Lifeboat Foundation

Jetson Founder Tomasz Patan is clearly getting very comfortable with the Jetson One eVTOL’s flight control system … Watch him wrench the controls around to show off how sharply – and safely – this thing can handle tight turns in flight.

Multicopter drones were revolutionary little gadgets when they started to appear on the scene for a number of reasons, but one was their highly automated fly-by-wire control systems. No human could manually control motor speeds on upwards of four rotors simultaneously, but a sensor-equipped flight control system certainly could – hence, drones like the DJI Phantom were able to automatically lift off and land, maintain altitude if required, and self-balance against wind gusts to hover in place, while also responding quickly to a pilot’s commands.

Continue reading “Watch: Jetson founder pushes the limits of ‘Freestyle’ eVTOL agility” »

Predicting it could drive the carmaker towards a $25 trillion valuation.

Experts believe this ambitious claim is within the realm of possibility. Optimus is still in development but will enter production next year, with Musk stating that Tesla could have “a few thousand” units working in its factories, reaffirming his earlier timeline.

Airbus U.S. Space & Defense announced on October 15, 2024, the successful completion of the first demonstration of the Lakota UH-72 drone helicopter for the U.S. Marine Corps, conducted at Marine Corps Air Station New River and Camp Lejeune. This demonstration showcased the capabilities of the Aerial Logistics Connector (ALC) system, designed to enhance logistical support in dispersed and challenging environments. As an autonomous platform, the Lakota UH-72 ensures a continuous supply flow without relying on traditional transportation methods, which are often vulnerable or limited.

It is morally wrong to use AI detectors when they produce false positives that smear students in ways that hurt them and where they can never prove their innocence.

While some educators…

About two-thirds of teachers report regularly using tools for detecting AI-generated content. At that scale, even tiny error rates can add up quickly.

Continue reading “AI Detectors Falsely Accuse Students of Cheating—With Big Consequences” »

It’s easy to marvel at the technical wizardry behind breakthroughs such as AlphaFold.

Pioneering crystallographer Helen Berman helped to set up the massive collection of protein structures that underpins the Nobel-prize-winning tool’s success.

Boston Dynamics and Toyota Research Institute (TRI) Wednesday revealed plans to bring AI-based robotic intelligence to the electric Atlas humanoid robot. The collaboration will leverage the work that TRI has done around large behavior models (LBMs), which operate along similar lines as the more familiar large language models (LLMs) behind platforms like ChatGPT.

Last September, TechCrunch paid a visit to TRI’s Bay Area campus for a closer look at the institute’s work on robot learning. In research revealed at last year’s Disrupt conference, institute head Gill Pratt explained how the lab has been able to get robots to 90% accuracy when performing household tasks like flipping pancakes through overnight training.

“In machine learning, up until quite recently there was a tradeoff, where it works, but you need millions of training cases,” Pratt explained at the time. “When you’re doing physical things, you don’t have time for that many, and the machine will break down before you get to 10,000. Now it seems that we need dozens. The reason for the dozens is that we need to have some diversity in the training cases. But in some cases, it’s less.”

The head of Singapore’s biggest lender said only about half of the banking industry has made sufficient progress in transforming their businesses to embrace digitalization and artificial intelligence.

Women worldwide could see better treatment with new AI technology, which enables better detection of damaged cells and more precisely predicts the risk of getting breast cancer, shows new research from the University of Copenhagen.

Breast cancer is one of the most common types of cancer. In 2022, the disease caused 670,000 deaths worldwide. Now, a new study from the University of Copenhagen shows that AI can help women with improved treatment by scanning for irregular-looking cells to give better risk assessment.

The study, published in The Lancet Digital Health, found that the AI technology was far better at predicting the risk of cancer than current clinical benchmarks for breast cancer risk assessment.

Multimodal AI models are powerful tools capable of both understanding and generating visual content. However, existing approaches often use a single visual encoder for both tasks, which leads to suboptimal performance due to the fundamentally different requirements of understanding and generation. Understanding requires high-level semantic abstraction, while generation focuses on local details and global consistency. This mismatch results in conflicts that limit the overall efficiency and accuracy of the model.

Researchers from DeepSeek-AI, the University of Hong Kong, and Peking University propose Janus, a novel autoregressive framework that unifies multimodal understanding and generation by employing two distinct visual encoding pathways. Unlike prior models that use a single encoder, Janus introduces a specialized pathway for each task, both of which are processed through a unified transformer. This unique design alleviates conflicts inherent in prior models and provides enhanced flexibility, enabling different encoding methods that best suit each modality. The name “Janus” aptly represents this duality, much like the Roman god, with two faces representing transitions and coexistence.

The architecture of Janus consists of two main components: an Understanding Encoder and a Generation Encoder, each tasked with handling multimodal inputs differently. For multimodal understanding, Janus uses a high-dimensional semantic feature extraction approach through SigLIP, transforming the features into a sequence compatible with the language model. For visual generation, Janus utilizes a VQ tokenizer that converts visual data into discrete representations, enabling detailed image synthesis. Both tasks are processed by a shared transformer, enabling the model to operate in an autoregressive fashion. This approach allows the model to decouple the requirements of each visual task, simplifying implementation and improving scalability.