Claude's 100k token support
Plus: Meta's Sandbox: AI meets Advertising. Stability AI Made Text-to-Animation Easy
Hello, Engineering Leaders and AI Enthusiasts,
Welcome to the 18th edition of The AI Edge newsletter. In today’s edition, we bring you Claude's 100k token support. Thank you everyone who is reading this. 😊
In today’s edition:
🧠 Anthropic giving Claude bigger “memory” than ChatGPT
🎬 Stability AI releases a powerful text-to-animation tool
🤖 Meta's Sandbox: AI meets Advertising
📚 Giving GPT "Infinite" knowledge by
Let’s go!
Anthropic introduces 100k context windows, giving Claude bigger “memory” than ChatGPT
Anthropic expands Claude’s context window from 9K to 100K tokens, corresponding to around 75,000 words! It means you can now submit hundreds of pages of materials for Claude to digest and analyze, and conversations with Claude can go on for hours or even days.
The context window size is an important parameter in language models like GPT. Models with small context windows tend to “forget” the content of very recent conversations, even their initial instructions– after a few thousand words or so. Claude’s larger “memory” can be game-changing for LLMs.
Why does this matter?
Poor memory has been a significant barrier to the usefulness of text-generating AI models. AI labs like OpenAI devoted an entire team to the issue. But Anthropic is trying to change that and how. Claude’s 100k tokens blow past ChatGPT’s 32k, putting Anthropic at the forefront for now.
Stability AI releases a powerful text-to-animation tool
Stability AI released Stable Animation SDK, a tool designed for developers and artists to implement the most advanced Stable Diffusion models to generate stunning animations. It allows using all the models, including Stable Diffusion 2.0 and Stable Diffusion XL. And it offers three ways to create animations:
Text to animation
Initial image input + text input
Input video + text input
The initial image/video inputs act as the starting point for the animation, which is additionally guided by text prompts to arrive at the final output.
Why does this matter?
The release of this SDK makes it easier for developers to create high-quality animations, accelerating AI adoption in the animation industry and other fields. It also represents a promising development in the intersection of AI and animation and the potential to drive further advancements and innovations in these fields.
Meta's Sandbox: Where AI meets advertising
Meta has introduced an AI Sandbox for advertisers, which includes features such as alternative copy generation, background creation through text prompts, and image cropping for Facebook or Instagram ads. This new tool aims to assist advertisers in creating more diverse and engaging content using AI.
The tools are still in beta, but they have the potential to revolutionize how ads are created and delivered.
Why does this matter?
The use of AI in advertising is a growing trend, with many companies exploring using AI to improve ad targeting and automate the creation process. Seems like Meta is following Google’s lead and catering generative AI customers to its customers.
Knowledge Nugget: Giving GPT "Infinite" knowledge
In this well-researched and thought-provoking nugget,
provides a detailed and insightful look at the evolution of GPT access to large amounts of data and how this has led to the development of GPT with infinite knowledge.GPT is a large language model that uses machine learning to generate human-like text. The early versions of GPT were trained on relatively small datasets, limiting their ability to generate high-quality text. And researchers have developed several methods for providing GPT with access to larger datasets, including pre-training on large amounts of text and fine-tuning on specific tasks.
Developing these methods has helped improve GPT's performance and capabilities, allowing it to generate more coherent and human-like text. However, there are concerns about the ethical implications of training language models on vast amounts of data, particularly regarding privacy and bias.
The core areas for providing LLMs with data are:
Tokens
Embeddings
Vector Storage
Prompting
Why does this matter?
Providing LLMs with real-time data can enhance their performance and capabilities. However, the output quality will depend on the data type fed to the models.
What Else Is Happening
🌋 Lord of the rings as cast by ChatGPT (Link)
💼 Transform enterprise workflows with Airtable AI(Link)
🔬 AI tools for heart attack diagnosis with 99.6% accuracy(Link)
🧙♂️ Try this popular secret password reveal game: Gandalf(Link)
🗞️ Boring Report, an AI app to desensationalize news(Link)
🤝 New offering to unite SAP with Google Cloud’s data & analytics tech(Link)
🛡️ Writer introduces a tool to reduce hallucinations in LLMs(Link)
Trending Tools
Stelvio: Tailor-made AI illustration styles. Create stunning illustrations for social media, articles, UI design.
Writesonic: No-code AI chatbot builder. Supercharge your website with custom GPT-4 powered chatbots.
OpenOS: AI-powered platform for data and financial analysis. Create reports, write queries, make forecasts using natural language.
Auto Gmail: AI agent that automates answering repetitive emails. Drafts answers based on context.
Molin: Multilingual AI copywriter. Writes in 10 languages with seamless cross-language operations.
AI Code Generator: AI tool for creating or fixing code. Say goodbye to manual coding and debugging.
Datalime: Create Jupyter notebooks with AI. Write code and analyze data in seconds.
Hexigon: Fine-tune custom models for higher performance at a lower cost using prompts to integrate and automate any AI-based model.
That's all for now!
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Thanks for reading, and see you tomorrow.