skysight-inc/HackerNews huggingface dataset:
* If 50% will use it, then it still might be a good buy at a low enough price.

* The desirable, safe, &c locations for fueling cars are for the most part already occupied by gas stations, so it might be that there are only a few locations available that 100% will use. It is rational to obtain those few and then add locations that 50% will use to reduce load on the locations that 100% will use.

The idea that women in the room would have changed this is not plausible, because the constraints are not any different for women planners in this situation than for men planners.
That sounds great and all, except that estimates are usually wrong. It’s a real problem when estimates are treated as deadlines, which is exactly what you’re describing.
I would love to know
Yeah, it was GPT-4.
So far, the "defense 2.0" companies seem to be following the path of Anduril - very high grade drones and cams. I'm not trashing them - there is a huge market for military grade observation tech.

I'm just wondering when one of these startups commits to making things that kill people.

Even then, a company isn't just limited to selling to the military...way waaaay more Sig P320s have been sold to civilians than will ever be sold to the army, and I expect the Spear to likewise be much larger for civilian sales than government sales (yes, a civilian Spear is coming).
IMO, the only realistic answer to that question is "we have no idea". Everything else is just taking wild, tenuous guesses.

But LLMs have no desires of their own, which to me settles the question of rights.
>Instead, government should support development of alternative technologies, and when those technologies are better, they will naturally take over.

I presume "naturally" is a euphemism for "subsidize the R&D, privatize the profits." I am shocked (shocked!) that a company might favor this option. ;)

The big problem in your example is a government choosing winners and losers by technology. Instead, a carbon tax should be used to tell us what to achieve (lower greenhouse emissions) and let the market decide the most cost-efficient way.
You’re right that it still needs a skilled person to ask the right prompts, and I don’t see that changing anytime soon

But if a few people asking the right prompts is all you need, what happens the to other 50-100+ people a game like Clash of Clans would normally employ?
LZ77 and LZ78 are ubiquitous, forming the basis of compression schemes used in ZIP, PNG, GIF, and Yann Collet's Zstandard.

[0] https://en.wikipedia.org/wiki/LZ77_and_LZ78
As a person whitout internet( pre open suse era), my first linux for personal use was suse (before the opensuse) it was nice to have all deps in the cds, also for the time, compatible nvidia drivers, cedega worked like a charm for warcraft 3. Those where the times
> I would think a military warning system would be implemented before a financial aid.

In this world? Eeeh, I doubt it.
> I've seen a lot of comments on here like "well LLMs are good at writing snippets, but could never write or maintain large codebases to accomplish a larger goal" - but are you SURE about that 'never' given the current generation is already doing things people suspected they could never do? "But they'll never be able to manage / interpret stakeholders", Are you SURE that LLMs will have to adapt to fit stakeholders, and not the other way round?

That sounds a lot like the self driving cheerleaders five or ten years ago. That work so far has resulted in some awesome features like adaptive cruise control and parking assist but it fell far short of what the hype was promising to deliver by now.

Five or ten years later Mercedes is the only company getting ready to ship level three self driving. Level four and five are still a pipe dream, practically restricted to a few companies like Waymo in a few controlled environments like Phoenix and San Francisco.

GPT4 is great and I can't wait to see what 32K or even 100K/1M token models can do, but I fear we're about to hit the point where progress grinds to a halt because going further requires something closer to AGI than what we have now.
Analyzing thousands of trends, both industry/niche specific and society wide. Tracking campaigns that work by monitoring social media likes, references to past slogans, etc. Potentially dedicating thousands of years worth of brain power and analysis to the coffee shop down the street's new slogan.
I thought I saw a very similar headline within the past month or so, also from WSJ if memory serves, though it could have been another publication, perhaps WaPo.

HN archive search-fu fails me.

Closest I'm finding is 5 months old, "State Department Urges Silicon Valley to Aid National Security Effort": <https://archive.vn/https%3A%2F%2Fwww.wsj.com%2Farticles%2Fst...>

<https://news.ycombinator.com/item?id=33215418>
> Why can't a "purely statistical" process have an understanding?

Once you understand long division, you can do it on infinite numbers without ever having seen the specific numbers. You can get this full understanding just from a handful of examples, no need for terabytes of them.

No matter how many examples of long division examples you fed to statistical model like GPT, there will always be infinite amount of numbers you can tell it where it will give the wrong answer*, unless you cheated and actually hard coded the understanding into the model.

If it cannot understand long division just from few examples it cannot ever understand it. The very reason it needs ridiculous amounts of data is precisely because it cannot understand. If you think it understands you simply aren't trying very hard to confirm otherwise.

* in a way that reveals there is no understanding of long division, obviously a human would also give wrong answer after being awake 100 hours writing numbers on paper
Get your bathroom remodeled in your home and have the worker tell you it'll be done whenever and you'll understand why developers (or any hands on worker) is lacking in their skill and practice if they have no clue how long it takes them to perform their work.

"Management" or a PMs job will then be to aggregate those estimates so they can estimate at a higher level than you, similar to a project management in a construction site. But each person needs some semblance of an idea of when their part will be done.

The same way if you were remodeling your bathroom and your garden and want to tell your kids when the construction around the house will be done, you'll need the gardener's estimate and the plumber's estimate, plus maybe some buffer at the end, plus some time to go with the wife to get a new shower curtain, etc.
> The "land of free speech" always has a big "not you" exemption that can be wielded against people who aren't WASPy enough

WASPs are the ones pushing pornographic books in schools. Florida is one of the most diverse states in the country.
The question is certainly naive, but the answers read like satire of the worst project managers I ever had.
That ChatGPT is capable of producing human-like sentences from pattern recognition without any universal grammar baked in

You didn't address or missed the main point: chatgpt requires something in the order of a trillion tokens to be capable of producing what you mentioned in one language.

There are 40 months old babies that are fairly conversant in both Chinese and English, and are able to detect sarcasm with something like 0.0000001% of the tokens, doesn't that give you pause that part of language acquisition is innate to humans and is not entirely acquired the way chatgpt is taught?
Yes it was the free version.
You are working under an assumption that this tech is an O(n) or better computational regime.

Ask ChatGPT:
“Assume the perspective of an expert in CS and Deep Learning. What are the scaling characteristic (use LLMs and Transformer models if you need to be specific) of deep learning ? Expect answer in terms of Big O notation. Tabulate results in two rows, respectively “training” and “inference”. For columns, provide scaling characteristic for CPU, IO, Network, Disk Space, and time. ”

This should get you big Os for n being the size of input (i.e. context size). You can then ask for follow up with n being the model size.

Spoiler, the best scaling number in that entire estimate set is quadratic. Be “scared” when a breakthrough in model architecture and pipeline gets near linear.
Galaxy distribution – Interactive 3D-Map of the Universe
Interactive 3D-Maps of the universe compare the distribution of galaxies based on real deep sky surveys like SDSS, 6df, 2df and DESI.
I generally agree, Hans came to mind when I read a recent post where people discovered they had been nudging gpt towards answers without realising it.

https://news.ycombinator.com/item?id=35306095
It’s not, statistically. But don’t let me stop you from clinging to ignorance.
Very few Russians disobey order to kill innocent people, see for example: https://youtu.be/gcVws482J30?t=151 . They are doing that intentionally.
More tame inflation for people who maintain their good paying jobs will be in an even better position
> Social media has been around a lot longer than the last few years.

Setting aside the (valid and important) point about the lockdowns, it also took time for social media to evolve into its current form. Snapchat and Vine came out in the early 10’s and TikTok in 2017. Twitter has never been especially widely used; it has outsized influence because corporate media types use it, but it’s not what the masses are scrolling. Social media might not be new, but the short-form video style of social media has taken off a ton in the past decade.

At the risk of speculation, I’d also surmise that this is a major part of the problem. Humans have a primal desire to interact face to face. Full motion video of people talking to the camera is sort of a junk food version of human interaction. It’s the same reason watching people play video games on Twitch is more engaging when you can see their face in the corner of the screen, and why so many YouTube thumbnails include some recognizable face either looking into the camera or expressing an emotion. Photos and text don’t scratch the same itch, so they’re less addictive.

(Some people can be addicted to text-based social media. I am one of these people. But we were the ones who got addicted to webforums and Usenet; probably not the typical user.)
Harry Potter by Balenciaga
AI-generated content is both exciting and terrifying.
This is absolutely something I was running into with LLaMA. I'm curious if this potentially extends into that particular use case...
The work developers do varies, so this question in general makes little sense.

Sometimes, developers literally do R&D without knowing, whether they will be able to solve the task at hand at all. Other times, they use proven solutions for typical problems.

I saw the most issues arises, when management starts to confuse between the two.

From the business perspective, you definitely have to balance the ratio of the two.
I don't care what you did. All due respect, you are one person. I care about what can observed by people in general.

Dunno what to tell you other than textbook and practice problems is far from the solution you think it is for a big chunk of the population.
I'm not demonizing anyone, I'm talking about incentives.

And I've come to my understanding of how they work on government employees by talking to, and working with (as a consultant), government employees. There's a certain bureaucratic counterproductivity that I've only ever encountered in government employees.

If that doesn't match your experience and government in your area is highly efficient and you wouldn't change a thing, that's great and I envy you, but it's not my experience at all.
If there was weather patterns that blew winds from either side to the terminator zone it would be very unpleasant. Imagine artic winds except way colder or hotter
That behavior is what people dislike about Google though. Filtering and biasing search returns is a huge area of mistrust for them. OpenAI's fix to this is an even more restrictive and opinionated version?

That is a total nightmare once they decide to monetize it.
Nooooooope
I find it kind of funny how both sides take an "it's just not realistic" approach to each other's complaints and then the compromise is usually "You pretend you can make an estimate and I'll pretend to believe it".
OpenAI isn't trying to become Google, though. ChatGPT is marketing.

OpenAI's market is more comparable to AWS. Each product is another backing service for some other product to integrate with and sell to _their_ users.
If it's a language model, yes. I think if it's in the initial prompt (first message) or second message to the AI doesn't make much difference for its role in the process.
You missed the point: it's enough money.

You could have enough, too. Anyone can.
Several deep sky surveys show us, that the space is filled with billions of galaxies forming the cosmic web with filaments and voids. Cosmic Structures is a 3D visualization where you can rotate, pan and zoom the map. The animations are based on real data derived from the largest spectroscopic sky surveys ever made: SDSS, 6dFGS, 2dFGRS and DESI.
Producing estimates is a necessary evil for multiple business reasons. A fundamental skill of any experience Software Engineer is to be able to make decisions under lots of uncertainty, and that includes allocating resources to a task for which a lot of information is not yet available. Yes it sucks but it needs to be done.
Off the top of my head, here's a short list of fields that could make some use of a professional 3d headset:

- 3d modeling/sculpting

- Architecture

- Interior Design (great uses for passthrough AR here)

- Product Design / CAD
If you want to see an engineer squirm, ask them to estimate how long something will take. If you want to see a manager / product person squirm, ask them what the net present value of the deliverable is.

We should just accept that significant uncertainty exists in both situations. If you can’t handle risk, liquidate company and invest in t-bills.
I agree that time estimates are a necessary evil, but I disagree that story points are useful for anything really.

This comment from the article was interesting and it matched my experience:

>
The law of large numbers only applies if a) the population is sufficiently large, otherwise outliers will dominate b) the estimates are at least mostly independent, otherwise systematic bias will skew the result and c) the estimate is in fact the expected value (mean average) of the probability distribution of time taken. If the estimate is a mode of the probability distribution, then you can not meaningfully add the estimate directly and be able to meaninfully say anything about the resulting distribution.
A surprisingly powerful fully functional miniature of the GWR King Class
But this is not phonetic. The Is in "Inglish" are hardly the same in any dialect.

And it doesn't solve the problem on the graphematic level. It ought to allow anyone to read the chosen dialect phonetically. But it suffers from an arbitrary choice of glyphs, so I have no idea how to pronounce eg. an (an actual problem I have pondered today is a-typical).

Conversely, if the spelling ought to be predictable for language learners, you are probably better of learning Mandarin (^_^)
Thanks very much for the write up. This was a fascinating read. It’s really interesting to see what chatgpt is capable of when challenged appropriately. It really does seem to be operating within some spectrum of human intelligence. Certainly beyond my capabilities in many many ways, which I was especially impressed with after reading that math problem example.
It is fitting that such a large name is expressed in so few characters. RIP.
The model does think but only when you tell it to think out loud.

This is less a weird quirk of the training data or a One Weird Trick That Makes Your Matricies Sentient, and more a limitation of the model architecture. Neural networks do not have the capability to implement 'for loops', the only looping construct is the process that runs the model repeatedly on each token. When you tell the model to "think out loud", you're telling it to use prior tokens as for loop state.

Another limitation is that the model can't backtrack. That is, if it says something wrong, that lie is now set in stone and it can't jump back and correct it, so you get confidently wrong behavior. I have to wonder if you could just tell the model to pretend it has a backspace button, so that it could still see the wrong data and avoid the pitfalls it dropped into before.
You don’t think it’s relevant that the source is the primary funder and arms provider of one side of this conflict?
Suggestions about how Canada could improve really ought to be directed to the Canadians. They might be amenable to different policies in sparsely populated regions like Hudson Bay. Continental USA has no analogous areas.

Non-Americans who wish to complain about USA have a broad range of topics from which to choose; there's really no need to complain that USA regulates the operation of airlines in USA airspace.
>which in both cases mean you spend less of your revenue on labor, which means "less jobs".

You can't view things in the void like this, though. Reducing the overall cost of doing something makes it viable for more businesses, which means more jobs. We have more jobs in software development overall now because of how much more productive it is today, which opens up so many more viable use cases for it. We have more CG artists for movies, games, etc. now in large part because more efficient workflows and more powerful software have increased productivity.

Will it be the same thing with generative AI? I think so, at least for the foreseeable future.
UBI is a dystopian nightmare it takes away the voice and power from the majority of the population.

I really don’t know why people are even considering this disaster.
One flavor of agile (XP) says that estimates longer than 3 weeks diverge from reality too much. If you want to be able to estimate accurately, you must break it down until every task's estimate is less than 3 weeks.

That doesn't give you an accurate estimate, but it does usually give you a less inaccurate one.
Cuz proj managers need some fall guy when they estimate a project timeline and submitting it to upper
What's wrong with mediocrity and laziness? Once you do enough to put a roof over your head and food on your table there's exactly zero reason to do more.
The result is a story that describes the princess as being funny, without actually saying anything funny:

"Once upon a time in the medieval kingdom of Gigglesworth, there was a princess named Miyu. Princess Miyu was known throughout the kingdom for her infectious laugh and love of practical jokes.

One day, the kingdom was thrown into chaos when the royal jester decided to quit his job. The king and queen were beside themselves with worry, knowing that they needed someone to fill the jester's shoes before the upcoming royal ball.

Princess Miyu, always eager to help, stepped up to the plate. "I can be the jester!" she exclaimed with a giggle.

The king and queen were skeptical at first, but Princess Miyu was persistent. She spent the next few days practicing her jokes and tricks, determined to make the royal ball the most entertaining event the kingdom had ever seen.

Finally, the day of the ball arrived. Princess Miyu donned her jester's hat and entered the grand hall, ready to perform.

She started with a classic joke, pulling a fake flower from her sleeve and presenting it to the king. But instead of a flower, a live frog jumped out, much to the king's surprise and the queen's horror.

The crowd erupted in laughter, but the king and queen were not amused. Princess Miyu quickly realized that perhaps she was not cut out for the job of the royal jester after all.

As she slunk out of the hall, she couldn't help but laugh at herself. "Maybe I should stick to being a princess," she thought to herself.

And so, the kingdom of Gigglesworth went on without a jester, but with the memory of Princess Miyu's hilarious attempt to fill the role."
every business exist to make money or something of value to its shareholder.

You can hire contractor to modernize you kitchen or paint wall.. you need some understanding of exactly what is scope of work, what is $ cost, what is the quality, and any other constraint.

the contractor cannot just say i have no idea how long it will take. there has to be some start and end date.

why some programmer think they do not have to provide any estimate at all is perplexing. is your paycheck an infinite money stream? does your employer not have commitment to deliver something by specific date? or do you want someone else to go create estimate of how long YOU need to complete some work?

this is why tools like scrum come in. estimates not a perfect science..so break down scope of work into small and small chunks until we can say “ok…this piece take 3 days”.
> The link I posted is data for the change in teen mental health between 2019 and 2021. The data incorporates all of the effects of COVID, Lockdowns and school closures

Yes, my point is that is exactly the problem with your data. It needs to be broken down by location to account for differences in lockdowns and school closures, to claim there was any or no effect.
Money now is more valuable than money later.

As someone who has both built and sold software, it's simply to be able to sell software before it's complete. Without estimates, you can't sell something that doesn't exist yet.

This doesn't change the fact that requiring estimates is a bad idea if you want great software. The best software is built well, then sold.[0]

Great software later is more valuable long-term than bad software now.

--

[0] The best case is actually to sell software without a timeline. But most organizations are not able to operate this way.
>> If it stopped making mistakes and produced complete fully working programs, no, there would be no way to say it doesn't understand.

There exist program synthesis systems that always produce "complete fully
working programs" and that in fact cannot make any mistakes because they are
based on algorithms with strong theoretical guarantees (like mathematical
proofs) of their correctness. You give them a set of examples and some
"background knowledge" and they spit out a program that's consistent with the
examples and the background knowledge, no ifs and buts and maybes.

I could point you to several systems like that, if you wish, but first I want
to make sure that we both agree what you are saying: a program synthesis
system that never makes mistakes "understands". Is that right? Could you also
please clarify what such a system "understands"?

Then I'm happy to link you to some systems like the ones I'm talking about.
> But LLMs have no desires of their own, which to me settles the question of rights.

For now. The whole point of this discussion is that LLMs have been rapidly improving to the point where emergent properties are bound to come out.

> IMO, the only realistic answer to that question is "we have no idea".

So we have no idea what’s happening in our own brains, what’s happening in other animal’s brains to cause us to feel empathy for them, or what’s happening in a LLM’s “brain” but we can confidently say they should never have rights and we should never feel empathy for them? That seems a bit premature.
This. A subtle issue is that your company may use saas services in the dev process like github or gitlab or various supply chain/security providers, since they have private source access, what's different about using ChatGPT? The difference is that your company has legal contracts with those other providers that require those providers to maintain confidentiality with your company's private intellectual property. If you share said intellectual property with any person, organization, or service with whom your company does not have such an agreement, you yourself have breached the terms of your employment and can be fired.