Quotations
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If your only way of making a painting is to actually dab paint laboriously onto a canvas, then the result might be bad or good, but at least it’s the result of a whole lot of micro-decisions you made as an artist. You were exercising editorial judgment with every paint stroke. That is absent in the output of these programs.
Sometimes, performance just doesn't matter. If I make some codepath in Ruff 10x faster, but no one ever hits it, I'm sure it could get some likes on Twitter, but the impact on users would be meaningless.
And yet, it's good to care about performance everywhere, even when it doesn't matter. Caring about performance is cultural and contagious. Small wins add up. Small losses add up even more.
Rye lets you get from no Python on a computer to a fully functioning Python project in under a minute with linting, formatting and everything in place.
[...] Because it was demonstrably designed to avoid interference with any pre-existing Python configurations, Rye allows for a smooth and gradual integration and the emotional barrier of picking it up even for people who use other tools was shown to be low.
LLMs may offer immense value to society. But that does not warrant the violation of copyright law or its underpinning principles. We do not believe it is fair for tech firms to use rightsholder data for commercial purposes without permission or compensation, and to gain vast financial rewards in the process. There is compelling evidence that the UK benefits economically, politically and societally from upholding a globally respected copyright regime.
For many people in many organizations, their measurable output is words - words in emails, in reports, in presentations. We use words as proxy for many things: the number of words is an indicator of effort, the quality of the words is an indicator of intelligence, the degree to which the words are error-free is an indicator of care.
[...] But now every employee with Copilot can produce work that checks all the boxes of a formal report without necessarily representing underlying effort.
Danielle Del, a spokeswoman for Sasso, said Dudesy is not actually an A.I.
“It’s a fictional podcast character created by two human beings, Will Sasso and Chad Kultgen,” Del wrote in an email. “The YouTube video ‘I’m Glad I’m Dead’ was completely written by Chad Kultgen.”
If you have had any prior experience with personal computers, what you might expect to see is some sort of opaque code, called a “prompt,” consisting of phosphorescent green or white letters on a murky background. What you see with Macintosh is the Finder. On a pleasant, light background (you can later change the background to any of a number of patterns, if you like), little pictures called “icons” appear, representing choices available to you.
— Steven Levy, in 1984
Find a level of abstraction that works for what you need to do. When you have trouble there, look beneath that abstraction. You won’t be seeing how things really work, you’ll be seeing a lower-level abstraction that could be helpful. Sometimes what you need will be an abstraction one level up. Is your Python loop too slow? Perhaps you need a C loop. Or perhaps you need numpy array operations.
You (probably) don’t need to learn C.
We estimate the supply-side value of widely-used OSS is $4.15 billion, but that the demand-side value is much larger at $8.8 trillion. We find that firms would need to spend 3.5 times more on software than they currently do if OSS did not exist. [...] Further, 96% of the demand-side value is created by only 5% of OSS developers.
— The Value of Open Source Software, Harvard Business School Strategy Unit
And now, in Anno Domini 2024, Google has lost its edge in search. There are plenty of things it can’t find. There are compelling alternatives. To me this feels like a big inflection point, because around the stumbling feet of the Big Tech dinosaurs, the Web’s mammals, agile and flexible, still scurry. They exhibit creative energy and strongly-flavored voices, and those voices still sometimes find and reinforce each other without being sock puppets of shareholder-value-focused private empires.
— Tim Bray
Tools are the things we build that we don't ship - but that very much affect the artifact that we develop.
It can be tempting to either shy away from developing tooling entirely or (in larger organizations) to dedicate an entire organization to it.
In my experience, tooling should be built by those using it.
This is especially true for tools that improve the artifact by improving understanding: the best time to develop a debugger is when debugging!
You likely have a TinyML system in your pocket right now: every cellphone has a low power DSP chip running a deep learning model for keyword spotting, so you can say "Hey Google" or "Hey Siri" and have it wake up on-demand without draining your battery. It’s an increasingly pervasive technology. [...]
It’s astonishing what is possible today: real time computer vision on microcontrollers, on-device speech transcription, denoising and upscaling of digital signals. Generative AI is happening, too, assuming you can find a way to squeeze your models down to size. We are an unsexy field compared to our hype-fueled neighbors, but the entire world is already filling up with this stuff and it’s only the very beginning. Edge AI is being rapidly deployed in a ton of fields: medical sensing, wearables, manufacturing, supply chain, health and safety, wildlife conservation, sports, energy, built environment—we see new applications every day.
We believe that AI tools are at their best when they incorporate and represent the full diversity and breadth of human intelligence and experience. [...] Because copyright today covers virtually every sort of human expression– including blog posts, photographs, forum posts, scraps of software code, and government documents–it would be impossible to train today’s leading AI models without using copyrighted materials. Limiting training data to public domain books and drawings created more than a century ago might yield an interesting experiment, but would not provide AI systems that meet the needs of today’s citizens.
If you learn something the hard way, share your findings with others. You have blazed a new trail; now you must mark it for your fellow travellers. Sharing knowledge is an unreasonably effective way of helping others.
Since the advent of ChatGPT, and later by using LLMs that operate locally, I have made extensive use of this new technology. The goal is to accelerate my ability to write code, but that's not the only purpose. There's also the intent to not waste mental energy on aspects of programming that are not worth the effort.
[...] Current LLMs will not take us beyond the paths of knowledge, but if we want to tackle a topic we do not know well, they can often lift us from our absolute ignorance to the point where we know enough to move forward on our own.
There is something so vulnerable and frightening about doing your own thing, because it’s your fault if it doesn’t work. And then there’s this other kind of work, where you’re paid an extraordinary amount of money, you’re the hero before you walk in the door, you’re not even held that accountable, because you have a limited amount of time, and all you can do is make it better.
Basically, we’re in the process of replacing our whole social back-end with ActivityPub. I think Flipboard is going to be the first mainstream consumer service that existed in a walled garden that switches over to ActivityPub.
— Mike McCue, CEO of Flipboard
Computer, display Fairhaven character, Michael Sullivan. [...]
Give him a more complicated personality. More outspoken. More confident. Not so reserved. And make him more curious about the world around him.
Good. Now... Increase the character's height by three centimeters. Remove the facial hair. No, no, I don't like that. Put them back. About two days' growth. Better.
Oh, one more thing. Access his interpersonal subroutines, familial characters. Delete the wife.
— Captain Janeway, prompt engineering
And so the problem with saying “AI is useless,” “AI produces nonsense,” or any of the related lazy critique is that destroys all credibility with everyone whose lived experience of using the tools disproves the critique, harming the credibility of critiquing AI overall.
gpt-4-turbo over the API produces (statistically significant) shorter completions when it "thinks" its December vs. when it thinks its May (as determined by the date in the system prompt).
I took the same exact prompt over the API (a code completion task asking to implement a machine learning task without libraries).
I created two system prompts, one that told the API it was May and another that it was December and then compared the distributions.
For the May system prompt, mean = 4298 For the December system prompt, mean = 4086
N = 477 completions in each sample from May and December
t-test p < 2.28e-07
When I speak in front of groups and ask them to raise their hands if they used the free version of ChatGPT, almost every hand goes up. When I ask the same group how many use GPT-4, almost no one raises their hand. I increasingly think the decision of OpenAI to make the “bad” AI free is causing people to miss why AI seems like such a huge deal to a minority of people that use advanced systems and elicits a shrug from everyone else.
I always struggle a bit with I'm asked about the "hallucination problem" in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.
We direct their dreams with prompts. The prompts start the dream, and based on the LLM's hazy recollection of its training documents, most of the time the result goes someplace useful.
It's only when the dreams go into deemed factually incorrect territory that we label it a "hallucination". It looks like a bug, but it's just the LLM doing what it always does.
Create a culture that favors begging forgiveness (and reversing decisions quickly) rather than asking permission. Invest in infrastructure such as progressive / cancellable rollouts. Use asynchronous written docs to get people aligned (“comment in this doc by Friday if you disagree with the plan”) rather than meetings (“we’ll get approval at the next weekly review meeting”).
We like to assume that automation technology will maintain or increase wage levels for a few skilled supervisors. But in the long-term skilled automation supervisors also tend to earn less.
Here's an example: In 1801 the Jacquard loom was invented, which automated silkweaving with punchcards. Around 1800, a manual weaver could earn 30 shillings/week. By the 1830s the same weaver would only earn around 5s/week. A Jacquard operator earned 15s/week, but he was also 12x more productive.
The Jacquard operator upskilled and became an automation supervisor, but their wage still dropped. For manual weavers the wages dropped even more. If we believe assistive AI will deliver unseen productivity gains, we can assume that wage erosion will also be unprecedented.
GPT and other large language models are aesthetic instruments rather than epistemological ones. Imagine a weird, unholy synthesizer whose buttons sample textual information, style, and semantics. Such a thing is compelling not because it offers answers in the form of text, but because it makes it possible to play text—all the text, almost—like an instrument.
A calculator has a well-defined, well-scoped set of use cases, a well-defined, well-scoped user interface, and a set of well-understood and expected behaviors that occur in response to manipulations of that interface.
Large language models, when used to drive chatbots or similar interactive text-generation systems, have none of those qualities. They have an open-ended set of unspecified use cases.
So something everybody I think pretty much agrees on, including Sam Altman, including Yann LeCun, is LLMs aren't going to make it. The current LLMs are not a path to ASI. They're getting more and more expensive, they're getting more and more slow, and the more we use them, the more we realize their limitations.
We're also getting better at taking advantage of them, and they're super cool and helpful, but they appear to be behaving as extremely flexible, fuzzy, compressed search engines, which when you have enough data that's kind of compressed into the weights, turns out to be an amazingly powerful operation to have at your disposal.
[...] And the thing you can really see missing here is this planning piece, right? So if you try to get an LLM to solve fairly simple graph coloring problems or fairly simple stacking problems, things that require backtracking and trying things and stuff, unless it's something pretty similar in its training, they just fail terribly.
[...] So that's the theory about what something like Q* might be, or just in general, how do we get past this current constraint that we have?
This is what I constantly tell my students: The hard part about doing a tech product for the most part isn't the what beginners think makes tech hard — the hard part is wrangling systemic complexity in a good, sustainable and reliable way.
Many non-tech people e.g. look at programmers and think the hard part is knowing what this garble of weird text means. But this is the easy part. And if you are a person who would think it is hard, you probably don't know about all the demons out there that will come to haunt you if you don't build a foundation that helps you actively keeping them away.
— atoav
This is nonsensical. There is no way to understand the LLaMA models themselves as a recasting or adaptation of any of the plaintiffs’ books.
To some degree, the whole point of the tech industry’s embrace of “ethics” and “safety” is about reassurance. Companies realize that the technologies they are selling can be disconcerting and disruptive; they want to reassure the public that they’re doing their best to protect consumers and society. At the end of the day, though, we now know there’s no reason to believe that those efforts will ever make a difference if the company’s “ethics” end up conflicting with its money. And when have those two things ever not conflicted?