LLMs
We identified 30 types of tasks that UX professionals used generative AI tools for in their work. We grouped these tasks under four roles: content editor, research assistant, ideation partner, or design assistant.
- Content editor : Generating and editing text, from microcopy to social media posts, based on specifications or copy given by UX
Mingjin Zhang • AI as a UX Assistant
Overview
MaxText is a high performance , highly scalable , open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference . MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.
MaxText... See more
MaxText is a high performance , highly scalable , open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference . MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.
MaxText... See more
google • GitHub - google/maxtext: A simple, performant and scalable Jax LLM!
How do models represent style, and how can we more precisely extract and steer it?
A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... See more
A commonly requested feature in almost any LLM-based writing application is “I want the AI to respond in my style of writing,” or “I want the AI to adhere to this style guide.” Aside from costly and complicated multi-stage finetuning processes like Anthropic’s RL with... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
GPT-4 Turbo performs better than our previous models on tasks that require the careful following of instructions, such as generating specific formats (e.g., “always respond in XML”). It also supports our new JSON mode, which ensures the model will respond with valid JSON. The new API parameter response_format enables the model to constrain its... See more
New models and developer products announced at DevDay
core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.
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... See more
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... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
These two components might be some of the most important ideas to improve all of AI.
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?
🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
- Why CrewAI
- Getting Started
- Key Features
- Examples
- Local Open Source Models
- CrewAI x AutoGen x ChatDev
- Contribution
- 💬 CrewAI Discord Community
- Hire Consulting
- License
joaomdmoura • GitHub - joaomdmoura/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
The Gemini API context caching feature is designed to reduce the cost of requests that contain repeat content with high input token counts.
When to use context caching
Context caching is particularly well suited to scenarios where a substantial initial context is referenced repeatedly by shorter requests. Consider using context caching for use cases... See more
When to use context caching
Context caching is particularly well suited to scenarios where a substantial initial context is referenced repeatedly by shorter requests. Consider using context caching for use cases... See more
Context caching guide | Google AI for Developers | Google for Developers
Take a look at our official page for user documentation and examples: langtest.org
Key Features
Key Features
- Generate and execute more than 50 distinct types of tests only with 1 line of code
- Test all aspects of model quality: robustness, bias, representation, fairness and accuracy.
- Automatically augment training data based on test results (for select models)
- Sup