OpenAI’s New Rival Jina AI Has Open-Source 8k Context 🤯
Plus: LLM hallucination problem will be over now, NVIDIA has announced new AI advancements.
Hello Engineering Leaders and AI Enthusiasts!
Welcome to the 133rd edition of The AI Edge newsletter. This edition brings you OpenAI’s New Rival, Jina AI Has an Open-Source 8k Context.
And a huge shoutout to our incredible readers. We appreciate you😊
In today’s edition:
😱 OpenAI’s new rival Jina AI has open-source 8k context
. 👏🔍LLM hallucination problem will be over with “Woodpecker”
🌟 NVIDIA Research has announced new AI advancements
📖 Knowledge Nugget: 3 ways to improve LLM Agent chains with debugging by
Let’s go!
OpenAI’s new rival Jina AI has open-source 8k context
Berlin-based AI company Jina AI has launched Jina-embeddings-v2, the world's first open-source 8K text embedding model. This model supports an impressive 8K context length, putting it on par with OpenAI's proprietary model. Jina-embeddings-v2 offers extended context potential, allowing for applications such as legal document analysis, medical research, literary analysis, financial forecasting, and conversational AI.
Benchmarking shows that it outperforms other leading base embedding models. The model is available in two sizes, a base model for heavy-duty tasks and a small model for lightweight applications. Jina AI plans to publish an academic paper, develop an embedding API platform, and expand into multilingual embeddings.
Why does this matter?
Jina AI introduces the world's first open-source 8K text embedding model. Though the context length is impressive, it will be more useful in legal document analysis, medical research, literary analysis, financial forecasting, and more.
This model's capabilities and open-source 8k context nature are increasing bars for competitors like OpenAI.
LLM hallucination problem will be over with “Woodpecker”
Researchers from the University of Science and Technology of China and Tencent YouTu Lab have developed a framework called "Woodpecker" to correct hallucinations in multimodal large language models (MLLMs).
Woodpecker uses a training-free method to identify and correct hallucinations in the generated text. The framework goes through five stages, including key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction.
The researchers have released the source code and an interactive demo of Woodpecker for further exploration and application. The framework has shown promising results in boosting accuracy and addressing the problem of hallucinations in AI-generated text.
Why does this matter?
As MLLMs continue to evolve and improve, the importance of such frameworks in ensuring their accuracy and reliability cannot be overstated. And its open-source availability promotes collaboration and development within the AI research community.
NVIDIA Research has announced new AI advancements
NVIDIA Research has announced new AI advancements that will be presented at the NeurIPS conference. The projects include new techniques for transforming text-to-images, photos to 3D avatars, and specialized robots into multi-talented machines.
The research focuses on generative AI models, reinforcement learning, robotics, and applications in the natural sciences. Highlights include improving text-to-image diffusion models, advancements in AI avatars, breakthroughs in reinforcement learning and robotics, and AI-accelerated physics, climate, and healthcare research. These advancements aim to accelerate the development of virtual worlds, simulations, and autonomous machines.
Why does this matter?
NVIDIA’s new AI innovations open doors to creative content generation, more immersive digital experiences, and adaptable automation. Additionally, their focus on generative AI, reinforcement learning, and natural sciences applications promises smarter AI with potential breakthroughs in scientific research.
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Knowledge Nugget: 3 ways to improve LLM Agent chains with debugging
This interesting article by
discusses three ways to improve Large Language Model Agent chains with debugging. The author shares their experiences with using LLM applications and highlights some limitations they encountered.The first surprise was the cost associated with using intermediate LLM calls in chains.
The second surprise involves using the Zapier Toolkit and the challenges of matching API parameters.
The third surprise was the agent hallucinating the call to a tool instead of actually using it.
The author concludes by mentioning the development of an open-source library called log10 to help debug LLM chains.
Why does this matter?
This article provides insights into practical issues when working with LLM chains, including cost considerations, integration challenges, and limitations, and offers a valuable solution in the form of an open-source debugging library.
What Else Is Happening❗
🔍 Google announces new AI tools to help users fact-check images and more
Also, prevent the spread of false information. The tools include viewing an image's history, metadata, and the context in which it was used on different sites. Users can also see when the image was first seen by Google Search to understand its recency. (Link)
📝 Grammarly announces new feature, "Personalized voice detection & application"
Uses gen AI to detect a person's unique writing style and create a "voice profile" that can rewrite any text in that style. The feature will be available to subscribers of Grammarly's business tier by the end of the year. (Link)
📱 AI features boost Motorola's new foldable phone
They've developed an AI model that runs locally on the device, allowing users to 'bring their personal style to their phone.' Users can upload or take photos to get an AI-generated theme to match their style. (Link)
🚀 Cisco rolls out new AI tools at the Webex One customer conference
These tools include a real-time media model (RMM) that uses generative AI for audio and video, an AI-powered audio codec that is up to 16 times more efficient in bandwidth usage, and the Webex AI Assistant, which pulls together all the AI tooling for users. (Link)
🖼️ Amazon reveals AI image generation to help advertisers create more engaging ads
By providing tools that reduce friction and effort for advertisers, Amazon aims to deliver a better advertising experience for customers. (Link)
That's all for now!
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