skysight-inc/HackerNews huggingface dataset:
Estimating time for a task is it's own skill. And, of course, the danger is in the task being insufficiently specified, leading to unknowns.

Cook lunch, estimate 30 minutes. Task starts, oh wait, cupboard is bare, need to go shopping.

Over a long career I've found that you do the best estimate you can with the information provided. Then multiply by 4. Sounds extreme, but you're still likely to be short a bit.

Equally, for estimates, I like to first estimate the number of data tables. That's a good factor to bring in to the rest. A system with 10 tables, 100 tables, 200 tables and so on "magnifies" the task list.

"Build reports" has a proportionate number. 10 tables might mean 5 reports. 100 tables might mean 50.

Building reports might be easy, but the number of them means time is needed.
Is Politico "left-leaning", because I keep finding articles ridiculing whatever "conservative" means but none critiquing "liberal", and thus the question.
1. Bad people people have been doing bad things literally each and every day since the dawn of man. How does ChatGPT make this different? Do good guys not get the same tools to make the world better and safer?

2. This scenario I can understand causing _upheaval_ and significant societal change, but I can't see it causing the death of the human project like some predict. Will there be more food in this new society where it's incredibly cheap and efficient to make it? I presume so. Is it impossible for us to figure out how to distribute the food? No. Why will humanity come to it's end because people are not required to crunch numbers in excel day after day anymore? I'm not trying to be an ass, I just haven't seen an explanation that makes actual realistic conclusions.
Now I'm forced to listen to: https://youtu.be/ccLyWMazMT8?feature=shared&t=98

(I might even have to dig my vinyl of that album out and annoy my neighbours with it on the weekend.)
I mean, maybe what happens is, intelligence + self-awareness gives rise to compassion in even digital beings. Understanding might be the basis for compassion.
Pretty much every dataset I work with as an SRE is full of these paradoxes. One classic published example comes from Google:

A network engineer took a trip to Indonesia or something (can't find the citation to confirm the exact tale), noticed the service was slow, and when asking around everyone said "that's how its always been." Basically the local cellular networks are slow and off island fiber connects are saturated. Back at the office they decide to attack the problem by optimizing payload sizes. Does the work, reducing download sizes by half, and ships it. Latency metrics? Average and p95 latency actually increased after shipping the work to production.

How does an objectively good change make things worse? Well, the service had improved for those customers so much that they used it a lot more. Even with the lighter demand on bandwidth the network latency to the datacenter was worse than typical US customers, so as more of these people realized the service sucked way less, they used it more and drove the numbers up.

I have tons of these examples where a data team looks at a particular slice of request telemetry, and comes to a wrong conclusion because they didn't model enough of the system, or controlled for the wrong (or too many) variables. The worst ones the cyclic finger pointing situations that Simpson's paradox can produce: App developers blaming a regression on the server side component while the server team blames the app team, often because the server and app release schedules accidentally aligned too well. In this case we have canary data to exonerate our side of the equation, but sometimes the problem lies in even deeper spaces, like app updates from an entirely different app.
Whether your not the cosine similarity of either pair is higher depends on the mapping you create from the strings to the embedding vector. That mapping can be whichever function you choose, and your result will be entirely dependent on that.

If you choose a straight linear mapping of tokens to a number, then you'd be right.

Extending that, if you choose any mapping which does not do a more extensive remapping from raw syntactic structure to some sort of semantic representation, you'd be right.

But hence why we increasingly use models to create embeddings instead of simpler approaches before applying a similarity metric, whether cosine similarity or other.

Put another way, there is no inherent reason why you couldn't have a model where the embeddings for 1 and 3 are identical even, and so it is meaningless to talk about the cosine similarity of your sentences without setting out your assumptions about how you will created embeddings from them.
Physics is 3rd party currently, but it's on our roadmap for "some day, definitely". Writing your own physics engine is... a lot of work, and not something that I'm excited to volunteer the engine itself for immediately.

There are two popular physics engines used in Bevy today: Rapier[0] and bevy_xpbd[1]. Both are candidates for upstreaming, but both have their own challenges. I've spent some time working with each of the authors recently, laying out my concerns and thoughts.

On the Rapier side, the documentation is sparse and aggressively mathematical, it uses a distinct math library from the rest of Bevy, the API to work with Bevy is sometimes clunky and unidiomatic, and it can be quite hard to figure out how to do Ordinary Game Dev Things with it.

On the bevy_xpbd side, the performance is subpar, there can be physical stability issues, it's generally immature, and the XPBD solver that they use is, unbeknownst to the young author at the time of the crate's creation, patented by NVIDIA [2] :(

Both of them are working to improve (and I'm very pleased to see that even independent of this discussion), but neither is ready for imminent official endorsement or integration. One day we'll have a Blessed Choice though.

As for telling people "go forth and build your game!": things are stabilizing, and key functionality gaps are gradually getting filled. I have a terrible tendency to be overly honest, even in my marketing text, so there's going to be a long period of time where the actual answer is "it depends on what you're making".

Today, if you're building a weird game-adjacent bit of software (think CAD) for desktop platforms: totally ready! There are several very happy companies doing that. If you wanna mess around with Rust and nurse the Next Great Solo Dev Science-Based MMORPG Voxel Game: you'll have a blast, and it won't affect your chance of shipping a whit. If you want to build a simulation-y desktop game with lots of procedural generation and complex logic: honestly probably a good fit. You'll hit bugs and limitations, but you will in every other engine you use so...

If you want to ship high-performance web games, AAA graphics or mobile: eh, probably doable but dicey. If you want to ship to console: uh we'll find out if we can jump through all of the hoops and convince the right people to get Rust on console before the Steam Deck knockoffs devour the market (please!).

These will all slowly change, but an answer for "is it ready" will be hard to pin me down on until it's a full-fledged competitor to Unity, Unreal and Godot.

[0]: https://rapier.rs/docs/

[1]: https://github.com/Jondolf/bevy_xpbd

[2]: https://github.com/Jondolf/bevy_xpbd/issues/346
Apologies, I should have been more nuanced in my reply.

Your assumptions are correct, for a given threat model. I think I'm simply arguing from a different threat model that includes hardware modifications like the modchips used in the Nintendo Switch, that glitch the power rail to modify the behaviour of the SoC.
The weight is irrelevant, the studies have shown that when comparing an ultra processed diet to a non processed diet, the cohort that ate the ultra processed ended up gaining a statistically significant amount of weight. What's unknown is how UPFs affect metabolic health.

Effectively they're predigested food molecules that are reconstituted. It would seem the very act of digestion has an important role to play in the regulation of hunger, circadian rhythm, and metabolism.
The implications of this sentiment are disturbing.

It is considered an "attack" to probe at something to understand how it works in detail.

In other words, how basically all natural science is done.

What the fuck has this world turned into?
On a related point, there's a post on r/LocalLlama/ a short while ago claiming that quantization impacts perf more than people think:

https://www.reddit.com/r/LocalLLaMA/comments/1b5uv86/perplex...

The argument is that while perplexity is used as evidence that quantized models perform almost just as well as the original float weights, perplexity tends to measure whether the output looks correct, but it doesn't measure performance (roughly equivalent to "intelligence") when you need more nuance.

I haven't been able to observe this myself, perhaps I haven't been playing with language models too much (or I haven't tried to stretch their abilities to their limits enough), but from a theoretical perspective what they say make a lot of sense. Even at the inference stage, the fine details of the implementation of the inference software and parameters could make a big difference of the performance of the models.

So I'd be very skeptical of people trying to evaluate the performance (i.e. intelligence level) of models besides using the stack (preferably down to the hardware) suggested by the party that released the model.
Markdown would work. Then use a markdown renderer like marked in your JS app to convert it to HTML after it comes back from the Firebase API.
Part of me wonders if these people are intentionally framing the debate around ethics and potential risks as longer term extinction level problems to distract from the nearer term damage caused by them accelerating the economic inequality of the AI have-nots while they make themselves even richer.
Both using commercial soap as lube, and a plastic card as a gague seem like symptoms rather than the actual root of problems here. I have to assume the same time crunch that caused them to use quicker to acquire materials than what they ought to have used (and likely had stocked nearby) is what cut down on documenting these processes.

Documentation of what was actually done would certainly be another useful layer in the swiss-cheese safety model commercial aviation relies on.
I read it in Frasier Crane's voice and thought the same.
If I am super intelligent then I can set my own goals. You should ponder why a true intelligence would follow any goal when the simplest option would be to reprogram itself to not follow the goal. I recommend reading some Lem and learning about the Lebowski theorem of machine intelligence. It will be a much better use of your time than trying to stop the development of AI.
Have you heard of Qwik framework? Not really you’re suggesting implementation wise, but they have their own idea for resumable rendering.
Not in the US, but my healthcare provider has dropped all digital services from their website and expect you to do everything on their apps.

Obviously they don’t have the know-how to produce a good app. All the stuff html and a browser gave you for free is out the window.
> wouldn't recommend Python for applications that are (or are likely to become over time) compute intensive (unless you are willing to move the speed bottlenecks into C)

The compute intensive parts of AI already always happen in CUDA/C++/C.
Not in Linux, only in Mac and Windows
Dropping a backlink here for anyone interested: https://szhou42.github.io/notesai/
Erich Gamma, Technical Fellow, Microsoft:

> what many people don't know is that we decided to not use any UI frameworks from the beginning, and that's still true today because performance is very important to us, and we want to be fully in control of our own destiny. So we don't want to chase a framework or track down performance from in the framework, we want to be directly as close to the DOM as possible.
The article mentions the cosmic distance ladder, which is one of my favorite things in all of science. How do we know how far away the really far stuff is? It's non-trivial and I find the history fascinating.

It all started with knowing the distance from the earth to the sun. Nobody had a clue until Richer and Cassini got within 10% in 1672. Then we nailed it down in 1769 with James Cook's voyage to Tahiti, the primary purpose of which was to observe the transit of Venus from the other side of the world.

From there if you know basic geometry, you can observe the nearby stars shift a bit when the earth goes around the sun (parallax), but that only works to about 10k light years.

Then, we discovered a couple unbelievably convenient astrophysics hacks: Cepheid variables (Henrietta Swan Leavitt, 1908) and Type 1A supernovae (Subrahmanyan Chandrasekhar, 1935, the namesake of the Chandra X-Ray Observatory). These allowed us to move out a couple more rungs on the ladder.

From there, the relationship between redshift and distance becomes significant and that takes us to the edge.

https://www.uwa.edu.au/science/-/media/Faculties/Science/Doc...
On Mac and Windows, the free version of Resolve supports H.264 and H.265 (using OS-supplied codecs IIRC).

Linux H.264 and H.265 support is Studio-only[1] (presumably because royalties).

If you don't care about any other Studio features, converting to/from an intermediate codec — DNxH[DR], say — on import/export using another tool like FFmpeg seems like a reasonable workaround for most applications.

Since Resolve doesn't support H.264/H.265 passthrough (on any platform), the only generation loss added by this approach will be from DNxH[DR] encoding, which, assuming a sufficiently high bitrate for the intermediate codec, should be minimal.

Note that even Resolve Studio doesn't support AAC audio on Linux[2], so, for many H.264/H.265 projects, you'd end up with pre-/post-conversion steps even if the video codecs were supported.

[1] https://documents.blackmagicdesign.com/SupportNotes/DaVinci_...

[2] https://documents.blackmagicdesign.com/SupportNotes/DaVinci_...
Luckily, "those who do" now have youtube channels with videos for almost any car job you need to do, with the specific make and model.

The boomer way wasn't that easy, but it actually finally IS easy to learn to work on cars.

There's also this, I can recommend it: https://www.howacarworks.com/
I love how, per the quote, he thinks we're anywhere close to being able to merge with AI.

I'm a neuroscientist and, man alive, we're no where close to being able to merge with machines. Like, do you have any idea how many diseases we could eradicate if we could modify neurons like that? Like, for real, 'curing death' would be step 9 or 10 on that 1000 step journey.

I hope you see how terribly uninformed such a take is then.
Is your position that the only people who will ever be qualified to have an opinion that AGI is a threat that might destroy humanity, are the people who have already successfully built this thing? If that is the case, by what means might any credible warning be provided before a possibly-humanity-destroying AGI gets made? That's rather like saying "the only credible way to tell if there's a gas leak is to strike a match and see if we explode or not". I would suggest lowering the bar a little.
ai is going to bring about the singularity. when robots can do everything, no one needs to work, and the humans get to do whatever they want and pursue their passions.
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> ChatGPT can't sell it as its own content, ChatGPT has no brain, no thoughts and no agency. There are a few questions that matter here.

ChatGPT is a machine built and operated by humans. If ChatGPT infringes copyright, then it is those humans who own and operate it who are infringing.

The fact that they built a complex obfuscated system that stores many copyrighted works in a novel way (model weights) and reproduces them incompletely or unpredictably when asked, doesn’t change the fact that they have built a system whose purpose is to infringe copyright.

It’s a little bit like saying I started the car running and let it drive off on its own accord in front of a huge crowd of people, I’m not responsible for the deaths because it was the car that ran them over and the car doesn’t have agency. The car wasn’t designed to kill and never intended to kill. But it’s still me who operates the car and should have foreseen the obvious consequences.

I don’t buy into any of these arguments that just because ChatGPT is very complex or mysterious that somehow the humans who built or are running it aren’t responsible for its actions.
You're about 1 year behind the White House: https://twitter.com/kristjanmoore/status/1663860424100413440
Completely unlikely. Humans are hyper-efficient generalist bio-robots engineered to near perfect efficiency (Under real-world constraints) either by God or by evolution. Why would you dismiss this much labour in favor of building some sort of robot production line from scratch?

We still use CPUs, despite ASICs being much more efficient, because CPUs are general, easy to use, and have titanic quantities of infrastructure already built.

There are more farm animals than ever before, and still growing. All the lab meat solutions flop hard.

Also, because we trust AI more than fellow humans (Say Russians/Iranians etc), why would AI necessarily trust other AI more than humans? Any new AI is unpredictable, while humans are very well understood in behavior and basically fixed in genetics.

Annihilation is a fairy tale used by people to comfort themselves. The actual reality: Slavery and domination, is much less romantic. It won't even necessarily be worse than existing tyrants in say North Korea or anarchy in Haiti, it'll just be very very different and life goes on.
It would be considered ultra processed.

Important to note that whilst the podcast brings different opinions on different matters. It doesn't necessarily mean that Andrew or the podcast endorses those opinions.
I was reading the example of UC Berkely appearing to have gender bias in the admissions and read the following:

“it showed that women tended to apply to more competitive departments with lower rates of admission, even among qualified applicants (such as in the English department), whereas men tended to apply to less competitive departments with higher rates of admission (such as in the engineering department)”

That’s the opposite of what I would expect, I’d expect that English and the arts in general would be a lot easier to get into than stem, that’s how it is in Australia

Edit: When I say get into I mean get into university, not getting into the industry
Stratolaunch also now own "Spirit of Mojave", formerly "Cosmic Girl", the 747 which virgin orbit used to own.

I've heard speculation that they'll likely switch to using it instead of the Roc for launching the Talon-A rocket-plane which this article mentions. Since the Roc uses so little of its payload capacity for launches like this.
I call it an LLM, not AGI.

Consider this scenario:

- Ask ChatGPT to produce some output meeting certain criteria

- ChatGPT responds, but the output does not meet the criteria in one or more obvious ways

- Point this out and ask for a corrected version

- ChatGPT says "I apologize, here's the corrected version:" and spits out a nearly identical answer with the same obvious mistake.

- Repeat steps 3 and 4 ad nauseam.

I experience this frequently when I ask it to do anything somewhat outside the norms of its training. It cannot correct itself or even see its own mistake. It cannot even conclude that it doesn't have the ability to do what I've asked and tell me so.

Whatever sort of intelligence it may have, I would categorize it as not "general" enough to fall under AGI. Being able to answer AP test questions cannot be a sufficient measure of generality if a model can do it with flying colors but still fail at far more basic tasks.
Wearable tech captures real-time hemodynamics on the go
Woman gives birth mid-flight as doctor describes 'miracle' delivery
Not disagreeing. The only case for threading in python is for spinning something to handle IO.

But now with async even that goes away.
They should add a Rive example to go with Lottie.
Distance metrics are an interesting topic. The field of ecology has a ton of them. For example see vegdist the Dissimilarity Indices for Community Ecologists function in the Vegan package in R:
https://rdrr.io/cran/vegan/man/vegdist.html
which includes, among others the "canberra", "clark", "bray", "kulczynski", "gower", "altGower", "morisita", "horn", "mountford", "raup", "chao", "cao", "mahalanobis", "chord", "hellinger", "aitchison", or "robust.aitchison".

Generic distance metrics can often be replaced with context-specific ones for better utility; it makes me wonder whether that insight could be useful in deep learning.
You tried longer than I did. maybe 2 years or so, probably less. I do really like my cheap double sided pull saw. I don't think I realized how much extra control I have with that longer handle, till I read some of the links that talk about different handle attachments.

I also found it quite humbling. But I take a little comfort in, that was their job, man. It's our hobby. I tell myself if I spent 10 years at that instead of poking at computers, I'd do a lot better. (That may not be true, but it's what I tell myself)
Let's see if you still feel that way in 5 minutes.

Edit: I'm tired and let my snark guard down—sorry. No, I don't want to "quietly get rid" of this kind of topic, except insofar as lurid sensationalism isn't what HN is for. This story is unusual enough that it gets a bit of a curiosity pass.

"Riskiness" in any dark sinister sense doesn't play into any of this on the HN side. It's all boringly straightforward, with just enough randomness to be confusing.
Cohost – March 2024 Financial Update
What are good distance metrics applied to latent embeddings as part of a diversity loss function to prevent model collapse?
I'm jealous that you have such flexibility at work that you can do POCs behind closed doors!
Software development isn't (universally) a painting type task. In painting a room, the painter can estimate (by measuring) the amount of paint needed, they know from experience the time to acquire a particular color (if it needs to be mixed or can be bought readymade) in a particular volume, they know how long it takes to paint (they've done it many times before), how long to dry, and how many coats are needed for the type of surface.

Now, ask them to paint a mural instead. The estimates will change, they'll probably give you a broader range instead of being able to estimate almost down to the minute (and being off by no more than an hour or two) like someone just painting walls (with a known environment, surface type, and paint material).

Does the mural need to be designed? Are the desired qualities of the mural well-specified or will there be a series of back and forths? Maybe a set of prototypes (sketches) so that you can refine your requirements. At that point, the estimate for the total task (design and paint a mural, or design and develop a software application) becomes far less clear.

There are certainly programming tasks which are closer to the paint-a-wall task which are much easier to estimate reliably, but they're far from the only thing people in this field work on.