ImageBlind

Meta engineering team released a paper with the model named ImageBlind, which can bind 6 modalities. It outperforms prior specialist models trained for a particular modality.

It joins data from:

• - Images
• - Text
• - Audio
• - Depth
• - Thermal
• - IMU data

It uses large-scale vision-language models and extends its zero-shot capabilities to new modalities just by using their natural pairing with images, such as video audio and image-depth data, to learn a single joint embedding space.

The paper shows that not all combinations of paired data are required to train a joint embedding, but only image-paired data is sufficient to bind the modalities together.

Ref: https://lnkd.in/gXNndk63

Harvard AI/ML free course

 

6 free online courses by Harvard University, in ML, AI, and Data Science.

◼️ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧

Link: https://lnkd.in/gygaeAcY

◼️ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

Link: https://lnkd.in/gUNVYgGB

◼️ 𝐇𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬

Link: https://lnkd.in/gv9RV9Zc

◼️ 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐑

Link: https://lnkd.in/gUY3jd8v

◼️ 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬

Link: https://lnkd.in/g8gQ6N-H

◼️ 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧

Link: https://lnkd.in/gAdyf6xR

From Introductory to Intermediate, great for beginners.

LangChain experimentation

Last month, LangChain experimentation was published at my company's blog at https://developer.trimblemaps.com/engineering-blog/experimenting-with-langchain-ai/

FACTOOL

This week, read an interesting research paper on "FACTOOL: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain."

FACTOOL is designed as a task and domain agnostic framework for detecting factual errors of text, which are generated by large language models like ChatGPT.

As part of this paper, the source code is released for FACTOOL associated with ChatGPT plugin interface at https://github.com/GAIR-NLP/factool

Happy AI coding!