LLMs
Document search and synthesis
Scores of organizations want to harness generative AI so employees can easily find the most relevant documents through improved search results and summaries. For example, your organization can reduce the time it takes employees to find answers to common HR- and process-related questions. Internal manuals and sites are... See more
Scores of organizations want to harness generative AI so employees can easily find the most relevant documents through improved search results and summaries. For example, your organization can reduce the time it takes employees to find answers to common HR- and process-related questions. Internal manuals and sites are... See more
Donna Schut • The Prompt: Takeaways from hundreds of conversations about generative AI - part 1 | Google Cloud Blog
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?
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
New models and developer products announced at DevDay
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The way that most RLHF is done to date has the entire response from a language model get an associated score. To anyone with an RL background, this is disappointing, because it limits the ability for RL methods to make connections about the value of each sub-component of text. Futures have been pointed to where this multi-step optimization comes at... See more
Nathan Lambert • The Q* hypothesis: Tree-of-thoughts reasoning, process reward models, and supercharging synthetic data
How can we make interacting with conversational models feel more natural?
Every conversational interface to a language model adopts the same pattern:
A chat history sidebar, with each conversation lasting just a few turns
New sessions always begin in a brand-new thread
Every user query must always elicit exactly one response
None of these assumptions... See more
Every conversational interface to a language model adopts the same pattern:
A chat history sidebar, with each conversation lasting just a few turns
New sessions always begin in a brand-new thread
Every user query must always elicit exactly one response
None of these assumptions... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
Giskard is a Python library that automatically detects vulnerabilities of AI models, from tabular models to LLM, including: performance biases, data leakage, spurious correlation, hallucination, toxicity, security issues and many more.
It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more
It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more
Giskard-AI • GitHub - Giskard-AI/giskard: 🐢 The testing framework for ML models, from tabular to LLMs
Zerox OCR
A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!
The general logic:
A dead simple way of OCR-ing a document for AI ingestion. Documents are meant to be a visual representation after all. With weird layouts, tables, charts, etc. The vision models just make sense!
The general logic:
- Pass in a PDF (URL or file buffer)
- Turn the PDF into a series of images
- Pass each image to GPT and ask nicely for Markdown
- Aggregat