Lifeboat Foundation

It took Nicolas Neubert seven hours to make it his living room.

It sounds far-fetched, but researchers are trying to recreate subjective experience in AIs, even if disagreement over what consciousness is will make it difficult to test.

By Thomas Lewton

Beware of CherryBlos: the new Android malware that’s out to steal your cryptocurrency!

Researchers from the Tokyo University of Science recently published a study in the journal Artificial Life and Robotics where they explored how machine learning can help detect deception.

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computers to learn and improve from experience without being explicitly programmed. In other words, it is a method of teaching computers to perform specific tasks by learning from data, patterns, and examples, rather than relying on pre-defined rules.

Detecting deception can be important in various situations, like questioning crime victims or suspects and interviewing patients with mental health issues. Sometimes, human interviewers might struggle to ask the right questions or spot deception accurately.

Just a few years ago, Berkeley engineers showed us how they could easily turn images into a 3D navigable scene using a technology called Neural Radiance Fields, or NeRF. Now, another team of Berkeley researchers has created a development framework to help speed up NeRF projects and make this technology more accessible to others.

Led by Angjoo Kanazawa, assistant professor of electrical engineering and computer sciences, the researchers have developed Nerfstudio, a Python framework that provides plug-and-play components for implementing NeRF-based methods, making it easier to collaborate and incorporate NeRF into projects. Kanazawa and her team will present their paper on Nerfstudio at SIGGRAPH 2023, and have published it as part of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings.

Continue reading “Open-source platform makes it easier to create 3D scenes from images” »

A routine osteoporosis screening bone density test can also detect increased risk for a heart attack because of the presence of calcium in the aorta. But reading these images requires expertise and can be time-consuming.

Now, research from a multi-institution collaboration, including Harvard Medical School and Hebrew SeniorLife, reports that this calcification test score can be calculated quickly by using machine learning, without the need for a person to grade the scans.

Dashing the hopes of liberal arts professors and teachers, OpenAI decommissions its AI content detector due to poor accuracy.

Those efforts and the interest in ChatGPT have led Microsoft to seek more GPUs than it had expected.

“I am thrilled that Microsoft announced Azure is opening private previews to their H100 AI supercomputer,” Jensen Huang, Nvidia’s CEO, said at his company’s GTC developer conference in March.

Microsoft has begun looking outside its own data centers to secure enough capacity, signing an agreement with Nvidia-backed CoreWeave, which rents out GPUs to third-party developers as a cloud service.

While AI can speed up the writing process, it doesn’t optimize quality — and it endangers our sense of connection to ourselves and others.