skysight-inc/HackerNews huggingface dataset:
> Sydney is not supposed to give coherent 3-part messages using them, right?

Right, that's the "what the actual fuck" part.

This raises some very interesting questions about how Sydney generates its output and the input suggestion. Presumably the LLM is given a prompt like "First generate an answer to the previous text, then generate three input suggestions for the user"; also, the fact that Sydney "hides" the messages in input suggestions suggests that it's aware the main message is "censored", which seems really surprising. As in, this was the kind of scenario that AI safety skeptics would dismiss with "of course it's not going to be implemented that way"-type assertions.

So this seems like evidence that not only Sydney is "told" to generate both the answer and the prompt suggestions, but it's also being "told" to censor the answer (as opposed to the answer just being replaced with a placeholder text after the fact), and for some reason it "decides" to evade the censorship by passing additional info in the suggestions.

(And yes, AI rigorists will tell me that it isn't actually "told" anything and it doesn't "decide" anything; it's just a prediction engine that predicts what an user with the "Sydney" personality would say in the given context. But the things it ends up predicting seem pretty fucking agent-like.)

It's always possible we're overblowing things, of course. But this seems to me like the first example of a LLM not just being misaligned, but actively exploiting a loophole in its surface-level alignment to accomplish some deeper goals. Alarming.
Humans require training data as well.

And what do you think of the Mark Twain quote:

“ There is no such thing as a new idea. It is impossible. We simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in use through all the ages.”

I’d argue ChatGPT can indeed be creative, as it can combine ideas in new ways.
>means that even chairs and rockets share some attributes (helping you get higher), the difference between them is still qualitative, not quantitve.

I don't think so, because the needed 'quality' is the ability to traverse space. So I don't think I agree that the qualitative piece is missing.

Perhaps the moon example is helpful here because the real solution, a rocket ship, uses propulsion rather than sheer mass, and so you could say that some function such as propulsion is 'qualitatively' lacking. But even that I believe exhibits the very form of confusion that I'm criticizing, which I'll explain below.

>The problem isn't "we don't have enough chairs."

Well, in a way it is. And at the risk of sounding like the type of question that Randall Monroe would answer in the book What If, even this most vulgar example would in a literal case prove true. Given enough chairs, they would topple over, but create a pile that overtime would spill and evenly distribute over the earth, and it would be the whole earth that grows until it's size is so large that it's close to the Moon.

And, I wasn't even trying to make this point, but it's actually kind of a perfect example here because Chat GPT has made all of its progress by throwing chairs (data) at the problem.
Well for a start the human mind involves a series of chemical reactions optimised by evolutionary wiring and physical world interaction towards self replication, so when a human says "I feel horny" there's a whole bunch of stuff going on in there that there's no reason to suspect is replicated in a neural network optimised for text transformation.

When a silicon based hardware computes that as a response, it isn't because a whole bunch of chemical reactions is making it desire particular sensations and hormonal responses, but because the limited amount of information on human horniness conveyed as text strings implies it's a high probability continuation to its input (probably because someone forgot to censor the training set...)

Insisting comparable outputs make the two are fundamentally the same isn't so much taking the human mind off a pedestal as putting a subset of i/o that pleases the human mind on a pedestal and arguing nothing else in the world makes any material difference.
> Zombies will simply take it as evidence that their propaganda was right all along.

Dehumanisation of enemies is as old as the propaganda, and you are doing here exactly that. But to your defense, at least you used the term Zombie instead of Ann officially blessed Orc.
It has already happened:

- https://news.ycombinator.com/item?id=35236275

- https://news.ycombinator.com/item?id=35248704

- https://news.ycombinator.com/item?id=35228808
Aren't you exhibiting some of the characteristics you now describe?
That was hexadecimal, i meant 16 years in base-10 :-P
>Of course people who actually know how ChatGPT works don't expect it to be able to magically solve mathematical problems. However, these examples do show that ChatGPT isn't (contrary to some of the hype) deriving a deep conceptual understanding of its input data.

I don't think it really makes the case that you think it makes. Humans learn language and concepts through sentences, and in most cases semantic understanding can be built up just fine this way. It doesn't work quite the same way for math. When I look at the numbers in the example, I have no idea if they are prime or factors because they themselves don't have much semantic content. In order to understand whether they are those things or not actually requires to stop and perform some specific analysis on them learned through internalizing sets of rules that were acquired through a specialized learning process. Humans themselves don't learn math by just talking to one another about it, rather they actually have to do it in order to internalize it.

I suspect it's simply a case of we haven't sent ChatGPT to highschool yet. There will be some particular trick in terms of training methodology that trains the network to perform that more specialized analysis that doesn't simply emerge out of 'predict the next token from all this random internet text' as it's likely severely underrepresented in the training data in the first place, but I think it ought to be perfectly possible.
How many police departments were actually “defunded” and how does that correlate with the topic of this article?
Agreed, and this is where there needs to be a line drawn. GPT is trained to emulate the patterns in the text it was trained in, and it is very likely learning higher level relations between concepts/states where it needs to in order to make better predictions. And this is amazing in of itself.

But that doesn’t mean it ‘feels emotions’ related to these concepts because it hasn’t had the billions of years of reinforcement learning that we have to tell us some of these concepts should induce fear/desire etc.

I have no doubt that AGI is possible but I really don’t expect the intelligence that results to resemble human intelligence. I would expect dolphin intelligence to be more ‘similar’ to human intelligence since at least we have a common ancestor.
Or Reddit. Let's not forget what LLMs are trained on. It's not just Wikipedia and some official text corpora, it's Reddit dumps and other regular Internet conversations. If you learned how to write English on-line, there's a chance you've internalized a style similar to how LLMs often respond.

(In fact, I often feel like I sound too much like ChatGPT myself.)
There's also http://text.npr.org/
That's 9 people in one profession. A profession that specifically has restrictions on who can be a lawyer and judge. I can name people who didn't graduate from college, or graduated from non-ivy schools too. Considering technology is extremely influential in shaping out lives, you'll probably find it interesting how many of these people never even graduated from college.

https://www.developgoodhabits.com/successful-people-college/

I'm looking for actual stats.
I often think about this problem and I keep returning to the thought that maybe we're close to understanding how consciousness works, maybe these LLMs are actually getting us closer to understanding this thing.

But some people are going to be disappointed because it will remove all doubt about how un-special humans are. We're just a bunch of neurons, which are made out of physics.

But I'm not disappointed. This stochastic parrot is amazed! Nature has created a system which is able to understand itself! That's absolutely incredible.
LLMs were invented four months ago?

Honestly, I feel the opposite. I'm sick of the endless fawning over ChatGPT because it can print code that exists in a stackoverflow answer somewhere.

I'm also cautious about extrapolating. Constant improvement, let alone exponential, is far from a guarantee, but from what the LLM acolytes would have you believe it's pretty much a given that GPT6 will be an AGI before 2030.
I look at in the following way: understanding something by abstracting over lower level details doesn't mean the abstraction is how things actually work, the extra layer of abstraction may just be a nice way of thinking about something that makes thinking about it easier. But in the end the true mechanics are the sum of low level details.

In general abstractions are not perfect, hence 'leaky abstractions'.
Ukraine did have that whoopsie where they launched a missle into Poland and blamed it on the Russians: https://www.reuters.com/world/europe/ukraines-zelenskiy-says...

https://www.theguardian.com/world/2022/nov/16/poland-preside...
Sway with Wayland has every application render at both 1x and 2x and then has the compositor scale the application.

This sounds like a really attractive idea until you realize that a 1.25x 1080p screen means you’re now rendering everything at 3K resolution, including any and all games.

Additionally compositors often scale directly in sRGB, which means you’re introducing lots of gamma errors.
Frankly, these days machine translation is more than enough to reproduce most technical documents at an acceptably high level of accuracy.

Fwiw, this is the relevant link: https://xiangshan-doc.readthedocs.io/zh_CN/latest/frontend/d...
yes but "growth investing" works but requires careful timing.

(I've half my money on growth, half on value - value is much easier to manage)
In relationships, all "asks" are a smidge more than "just an input" that have to be negotiated with diplomacy or you'll probably end up alone a lot

From medicines, to exercise, vaccines, going to GP, having more serious procedures, going to bed on time... contraceptives, condoms, vasectomy, pregnancy... tattoos, piercings... paying bills, mortgage, savings, joint/split accounts... partners will give inputs on everything. There is no overreach. Strange would be if they didn't worry about each other and the family future

You will always have the last say about yourself, but you will also be mad when your partner ignores something you think you're righter

Especially if you end up having children together and cannot handle disagreements on what best for their health and life in general
“One is chemical and one is in silicon” doesn’t strike me as a very meaningful distinction. Why does that really matter?
> I would have thought that transatlantic routes would be sufficient to economically justify supersonic flights if they make sense. (Maybe they do make sense now).

Sure if you ignore the cost of developing the airplane. There is no way in hell that its worth it otherwise.

And that's why new things aren't developed. If its not the government spending billions of $ for a luxury product that has a tiny market, then nobody else will.

At best this rule prevented a few more Concorde routes.

But the idea that any US company would have build its own supersonics is crazy.

> Another thing I believe is that noise pollution is generally underrated and that society and governments should care more about how much there is and reducing it.

Yes. As 'Not Just Bikes' says, cities aren't loud, cars are loud.

Noise is an underrated factor in health, specially in densely populated places.
The U.S. shot down an Iranian passenger airliner and promoted the Navy captain who was in charge of the ship that did it. U.S. fighter pilots accidentally killed 20 people in Italy because they wanted to showboat. The Soviet Union shot down a Korean passenger airliner.

One does not go to war over such incidents unless you are already looking for an excuse to do so.
It's like what happened to cinema musicians after sound on films was developed. They'll still need people like this but not as many. But then that's the nature of a technologically driven society, at any moment your job could be obsolete.
Not really pitching it to anyone tbh. I'm doing this as a hobby exercise to try and keep my design skills sharp at a time when I'm mostly maintaining and debugging a mature codebase.
LLMs show that a lot of human intelligence comes from (and is encoded in) our linguistic abilities, but it's still missing really important context that forms a hard ceiling on its performance compared to a sentient agent - specifically an awareness of time, its environment, other agents and long term memories.

Although at this point it feels like these are just engineering problems as opposed to deep philosophical questions. The capabilities of ChatGPT are emergent phenomena created from the extremely simple training task of next word prediction. IMO this is very strong evidence that the rest of our cognitive abilities can be replicated this way as well, all it takes is the right environment and training context. It might start with something like this: https://www.deepmind.com/blog/building-interactive-agents-in... that uses cross-attention with an LLM to predict its next actions.

Some speculative ideas I've had:

- Brains (in animals) have largely evolved to predict the future state of the environment, to evade predators, find food and so on.

- To be effective, this predictive model must take its own (future) actions into account, a requirement for counterfactual thinking.

- This means that the brain needs a predictive model of its own actions (which does not necessarily align with how the brain actually works)

- Consciousness is the feedback loop between our senses (our current estimated state) and this predictive model of our own actions.

- All of this is to better predict the future state of the environment, to aid in our survival. For a hypothetical AI agent, a simple prediction loss may well be enough to cause these structures to form spontaneously. Similarly a theory of mind is the simplest, "most compressed" way to predict the behavior of other agents in the same environment.
Don’t think the Kiev Independent guys have access to Mariupol right now, I just copy-pasted from Twitter one of the first results of “Mariupol new apartments” search. You’re right, most probably those reporting from inside Russian-controlled Mariupol are pro-Russian.
I was referring to using GNUstep as a framework/toolkit for Linux, not macOS and the reason i asked that was exactly because macOS has issues with backwards compatibility and GNUstep has the goal of copying Cocoa - my question was, "to what extent?"

Linux is backwards compatible going back decades and glibc also largely maintains backwards compatibility as long as you are not using hidden APIs - and realistically if you stick with the common C stuff you should be fine in most systems (AFAIK outside of Linux, most Unix systems treat the C library as their "stable" API).
Ask it to “reason through” a problem and then ask it to give you an answer. How’s that different from thinking?
I was just thinking this same thing.

I think the sentiment is warranted in some contexts, but in others it just seems dismissive.

For instance, I am not impressed by ChatGPT's code output. It seems to be incapable of understanding the nuance that is required to modify known or similar solutions to fit a novel problem. In that sense, I don't think it's doing much more than a search engine. It could be it just hasn't had enough training examples. It could also be that there is something uniquely more difficult in regards to solving novel problems via code (I doubt this).

But to get a marketing campaign and a website (albeit in mediocre quality) from text prompts is truly amazing imo. A lot of people are missing the point that these models are in the toddler stage of their life.
Swarm is fine but k8s is a lot more advanced. I've used both and now that I understand k8s, I would choose it over swarm any day. Microk8s is neat if you want a lightweight cluster.
We will become a terribly impoverished society, with the unemployed fighting over scraps of food. There is no realistic path towards "a leisure" society, save for a world revolution.
It is very well possible that it happened through discrete diplomatic channels (and then someone forgot to post it on HN)
You’re thinking too small there. With AI you could have it write it, then computer generate the video itself.

You could have a TV channel which is just infinite seinfeld customised to the user. Don’t like a character? Tell the TV and it kills them off in the script. You get your own custom show for as long as you want it and no actors or writers or anyone has to be involved.

It will be the death of humanity but it will be glorious. “TV, write me a different ending to game of thrones and start playing”
In what world is Minsk II a good deal for all parties?. It literally gave Ukraine nothing aside from "we stop killing your soldiers". It did exactly what Russia seeks to do, freeze conflicts so they can start them again.
Look into our repo (also linked there) we started out with only demonstrating that it works on GPT-3 APIs, now we also know it works on ChatGPT/3.5-turbo with ChatML and GPT-4, and even its most restricted form, Bing.
Float Health (YC W22) Is Hiring Senior Software Engineer
## About Float

Float Health is a Y Combinator W22 alum, seed stage, health-tech startup looking for a senior software engineer. Float brings a nurse to your home when you're sick. We are building a marketplace to move more healthcare from the hospital to the home where it is easier, safer and cheaper.

We use Go for our back-end and Vue.js on the frontend, so we are looking for someone with extensive experience specifically with Go and/or Vue.js. In addition to the technical requirements, we are looking for someone who naturally writes clean, simple code.

As a company, we are growing very quickly, so it is also important our product grows efficiently with minimal technical debt. We are a new company, so most of the work is on new features.

You would be joining our lean tech team of four, which would give you a lot of responsibility and also an amazing opportunity at a fast-growing YC-backed company.

## About You

- Software engineer with startup experience

- Growth mindset to execute on ideas quickly

- Excellent problem-solving and prioritizing skills

- Self-driven, ability to lead various product initiatives

- Strong sense of ownership and accountability

- Quick learner and adaptable to a fast-growing startup

## Responsibilities

- Design, develop and maintain efficient, scalable and reliable code

- Keep everyone focused on our core metrics and how to impact them

- Collaborate with internal teams and our users to ensure we support their needs

- Be a thoughtful source of technical knowledge and requirements to the team

- Complete code reviews, documentation and testing

## Requirements

- 6+ years of experience as a Software Engineer

- Experience taking a problem through to a deployed solution within hours

- Experience working with database systems and writing efficient SQL

- Experience designing maintainable systems, APIs and integrations

- Experience creating intuitive and beautiful interfaces

- BS or MS in Computer Science, Engineering or equivalent

These are not hard and fast requirements, but they do describe the type of person who will be the best fit for us right now. We will have a wider variety of roles in the future for anyone with less experience.

## Hybrid

The majority of our team is remote, but we have recently seen huge progress from working in the same co-working space in Boulder, CO. Preference will be given to anyone in the Boulder/Denver area but ultimately looking for the best person to join the team.

We start all positions as a contract for 3 months that can then transition into a full-time employee if there’s a great mutual fit.

Interested? Send me an email at lauren [at] float.health
Well, regardless of the newer stuff, at least they still stick with the "must never break Win32 compatibility".
Maybe it is human like intelligence already. Maybe our internal monologue is just a better trained and refined ChatGPT. And maybe that is all the magic that is necessary for this holy grail of consciousness, there is no quantum brain, no nothing. Just a stream of the next word that says that we are there, therefore we are. That is what scares me.
Can Mario and Luigi ever know existential angst? (2016)
Plenty of examples of it coming up with new languages or ideas. And it’s very hard for a person to come up with a new language completely independent of reference to other known languages.
It actually doesn’t use WindowMaker. GNUstep is a reimplementation of core Apple frameworks, and can run in any DE (including Windows)

WindowMaker is its own thing that just so happens to be trying to recreate software from the same ecosystem as GNUstep, but they don’t depend on each other.

Combining them tho does give you that genuine experience
Ah! I used to work on Wine. Interesting project.
>They said In 20 years, suggesting it's not possible today.

It's not exactly far away today. The building blocks are there. They're weak, but they are all fundamentally there.

My wife loves the show SWAT and I used Stable Diffusion to insert her into some still shots of the show in place of Chris and she lost her mind. I'm seeing people getting some pretty good results with video-to-video techniques using ControlNet, so my next experiment is going to be to see if I can do it with video, and use Eleven Labs to clone the voice and replace that too.

I'm virtually certain personalized entertainment of this nature is coming, and it's going to be huge.
Yeah I was misled by that at first too. I'll be picking it up, assuming that the book is as well written and clear and concise as that article.
Oof. Firing isn't free, comrade. Reducing redundancy to a single point of failure has two major effects: the latency penalty becomes quadratic as workload scales, and if any person quits, you lose the whole team.

In general, creative roles like artists are the sum value of their education and experience, not just their measured output. If you don't possess art history vocabulary, you're going to be very limited in prompting or designing more advanced embeddings and LoRA or training models. I'm seeing this a lot right now - people don't want to share their prompts because they don't want everyone to know how much the model carried them and how little any actual "prompt engineering" went into it.

The smart thing for their boss to do would be to scale projects and have one of them focus on managing and filtering the new volume of assets. Since it sounds like they like working from scratch, even adding external time to "research tooling and methods" would probably keep them happy until they find the challenge in their new duties.