LLMs
I’ve been giving talks and speaking with engineers and non-technical audiences about interpretability since 2022, and I still struggle to explain exactly what a “feature” is. I often use words like “concept” or “style”, or establish metaphors to debugging programs or making fMRI scans of brains. Both metaphors help people outside of the subfield... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Two ways for an AI company to protect itself from competition: (a) depend not just on AI but also deep domain knowledge about a particular field, (b) have a very close relationship with the end users.
Paul Graham • Tweet
How enterprises are using open source LLMs: 16 examples.
Many use Llama-2: Brave, Wells Fargo, IBM, The Grammy Awards, Perplexity, Shopify, LyRise, Niantic....
Quote: “A lot of customer are asking themselves: Wait a second, why am I paying for super large model that knows very little about my business? Couldn’t I just use one of these open-source... See more
Many use Llama-2: Brave, Wells Fargo, IBM, The Grammy Awards, Perplexity, Shopify, LyRise, Niantic....
Quote: “A lot of customer are asking themselves: Wait a second, why am I paying for super large model that knows very little about my business? Couldn’t I just use one of these open-source... See more
Paul Venuto • feed updates
- Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion of self-play will look like AI Feedback rather than competitive processes.
Nathan Lambert • The Q* hypothesis: Tree-of-thoughts reasoning, process reward models, and supercharging synthetic data
In the simplest form, we can use the model’s detection confidence to determine a score. But even here there are quite a few options to choose from:
- Lowest confidence - the score is the lowest confidence of all detected objects
- Average confidence - average of all confidences of detected objects
- Minimizing confidence delta - difference between
Active Learning with Domain Experts, a Case Study in Machine Learning
What data to label?
MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.
- Multi-model serving, letting users run multiple models within the same process.
- Ability to run inference in parallel for vertical
GitHub - SeldonIO/MLServer: An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more
Ask HN: What are some actual use cases of AI Agents right now? | Hacker News
However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more
Understanding the Cost of Generative AI Models in Production
Developers can now generate human-quality speech from text via the text-to-speech API. Our new TTS model offers six preset voices to choose from and two model variants, tts-1 and tts-1-hd . tts is optimized for real-time use cases and tts-1-hd is optimized for quality. Pricing starts at $0.015 per input 1,000 characters. Check out our TTS guide to... See more