skysight-inc/HackerNews huggingface dataset:
> To know something one must be able to see all of it, fairly basic but impossible for us to achieve

self contradiction

> We need only a working model of reality as we cannot have a functional correct model of reality

self contradiction

> I have, in a sense just made up my "religion" but it's built on all that which have come before it to be a functional working model of reality, which is all I believe it needs to be. That's all the prophets have ever done - say the truth, as can be heard, in their time and place.

Please do not cheat yourself and others, or slander the saints, by saying (a) they made up or wanted to start religions or (b) that you can speak of a truth approaching theirs yet while saying you can't. Buddha didn't say "I'm enlightened but not really". He claimed he actually reached the real thing and there was proof if not for the destruction perpetrated upon him by those who couldn't admit their own issues even to themselves.
Bitcoin originally evolved from the "hashcash" proof-of-work system, which was intended to be a scalable anti-spam measure (force an attacker to generate a unique proof that is expensive to generate but cheap to verify). And you are basically describing proof-of-stake (force participants to stake, and if they misbehave you slash their stake). I wasn't kidding about "nothing is new under the sun" ;)

In general though others are right that the problem often is that attackers are more willing to play these games than individuals - you are not willing to put up 10 bucks to post on a web forum, but an attacker might think $10 to get a wonderful commercial offer delivered to 1000 people sounds really good! You won't spend 10c to upvote each individual song, but the RIAA will! Etc. It needs to be highly asymmetrical, and ideally have minimal/zero cost to "honest" users with an exponential penalty for attackers.

Obviously we are not living in a post-email-spam world unfortunately, and what we have is basically the "lightning network" with gatekeepers offloading and centralizing a lot of the problem so users don't have to deal with it. But you aren't the first person to make the observation that these are related problems!
I wonder if it's a situation where the knowledge required to improve and enhance these models at a fundamental level vs just more data for testing or screwing around with system prompts is so deep and rare that a single person leaving an organization can have a gigantic impact on the overall trajectory of a startup. No amount of money will fix that problem if the skills required are that rare. Established large companies like Google, Facebook, etc have a much deeper bench of specialists than a startup I would assume and so can survive some people jumping ship.
Big Tech Is Unimpressed by Apple's EU App Store Changes
Cheez-its also work very well, and are easy to store in an altoids tin
It's condescension because you continue to refuse to believe what I'm saying, and you keep insisting that something difficult is easy.

Moreover, if I needed help with a Swift issue, I would go to my many friends and associates in the Apple developer community. In fact, I've discussed the issue with some members of the Apple Swift engineering team, and I have an open bug report that Apple hasn't addressed. The very last person in the world I would consult about a programming issue is a random, condescending HN replier.
> The future is model graphs with networked mixtures of experts, where models know about other models and can call them as part of recursive prompts, with some sort of online training to tune the weights of the model graph.

What's the difference between that and combining all of the models into a single model? Aren't you just introducing limitations in communication and training between different parts of that über-model, limitations that may as well be encoded into the single model if they're useful? Are you just partitioning for training performance? Which is a big deal, of course, but it just seems like guessing the right partitioning and communication limitations is not going to be straightforward compared to the usual stupid "throw it all in one big pile and let it work itself out" approach.
I think this article's full import is not being properly processed yet by a lot of people. The stock market is in an absolute AI frenzy. But this article trashes one of the current boom's biggest supposed markets. If AIs can't be put in contact with customers without exposing the company to an expected liability cost greater than the cost of a human customer representative, one of their major supposed use cases is gone, and that means the money for that use case is gone too. There's probably halo effects in a lot of other uses as well.

Now, in the medium or long term, I expect there to be AIs that will be able to do this sort of thing just fine. As I like to say I expect future AIs will not "be" LLMs but merely use LLMs as one of their component parts, and the design as a whole will in fact be able to accurately and reliably relay corporate policies as a result. But the stock market is not currently priced based on "AIs will be pretty awesome in 2029", they're priced on "AIs are going to be pretty awesome in July".

LLMs are a huge step forward, but they really aren't suitable for a lot of uses people are trying to put them to in the near term. They don't really "know" things, they're really, really good at guessing them. Now, I don't mean this in the somewhat tedious "what is knowing anyhow" sense, I mean that they really don't have any sort of "facts" in them, just really, really good language skills. I fully expect that people are working on this and the problem will be solved in some manner and we will be able to say that there is an AI design that "knows" things. For instance, see this: https://deepmind.google/discover/blog/alphageometry-an-olymp... That's in the direction of what I'm talking about; this system does not just babble things that "look" or "sound" like geometry proofs, it "knows" it is doing geometry proofs. This is not quite ready to be fed a corporate policy document, but it is in that direction. But that's got some work to be done yet.

(And again, I'm really not interested in another rehash of what "knows" really means. In this specific case I'm speaking of the vector from "a language model" and "a language model + something else like a symbolic engine" as described in that post, where I'm simply defining the latter as "knowing" more about geometry than the former.)
Meet the divers trying to figure out how deep humans can go
Rajit has since moved to Yale and has made quite a bit of progress on the tooling problems

https://github.com/asyncvlsi/actflow

https://avlsi.csl.yale.edu/act/doku.php

https://avlsi.csl.yale.edu/chips.php

There's also a bit of documentation up on Wikipedia now

https://en.wikipedia.org/wiki/Quasi-delay-insensitive_circui...
Well, if the information on the website is too inaccurate to trust then it is also too inaccurate for purposes of contract. The logical next step is to say that all contracts made with chatbots are unenforceable as they lack any trustworthy meeting of the minds.
Though I posted this, I think the bottom line is many big tech companies are OK not retaining best employees. They now have stable products they need to incrementally improve. The “best employees” are seen as a hassle now. Instead they want to manage to consistent mediocrity over occasional brilliance.

In other words they’ve given up all pretense of being different from other large companies out there.
If they could reasonably expect to be able to hire people who would agree to accept all liability incurred during their work for the company, they absolutely would.

Same with chatbots. Even better, because once it's "trained", you don't have to pay it.

There's a few instances of expecting digital entities to shoulder the entirety of legal liability here in the last few years; DAOs are another example of this in the crypto space.
I can sort of see it. On the one hand, it's reasonable to hold them accountable when an employee gives you the wrong discount. But if an employee, on their last day at work, decides to offer the next person calling all of the seats on a single flight for just $10, I think we'd all agree that it would be unreasonable to expect the airline to honor that offer.

It's the degree of misinformation that's relevant.
> this trend is after they busted a billion dollar auto parts company for being heavily involved in fencing these parts

Do you mean DG Auto Parts (https://en.wikipedia.org/wiki/2020–2022_catalytic_converter_...) or is there another auto parts chain I should avoid.
By bulk messaging every possible phone number... and you can't detect that because signal can't read the messages to look for spam.
Noprocast alternative in uBlock: hides only the front page
news.ycombinator.com##.rank:has-text(/\d/):xpath(../../..)

You can still read any thread you intentionally sought out, e.g. something you found in a search engine. But this stops you from seeing lists of threads: the front page, /newest, and /best.

On a theory that one aligns with a usage pattern like "constructive search for information", and the other aligns more towards doomscrolling.
A lot of people now find themselves in situations which are bad/lead to very strong ADHD symptoms, like lack of regulated people around them, less/no exercise, spending more and more time around devices/screens (instead of people/nature), lots of junk food, getting lots of narcissistic manipulation in their ‘daily diet’, stressful situations with no clear way out, etc.

It used to be a small percent of the population got the symptoms - they were either those particularly predisposed to it, or in particularly bad situations.

As it becomes more prevalent across the population, more and more folks are showing problematic symptoms.

Notably, anyone who even glances at the news, social media, or politics is getting a ton of narcissistic abuse every day. As are many people at work.

And the symptoms of narcissistic abuse line up very strongly with certain ADHD symptoms - and issues we’re seeing across the entire population.
You could use a divider and just encode it in log(9!)/log(2) ~ 19b? I think the point of the article is you can encode every game in something like a kilobyte of data, though, right?
I agree with the sentiment here but..

> You barely have any .. anti-competition regulations

this is not true. However, the secrecy implemented around enforcement (bad publicity) causes the casual observer to think so..

There are very large enforcement actions that take place regularly.. they are far from perfect, and the failures tend to be the ones that are amplified in media..
isn't that kind of like Toyota financing my car loan
They still do. Bank errors are an example.
It's very innacurate. I wrote a single message: "I need to talk to you" without further context, and its anxiety is on the greens.
I saw it but glanced over as I'm not big into the accelerator part of things, and I was so intrigued by the rest of the writing. Then I googled, and there's a real company from YC doing exactly what you describe, and then my mind exploded! Fortunately, just a little bit, and I have since recovered with some help from the responses here.

S14 - https://www.ycombinator.com/companies/checkr
I'd love to see the responses people started writing but then deleted after reading the whole comment!
I think its great that some consideration was given by Gemma to the 2.3 million Norwegian immigrants. However it is/was very consistent in which kind of Norwegians it decided to show regardless of the prompt 100% of the time.

In fact it was quite adamant regardless of the time period or geography.

Rather mysteriously if you try it now as opposed to when it came out the results currently only show non-immigrant Norwegians. So is it wrong now? Because now it switched to exclusively ignoring the 4.5 million immigrants and only showing me the boring OG Norwegians.

I for one am outraged that the 8.9 million people of color Norwegian immigrants are presently under represented by Google. There is a serious risk of misleading people.
Huh? The official numbers are 877k or 16% [0]. Are you just pulling numbers out of thin air?

[0]: https://www.ssb.no/en/innvandring-og-innvandrere/faktaside/i...
I like the idea of siting them underground, which completely addresses the aircraft strike vulnerability. Physical security is also easy when they're underground.

Thorcon was pursuing this, but has switched to a ship-based approach.

Searching today, I found a new (to me at least) company that's working on a truly small reactor, 3-15 MW electricity, and is planning on underground siting: https://www.usnc.com/mmr/

It also supplies 10-45 MW of thermal, so great for poleward climes.

Fission will be a critical technology going forward, until fusion generation can compete on power to size/weight ratio...and cost. And until aneutronic fusion power is practical, there's no large benefit to fusion regarding waste.
that's good, you aren't my audience

there's no code
Wages in the tech industry are still much higher than equivalent jobs factoring hours worked and additional compensation.

As long as that remains true, tech will remain a hot industry.

The ability to spend almost no money to generate ridiculously high margins (50-70%) is hard to beat. The only similar industry is Finance, but that is heavily gatekept due to the limited need for staffing.
in a gold rush, sell shovels.
Particularly since it's been discussed and plausible explanations given.
Ridiculous comparisons to be honest - so lazy they don't deserve a response.
Apple and Google don't sell insecure cheap phones, but lots of other manufacturers do.

I suppose organized crime doesn't systematically take advantage of that because cheap phones are cheap, and the people who own them are poor. You don't get that much benefit from pwning them.

Alternatively, maybe organized crime does take advantage of them but we haven't heard about it. They could have a giant botnet of them for all we know.
I'm a bit puzzled. Suppose you do single Kyber key exchange, and you then hash the shared secret with a domain separator to get a fingerprint, wouldn't that mean you now have a shared secret that you can verify wasn't under MITM.

You then can build post quantum future secrecy / key rotation on top of that, by mixing in new key material and it remains secure from MITM, as long as the internal state of the endpoint isn't compromised. The endpoint compromise is outside networked TCB threat model, as such compromise could also be used to exfiltrate long term post-quantum identity keys for undetectable MITM).
>All the costs and complexity are tied up in authoring good training data, not compute.

No it's compute. Post-Training by Human reinforcement is not necessary. Anthropic employs RLAIF and it works just fine. Some don't bother with reinforcement learning at all and just leave it at fine-tuning on Instruction-response pairs.

The work being done to pre or post-training data is insignificant in comparison.

You don't need 100k instruct-tuning examples. The vast majority of instruct-tuning data is nowhere maxing even a 4k context.

100k pre-training runs would probably be very helpful but the thing stopping that from happening even in domains that regularly match or exceed that context (fiction, law, code, etc) is the ridiculous compute it would require to train with that much context.
ok - well I agree with you. You had a good comment, and I appreciate it :)
If you want to steal a single print, you would need a moderately long game and pliable player along with wide angle synchronized video that imaged hand/finger orientation. If the video isn't good, or they use different fingers intermittently, or only one orientation, or they have a callus on their finger, or they have very fine/course ridges, too many forks/ends, an irregular accidental whorl or arch, or there's noise, etc... you wont' get a single print with high enough resolution to match. That still may not be a print they use for authentication.

Most people use their forefinger or a thumb to swipe, and their thumbs to match/type, and multiple fingers or both thumbs to scroll/zoom.

More interesting is that if one had full knowledge of a persons prints then perhaps with microphone, touch tracing, and wide angle video, one could compare expected vs measured sounds. This is a one-one confirmation rather than one-many match. Perhaps it could prevent long term usage after a person had unlocked their device and another was using it.
So when the AI fluffers are saying that LLMs just do what humans do - predict the next most likely word, how do you explain this?
Was gonna say something like this. In software security land, security measures are cleaner and hacking tools are impossible to regulate, so it's really on the creators to make things secure up to a point. The real world is different. Even the software security philosophy kinda stops at DDoS.
We handle the audio bytes in / out, and also connect to your user's server for response. We handle the interaction and decide when to listen and when to talk, and send live updates to our users. When a response is needed, we ask for it and get it from our user.

Our homepage https://www.retellai.com/ has a GIF on it that illustrates this point.
Meh. I've seen taller flames from a lone page of crumpled newsprint.

If it's '90's Pyromania Day, then look for the old videos of charcoal BBQ's being fired up with LOX.
No. LLMs are worse for customer experience than their predecessors: LLMs confabulate, and their language is so smooth that you often need expertise to catch them in it.

People call customer service because they don’t know what to do. It would be better for most customers to talk to a bot that they can catch making a mistake.

Recent example:
https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-th...
I haven't listen to your great podcasts so hard to say what is not covered.

Architectures matter a lot less than data. "Knowledge" and "reasoning" in LLMs is a manifestation of instruct-style data. It won't matter how much cheaper training gets if there is limited instruct data for use cases.

How do you make 100k context window data for example? Still need thousands of people. Same with so-called niches.

Maybe it turns out to be a complex coordination problem to share the data. That's bad for equity investors and giant companies. Anyway, all of this would cost less than the moon landing so it's practicable, you don't need cheaper, you just need risk-taking.

The obviousness of the path from here to there means it's not about innovation. It's all about strategy.

If Google could marshal $10b for Stadia it could spend $10b on generating 100k context window instruct style data and have the best model. It could also synthesize videos from Unity/Unreal for Sora-style generation. It would just be very hard in an org with 100,000+ people to spend $10b on 10 developers and 10,000 writers compared to 400 developers and 3,600 product managers and other egos. At the end of the day you are revisiting the weaknesses that brought Google and other big companies to this mess in the first place.

Anyway I personally think the biggest weakness with ChatGPT and the chat-style UX is that it feels like work. Netflix, TikTok, etc. don't feel like work. Nobody at Google (or OpenAI for that matter) knows how to make stuff that doesn't feel like work. And you can't measure "fun." So the biggest thing to figure out is how much technical stuff matters in a world where people can be poached here and there and walk out with the whole architecture in their heads, versus the non-technical stuff that takes decades of hard-worn personal experiences and strongly held opinions like answers to questions "How do you make AI fun?"
Sure, to get started just ask it about people/Samurai from the Heian period.

https://g.co/gemini/share/ba324bd98d9b
EDIT: it seems this is likely an Ollama bug, please keep that in mind for the rest of this comment :)

I ran Gemma in Ollama and noticed two things. First, it is slow. Gemma got less than 40 tok/s while Llama 2 7B got over 80 tok/s. Second, it is very bad at output generation. I said "hi", and it responded this:

```
Hi, . What is up? melizing with you today!

What would you like to talk about or hear from me on this fine day??
```

With longer and more complex prompts it goes completely off the rails. Here's a snippet from its response to "Explain how to use Qt to get the current IP from https://icanhazip.com":

``` python
print( "Error consonming IP arrangration at [local machine's hostname]. Please try fufing this function later!") ##
guanomment messages are typically displayed using QtWidgets.MessageBox
```

Do you see similar results on your end or is this just a bug in Ollama? I have a terrible suspicion that this might be a completely flawed model, but I'm holding out hope that Ollama just has a bug somewhere.
> I'm not telling them how to act.

> It probably sounds extreme. It keeps people from doing what you're describing though.

Sounds like you are.
I think a case could be made (although I'm struggling to do so myself) that the growth of mercantilism, and then capitalism, could be understood as direct challenges to Abrahamic-religion-based ethics, especially as capitalism directly discourages altruism.

I think this is a thesis I need to do some work on to either reject it or let it mature, but I think this is an interesting starting place. It is worth noting that the early Christians frequently practiced collectivism and rejected the concept of individual property rights, although that was ~2000 years ago, the faith has evolved sine then.

All of this to say, I do not believe its that secular ethics per se are the cause of the decay, but rather that the religions of the world have not made a compelling enough case to sway people away from rejecting altruism in the name of personal enrichment. The situation is made considerably worse by the fact that a fair number of the global religions see the spoils of personal enrichment as evidence of righteousness, and altruism as at least adjacent to sin.
That sounds like a state actor thinking, not a terrorist.
Yes, we resdesigned our programming languages to be easy to parse with limited lookahead.
> When you are working for a company, privacy isn't to be expected outside of the bathrooms.

For better and for worse, that is the case ^^^

Back in the 90s (before cell phones), I had to give a training to a bunch of managers to teach them how to eavesdrop on employees' phone calls. I asked if this was legal and was told yes, the phones are company property. That never sat well with me. However, it got worse: I later found out that one of the managers was having an extramarital affair with an employee and wanted to monitor what she was saying to her husband or something weird like that.
One could argue that large reactor plants make for easier targets since the surface area of attack vectors is much larger.
Like many other people in this thread, I think we disagree that a PCI switch means that an SSD "is connected over a network" to the host bus.

Now if you can show me two or more hosts connected to a box of SSDs through a PCI switch (and some sort of cool tech for coordinating between the hosts), that's interesting.
I'm way out of my depth in terms of the math here.

But my 'software engineer brain' likes the ideal of using the prime factoring problem, because it's so simple to understand, and feels like some kind of universal primitive. "It's easy to multiply but hard to factor." It just seems so intuitive.

But I'm reading the 'learning with errors' wiki page and it's beyond my comprehension.

There's a weird fear in my mind that all these "post quantum algorithms" are so complicated, with such a large surface area, that they may hide flaws. While prime factoring, or even the elliptic key stuff, is so simple to comprehend.

that said, obviously the experts know what they're doing, and I'll use what they suggest. just saying that this thought has crossed my mind.