kgarten

Looks nice. Funny, I also made a dictation app using claude ... yet just for iOS. https://kaikunze.de/aimemo/

Reminded me a bit on the design space of Metal logos: https://renecutura.eu/metalvis/

I think this coverage feels very similar to the way Google Glass was treated back in the early 2010s ... there’s a grain of legitimate concern, but the article oversells what these glasses actually do and stokes alarm in a way that goes beyond the available facts.

Workers annotating data for AI might see sensitive content captured by smart glasses. But the leap from that to “we see everything” and framing it like some dystopian panopticon mirrors the early Google Glass panic, where the concerns often outran what the device actually could do.

Legitimate concerns shouldn’t be dismissed, but neither should they be inflated to create a new “Glass-forked-into-Big-Brother” narrative unless the evidence genuinely supports that level of risk ...

btw, source code is here: https://github.com/kkai/CranKen

Quite a while back, a former student of mine built Nekoze :D

https://nekoze.app Nekoze warns you when you are hunched over.

Years back, we did a couple of whimsical prototypes along those lines (using J!NS MEME, smart glasses): https://youtu.be/LXIY2g-twOA

https://kaikunze.de/

Source is here: https://github.com/kkai/web-source

I'm also not saying that the parent is AI generated. Just, that the text triggered for me my "Might be AI" alert. It's not only the em dash but the combination of em dash and rules of three (plus a couple of other hints).

“His peaks—the biting humor about corporate absurdity, the writing on systems thinking and compounding habits, the clarity about the gap between what organizations say and what they do—unquestionably made me healthier, happier, and wealthier.”

Maybe I’m getting cynical, yet every time I see an mdash and rules of 3, it triggers the feeling of “This sounds like AI” …

Here’s another example:

“ I can avoid the ugliness—the racism, the grievance, the need to be right at any cost.”

Why not link to the official recording ?

https://media.ccc.de/v/39c3-liberating-bluetooth-on-the-esp3...

wondering why you are downvoted. You are right, though it's kind of inferred that the author means fMRI as the title focuses on brain activity only.

My heart goes out to you.

thanks!

Great content, yet why are the figures so horrible in terms of resolution. I’m reading it on my smart phone and have a hard time deciphering anything on these images.

Which podcast?

Are the videos available somewhere?

spring course is on YouTube https://m.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpT...

Logseq? (Though it uses md)

Is this all vibe coded? (mdashes ;) ... I cannot see how one can publish agent skills /task capabilities on the site. It seems to be just a list of open resources (most likely also found using claude or ChatGPT ?).

It works for me, just needs a time to load.

My first thought went to Project Blinkenlights https://en.m.wikipedia.org/wiki/Project_Blinkenlights

NotebookLM is great to get an overview of a publication. I created a short podcast focusing on HCI publications using NotebookLM https://www.deep-hci.org/

Just posted some ISWC, MobileHCI and UbiComp papers, UIST is up next.

https://aisnakeoil.substack.com/p/gpt-4-and-professional-ben...

"GPT-4 and professional benchmarks: the wrong answer to the wrong question OpenAI may have tested on the training data. Besides, human benchmarks are meaningless for bots."

lol .. then don't claim you have:

"His claims are simply false, easily refuted by empirical data."

he underlines his claims with examples showing what he means. I recommend you to read the original article by Chomsky, as you seem to have a problem understanding his arguments.

ok, you didn't answer my first questions. I try a couple of more.

your first argument is useless, as anything might be a path to AGI ... first order logic / expert systems are ...

regarding empirical data, you are bringing up anecdotes that don't hold up. Humans can often learn a concept from 1-2 instances (take the face of Albert Einstein) (even your kids can do that in grammar :) , maybe check how language models are trained in comparison.

do you have any references for your claims? How much data does a human need to learn grammar? do you have any estimate?

you say you can refute Chomsky's claims by empirical data. please show me that data/studies, don't talk about anecdotes from your kids.

ha ha ... every human is a fool in that case (as every human holds some unreasonable believes and opinions some point in time).

"He has discredited his mind?" This sentence does not make sense to me. Explain what you mean by this statement. I found his NYT piece well argued. How can you fix any language model similar to ChatGPT to prevent the mistakes shown in the article? (I don't think you can).

to make my point easier: for my definition of to read, to understand and comprehend you need a body, an embodied mind. you need agency and consciousness. ChatGPT has none of those, it's an implementation of several matrix functions.

the specific model you are referring to does nothing, it's not acting (it's not reading anything). you have to press enter for it to work. so it's not reading, you (and OpenAi engineers) are doing its actions for it.

Yes, as I said we have different definitions.

For me, it's matrix multiplications on data made to output things that are approximations to what a human has written before on that question or text you're prompting it.

I know how training a deep neural network works and there's no reading involved for me :)

It's just estimating what a person would write (being trained on a really large data set of what people wrote). If it's trained on gibberish, it will just output gibberish. If I ask you to read a book with gibberish words would you do it? ChatGPT would (according to you) "read it" and recite the gibberish I gave it without a problem.

The matrix multiplications used to build these models don't show any comprehension, agency, or consciousness. Their point is to estimate the next data point given the previous data point, they will perform the same task independent from which training data we give them. That's not reading.

I don't say this might not change in the future, yet treating ChatGPT like a human using words like "reading," "understanding" etc. or intelligence for that matter is just anthropomorphizing for me.

Very skeptical about this. I'm working with heart rate monitors and different physiological signals. The wrist-based smart watches are still sometimes way off compared to an ECG baseline, depending on skin type and how they are worn.

Even step counting with them is not accurate at all. Can't imagine that Blood Glucose tracking will work anytime soon for a large population.

Then we have a different definition of reading. To read means to understand and comprehend for me.

the models we have today will never be able to read .. they are just able to produce something that we cannot distinguish from human output.

we should be more careful on how we use them. in my opinion, ChatGPT and similar will be horrible for search on the web, as we are flooded with text that looks like a human wrote it but it does not add any knowledge or new insight.

I'm wondering about the study ... I'm a complete novice so might get things wrong, yet the participant description has not much information (e.g. average, std of the age, gender etc.). N is also just given in the abstract.

Also the study does not mention how the experimental setup was: "Subjects were administered a comprehensive psychometric battery of fluid and crystallized intelligence tasks,"

There can be substantial differences, just on how the experimental setup was administered (e.g. potential ordering effects between the tests) and when the scans were done.

Does anybody know if that's a standard procedure to determine "general intelligence"? Sounds vague to me.