Inspired by the map makers of the 20th Century, Imperial researchers have demonstrated a new way to identify copyright holders’ work in LLMs.

The technique was presented at the International Conference on Machine Learning in Vienna this week, and is detailed in this preprint on the arXiv server.

Generative AI is taking the world by storm, already transforming the day-to-day lives of millions of people.

An analysis of how rhinoceros beetles deploy and retract their hindwings shows that the process is passive, requiring no muscular activity. The findings, reported in Nature, could help improve the design of flying micromachines.

Among all flying insects, beetles demonstrate the most complex wing mechanisms, involving two sets of wings: a pair of hardened forewings called elytra and a set of delicate membranous hindwings. Although extensive research exists on the origami-like folds of their wings, little is known about how they deploy and retract their hindwings.

Previous research theorizes that thoracic muscles drive a beetle’s hindwing base movement, but experimental evidence to support this theory is lacking.

Proving earlier rumors to be true, chip manufacturer Intel has announced its plans to fire over 15% of its total workforce, amounting to between 15,000 and 19,000 employees, in an attempt to save $10 billion in 2025.

In a note sent by Intel CEO Pat Gelsinger to the workers, the director sang a familiar song of the firings being very painful news and the hardest thing he’s ever done in his career, attributing the decision to leave thousands of people unemployed by declining profits as Intel is “yet to fully benefit from powerful trends, like AI”

To address the issues the company faces these days, the CEO aims to cut operational costs, simplify Intel’s portfolio, eliminate overlapping responsibilities, halt non-essential work, and reduce Intel’s capital expenditures for 2024 by over 20%, leaving the company’s overall IDM 2.0 strategy, which includes priorities such as “expanding manufacturing capacity in the US and EU” and “delivering AI everywhere”, unchanged.

Some models are available to users without Chinese phone numbers, while open-source platforms provide other workarounds.

An extremely cool application of large language models in combination with other AI tools such as models for text-to-speech and speech-to-text, image recognition and captioning, etc.

We created a robot tour guide using Spot integrated with Chat GPT and other AI models to explore the robotics applications of foundational models.

One of the significant challenges in AI research is the computational inefficiency in processing visual tokens in Vision Transformer (ViT) and Video Vision Transformer (ViViT) models. These models process all tokens with equal emphasis, overlooking the inherent redundancy in visual data, which results in high computational costs. Addressing this challenge is crucial for the deployment of AI models in real-world applications where computational resources are limited and real-time processing is essential.

Current methods like ViTs and Mixture of Experts (MoEs) models have been effective in processing large-scale visual data but come with significant limitations. ViTs treat all tokens equally, leading to unnecessary computations. MoEs improve scalability by conditionally activating parts of the network, thus maintaining inference-time costs. However, they introduce a larger parameter footprint and do not reduce computational costs without skipping tokens entirely. Additionally, these models often use experts with uniform computational capacities, limiting their ability to dynamically allocate resources based on token importance.

🚀 [FREE AI WEBINAR] ‘Learn how to fine-tune SAM 2 with your own data.’ (August 8, 2024 10 am PST) [Promoted].