loudmax

At some level all fossil fuels come from the sun. Fossil fuels come from biomass accumulated over millions of years. The energy that went into gathering all that carbon and hydrocarbons came from the sun.

Take it a step further and nearly all our energy comes from nuclear fusion, with the exceptions you noted.

There is plenty of natural selection pressure on not being eaten. If a mold or something could evolve to live off of a dry atmosphere plus sunlight and whatever minerals it can eek out of rocks, it could blanket millions of square kilometers of desert. Presumably other living things would find it hard to digest because its protein and chemical structures are so different. Nature is constantly finding weird little niches at the edge of sustainability. Waterless deserts should present an enormous opportunity for something to fill, but it hasn't happened.

LLMs are really good at processing vague descriptions of problems and offering a solution that's reasonably close to the mark. They can be a great guide for unfamiliar tools.

For example, I have a pretty good grasp of regular expressions because I'm an old Perl programmer, but I find processing json using `jq` utterly baffling. LLMs are great at coming up with useful examples, and sometimes they'll even get it perfect the first time. I've learned more about properly using `jq` with the help of LLMs than I ever did on my own. Same goes for `ffmpeg`.

LLMs are not a substitute for learning. When used properly, they're an enhancement to learning.

Likewise, never mind the idiot CEOs of failing companies looking forward to laying off half their workforce and replacing them with AI. When properly used, AI is a tool to help people become more productive, not replace human understanding.

The 19th century US cotton industry was built on far worse crimes than patent infringement.

There's the problem of scale, and also of duration. Let's say Lee Kuan Yew genuinely wants what's best for Singapore as a whole. How do you ensure that the next autocrat will be equally benign?

> It's why I recently have been convinced that we need something like election my jury

I think this is how the electoral college was intended to function. This is a hard problem to solve, especially when some of the players aren't operating in good faith.

Most of these models have been trained using 16-bit weights. So a 1 billion parameter model takes up 2 gigabytes.

In practice, models can be quantized to smaller weights for inference. Usually, the performance loss going from 16 bit weights to 8 bit weights is very minor, so a 1 billion parameter model can take 1 gigabyte. Thinking about these models in terms of 8-bit quantized weights has the added benefit of making the math really easy. A 20B model needs 20G of memory. Simple.

Of course, models can be quantized down even further, at greater cost of inference quality. Depending on what you're doing, 5-bit weights or even lower might be perfectly acceptable. There's some indication that models that have been trained on lower bit weights might perform better than larger models that have been quantized down. For example, a model that was trained using 4-bit weights might perform better than a model that was trained at 16 bits, then quantized down to 4 bits.

When running models, a lot of the performance bottleneck is memory bandwidth. This is why LLM enthusiasts are looking for GPUs with the most possible VRAM. You computer might have 128G of RAM, but your GPU's access to that memory is so constrained by bandwidth that you might as well run the model on your CPU. Running a model on the CPU can be done, it's just much slower because the computation is so parallel.

Today's higher end consumer grade GPUs have up to 24G of dedicated VRAM (an Nvidia RTX 5090 has 32G of VRAM and they're like $2k). The dedicated VRAM on a GPU has a memory bandwidth of about 1 Tb/s. Apple's M-series of ARM-based CPU's have 512 Gb/s of bandwidth, and they're one of the most popular ways of being able to run larger LLMs on consumer hardware. AMD's new "Strix Halo" CPU+GPU chips have up to 128G of unified memory, with a memory bandwidth of about 256 Gb/s.

Reddit's r/LocalLLaMA is a reasonable place to look to see what people are doing with consumer grade hardware. Of course, some of what they're doing is bonkers so don't take everything you see there as a guide.

And as far as a decade from now, who knows. Currently, the top silicon fabs of TSMC, Samsung, and Intel are all working flat-out to meet the GPU demand from hyperscalers rolling out capacity (Microsoft Azure, AWS, Google, etc). Silicon chip manufacturing has traditionally followed a boom/bust cycle. But with geopolitical tensions, global trade barriers, AI-driven advances, and whatever other black swan events, what the next few years will look like is anyone's guess.

Fellow Virginia Tech alumnus here. "VA Tech" is perfectly legible and unambiguous. "VT scientists" is straightforward to Americans, but not everyone in the world will know what "VT" is in this context. It could be Vermont scientists, or who knows what else.

Incidentally, the author is the wife of physicist Sean Carroll. She certainly knows how to abbreviate Virginia Tech, but Ars Technica's editors will want to influence the title to appeal to a global audience.

"VA Tech" is a reasonable balance between writing something unambiguous for a global audience, but short enough to be interesting. Nobody's going to be interested in an article titled "Virginia Polytechnic Institute & State University scientists build a better fog harp."

Of course, you still need to read the article itself to find out what the heck is a "fog harp," and why we need a better one.

As a system administrator, I vastly prefer to deploy Go programs over Java programs. Go programs are typically distributed as a single executable file with no reliance on external libraries. I can usually run `./program -h` and it tells me about all the flags.

Java programs rely on the JVM, of which there are many variants. Run time options are often split into multiple XML files -- one file for logging, another to control the number of threads and so on. Checking for the running process using `ps | grep` yields some long line that wraps the terminal window, or doesn't fit neatly into columns shown in `htop` or `btop`.

These complaints are mostly about conventions and idioms, not the languages themselves. I appreciate that the Java ecosystem is extremely powerful and flexible. It is possible to compile Java programs into standalone binaries, though I rarely see these in practice. Containers can mitigate the messiness, and that helps, up until the point when you need to debug some weird runtime issue.

I wouldn't argue that people should stop programming in Java, as there are places where it really is the best choice. For example deep legacy codebases, or where you need the power of the JVM for dynamic runtime performance optimizations.

There are a lot of places where Go is the best choice (eg. simple network services, CLI utilities), and in those cases, please, please deploy simple Go programs. Most of the time, developers will reach for whatever language they're most comfortable with.

What I like most about Go is how convenient it is, by default. This makes a big difference.

My theoretical explanation for the gap is that women are sensitive to much more than physical appearances when selecting a mate. When men select a mate, they over-index on physical characteristics, relative to women.

So, women will take into account a man's physical appearance, but also his social standing is also very important, plus perceived intelligence and dependability and so on.

When men look at women, physical beauty is paramount. Intelligence and so on is a plus, but not nearly to the extent that it is for women looking at men.

The other part of the explanation is menopause. Humans live long past the age of sexual fertility, which is somewhat unusual in the animal kingdom. Presumably, ancient human social structures made it beneficial to have grandparents around, in a way that does not exist for most other animals. Relative to other species, this puts more evolutionary pressure on human males to seek out females that are of reproductive age, and ignore older women.

The combination of these factors means that female physical appearance comes out as a prime selector in humans, in a way that it doesn't for other animal species. Male physical appearance is also significant, but relatively less important because of other factors, especially a male's status in the social hierarchy.

Anyway, that's my explanation, but I wouldn't hold any of this as a firm belief. Coming up with evolutionary psychological explanations for sex differences is easy. Actually testing them is much harder.

It's the Latent Space podcast, published June 13. It's a pretty decent podcast for keeping on top of AI stuff. Swyx, one of the co-hosts, is active here on HN.

Programmers aren't deterministic either. If I ask ten programmers to come up with a solution to the same problem, I'm not likely to get ten identical copies. Different programmers, even competent experienced programmers, might have different priorities that aren't in the requirements. For example, trading off program maintainability or portability over performance.

The same could apply to LLMs, or even different runs from the same LLMs.

There is a proposal to build an apartment complex in my neighborhood in Northern Virginia. The houses that had Trump lawn signs up last year, now have lawn signs arguing to block the new housing (or "preserve the neighborhood's character").

NIMBYism is an economic issue, not a culture issue. It has far more to do with how much impact the new housing is expected to have on the value of people's property. Or more saliently, the equity they have in the property. If people expect that nearby housing will cut their $500k equity in half, they're likely to petition against it, regardless of whether their governor is a Republican or a Democrat.

NIMBY isn't red or blue, it's just fear and greed.

As a pro-nuclear-power fan, this does sound like it could be good news. I do have a few concerns.

First, the power source is orthogonal to AI. Datacenters will run just as well on nuclear, or coal, or wind and solar. And the electricity generated from nuclear could be used to run datacenters, or power electric cars, or replace decommissioned coal plants. In this context, the AI stuff is just confusing matters.

Second, this administration is about the last people I'd want involved in assessing or enforcing precautions around nuclear safety.

I'm afraid a lot of Americans consume the news like they consume sports media. They root for their team and select a news stream that presents them with the most favorable coverage.

The OP is not equating contextual with insidious. They're pointing out, correctly, that contextual ads can be insidious. And if they're profitable, they probably will be.

A lot of the companies offering LLM services are in a race gain market share and build expertise. Right now, they can burn through millions of dollars of VC money, with the expectation that they'll turn a profit at some point in the future. If that profit comes from advertising, and critically, if users don't expect advertising in their free LLMs, because they didn't see ads in generated output in the past, that will be very insidious.

The current US copyright duration is 70 years after the life of the author. This is absolutely bonkers. 50 years from publication would be a significant improvement.

50 years ago was 1975. If copyright were limited to 50 years, we'd be looking at all of the Beatles works being in the public domain. We'd be midway though Led Zeppelin, and a lot of the best work from Pink Floyd and the Rolling Stones.

Also, Superman, Batman, and Spider-Man. Disney would still profit from the MCU films which they produced in the 2010's, but they couldn't stop you from releasing your own Batman vs Spider-Man story.

The Harry Potter books would still belong to JK Rowling, but the Narnia stories would be available for all.

The Godfather 1 and 2 would be in the public domain, as would be original Star Trek TV show, and we'd be coming up on Star Wars pretty soon.

If there were no copyright protection, these works wouldn't have been created. It is good that Paul McCartney and George Lucas and JK Rowling have profited from their creative output. It would be okay if they only profited for the first 50 years. Nobody is counting on revenue over half a century in the future when they create a work of art today.

This is our culture. It should belong to all of us.

Yes, but do artists have the ability to actually monitor and enforce this? You have to have the capacity and the wherewithal and to test these models to even know that your data is being ingested into AI.

Big companies like the New York Times and Twitter/X have the funds to pay for this. Miscellaneous artists probably don't.

The amount of exposure matters here. It's one thing for a teenager to manage to occasionally access clandestine pornography (as was kind of the norm growing up in the 1980's). It's a much worse thing to spend many hours per week looking at porn. The former is probably not a terrible thing, the latter is not healthy for emotional or social development.

Plus, there are other positive aspects to keeping kids busy and involved with healthy social activities.

When a news organization hires a journalist to file a report, they put their reputation on the line in the hands of the journalist.

As a consumer of news, you put your trust in the institution to have a reasonable vetting process, and also a process to retract a story if it's later shown to be false.

None of this is completely foolproof. It relies on institutions taking a long term view, and people working out which individuals and institutions are worthy of their trust. This isn't like the the blockchain where you have mathematical proof of veracity that's as strong as your encryption algorithm. I don't see how that level of proof is achievable in the real world.

I think placing trust in technology is misguided. Bad actors will always figure out a way to abuse the machines.

What we can do is place trust in particular institutions, and use technology to verify authenticity. Not verify that what the institution is saying is true, but verify that they really are standing by this claim.

This is challenging because no institution is going to be 100% trustworthy all of the time in perpetuity. But you can make reasonable assessments about which institutions appear more credible. Then it's a matter of policing your own biases.

I don't think I'd want to read a novel that was generated by an algorithm, but I might be up for a Choose Your Own Adventure style game, which might be a better analogy to Minecraft or nethack.

Companies that leverage LLMs and AIs to let their employees be more productive will thrive.

Companies that try to replace their employees with LLMs and AIs will fail.

Unfortunately, all that's in the long run. In the near term, some CEOs and management teams will profit from the short term valuations as they squander their companies' future growth on short-sighted staff cuts.

Along the same lines, it's often useful to think of an LLM's "understanding" a question or a context. Sometimes responses from smaller models show a shallower "understanding" of the question than you'd get from a bigger model.

Obviously, we shouldn't anthropomorphize too much here, and even the most powerful LLMs don't "understand" or "reason" or "think" the way humans do. But whatever they're doing, it's at least analogous to what we do. These concepts are genuinely useful for making better use of these tools.

It can be hard to convey sarcasm, especially in writing, and especially when you don't know the actual stance of the person writing.

If I mention "big beautiful tariffs" in a peer group of liberals, they can assume I'm being sarcastic. If I'm posting anonymously on the internet, then who knows, maybe I genuinely reject the economic orthodoxy of Adam Smith and David Ricardo.

In this context, emoji might actually be helpful in expressing sarcasm. Big beautiful tariffs :roll_eyes:

If the diffusion models are an improvement over autoregression models, then the answer is No, due to Jevon's paradox. That is, as these models get cheaper and better, they provide more utility, driving more demand. Even as your datacenters become more productive, the demand for their compute power increases at an even faster pace.

The thing that will limit demand for compute is when the world decides it has sufficient capacity of the form of "intelligence" these models provide. I don't think anyone has any idea what that world will even look like.

The target market for these offerings are corporations, or self-employed developers. If these tools really do make your developers more productive, $250 a month is easily justifiable. That's only $3000 per year, a fraction of the cost of a full time developer.

Obviously, the benefit is contingent on whether or not the models actually make your developers more productive.

This is approximately the premise of the Black Mirror episode "Hang the DJ"

Socrates had this to say about literacy:

> In fact, [writing] will introduce forgetfulness into the soul of those who learn it: they will not practice using their memory because they will put their trust in writing, which is external and depends on signs that belong to others, instead of trying to remember from the inside, completely on their own.

Presumably very few people since Socrates would argue that society would be better off without writing. But it's a legitimate point. There is a cost to any new skill or technology. We should be conscious of what we're giving up in this exchange.

These are conservative values:

  * The rule of law
  * Moral integrity
  * Respect for traditions and institutions

There's a tension between those values and other values such as a sense of fairness. People could overlook a politician who fell short of these conservative values when there was a perception that these values were holding us back from getting to a more just society.

Today we have a president who openly defies the court orders, engages in blatant corruption, and undermines American institutions. The Trump administration's vehement rejection of all of these values highlights just how critical they really are.

It's time for American conservatives to vote Democrat.