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LatchBio researcher arjun reveals Grok's refusals often come from the model itself while Fable and GPT-5.6 Sol rely more on external safety layers: "There might be input classifiers where when a user puts in a query, the model initially might say, this is a bad query, we can't respond...

13,719 Aufrufe • vor 10 Tagen •via X (Twitter)

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🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

Anthony Pompliano 🌪

76,176 Aufrufe • vor 25 Tagen

Will we ever be able to simulate a living cell? In theory. Molecules collide, proteins fold, and all of these things can be modeled on a computer. But there are so many unknowns, and so much compute would be required, that a mechanistic model of the cell remains a distant dream. Still, many research groups are trying to build cells "from the bottom up," mostly by stringing together mathematical equations that represent different parts of the cell. By *trying* to build an accurate model of the cell, they hope to improve our own understanding of how biology works. In doing so, they also maintain legibility, meaning that humans can understand and interpret all the equations used to construct the model. But Adam Green argues that legibility is a constraint on our models. He thinks that "human legibility, our ability to understand a system...has been limiting us." To truly accelerate biomedical progress, Green thinks that we should discard assumptions about legibility in favor of "more black box" models. Neural networks can make sense of biological data in a way that hard-coded equations cannot. This is not a new argument. A 2019 essay by Bert Hubert, called "Is biology too complex to ever understand?" makes much the same point. In that essay, Hubert writes: "There is no rule that says nature cannot be more complex than our brains can handle." Instead of trying to make sense of biology via reductionist observations and mechanisms, Hubert argues that we should just gather everything into databases "that might enable computers to make sense of what we have learned." (This vision is now playing out in many research groups.) This "black box" approach might be useful (in terms of modeling cells to cure diseases, say) but would be unsatisfying in the sense that it doesn't deepen our own understanding of the universe. And isn't that a key part of science? Is science for humans -- an act that satisfies some itch -- or rather a means to an end? I think both approaches are useful. The black box models may help us cure diseases faster by making useful predictions that our brains cannot (currently) comprehend, but the mechanistic models deepen our own understanding of how cells work.

Niko McCarty.

16,382 Aufrufe • vor 2 Monaten

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 Aufrufe • vor 3 Jahren