Welcome to AI Daily, your go-to podcast for the latest updates in artificial intelligence! In today's episode, we have an exciting lineup of stories, covering Salesforce AI, OpenAI updates, and the remarkable LLaMA Adapter. Get ready for a deep dive into the world of AI!
LLaMA Adapter is a bilingual multimodal instruction model that integrates various inputs such as images, audio, and 3D point clouds.
It is designed for composability and compatibility, allowing it to connect with other projects and models like Falcon, ImageBind, StableDiffusion, and LaneChain.
LLA Adapter enables fine-tuning of models for image processing and specific instructions, expanding the capabilities of traditional LAMA.
The combination of models through LLA Adapter is becoming more accessible, cost-effective, and practical, making it an exciting development for multimodal abilities.
Salesforce made significant updates to its AI offerings across various domains, including sales, marketing, code, and Tableau integration.
The release of multiple AI tools directly into Salesforce demonstrates their commitment to the field and their intention to make a strong impact.
One notable feature is the ability to customize sales pages and emails based on CRM data, allowing for powerful AI-driven personalization.
Salesforce's extensive customer data puts them in a prime position to leverage these new tools and technologies effectively, signaling more exciting developments to come. Additionally, they may pursue acquisitions to enhance their AI capabilities further.
OpenAI released a comprehensive suite of updates, including cheaper models, steerable versions of GPT-4, and enhanced function calling capabilities.
The function calling feature stood out as a game-changer, eliminating the need for multiple startups by enabling users to obviate their functions within GPT models.
OpenAI's pace of innovation remains impressive, with more significant updates expected in the future, indicating that they have many more exciting developments in the pipeline.
The expanded context window from 4,000 tokens to 16,000 tokens, with plans for 32,000 tokens, opens up new possibilities and enhances the model's capabilities for handling large amounts of context. Integration with other models like CLIP and DALL·E further enhances functionality.