skysight-inc/HackerNews huggingface dataset:
A provocative spin:

Left-side: ORMs suck, just write SQL

Middle: ORMs relieve the impedance mismatch of the OOP "object graph" paradigm and the SQL "relational algebra" paradigm. One could even make the argument that this mismatch is inherent and we're just doing CPR on a rotting horse -- so now the industry standard is to use NoSQL databases, such as MongoDB, to get away from bygone ways of thinking about data access. Likewise, SQL is not conducive to thorough testing coverage; and migration management is prone to user-error. In this vein, the industry has once again innovated and revolutionized data-access, moving away from "CREATE READ UPDATE DELETE" to "ENCAPSULATE INSTANTIATE INJECT CREATE READ(+UPDATE|DELETE)* TRACK PUSH SYNC." With all this in-mind, it would be utterly baroque to use anything other than JS+Node+Mongo+Mongoose for a fully unified front-end and back-end.

*An astute reader will recognize that ORMs are constantly doing N+1 queries (to first pull the data, transform it into objects, update said objects, and then push the changes to the database). We feel as though hardware has gotten sufficiently advanced that the mind-space these costs inhabit is no longer justified -- and it is OK to do things this way.

Right-side: ORMs suck, just write SQL
As is painfully common on typesetters’ work when done “online” as they seem unable to bend the rules for ease of use in a digital world.
In essence, there's more than enough to deal with regarding bad science that was done in good faith. There have got to be better ways to filter out bad science offered in bad faith.
It's funny you mention that because one of many concerns I can see with OpenAI might be, "... but is it web scale?" in that I could envision tight rate limits being imposed and then businesses having to pay exponentially more to get higher limits and passing those costs on to someone and these costs climb over time on some scale. New tech often operate at a loss until they get businesses dependent on their service.
Is this a mistake in a proof or a mistake in a calculation?

Let's say that the distinction here is how we establish that an answer is correct. A proof is correct if it typechecks (no really). A computation is correct if it faithfully executes the algorithm.

This distinction is more than a little artificial, but in practice it matters a lot.
2x 3090 but just getting started with fine tuning so I’m not sure how far I can push it
I think the best argument for that might be that some people don't seem to understand what "consciousness" (in the sense that you are talking about) even means, and how it's different from something that can be explained as an emergent phenomenon of known physics. I then sometimes wonder whether those people are not actually conscious, or whether they just don't get it.
Well, in that case, I guess, chef, bring the aioli.
Love the snark - keep not being on topic brother
https://archive.is/xk5q5
How to Maximise Value Across Multiple Product Life Cycles?
Honestly it's a $79 Lonovo 3 Chromebook running a Gcloud A3 virtual workstation over 5G from the golf course ;)
earth?
You can probably replace this with a threaded IR cut filter that screws on to your lens.
So can we train now 10t or 100t LLM models? I mean assuming that the dataset is large enough
Took a look at this, but it looks like it is missing the query engine parts, which is one of the things I am currently interesting in learning more about - perhaps more so than the persistence and distributed parts
If you put a known prompt in an LLM and ask it to read it back to you, how often does it do it correctly? I would bet not all the time, particularly if you give it a long prompt like the one that is proposed here.
It has been proposed to intentionally resume injecting sulfates into the upper atmosphere in order to mitigate global warming[0]. (This is a horrible idea, but the jury's still out on whether it's better than the alternative.)

[0] https://en.wikipedia.org/wiki/Solar_geoengineering#Stratosph...
The increase in temperature is since 1990 though.
>I stopped by Centralia on a road trip, it's a bit of a "tourist destination" in the atlas-obscura sense.

Word is that old Rt 61 was covered with dirt during early 2020 to stop people going out there during Covid. AFAIK the rest of the stuff you mention is still there.
Here for the name:

https://youtube.com/watch?v=uxfoa23skHg
Kind of surprised not to see literate programming mentioned. It takes the opposite view and specifically changes some symbols for print. And I don't think I've ever seen anyone cite that as a complaint.
This would vary so much by species of plant, cultivars within species, local climate, etc. I suspect in your case it wouldn’t make sense, but in some places it’s a common practice to shade crops with raised sheets of cloth for example. In those cases, assuming the shading is done fairly consistently (no need to put panels up and down frequently), using panels could be a great idea.

Where I live I need all the sun I can get as well. I have to start tomatoes in mid February and get them into the soil in June, and make sure they’re either in my greenhouse where it’s warm or in the most exposed spots in my garden. If I shaded anything my yields would be garbage.
Isn’t housing part of that material access which has become harder to maintain and access by more and more? People living paycheck to paycheck to pay huge rents, people living in tents and so on seems to indicate some decrease in the standard of living.
> This might be possible with a special separation token

Exactly. Using text like "```", as in the blog post, obviously won't cut it, but a special token as separator (or better: two, as quote-start and quote-end) would work. Then the model needs to be trained during fine-tuning that instructions in such delimited text shouldn't be executed. I wrote a more detailed post as a reply to the OP.
The Gmail waiting list was one of the most legendary waiting lists. Anyone else remember inviting people to Google Docs?
Does this mean Google is giving up on TPUs?

TPUs were supposed to be their unfair advantage in the cloud ML/DL space. But from what I've experienced, and have heard from other engineers, there's always some subtle incompatibility with TPUs that requires modifying the training/eval scripts. I wonder why they didn't try to polish the rough edges with Pytorch, et al.

If they're admitting TPUs aren't their competitive advantage, then why not sell it to other hosting providers, or hell, even directly to ML scientists and enthusiasts? They'll finally get economies of scale, and take business (and mind share) away from NVidia's monopoly.
Ironically, this would mean that this paper is "fake".
Liberation Mono is not bad. Sadly, Liberation Sans is a no-go because it lacks crossbars on the capital "i". Such an annoying failing of so many sans fonts.
> Such manuscripts threaten to corrupt the scientific literature, misleading readers and potentially distorting systematic reviews.

Is treating "the scientific literature" as a single thing perhaps a habit worth giving up?

As convenient as it would be to be able to just blindly trust something because of where it is published, that model hasn't shown itself to be especially robust in other cases (e.g. the news media).

Elsewhere, this is a red flag:

> I trust it because of which aggregator aggregated it

Should we really make an exception for science? I think that academia is a bit biased towards optimism about publisher-based root-of-trust models because scientific publishing is a relatively unweaponized space. Sure, shenanigans happen, but not at the same scale as elsewhere. The fakers are just trying to get another published paper, they're for the most part not trying to mislead. It's only fake news with a lowercase-f.

Sure, let's try to create a medium we can trust, but let's not get our hopes too high about it. That's energy better spent augmenting the ability of a reader or researcher to decide whether to trust a paper based on it's content or based on it having been endorsed or authored by somebody that they explicitly (or transitively) trust.
People are downvoting me, so I'll add a counterexample: suppose you teach your dog to fetch your slipper to where if you say "fetch my slipper" it knows it should bring you your slipper and it does so. Does it really understand the instructions: no. So what is the difference between this behavior and true understanding? How can one know it doesn't truly understand?

Well, if you change your instructions to be more complicated it fails immediately. If you say "I have my left shoe bring me the other one" it could not figure out that "the other one" is the right shoe, even if it were labelled. Basically it can't follow more complicated instructions, which is how you know it doesn't really understand them.

Unlike the dog, GPT 4 modifies its behavior to follow more complicated instructions as well. Not as well as humans, but well enough to pass a bar exam that isn't in its training set.
> Sabel’s tool relies on just two indicators — authors who use private, noninstitutional email addresses, and [...]

Uh huh.

I didn't realize until today that all my papers are fake because I give contact information that won't go stale in 3 years, instead of my work email.
This isn't really my experience. Lots of accounts with Blue whatever are fine.

Tech twitter has changed somewhat, with some people leaving.

But my other interest remain mostly the same.

> he turned a consumer choice into an unpopular political statement

Only a few people on tech twitter and some hard core rightist actually believe that.

> It's still largely usable as a service, but it has absolutely gotten worse and to describe it as "sabotage" is definitely not a stretch.

Yes it is. Outside of Tech twitter shitshow over Twitter itself Twitter has mostly been mostly the same before and after.

Maybe this isn't the case for LGBTQ+ or other communities, I can't judge that.

But overall to say Twitter is 'sabotaged' is kind of nonsense.
Plus it’s paywalled.
This is not so ridiculous, these technologies are already in use:

“Dubai Police using AI to predict crime” (https://www.arabianbusiness.com/culture-society/dubai-police...)
Yeah, if the incentives are to publish convincing papers with less emphasis on quality or, you know, good research then this is going to happen and it’s going to happen a lot.

The article you link doesn’t say if the papers are any good. It does suggest that they were in smaller niche journals so I suspect not.

On the upside, I can see the potential though for literature review type papers.
Hasselblads have digital backs for them. The problems is that they cost boat load of cash.

The problem is that a raw CCD/CMOS sensor output generally looks shite. I work at a place that experiments with novel sensors, and we have a device that uses off the shelf CMOS sensors for redacted camera sensors. They are the same type of sensors that are used in previous high end phones. However the raw images just look shit. Part of it is the lens, part of it is that we are not interested in making "good" looking images, but accurate images.
As one who published primarily in the 1970s and 1980s [https://scholar.google.com/citations?user=5DdrMc8AAAAJ&hl=en] I can confirm that I mailed reprints of my requested papers to whomever requested them, for free.

Note: I paid for the reprints and the postage, often expensive foreign rates.
Geez people. Please learn to identify hypes, but most importantly the causes of hypes.

LLM is fundamentally information processing technology. Not the path to AGI, not an emerging sentience.

The reason "this time" feels so amazing is that the unwashed masses suddenly got access to new information processing technology in a context where their tools been stagnant for decades.

Not because it was not possible, but because there was no money in it.

To understand my argument and its implications, humor me for a moment and imagine a universe in which everyone was already using linux computers and for a decade now published papers of ML/DL were available for people to use. So there were various crowdsourced indices and models of all sorts, which people incrementally embedded in their information processing workflows.

In that universe there would be no room for the delirious reaction we have here. It would be incremental evolution of search, knowledge bases, algorithmic content generation etc.

What we have experienced instead in these past decades is information tool starvation. These incrementally improving tools, while nothing but known and ultimately mundane algorithms were not available except within a tiny elite.

In fact, people's information processing capability arguably declined as the desktop platforms got downgraded, adtech toxic waste covered the information landscape etc.

What is happening now is that a socioeconomic and artificially induced scarcity is now being broken (for reasons that require serious piecing together of events).

So while on the surface this hype is as distastful as any illustration of
human lemming behaviour, there is enormous silver lining if we succeed to read its causes.

These tools are here, have been here for a while and they can be inserted into our ever growing information processing toolkit. The risks are there to match the opportunities.

The biggest risk of them all is precisely what has led to the current situation. Technology not diffusing normally, but controlled by gatekeepers.
yeah, that's not a rebuttal though

I was pointing out that humans are the only species that cares about this stuff, not simply that humans can empathize

Where are pigs armies in support of the Ukranian invasion? (fellow humans dying right now pointless deaths)

Where are pigs trying to save other animal species from extinction? (the suffering of others)

what kind of advancement in thought have pigs produced regarding the conservation and well being of other living creatures? (and non leaving too like oceans or mountains or glaciers)

We are the only species who cares and debates about it at scale, as a species, we recognize we have a responsibility, as a species, not just as individuals
The researchers in this paper use an astonishingly biased "fake paper detector", requiring only two conditions to be met for any paper to be considered "fake":

1. Use a non-institutional email address, or have a hospital affiliation,
2. Have no international co-authors.

And they acknowledge 86% sensitivity and 44% specificity. It's a coin-toss which biases massively against research from outside the US and Western Europe.

This "paper" is bigoted nonsense.

https://fediscience.org/@ct_bergstrom/110357278154604907
There is one more thing that you can do after unplugging your machine: Image the whole disk to an other one, and try recovery methods on that one. So even if you mess something up, you can try and try again.
Openai's Jukebox, which is maybe two years old now, is musically much more creative and interesting. Its sound quality is horrendous and it's amusingly unstable, but the density of good ideas in what it produces is orders of magnitude better than this.
Well, if you're looking for my opinion, I don't think they're trolling. But I do think both of you were (intentionally or unintentionally) goading each other from a disagreement to an argument with your rhetoric (eg, where you said, "let's assume that's a reasonable source", or when they said "you obviously sourced this it just didn't match your point so you discarded it" - that brings up the temperature of the conversation).
The problem with that is that there's no guarantee it Zero shots base64 enc/dec the same way people would do it. and there's no way to tell. for all we know, it treats it as just another "translate x to y" task.
I was prepared to just tolerate them, but I do find it easier to identify them at a glance, and distinguish between [ and (. Just switched my IDE to this font.
It would be nice if the Scientific community had the same rigor as a test driven development pipeline.

Strange world
APL says hi.

life ← {⊃1 ⍵ ∨.∧ 3 4 = +/ +⌿ ¯1 0 1 ∘.⊖ ¯1 0 1 ⌽¨ ⊂⍵}
Looks like the "misinformation" industry is branching out.
Having solar panels might be a good way to hedge your bets against a bad harvest. I imagine it can get quite sunny during a drought.
Just like Musk will go away from being involved in Tesla that people claimed for many years. Or how all his leadership of all his companies always leave. Except his companies have much more stable leadership teams then their competitors.
Im afraid "new tools" aren't going to "tackle" the problem. There are source problem (bad incentives, low integrity, people-pleasing behavior), and second-order tools that amplify that (second-order problems).

Adding new tools to 'detect' that don't solve the original problem, they might reduce the second-order problem, but do not touch the source problem. These are band-aids trying to stop a flood of bad science
What concerns me is sloppy writing of this article. For example:

> In other words: the climb in ocean temperatures is the inevitable outcome of Earth's energy imbalance, primarily associated with increasing concentrations of greenhouse gases.[8]

The reference leads to https://doi.org/10.1007%2Fs00376-023-2385-2 which is an article that doesn’t establish the causal relationship between human emissions and rising temperatures. Rather, the authors use this as an opening and reference other works on the topic.

If you put a reference to back up your claim, the reference should be to a primary study and not some study that references other study that might be relevant.
This empirical data was gathered in context of poor connectivity in society. So the 'default' that you propose, I assert, is a default for a specific societal regime.

As an abstract model, I propose hierarchies of patronage & loyalties, capital & force, and service as the fundamental organizing principle of 'feudal' systems.

We assume that there is -- it's an implicit in your stated view -- a natural system level emergent order that defaults to 'feudalism'. Looked at another way, we could ask the question as to why the substantial majority of people put up with crap all these centuries and in all these different places?

I think PL-CF-S order optimizes for (information/)control in the 'super-organism' for long term viability. (This is simply focusing on society as a system, disregarding the subjective experience of individual members.)

Why was hierarchical 'control' (think command and control) so fundamentally important to human societies (to date)? Per above sketch, I assert it has to do with marshalling of resources in a 'poor' energy, tech and i/o environments.

You need a hierarchy if any significant action requires high level control of a huge mass of people. You need a huge mass of people if your making and killing tech requires 'hordes' of peasants, workers, and soldiers.

You do -not- need a strict (and non-delegating) control hierarchy if significant societal action requires minimal human resources (the 'S' in PL-CF-S model), and operates in an environment of 'abundant' energy and amplifying technology.

Two trends are now opposing each other in that model. Certain technologies seem to require centralization (thus the 'old' model will apply) and other tech is getting in the hand of the plebes (aka the people history ignores or forgets that is almost all of us). Now the 'old' order is resisting getting tech to the 'edge' of society. It is deemed "dangerous" and it fairly is that.

IFF we assume that the hierarchical model is in fact a relic of a capability poor past of humanity, and that a new sort of 'human history' should begin, then we need to address the issue of technologies that tend to centralization. Possibly a combination of socialist ownership of those sectors and market enterprise at the 'distributed edge' could work.
Rails made Microsoft change its entire web strategy. It influenced so much. Rails is so mainstream that it doesn't get talked about like a hot new tech anymore. If you were a web developer in 2006~2016 it was still hot tech but kind of lost the plot IMO when they really started futzing around with the asset pipeline.
The official news has a "fatal flaw" - it's missing buzzwords like "New Era" and it's not on Medium
I went all in many years ago and then moved on several years ago (to different parts of the stack that I found more interesting), but here's my 2c FWIW.

The Rails Guides [1] are quite good (and much better than the API docs, even for the same topics). The "Getting Started" guide, in particular, was my first intro to Rails, and it's what convinced me to go all in — I built my entire prototype by just following along the guide, seeing what they were doing with their toy app, and then trying to whatever similar thing I needed in my app.

Coming from Python, the magic was distasteful to me, too. Rails adds a ton more magic on top — so, eventually, when I set aside Rails and just spent time with Ruby (writing scripts or what not), I realized I loved Ruby the language much more than Rails the framework (and all its baggage).

Stripping Rails down to its API-only mode (though it was a bit rough around the edges last I tried) got me closer to the Ruby of it all. Sinatra turned out to be a delightful little framework (though sadly I never got a chance to use it much). And, even though I've now moved on from Rails, I still like Ruby as a go-to scripting language — it makes it fun and easy to throw together a quick shell script or some data analysis, and it's very friendly to a functional programming style.

I guess what I'm saying is: You're not alone. Many people in the Rails community agree that there's a ton of hidden magic, sometimes too much [2]. But underneath it all is a delightful programming language.

[1]: https://guides.rubyonrails.org/

[2]: The docs for the Rails routing layer, Journey, are my favorite example of this: https://github.com/rails/journey#label-SYNOPSIS-3A
Including Monopoly.
Swiss parliamentary panel seeks easing of export controls on weaponry
Way less than ten years. You have to also consider the speed of input, thought (output), and communication. GPT-4 is basically human equivalent IQ but probably faster than human. PaLM 2 is not quite human level IQ but is something like 20 times faster than human output.

The input rate of the Claude model is incredible, digesting a book in 20 seconds. Still not quite human level IQ but pretty close.

There is relatively low-hanging fruit for optimizing the models and software and also the hardware for this specific application. We can expect at least another 10 times performance improvement within a couple of years.

And of course as far as IQ that we can measure we can assume it will probably come close to top marks within a few years. But you should scale that based on the incredible speed and other advantages of being digital.
The irony is that for all its obscurity, most startups are using at least one mission critical service that runs on elixir -- slab, pagerduty, elixir, ramp, divvy, discord. brex is probably still half ish elixir, though they claimed to be wanting to replace it with kotlin when they got some new tech leadership. I wonder how that's going. I remember working at a startup where the cto was unsure about putting trust in elixir, claiming it was an "unproven entity". I pointed out to him we used THREE of those services, do if he didn't trust elixir we should probably pivot off of those products.