Quotations tagged ai
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There is superstition about creativity, and for that matter, about thinking in every sense, and it's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something - play good checkers, solve simple but relatively informal problems - there was a chorus of critics to say, but that's not thinking.
— Pamela McCorduck, in 1979
o1-mini is the most surprising research result I've seen in the past year
Obviously I cannot spill the secret, but a small model getting >60% on AIME math competition is so good that it's hard to believe
— Jason Wei, OpenAI
Telling the AI to "make it better" after getting a result is just a folk method of getting an LLM to do Chain of Thought, which is why it works so well.
history | tail -n 2000 | llm -s "Write aliases for my zshrc based on my terminal history. Only do this for most common features. Don't use any specific files or directories."
— anjor
Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. […] to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.
If an A.I. generates a ten-thousand-word story based on your prompt, it has to fill in for all of the choices that you are not making.
I think that AI has killed, or is about to kill, pretty much every single modifier we want to put in front of the word “developer.”
“.NET developer”? Meaningless. Copilot, Cursor, etc can get anyone conversant enough with .NET to be productive in an afternoon … as long as you’ve done enough other programming that you know what to prompt.
We have recently trained our first 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels.
For each decoded token, LTM-2-mini's sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B for a 100M token context window.
The contrast in memory requirements is even larger -- running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache. In contrast, LTM requires a small fraction of a single H100's HBM per user for the same context.
— Magic AI
Everyone alive today has grown up in a world where you can’t believe everything you read. Now we need to adapt to a world where that applies just as equally to photos and videos. Trusting the sources of what we believe is becoming more important than ever.
We've read and heard that you'd appreciate more transparency as to when changes, if any, are made. We've also heard feedback that some users are finding Claude's responses are less helpful than usual. Our initial investigation does not show any widespread issues. We'd also like to confirm that we've made no changes to the 3.5 Sonnet model or inference pipeline.
[...] here’s what we found when we integrated [Amazon Q, GenAI assistant for software development] into our internal systems and applied it to our needed Java upgrades:
- The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer-days to just a few hours. We estimate this has saved us the equivalent of 4,500 developer-years of work (yes, that number is crazy but, real).
- In under six months, we've been able to upgrade more than 50% of our production Java systems to modernized Java versions at a fraction of the usual time and effort. And, our developers shipped 79% of the auto-generated code reviews without any additional changes.
— Andy Jassy, Amazon CEO
With statistical learning based systems, perfect accuracy is intrinsically hard to achieve. If you think about the success stories of machine learning, like ad targeting or fraud detection or, more recently, weather forecasting, perfect accuracy isn't the goal --- as long as the system is better than the state of the art, it is useful. Even in medical diagnosis and other healthcare applications, we tolerate a lot of error.
But when developers put AI in consumer products, people expect it to behave like software, which means that it needs to work deterministically.
Examples are the #1 thing I recommend people use in their prompts because they work so well. The problem is that adding tons of examples increases your API costs and latency. Prompt caching fixes this. You can now add tons of examples to every prompt and create an alternative to a model finetuned on your task with basically zero cost/latency increase. […]
This works even better with smaller models. You can generate tons of examples (test case + solution) with 3.5 Sonnet and then use those examples to create a few-shot prompt for Haiku.
But [LLM assisted programming] does make me wonder whether the adoption of these tools will lead to a form of de-skilling. Not even that programmers will be less skilled, but that the job will drift from the perception and dynamics of a skilled trade to an unskilled trade, with the attendant change - decrease - in pay. Instead of hiring a team of engineers who try to write something of quality and try to load the mental model of what they're building into their heads, companies will just hire a lot of prompt engineers and, who knows, generate 5 versions of the application and A/B test them all across their users.
Some argue that by aggregating knowledge drawn from human experience, LLMs aren’t sources of creativity, as the moniker “generative” implies, but rather purveyors of mediocrity. Yes and no. There really are very few genuinely novel ideas and methods, and I don’t expect LLMs to produce them. Most creative acts, though, entail novel recombinations of known ideas and methods. Because LLMs radically boost our ability to do that, they are amplifiers of — not threats to — human creativity.
The RM [Reward Model] we train for LLMs is just a vibe check […] It gives high scores to the kinds of assistant responses that human raters statistically seem to like. It's not the "actual" objective of correctly solving problems, it's a proxy objective of what looks good to humans. Second, you can't even run RLHF for too long because your model quickly learns to respond in ways that game the reward model. […]
No production-grade actual RL on an LLM has so far been convincingly achieved and demonstrated in an open domain, at scale. And intuitively, this is because getting actual rewards (i.e. the equivalent of win the game) is really difficult in the open-ended problem solving tasks. […] But how do you give an objective reward for summarizing an article? Or answering a slightly ambiguous question about some pip install issue? Or telling a joke? Or re-writing some Java code to Python?
[On release notes] in our partial defense, training these models can be more discovery than invention. often we don't exactly know what will come out.
we've long wanted to do release notes that describe each model's differences, but we also don't want to give false confidence with a shallow story.
— Ted Sanders, OpenAI
When Noam and Daniel started Character.AI, our goal of personalized superintelligence required a full stack approach. We had to pre-train models, post-train them to power the experiences that make Character.AI special, and build a product platform with the ability to reach users globally. Over the past two years, however, the landscape has shifted – many more pre-trained models are now available. Given these changes, we see an advantage in making greater use of third-party LLMs alongside our own. This allows us to devote even more resources to post-training and creating new product experiences for our growing user base.
The [Apple Foundation Model] pre-training dataset consists of a diverse and high quality data mixture. This includes data we have licensed from publishers, curated publicly-available or open-sourced datasets, and publicly available information crawled by our web-crawler, Applebot. We respect the right of webpages to opt out of being crawled by Applebot, using standard robots.txt directives.
Given our focus on protecting user privacy, we note that no private Apple user data is included in the data mixture. Additionally, extensive efforts have been made to exclude profanity, unsafe material, and personally identifiable information from publicly available data (see Section 7 for more details). Rigorous decontamination is also performed against many common evaluation benchmarks.
We find that data quality, much more so than quantity, is the key determining factor of downstream model performance.
The key to understanding the pace of today’s infrastructure buildout is to recognize that while AI optimism is certainly a driver of AI CapEx, it is not the only one. The cloud players exist in a ruthless oligopoly with intense competition. [...]
Every time Microsoft escalates, Amazon is motivated to escalate to keep up. And vice versa. We are now in a cycle of competitive escalation between three of the biggest companies in the history of the world, collectively worth more than $7T. At each cycle of the escalation, there is an easy justification—we have plenty of money to afford this. With more commitment comes more confidence, and this loop becomes self-reinforcing. Supply constraints turbocharge this dynamic: If you don’t acquire land, power and labor now, someone else will.
Among many misunderstandings, [users] expect the RAG system to work like a search engine, not as a flawed, forgetful analyst. They will not do the work that you expect them to do in order to verify documents and ground truth. They will not expect the AI to try to persuade them.
Our estimate of OpenAI’s $4 billion in inference costs comes from a person with knowledge of the cluster of servers OpenAI rents from Microsoft. That cluster has the equivalent of 350,000 Nvidia A100 chips, this person said. About 290,000 of those chips, or more than 80% of the cluster, were powering ChartGPT, this person said.
One interesting observation is the impact of environmental factors on training performance at scale. For Llama 3 405B , we noted a diurnal 1-2% throughput variation based on time-of-day. This fluctuation is the result of higher mid-day temperatures impacting GPU dynamic voltage and frequency scaling.
During training, tens of thousands of GPUs may increase or decrease power consumption at the same time, for example, due to all GPUs waiting for checkpointing or collective communications to finish, or the startup or shutdown of the entire training job. When this happens, it can result in instant fluctuations of power consumption across the data center on the order of tens of megawatts, stretching the limits of the power grid. This is an ongoing challenge for us as we scale training for future, even larger Llama models.
As we've noted many times since March, these benchmarks aren't necessarily scientifically sound and don't convey the subjective experience of interacting with AI language models. [...] We've instead found that measuring the subjective experience of using a conversational AI model (through what might be called "vibemarking") on A/B leaderboards like Chatbot Arena is a better way to judge new LLMs.
I believe the Llama 3.1 release will be an inflection point in the industry where most developers begin to primarily use open source, and I expect that approach to only grow from here.
I have a hard time describing the real value of consumer AI because it’s less some grand thing around AI agents or anything and more AI saving humans a hour of work on some random task, millions of times a day.
Stepping back, though, the very speed with which ChatGPT went from a science project to 100m users might have been a trap (a little as NLP was for Alexa). LLMs look like they work, and they look generalised, and they look like a product - the science of them delivers a chatbot and a chatbot looks like a product. You type something in and you get magic back! But the magic might not be useful, in that form, and it might be wrong. It looks like product, but it isn’t. [...]
LLMs look like better databases, and they look like search, but, as we’ve seen since, they’re ‘wrong’ enough, and the ‘wrong’ is hard enough to manage, that you can’t just give the user a raw prompt and a raw output - you need to build a lot of dedicated product around that, and even then it’s not clear how useful this is.
The reason current models are so large is because we're still being very wasteful during training - we're asking them to memorize the internet and, remarkably, they do and can e.g. recite SHA hashes of common numbers, or recall really esoteric facts. (Actually LLMs are really good at memorization, qualitatively a lot better than humans, sometimes needing just a single update to remember a lot of detail for a long time). But imagine if you were going to be tested, closed book, on reciting arbitrary passages of the internet given the first few words. This is the standard (pre)training objective for models today. The reason doing better is hard is because demonstrations of thinking are "entangled" with knowledge, in the training data.
Therefore, the models have to first get larger before they can get smaller, because we need their (automated) help to refactor and mold the training data into ideal, synthetic formats.
It's a staircase of improvement - of one model helping to generate the training data for next, until we're left with "perfect training set". When you train GPT-2 on it, it will be a really strong / smart model by today's standards. Maybe the MMLU will be a bit lower because it won't remember all of its chemistry perfectly.
Update, July 12: This innovation sparked a lot of conversation and questions that have no answers yet. We look forward to continuing to work with our customers on the responsible use of AI, but will not further pursue digital workers in the product.
— Lattice, HR platform
OpenAI and Anthropic focused on building models and not worrying about products. For example, it took 6 months for OpenAI to bother to release a ChatGPT iOS app and 8 months for an Android app!
Google and Microsoft shoved AI into everything in a panicked race, without thinking about which products would actually benefit from AI and how they should be integrated.
Both groups of companies forgot the “make something people want” mantra. The generality of LLMs allowed developers to fool themselves into thinking that they were exempt from the need to find a product-market fit, as if prompting is a replacement for carefully designed products or features. [...]
But things are changing. OpenAI and Anthropic seem to be transitioning from research labs focused on a speculative future to something resembling regular product companies. If you take all the human-interest elements out of the OpenAI boardroom drama, it was fundamentally about the company's shift from creating gods to building products.
We've doubled the max output token limit for Claude 3.5 Sonnet from 4096 to 8192 in the Anthropic API.
Just add the header
"anthropic-beta": "max-tokens-3-5-sonnet-2024-07-15"to your API calls.