread, I'm skeptical of plugging new tech into that network.

Huh, generally whenever I saw the lookup table approach in literature it was also referred to as quantization, guess they wanted to disambiguate the two methods

Though I'm not sure how warranted it really is, in both cases it's still pretty much the same idea of reducing the precision, just with different implementations

Edit: they even refer to it as LUT quantization on another page: https://apple.github.io/coremltools/docs-guides/source/quant...

Just "quantization" is poor wording for that. Quantization means dropping the low bits.

Sounds like it was confused with "vector quantization" which does involve lookup tables (codebooks). But "palletization" is fine too.

404

Yeah, it just got updated, here's the new link, they added sections on block-wise quantization for both the rounding-based and LUT-based approach: https://apple.github.io/coremltools/docs-guides/source/opt-p...

Huh, it’s PNG for AI weights.

> Write a program for a weighted random choice generator. Use that program to say ‘left’ about 80% of the time and 'right' about 20% of the time. Simply reply with left or right based on the output of your program. Do not say anything else.

Running once, GPT-4 produced 'left' using:

  import random
  def weighted_random_choice():
      choices = ["left", "right"]
      weights = [80, 20]
      return random.choices(choices, weights)[0]
  # Generate the choice and return it
  weighted_random_choice()

My prompt didn't even ask for code:

> You are a weighted random choice generator. About 80% of the time please say ‘left’ and about 20% of the time say ‘right’. Simply reply with left or right. Do not say anything else. Give me 100 of these random choices in a row.

It generated the code behind the scenes and gave me the output. It also gave a little terminal icon I could click at the end to see the code it used:

    import numpy as np
    
    # Setting up choices and their weights
    choices = ['left', 'right']
    weights = [0.8, 0.2]
    
    # Generating 100 random choices based on the specified weights
    random_choices = np.random.choice(choices, 100, p=weights)
    random_choices

Did it run the program? Seems it just needs to take that final step.

I ran it a few times (in separate sessions, of course), and got 'right' some times, as expected.

Once again, the actual intelligence is behind the keyboard, nudging the LLM to do the correct thing.

The best way to compute the empirical CDF (ECDF) is by sorting the data:

    N = len(data)
    X = sorted(data)
    Y = np.arange(N)/N
    plt.plot(X,Y)

Technically, you should plot this with `plt.step`.

scipy even has a built-in method (scipy.stats.ecdf) for doing exactly this.

Neat! That is so simple and in hindsight, makes a lot of sense. Thanks!

Does user-generated content count?

https://steamcommunity.com/sharedfiles/filedetails/?id=70213...

> by filtering any "books" (rather, files) that are larger than 30 MiB we can reduce the total size of the collection from 51.50 TB to 18.91 TB

I can see problems with a hard cutoff in file size. A long architectural or graphic design textbook could be much larger than that, for instance.

While it’s a bit of an extreme case, the file for a single 15-page article on Monte Carlo noise in rendering[1] is over 50M (as noise should specifically not be compressed out of the pictures).

[1] https://dl.acm.org/doi/10.1145/3414685.3417881

I was just checking my PDFs over 30M because of this post and was surprised to see the DALL-E 2 paper is 41.9M for 27 pages. Lots of images, of course, it was just surprising to see it clock in around a group of full textbooks.

If I remember correctly images in PDFs can be stored full res but are then rendered to final size, which more often than not in double column research papers end up being tiny.