Quotations
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Overnight, tens of thousands of businesses, ranging from one-person shops to the Fortune 500, woke up to a new reality where the underpinnings of their infrastructure suddenly became a potential legal risk. The BUSL and the additional use grant written by the HashiCorp team are vague, and now every company, vendor, and developer using Terraform has to wonder whether what they are doing could be construed as competitive with HashiCorp's offerings.
llama.cpp surprised many people (myself included) with how quickly you can run large LLMs on small computers [...] TLDR at batch_size=1 (i.e. just generating a single stream of prediction on your computer), the inference is super duper memory-bound. The on-chip compute units are twiddling their thumbs while sucking model weights through a straw from DRAM. [...] A100: 1935 GB/s memory bandwidth, 1248 TOPS. MacBook M2: 100 GB/s, 7 TFLOPS. The compute is ~200X but the memory bandwidth only ~20X. So the little M2 chip that could will only be about ~20X slower than a mighty A100.
Someone asked me today if there was a case for using React in a new app that doesn't need to support IE.
I could not come up with a single reason to prefer it over Preact or (better yet) any of the modern reactive Web Components systems (FAST, Lit, Stencil, etc.).
One of the constraints is that the team wanted to use an existing library of Web Components, but React made it hard. This is probably going to cause them to favour Preact for the bits of the team that want React-flavoured modern webdev.
It's astonishing how antiquated React is.
You can think of the attention mechanism as a matchmaking service for words. Each word makes a checklist (called a query vector) describing the characteristics of words it is looking for. Each word also makes a checklist (called a key vector) describing its own characteristics. The network compares each key vector to each query vector (by computing a dot product) to find the words that are the best match. Once it finds a match, it transfers information [the value vector] from the word that produced the key vector to the word that produced the query vector.
Much of the substance of what constitutes “government” is in fact text. A technology that can do orders of magnitude more with text is therefore potentially massively impactful here. [...] Many of the sub-tasks of the work of delivering public benefits seem amenable to the application of large language models to help people do this hard work.
Increasingly powerful AI systems are being released at an increasingly rapid pace. [...] And yet not a single AI lab seems to have provided any user documentation. Instead, the only user guides out there appear to be Twitter influencer threads. Documentation-by-rumor is a weird choice for organizations claiming to be concerned about proper use of their technologies, but here we are.
Not every conversation I had at Anthropic revolved around existential risk. But dread was a dominant theme. At times, I felt like a food writer who was assigned to cover a trendy new restaurant, only to discover that the kitchen staff wanted to talk about nothing but food poisoning.
At The Guardian we had a pretty direct way to fix this [the problem of zombie feature flags]: experiments were associated with expiry dates, and if your team's experiments expired the build system simply wouldn't process your jobs without outside intervention. Seems harsh, but I've found with many orgs the only way to fix negative externalities in a shared codebase is a tool that says "you broke your promises, now we break your builds".
It feels pretty likely that prompting or chatting with AI agents is going to be a major way that we interact with computers into the future, and whereas there’s not a huge spread in the ability between people who are not super good at tapping on icons on their smartphones and people who are, when it comes to working with AI it seems like we’ll have a high dynamic range. Prompting opens the door for non-technical virtuosos in a way that we haven’t seen with modern computers, outside of maybe Excel.
Once you've found something you're excessively interested in, the next step is to learn enough about it to get you to one of the frontiers of knowledge. Knowledge expands fractally, and from a distance its edges look smooth, but once you learn enough to get close to one, they turn out to be full of gaps.
Every year, some generation of engineers have to learn the concepts of "there is no silver bullet", "use the right tech for the right problem", "your are not google", "rewriting a codebase every 2 years is not a good business decision", "things cost money".
— sametmax
Back then [in 2012], no one was thinking about AI. You just keep uploading your images [to Adobe Stock] and you get your residuals every month and life goes on — then all of a sudden, you find out that they trained their AI on your images and on everybody’s images that they don’t own. And they’re calling it ‘ethical’ AI.
Cellphones are the worst thing that’s ever happened to movies. It’s awful. [...] I think you could talk to a hundred storytellers and they would all tell you the same thing. It’s so hard to manufacture drama when everybody can get a hold of everybody all the time. It’s just not as fun as in the old days when the phone would ring and you didn’t know who was calling.
If you give feedback that isn't constructive your feedback is worthless. I know that sounds harsh but it is. If you give unconstructive feedback you might as well not be saying anything. If you just look at something and go "That's stupid" or "I don't like that" - that's worthless feedback, nobody can do anything with that. They're not going to start throwing darts against the wall until you say "Oh OK, I like that". You have to say something more.
There was an exchange on Twitter a while back where someone said, ‘What is artificial intelligence?’ And someone else said, ‘A poor choice of words in 1954’. And, you know, they’re right. I think that if we had chosen a different phrase for it, back in the ’50s, we might have avoided a lot of the confusion that we’re having now.
He notes that one simulated test saw an AI-enabled drone tasked with a SEAD mission to identify and destroy SAM sites, with the final go/no go given by the human. However, having been ‘reinforced’ in training that destruction of the SAM was the preferred option, the AI then decided that ‘no-go’ decisions from the human were interfering with its higher mission – killing SAMs – and then attacked the operator in the simulation.
[UPDATE: This turned out to be a "thought experiment" intentionally designed to illustrate how these things could go wrong.]
— Highlights from the RAeS Future Combat Air & Space Capabilities Summit
If I were an AI sommelier I would say that gpt-3.5-turbo is smooth and agreeable with a long finish, though perhaps lacking depth. text-davinci-003 is spicy and tight, sophisticated even.
A whole new paradigm would be needed to solve prompt injections 10/10 times – It may well be that LLMs can never be used for certain purposes. We're working on some new approaches, and it looks like synthetic data will be a key element in preventing prompt injections.
— Sam Altman, via Marvin von Hagen
In general my approach to running arbitrary untrusted code is 20% sandboxing and 80% making sure that it’s an extremely low value attack target so it’s not worth trying to break in.
Programs are terminated after 1 second of runtime, they run in a container with no network access, and the machine they’re running on has no sensitive data on it and a very small CPU.
The benefit of ground effects are: - 10-20% range extension (agreed, between 50% and 100% wingspan, which is where seagliders fly, the aerodynamic benefit of ground effect is reduced compared to near surface flight) - Drastic reduction in reserve fuel. This is a key limitation of electric aircraft because they need to sustain powered flight to another airport in the event of an emergency. We can always land on the water, therefore, we can count all of our batteries towards "mission useable" [...] Very difficult to distribute propulsion with IC engines or mechanical linkages. Electric propulsion technology unlocks the blown wing, which unlocks the use of hydrofoils, which unlocks wave tolerance and therefore operations of WIGs, which unlocks longer range of electric flight. It all works together.
— Billy Thalheimer, founder of REGENT
I find it fascinating that novelists galore have written for decades about scenarios that might occur after a "singularity" in which superintelligent machines exist. But as far as I know, not a single novelist has realized that such a singularity would almost surely be preceded by a world in which machines are 0.01% intelligent (say), and in which millions of real people would be able to interact with them freely at essentially no cost.
I myself shall certainly continue to leave such research to others, and to devote my time to developing concepts that are authentic and trustworthy. And I hope you do the same.
According to interviews with former employees, publishing executives, and experts associated with the early days of AMP, while it was waxing poetic about the value and future of the open web, Google was privately urging publishers into handing over near-total control of how their articles worked and looked and monetized. And it was wielding the web’s most powerful real estate — the top of search results — to get its way.
There are many reasons for companies to not turn efficiency gains into headcount or cost reduction. Companies that figure out how to use their newly productive workforce should be able to dominate those who try to keep their post-AI output the same as their pre-AI output, just with less people. And companies that commit to maintaining their workforce will likely have employees as partners, who are happy to teach others about the uses of AI at work, rather than scared workers who hide their AI for fear of being replaced.
For many, crypto had become an identity, a way to feel smart and subversive and on the cutting edge of a new technology. What happens to that self-image when its foundation erodes? When instead of being someone’s savvy son or daughter, you are the sheepish adult child who has to explain where the family savings went?
The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.
— PaLM 2 Technical Report, PDF
When trying to get your head around a new technology, it helps to focus on how it challenges existing categorizations, conventions, and rule sets. Internally, I’ve always called this exercise, “dealing with the platypus in the room.” Named after the category-defying animal; the duck-billed, venomous, semi-aquatic, egg-laying mammal. [...] AI is the biggest platypus I’ve ever seen. Nearly every notable quality of AI and LLMs challenges our conventions, categories, and rulesets.
What Tesla is contending is deeply troubling to the Court. Their position is that because Mr. Musk is famous and might be more of a target for deep fakes, his public statements are immune. In other words, Mr. Musk, and others in his position, can simply say whatever they like in the public domain, then hide behind the potential for their recorded statements being a deep fake to avoid taking ownership of what they did actually say and do. The Court is unwilling to set such a precedent by condoning Tesla's approach here.
Because we do not live in the Star Trek-inspired rational, humanist world that Altman seems to be hallucinating. We live under capitalism, and under that system, the effects of flooding the market with technologies that can plausibly perform the economic tasks of countless working people is not that those people are suddenly free to become philosophers and artists. It means that those people will find themselves staring into the abyss – with actual artists among the first to fall.
At this point the lawsuits seem a bit far-fetched: “You should have warned us months ago that artificial intelligence would hurt your business” is unfair given how quickly ChatGPT has exploded from nowhere to become a cultural and business phenomenon. But now everyone is on notice! If you are not warning your shareholders now about how AI could hurt your business, and then it does hurt your business, you’re gonna get sued.
We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. [...] We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time.
— SparseGPT, by Elias Frantar and Dan Alistarh