skysight-inc/HackerNews huggingface dataset:
>For one, the majority of datasets aren't comprised of capital-A Art with specific authors and attributions - a ton of it is just random information from around the internet, or just downright junk data.

Random means there can be copyrighted content in it. It shouldn't be "random". You can't do such things when making your own piece of media, so I don't see why it's okay when it's a bot scraping massive pieces of the internet for use in some for-profit venture. Scraping was already this gray area in the internet and practices like these argue against such techniques.

>Then, when a model is trained on that dataset and that model is used to make other outputs, are those outputs actually derivative with a direct link to some origin?

That's the million dollar question (literally. Or more like 10/100 million). I imagine like existing copyright it depends on how far out you derive. Arguments above that suggest the ability to regurgitate the exact or very close to piece of data would certainly lean towards "yes". In which case it implies they have that piece of data in their database, instead of just being a "reference".

That feels too close hosting off of copyrighted material in my eyes, but I don't have the full picture of how and what these LLMs contain.

>If I generate a landscape using an AI, to whom exactly would I even owe money?

Like any media creator/producer would tell you: depends on thr license. For example, I Google "mountain" and my first result is this :

https://unsplash.com/photos/aerial-photography-of-mountain-r...

License says "free to use under the unsplash license", which is surprisingly lax. But there are two stipulations

Photos cannot be sold without significant modification.
Compiling photos from Unsplash to replicate a similar or competing service.

So we come back to the whole issue in the beginning with the first point. The 2nd point is a much more nebulous one to consider but not to be completely dismissed (maybe an AI service can be argued to generate a competitor).

And that's one of the more generous results. Another result came from CNN but sourced from Shuttershock. Here's their license :

https://www.shutterstock.com/license

I'm not going to read nor summarize the license, but I'm sure CNN at the bare minimum needed to ask permission and likely negotiate to use it in their news post.

So yeah, an "ethical" training model will be doing this for every single image they use, if the aren't creating nor taking the pictures themselves.

>Equally to all contributors in a dataset?

Don't know. That would be for the courts to decide. If it's anything like streaming music and movies, you would be compensated proportionally to the amount of "usages" your work gets when generating pieces. Doesn't necessarily have to be equal, but it may surprisingly equalize out since algorithms are at the helm and not brands trying to stand out.

It's certainly be it's own rabbit hole to explore though. If we ever get that far.
The Linux Programming Interface and The Art of Electronics, perhaps?
Can AI replace a co-founder?
Founders, I am building an AI Co-founder to automate your efforts.
We are starting with automating fund raising which is one of the most crucial part during an early stage startup.

We imagine that an AI partner can further helps you with brainstorming ideas, is good with tech, helps you to structure your business, helps you with automation of the work , has tools for different skills like lead generation marketing etc. Most important can identify what's right and wrong with your process and provide the optimal feedback to it.

What else should we add in the roadmap? Open to any support or advice, thanks!
"Ask HN: What's the best out-of-box Document OCR/Analyzing/recognition API?" (2024) https://news.ycombinator.com/item?id=38829242 ; BetterOCR :

> Better text detection by combining multiple OCR engines (EasyOCR, Tesseract, and Pororo)

FWIU there's a way to image multiple stacked sheets of ancient scrolls without unrolling them?
I would've preferred that the identifying information of the person had been edited out.

The phone's owner experienced a traumatic incident. Which furthermore is under investigation.

Posting some kind of photo feels OK, however, since finding the phone is arguably newsworthy. And the Twitter poster says that it was open to that email, suggesting that they didn't go snooping through the phone. A little privacy redaction/cropping would've helped.
I don't understand why fuzzing hardware is being presented as a new thing here...
In silicon validation, constrained random testing has been the standard methodology for at least 10 years. With the complexity of modern CPUs, it's effectively impossible to validate the hardware _without_ using some kind of randomized testing, which looks a whole lot like fuzzing to me.
What is new here? Or is this a case of someone outside the industry rediscovering known techniques.
HEP is a somewhat peculiar community. They tend to rely on their own tools, their own libraries. I wouldn't take them as a model to understand historical python adoption in science.

I'd bet that their python usage is still mostly as a REPL to ROOT (which by the way has its own REPL), so no numpy, maybe little pandas, no matplotlib.
Missing part of Alaska Airlines plane found in Portland man's backyard
I lean toward thinking that if it seems nonobvious, then you're clearly not in a great position to be making "long bets", no? If you're making $1MM bets with 10-year timescales, I expect you to either understand this or rightly burn a quarter of your bet value over it. It's not specialized knowledge or anything, it's practically foundational to American retirement planning.
You can try it yourself on SageMaker using NVIDIA. There's a free trial.

https://aws.amazon.com/marketplace/seller-profile?id=seller-...

For AMD, you'll have to wait until these cards become available on the cloud.
That happens when you finish your roadmap and call it done. It’s frustrating that the modern sense of it became automatically negative.

This is an actual claim if someone is interested: https://www.hmailserver.com/state

On SHA-1: https://en.wikipedia.org/wiki/SHA-1

As of 2020, chosen-prefix attacks against SHA-1 are practical.[6][8] As such, it is recommended to remove SHA-1 from products as soon as possible and instead use SHA-2 or SHA-3. Replacing SHA-1 is urgent where it is used for digital signatures.

Digging further: https://www.hmailserver.com/forum/viewtopic.php?t=40568

Tl;dr: it uses sha256 by default and only has sha1 for backwards compatibility, which is considered insecure today. Critical updates are still there.
What is "MSP"?
Do you mind sharing what you tried to do? So I can improve the feature discovery or add the features later on.

Editor.js looks interesting. Nino Doc is based on ProseMirror. Never heard of grist before but I think react-datasheet-grid is a pretty good. I've been itching for a frontend rewrite of Nino Sheet.
How would you fuzz an app in LAMP stack or some enterprise CRM?
Well at least one did in the 90s https://en.wikipedia.org/wiki/Sinking_of_the_MS_Estonia#Offi...
I looked at those but felt support for WhatsApp is somewhat limited.

I am also considering a two-device approach: a candy bar dumb phone from the company that bought Nokias line, and a something like Onyx Palma (android, eink, no sim slot) tethered to dumb phone.

This should give me a comfortable reading experience, a camera, all basic apps (Uber, authenticator, WhatsApp,…) with lower “addiction” (no shiny colors) but also the chance to leave the Palma behind and just take the dumb phone with me (for instance riding by MTB)
I know this isn't something the authors can fix just like that, but shipping and import duties mean that the hard cover book is about as expensive for me as the smartphone I'm writing this on ($96.32 for a Samsung Galacy M04 as compared to $96.6 for the book).

The book is great though. If you can't afford it I highly recommend reading it on the website. I haven't gotten through the last section just yet due to family obligations unfortunately but I look forward to finishing it soon.
Sounds a bit like Office + Google Wave. Will check it out later.
Does it have task automation via some macros?
Google does research on both their own and other people's models all the time. Here's the blog post and paper if you want to know more: https://not-just-memorization.github.io/extracting-training-...

You appear to be refuting a slightly different point, though. When OpenAI was making Dall-E 2, they found that duplicates in the training set would incentivize memorization of specific images, as you said. This is more like finding a secret cheat code that would make the model regurgitate everything it had remembered, regardless of how "incentivized" it is to do so or even if it had been aligned to not do that.

My personal argument is this: the primary metric that the training process attempts to minimize is perplexity. This is how good the model is at guessing the training set data. Base models are specifically being designed to compress huge amounts of text and we just so happen to accidentally get a decent word calculator out of it. The alignment fine-tuning that happens later adjusts the model to prefer answering questions, but the underlying memorized data is still there.

>The way these algorithms work is public knowledge, there's not really any black boxes in the hands of OpenAI that would be relevant here.

Nope. Modern GPT is entirely a black box. OpenAI stopped publishing model weights the moment Elon Musk stopped writing the checks. Hell, they don't even publish the model architecture anymore. How GPT-4 works, even on a basic "this is how many transformer layers and attention heads we're using" basis, is a trade secret.

Even if you have model weights, the actual meaning of the learned model parameters has never been known; there is active research on figuring them out. One particular problem is polysemanticity. If you look inside a particular hidden layer, you won't see a single "dog" or "cat" neuron in its hidden layers. You'll have a 512-dimension concept bouillabaisse with "dog", "cat", "bird", "guinea pig", "kangaroo", and so on all floating around whatever shape made sense at training time (even if it implies absurdities like "desk is the opposite of loin cloth"). To untangle this, you have to train another AI to pick out monosemantic clusters of neurons that can then be inspected, and that requires extreme amounts of GPU resources.
Unlikely; that is almost in no-one's interests. The story down that path is ROCm gets 95% of the way there feature wise, then Nvidia catches a sudden case of community spirit and opens up CUDA.

Although that is mostly wishful thinking. I expect ROCm probably works quite well on the server, the issues in my experience is that it assumes that the AMD card is dedicated to processing and once it tries to run graphics and compute at the same time it becomes Too Hard to manage memory for both and then the driver locks up. So their buggy drivers basically kill any sort of hobbyist or researcher effort unless there is a dedicated supercomputer on hand. Much better plan to go with Nvidia I assume.
Art will take more skill to produce, not less, at faster speed by select artists. GenAI will become another tool that artists and clients alike must understand and use effectively within unspoken guild rules, that is, if it stays.
"HotBits: Genuine random numbers, generated by radioactive decay", since 1996.

https://www.fourmilab.ch/hotbits/
Docs are the place people go to to learn the concepts. Examples is one way how docs teach those concepts.
Heh not sure if you're aware, but our brain seems to have a special treatment or logic for contextualizing "high technology", as indicated by one well-documented failure modes: the "influencing machine" is a feature of schizophrenia which features a delusion that contemporary high technology (magnets, pneumatics, gears, mind-control drugs, satellites, prob AI now, etc) is being used by mysterious attackers to control the sufferers body and mind:
https://en.m.wikipedia.org/wiki/On_the_Origin_of_the_%22Infl...

Though not mentioned in the post, schizophrenia (oddly enough) is also tied to cerebellar dysfunction: https://neuro.psychiatryonline.org/doi/10.1176/jnp.12.2.193#...
Replacing Linux is hard even for the BSDs, and those are very well established and considered basically next-in-line for similar tasks.
A100 has no display outputs or integrated fan which makes it a poor fit for a workstation.
I WORK at Microsoft 365 (not on Word), and it's actually good LibreOffice can read old Word files even if Word can't. I wasn't even born until 1997, tho when I was young I played a lot with MS-DOS and Win9x, since my toys were old PCs.

The reality is, both MS Office and LibreOffice are legacy messes. In the 90s MS aggressively broke compatibility to get users to upgrade, which is why LO can read it. Now MS is excellent at compatibility, because the focus is subscription software your employer pays for.
Obscured the credit card # but didn't think to grant the passenger some privacy and obscure their full name?
Working with partners who have integrity is a massive boost to everything you do. People with high integrity attract more people with high integrity, compounding that effect.
Damn! Apple has better QC than Boeing!

Apple should seriously think about getting into the Aeroplane Manufacturing business; its there for their taking :-)
I've been wanting to run old Photoshop in this way.

Was the process straightforward?
Its expensive just because its desirable? Housing doesn't work like a commodity you can ship around the world. Everyone needs a place to live, and having more housing for everyone who wants to live in an area only increases economic productivity. There is still more than enough space, even in the bay area, even near LA, to build housing, its just that the people elected to local offices also happen to be the same people who don't want the value of their houses to decrease. So instead of having to live next to lower-income residents, they get to live next to the homeless, who come in surging, ever-increasing waves. Why is it better for California that all these people who could be housed, educated, fed, are left without a private domicile--it seems better only for those select few who happen to own good property.
Would this work indoors through walls as well? It would be cool to have a wireless standard that only required one base station placed almost anywhere to guarantee a signal in every room of a house and outside in the whole yard, even if the bandwidth was limited. Seems like a lot of products could benefit from that.
Depends how fast they are moving relative to the phone.
This must be some elaborate joke. How did that iPhone stay unlocked? The screen stayed on the entire time? And if the screen stayed on the entire time, how does the battery last that long? (In winter weather no less)
> encrypted messaging of some form, GPS and NFC payments

That’s exactly what “modern” is.
Asianometry is really great. Some of the points in the DNA sequencing videos, an area I know well unlike chip fabbing, had very slight inaccuracies or maybe just incorrect focus, but they are so much better than other sources that I trust all his videos. The detail was fantastic and my harshest complaints were minor.
All three apps you mentioned are awful, the fact that they were there doesn't confer a non-nostalgic benefit to mourn their disappearance
Never mind.. your site works perfectly well with "www." prepended before the domain name.

Maybe something to do with redirecting non www traffic to www.
The silicone cases provice protection to 5 sides of the phone + create a "frame" around the edges of the screen. Wont help you when the screen hits something sharp, but it is still nice protection.
Wow that's a very interesting summary of the encoding of opcode itself. Thanks!

I find this part the most challenging. It is relatively easy to figure out the "mapping between the assembly and instruction" when you have both in front of you already, as I did in my posts.

But I would have difficulty translating from one to the other, because the opcode encoding is difficult. You can actually see me intentionally handwaving it in an earlier post: https://blog.kenanb.com/code/low-level/2024/01/04/x86-insn-e...

Note for other people reading both my post and the comment above:

The terms r0 and r3 in the last paragraph corresponds to what I describe in my post as: The "register code" or "opcode extension" values stored in the "ModR/M.reg" field. In this case, the values 0 and 3 are meant to be "opcode extensions". You can see both instructions here: http://ref.x86asm.net/coder64.html#x0F01 . The values 0 (for SGDT) and 3 (for LIDT) are shown in the column called "o", which is defined as: "Register/ Opcode Field"
Comparisons between different chip architectures are imperfect. In our opinion the most fair thing to do is 1) match the TFLOPs (since these workloads are compute bound), and 2) find a similar card that can run the same size models. Since MI210 has 64GB, the closest one is the A6000 with 48GB. Many of the less expensive ADA cards can't even fit a 13B model in VRAM, so a comparison could not even be made.
For example, when sharing a google doc in Gmail, the experience is more feature rich. And if an organization is already paying for gmail, it's hard to stop people from using gdoc and gsheet. In real work, I can't just reply a gdoc share with "use nino or I refuse to read".
KyberSlash attacks put quantum encryption projects at risk
I'm talking more about methodology. There are a lot of "forgot to set the dirty bit" type bugs in GNOME-based applications. UI moved on to declarative rendering ages ago, and GNOME never got the memo. When you are writing Flutter or React it's virtually impossible to make those mistakes.
I have my phone set to never lock automatically, so that’s one part of the equation plausible.
Also, new chat blows away previous chat.
Addition is a different problem for LLMs than code solving, since the latter is easier to memorize.
Why drugs got harder to develop and what we can do about it
How old is the Photoshop? I was recently able to run Macromedia Flash 2004 in Windows XP compatibility mode without any hiccups.
A meteorite on planet X which came from planet Y means via an extraterrestrial impact that launched the matter into space, to land on Y?
Honestly I think being able to run any kind of LLM on a phone is a miracle. I'm astonished at how good (and how fast) Mistral 7B runs under MLC Chat on iOS, considering the constraints of the device.

I don't use it as more than a cool demo though, because the large hosted LLMs (I tend to mostly use GPT-4) are massively more powerful.

But... I'm still intrigued at the idea of a local, slow LLM on my phone enhanced with function calling capabilities, and maybe usable for RAG against private data.

The rate of improvement in these smaller models over the past 6 months has been incredible. We may well find useful applications for them even despite their weaknesses compared to GPT-4 etc.
Because

1) It’s neither broken nor weird. It’s just that same windows without preinstalled crapware, think XP times. I’m using it since 2018 and never met any version-related issue.

2) https://news.ycombinator.com/item?id=38529637

I don’t get the point of going through this effort either.
Sadly:

Consequently, the radioactively-generated HotBits server has been retired as of the end of 2022 (more precisely, at 00:00 UTC on 2023-01-01).

https://www.fourmilab.ch/hotbits/retired.html

The OG John Walker merch is still available though ...

https://www.fourmilab.ch/evilempire/
On airplane mode, not doing anything.
Colorectal cancer is rising among Gen X, Y and Z. 5 ways to protect yourself
> where that last ‘+’ is addition mod 5

It can just be integer addition. 2 + 4 = 6 = 1 mod 5 is a true statement.
They share characteristics (ie the "advanced knowledge" aspect), but differ in how these are applied: they deliver and maintain their own systems.
For my particular cases there are features like shared-mailbox and distribution list that requires additional setups. I don't see a clear advantage to move over a critical provider so I kept things the same. If from dayone I have a choice, I would go with all-in-one.
Somebody more knowledgeable than me can correct me, but iirc some of the earlier versions of Rollercoaster Tycoon 2 were incompatible with the latest version of Windows at the time that was unreleased at the time of the game's launch (because RCT2 used a memory management bug? I don't remember) so the Windows team coded up a special exception for RCT2 because they knew that users would blame Windows rather than the RCT2 team if the game failed to launch). This patched version of Windows ended up shipping.

I have no source to back me up. This is something I read about sometime back and it's probably lost in some deeply nested bookmark folder I'll never open.
Its true, but the land that is available, like I said, is still being underutilized. Look a Japan, which is in a very similar situation near the major metropolitan areas: very little land where people can actually settle, and yet housing is relatively affordable for most. Its not an accident, they're just better at managing urban density--and Tokyo especially has the benefit of a fantastically complex and well managed metro system, but not all the cities do, and they're still fine.
Conceptually, to the best of my understanding, nothing too serious; perhaps the inefficiency of processing a larger input than necessary?

Practically, a few things:

If you want to have your cake & eat it too, they recommend Enumerated Shapes[1] in their coremltools docs, where CoreML precompiles up to 128 (!) variants of input shapes, but again this is fairly limiting (1 tok, 2 tok, 3 tok... up to 128 token prompts.. maybe you enforce a minimum, say 80 tokens to account for a system prompt, so up to 200 tokens, but... still pretty short). But this is only compatible with CPU inference, so that reduces its appeal.

It seems like its current state was designed for text embedding models, where you normalize input length by chunking (often 128 or 256 tokens) and operate on the chunks — and indeed, that’s the only text-based CoreML model that Apple ships today, a Bert embedding model tuned for Q&A[2], not an LLM.

You could used a fixed input length that’s fairly large; I haven’t experimented with it once I grasped the memory requirements, but from what I gather from HuggingFace’s announcement blog post[3], it seems that is what they do with swift-transformers & their CoreML conversions, handling the details for you[4][5]. I haven’t carefully investigated the implementation, but I’m curious to learn more!

You can be sure that no one is more aware of all this than Apple — they published "Deploying Transformers on the Apple Neural Engine" in June 2022[6]. I look forward to seeing what they cook up for developers at WWDC this year!

---

[1] "Use `EnumeratedShapes` for best performance. During compilation the model can be optimized on the device for the finite set of input shapes. You can provide up to 128 different shapes." https://apple.github.io/coremltools/docs-guides/source/flexi...

[2] BertSQUAD.mlmodel (fp16) https://developer.apple.com/machine-learning/models/#text

[3] https://huggingface.co/blog/swift-coreml-llm#optimization

[4] `use_fixed_shapes` "Retrieve the max sequence length from the model configuration, or use a hardcoded value (currently 128). This can be subclassed to support custom lengths." https://github.com/huggingface/exporters/pull/37/files#diff-...

[5] `use_flexible_shapes` "When True, inputs are allowed to use sequence lengths of `1` up to `maxSequenceLength`. Unfortunately, this currently prevents the model from running on GPU or the Neural Engine. We default to `False`, but this can be overridden in custom configurations." https://github.com/huggingface/exporters/pull/37/files#diff-...

[6] https://machinelearning.apple.com/research/neural-engine-tra...