skysight-inc/HackerNews huggingface dataset:
It appears that even after nonapology they still don't get the fucking problem.

The whole thing only needs docker infrastructure getting hacked because it used some of the now-orphaned containers to complete the shitshiw
Not in one of the oil rich exporting countries. Also if you don't do the right thing everytime this doesn't mean you can't do a good thing from time to time.
Keep in mind SVB was not the only bank. 3 banks went under (at least so far).
Show HN: Hey Sunrise – an iOS app that displays sunrise and sunset times
Hey folks! This is my first iOS app. I’m an Android developer by trade, but have recently been interested in learning some iOS as well. This app is the product of my learning over the last month or so. It’s built entirely with Swift UI. I built an Android app with the same functionality a number of years ago and it has been relatively successful (200k+ installs, 4.8* rating), so I wanted to make an iOS equivalent. There are a number of apps already on the App Store that have similar functionality, but I’m not a huge fan of their UI and most of them require a subscription for the main features.
> And the reason it matters is because people start spouting woo about how the host's knowledge or intentions are what mattered, when that isn't true at all.

Knowledge and intentions don't matter? They most certainly do.

Imagine a third version of the problem (I'm afraid I don't have a good name for it) where the host actively tries to lure you away from the correct answer, and only opens an empty box if you've picked the correct box. Now, if you pick a box and the host opens an empty box, you should absolutely not switch, because the remaining box has 0% of being the right one.

Knowledge and intentions matter. The real problem happens when you don't know what strategy the host is using.
Mobile was a huge boom for indie development too, across all categories. AI will be the same.
> cuddled by free interest rates

thanks for the chuckle
We used Screen Studio! https://www.screen.studio/

Great product
Models that can do this or are involved in robotics have existed for YEARS in the military and presumably industry, I'm not sure really buying GPT4 represents a revolutionary leap forward and it seems really backwards to use an autoregressive language model for this use case..

Does any reputable academic or expert in LLMs actually support the hype behind GPT/autoregressive models as much as the HN crowd seems to?

Percy Liang and particularly Yann LeCun are pretty meh about them despite being thought leaders in this space and running leading groups in the NLP space.

I'm not sure where along the way we confused next-token prediction which by literal definition results in output that must SOUND really smart and coherent because it's trying to make a plausible sounding output with any real intelligence built in. To our knowledge (GPT-4 isn't really open) there is zero grounding happening with the outputs, certainly was not part of ChatGPT.

Someone tweeted a thread about GPT4 modifying a molecule in an anti-malarial and it didn't even get the original base molecule correct or the substitution, something that can be trivially done without using an LLM querying ope biochemical databases...

> Sure, it's "just generating text". But if it can sensibly and correctly generate arbitrary text, it's an AGI - it can solve any problem presented to it, as long as you present it in text form and accept its output in text for

No it doesn't. It APPEARS to solve problems like these where the actual task is either solved or trivial. If you read OpenAI's disclosures, and other papers by the FAIR group the all disclosure that the answers are routinely incorrect and just SOUND right. 'Write a snake game in python' is like lecture 2 of a 'python for non-engineers' course.

> but it seems no longer obvious that the same approach, just with more training data and compute thrown at it, won't be able to solve this.

Is it? Says who? Once again there are many major experts criticizing the end-game of RLHF. The problem space is much larger than what you can correct with a reward function.

If anything recent work by FAIR and Stanford NLP is suggesting that more compute is not the end game.

OpenAI themselves acknowledge that we still haven't figured out how to reliably ground a language model in truth and avoid spewing BS the moment you're not showing it some trivial thing.

At best the current approaches seems like glorified STS/IR models with the ability to output reasonable sounding text (again by definition given that they work by next token).
It’s someone who does the AI equivalent of social engineering.
Schools have always been like that. There's a reason that all of those skilled trades require multi year apprenticeships.

School teaches everybody to read, write and reason about things in general to a decent level. You can't teach high school kids the basics of all the careers out there beyond stuff that's generally applicable - you wouldn't have the time or the equipment. And schools do often have elective shop, cooking, electronics etc classes for those who want to do them.
Predicting a set of answers to some confidence interval would still result in hallucinated answers.
Israel has a lot more sunny days than most of the US.
An Airway-to-Brain Sensory Pathway Mediates Influenza-Induced Sickness
That is a good outcome I think. You can still use it anywhere, you just can't "claim it"
Testing with GPT4, it didn't catch on with "philosophers" (even after correcting the typo) but it did figure it out for "logicians":

> User
> Please explain this joke: Three logicians walk by a fortune teller's booth at the fair. The fortune teller asks, "do none of you want your fortunes read?" The first logicians says "I don't know." The second logicians says "I don't know." The third logicians says "No thank you."

> Assistant
> This joke is based on the principles of logic and the way logicians would answer questions. The fortune teller asks if none of them want their fortunes read, which is a question about the collective preferences of all three logicians.

> When the first logician says "I don't know," it implies that they might want their fortune read, but they are not sure about the preferences of the other two logicians. The second logician also says "I don't know," which means they might want their fortune read as well, but they now know that at least one of them (the first logician) might want it.

> Finally, the third logician says "No thank you," which implies that they don't want their fortune read. Since the first two logicians have already expressed that they might want it, the third logician's answer confirms that not all three of them want to refuse the service, and therefore, the answer to the fortune teller's question is "No."

> The humor comes from the indirect and logical way the logicians communicate their preferences, which is quite different from how ordinary people would respond in such a situation.
Nice, I was playing with documentation q&a with gpt in https://github.com/rmuller-ml/gpt_table_of_contents_doc_qa and found that if the documents your are doing semantic search on is structured, you can prompt gpt with the table of contents so gpt does the chunk prefiltering for you. I guess this is similar to your tagging explanation. Nice work
This is a lie. It says on their SEC filings.

They state clear as day that they had 91 billion of securities, currently worth 76 billion in their hold to maturity portfolio.

In their avalable for sale (AFS) portfolio, they had 28 billion, currently worth 26.

They break it all down by asset type, MBS, treasuries, foreign debt, ect. They break it down by the duration, eg <1yr, 5-10, 10+ yrs

There is no mystery. I dont know why people keep saying this.

Check out page 124 onward if you are curious:
https://d18rn0p25nwr6d.cloudfront.net/CIK-0000719739/f36fc4d...
The end result does not look like a "good thing" at all.
I see - we'll reconsider then! Appreciate the feedback
TBF he said commit, not attempt.
Wait, didn't it get the Bible quote correct?

"The thief does not come except to steal, and to kill, and to destroy. I have come that they may have life, and that they may have it more abundantly."
Delete them.

Keeping them read only is literally the worst solution. Old images that can't be updated and accrue security flaws, all while uninformed users see address still work and assume nothing needs to be changed.

Your corporation picked literally worst way to do it.
This is a very honest assessment.
If a 3rd party service like Karat has more trust with an employer than their own employees in terms of what is needed for that specific employee's role within their own company then that is a sign of poor management and practices.
Any group that can create 'others' has these tendencies. If you like podcasts, checkout "My year in Mensa' where the host passes the mensa test and then finds that all the nice mensa members you meet in person seem to be online trolls. Same thing really.
I'm not really fond of the over excitement of AI. It has traveled to almost everywhere I go online. But if there's one thing fun about it, it's this. It's amusing to me how much effort and creativity has been put into this - both from OpenAI and from the jailbreakers. It's like seeing DRM vendors vs crackers race to outdo each other in real time.
Security and morality may need to be baked in from the ground up instead of slapped on after the fact RLHF style. The problem is it’s hard to codify (or reach consensus) on security and morality.
The person using the paintbrush, or the instrument, or photoshop, or AI, is the artist.
What skills are you using in creating AI content? Did you create the LLM?
It would be useful to know whether you know both languages or just English.

Not trying to poke holes, just clarifying.
In their pricing calculator I put in 8 cores, 16GB of RAM, 1TB of egress and get the ludicrous price of $715.95 per month per location.

Is this a joke or do some people/orgs actually pay these amount of those specs (+ the other stuff that's included, obviously)?

If so, I need to rethink which market segment I'm in...
This might be a longer answer than you expected/wanted; sorry.

Let's say you have a bunch of machines, mounted in racks, installed in datacenters. You might have a naming convention like `m3r5dls1.acme-prod.com` to identify "machine #3 in rack #5 in datacenter #1 in The Dalles, Oregon". The acme-prod.com TLD is used to avoid cross-contamination between internal and public-facing identities. You'll have remote management services running on those machines, which identify themselves with an mTLS certificate issued to the machine hostname.

For services it's a little more complex, because there are at least three common use cases:

1. You have a service like `my-namespace/hello-world`, and you'd like to be able to send it RPCs. The clients don't care where the service backends are located, they just need to be able to resolve that service name to a set of (IP, port) pairs.

2. The same service as above, but now there's a locality requirement -- you want to send RPCs to a service in a specific place. Maybe for latency (closer = faster), maybe cost (local datacenter = no egress = cheaper), maybe regulatory ("all customers in India must have their data fetched from the Mumbai datacenter").

3. You have a specific instance of a service, for example when tracking the progress of a long-running operation. In Kubernetes this would be a specific pod.

It would also be nice if the services' mTLS certs are served from the same CA as machine certs, since that allows you to write tools that send RPCs without having to do their own parsing of the destination address.

The `{service_name}.cluster.local` format is not flexible enough to accommodate these goals, but if you're willing to use longer service identities then there's no problem:

1. `{service_name}.{namespace}.any.acme-prod.com` can resolve to any instance of that service, regardless of location.

2. `{service_name}.{namespace}.dls1.acme-prod.com` can resolve to any instance of that service in the datacenter `dls1`.

3. `{pod_id}.{service_name}.{namespace}.dls1.acme-prod.com` resolves to a specific instance.

Now you have consistent identities and universal mTLS authentication, so you can write authorization policies that grant services specific permissions they need to do their work.
To give folks a term to search for, also known as "bargaining for the common good."
> The owner class gets enlightened and makes sure that the govt taxes them and implements a solid Universal Basic Income

But where will they make their billions from if everyone will be living on a basic income? Less money for them will mean less tax money, and less UBI. It will spiral out of control into complete societal collapse if AI doesn't hit a plateau soon.
Sunk cost! This is why so many AI scientists are so skeptical. They don’t use chatGPT because they spent years developing skills that enabled them to use LLMs and now any kid on the street can.
Those values are probably not intelligible as confidence scores. For example if it answers a question with "They died in 1902", since there are a lot of euphamisms and rephrasings of 'died' it will get a relatively low probability. 1902 probably gets a high score, but you can't really rely on that since it might just as well be hallucinating and pulled the year from some famous event in that person's life.
> there will probably be co-processors added into consumer devices that excel specifically at the kind of processing that these models need.

There already are, e.g., Google Edge TPU, Apple Neural Engine, etc.
It's almost as if making something artificial more human-like also makes it harder to control just like a real human.
> For those of you catching up, we recently emailed accounts that are members of Free Team organizations, to let them know that they will lose features unless they move to one of our supported free or paid offerings. This impacted less than 2% of our users.

Why do you really want to kill those 2% of your users?
A topic I haven't seen brought up enough. Does ChatGPT contain publicly accessible, yet classified information? Will it divulge such information? Anything that can be done to mitigate divulging that?

Often two unclassified statements can be brought together to form one statement that is classified.
I agree a lot with this. I’ve probably spent more time crafting system prompts to find ways to avoid it doing this. As far as I can tell the best way so far has been to say something like “you are a terse helpful AI who responds in 100 words or less”. The lack of extra words to work with forces the bot to focus hard on exactly the answer I asked for because that’s the part that is useful to me and therefore rewarded.
This is really harshing my bag of kludgy workaround scripts mellow.
For anyone else who does not speak Aussie, a pokie is a video poker or slot machine.
Thieves Try to Steal Vintage Brabham Race Car, Foiled by Manual Transmission
You can work around perverse incentives by making the programs revenue neutral (like, any extra revenue gets mailed to transit riders, or something like that, and don't contract out red light cameras to some private company, have them be run by the DOT or something).

But yes, traffic issues run much deeper than that, but the fact that any attempts at efficient traffic enforcement generate so much outcry is telling.
There is, but it's in deployment not in the model, which is part of why I really don't understand why the approaches are so dumb right now from such smart people.

It may be from the odd perspective of trying to create a monolith AGI model, which doesn't even make sense given even the human brain is made up of highly specialized interconnected parts and not a monolith.

But you could trivially fix almost all of these basic jailbreaks in a production deploy by adding an input pass where you ask a fine tuned version of the AI to sanitize inputs identifying requests relating to banned topics and allowing them or denying them accordingly and an output filter that checks for responses engaging with the banned topics and rewrites or disallows them accordingly.

In fact I suspect you'd even end up with a more performant core model by not trying to train the underlying model itself around these topics but simply the I/O layer.

The response from jailbreakers would (just like with early SQL injection) be attempts at reflection like the base64 encoding that occurred with Bing in the first week in response to what seemed a basic filter. But if the model can perform the reflection the analyzer on the same foundation should be able to be trained to still detect it given both prompt and response.

A lot of what I described above seems to have been part of the changes to Bing in production, but is being done within the same model rather than separate passes. In this case, I think you'll end up with more robust protections with dedicated analysis models rather than rolling it all into one.

I have a sneaking suspicion this is known to the bright minds behind all this, and the dumb deploy is explicitly meant to generate a ton of red teaming training data for exactly these types of measures for free.
« Bonjour, comment êtes-vous? » barely translates to « Hi, how are you feeling today? » or, depending on the context, to something like « Hi, please describe yourself » to a native French speaker.
It's really just artistry, trial and error-based knowledge, and creativity behind figuring out what particular inputs to the program generate desirable outputs.
> This impacted less than 2% of our users.

"We apologise, and also here are some our 'lie by statistics' excuses where we don't count who used those org repos but just the org owners"
Tourist centers of London/Paris/Rome/Venice/etc. in the summer are becoming asymptotically closer to theme parks like Disneyland. Themed experiences, picture locations, vendors selling merchandise and junk food, families running through trying to "do everything."

At least with Disney the workers aren't trying to scam you.
Maybe they realize forcing people to switch from gas ranges would cause a huge uproar. People love gas ranges even though they pollute the air in your home and induction ranges are superior in almost every way except price.

This is a pretty good video to convince people to switch from gas: https://www.youtube.com/watch?v=X440BHdy35g
Title needs (2010).
AI was developed with solid used cases - mostly image recognition or other classification problems. Remember when you couldn’t select text in an image file and paste it into a document? Well that’s a solved problem. The fact that AI has been overhyped and business struggle to find uses for it doesn’t diminish the cases where it does work well.
> with AI-powered cost optimization

I am missing a blockchain reference
If one coder can do the work of four, the other three are out of a job.
One difference is that you can continue using your old Atom install, and if it had been "extinguished", you couldn't[1]. The other difference is that EEE was largely about interoperable protocols, not what one person could do with one program - it was about TCP/IP and SMTP and HTML and trying to take those over to Microsoft's benefit[2].

> "And blaming it on the end-user because they fail to pick up a massive product like Atom and develop it themselves seems disingenuous at best.

Yes, welcome to the elephant in the room of Open Source, 'many eyes makes bugs shallow', and the other pretenses we don't question. But at least the principle stands - you can still get the Atom source code[3] and pay someone as a contractor to change it for you, employ some people to work on it, start up a foundation, beg someone. Compared to a closed source program built on patented algorithms when the company shuts down and nobody can practically or legally improve it, or compared to a multiplayer game with online servers which the company shuts down and you can't use the game ever again, it is different.

[1] If you think it's impossible for Microsoft to reach into your Linux machine and take your Atom editor away, you'll see why EEE is a moral-panic rather than a real thing which could ever happen.

[2] you know, like Google actually does with HTML/Chrome strong-arming through Google-custom extensions, AMP pages, and the like. You'll see that this still hasn't resulted in other people being unable to make browsers or webservers or web-like protocols (Gemini).

[3] https://github.com/atom/atom
To be fair, if your usage doesn't hit any edge cases you would probably not notice any difference.

It supports using multiple monitors with different refresh rates, relative touch pad gestures, fractional scaling, more secure, easier to work with (for drivers) and more.
Some of it seems like gambling minus the fun. Especially when people bet on sports online anonymously against strangers. Still, just because it doesn't sound fun to me doesn't mean it isn't fun. Maybe they tell their stories to friends and still have social experiences, or it heightens the experience of following sports with friends.
The promise is so huge and the potential for abuse is so large, I'm glad OpenAI is limiting usage, even if now it's only to stay within capacity limits.

Off topic on business model:

I'd also support requiring people to provide feedback for each interaction; that's a fair trade.

I think a major, major portion of the value of Apple and (early) Google is the goodwill they maintained. People generally believed they were/are doing good, and could be trusted with the huge disruptions to everything from music to journalism, and consumers were willing to participate. While it has been problematic in many cases, I don't think those companies intended harm, and most of the benefit went to the consumer, not the company.

If there's a winner in the AI race, I believe it will be those who can maintain goodwill by exercising responsible use. It won't be by making early-adopters and researchers or competitors excited.

I also hope we've learned our lesson about cross-subsidization. I'd much rather have the company charge for services in close relationship to cost, to avoid business models built on gleaning customer data, preferences, or cognitive styles.

I dream that the company that wins will come out forcefully with a statement of its operating principles and enshrine them in irrevocable corporate governance, to avoid the bait and switch growth model we've reluctantly accepted (often in the name of open-source).

The question is whether people at those companies actually have the vision for that while they're making such quick progress. In this retrospect I'm surprised at the wisdom of many of the policy decisions made in the early days of Java, that ended up sustaining it for almost 30 years.
The comment I was replying to stated that that lifestyle is achievable for traditionally minimum wage (or close to it) jobs as long as you choose to live in a small city though
Decision-Making Under Uncertainty
> I am really surprised, I don't always agree with the ACLU but I've always seen them as a serious and respectable organization. I honestly wonder if there is external influence at play here.

People said that when they defended terrorists. People said that when they defended Nazis. People said that (correctly) when they refused to defend Communists.

This is a silly point to realize they are in favor of free speech, and believe free speech means that the government can't single out particular speech for consequences. (And yes, requiring someone to stop "engaging in commerce" because of the content of their speech clearly counts as that).
They are trying to use small number they conjured from the statistics to diminish the scale of the problem.

99% it is "the number of accounts affected" and not anything to do with number of downloads of images hosted there.
Worse, Tencent owns like 40% of Epic. Ironically would have been better if Bandcamp were acquired by Spotify, because then it would at least be owned by a public American company.