Archive 16.11.2023

Cambridge engineers investigating the load-bearing capacity of conical shells, made from soft materials, have discovered performance-limiting weaknesses that could have implications for soft robotics—affecting the ability of morphing cones to perform fundamental mechanical tasks.

To enable the industry to keep pace with trends through quick and cost-efficient development, Markus Fenn and Prof. Dr. Stefan May developed Eduard, a teaching and prototyping platform that can be used to map current trends.

Announcing our most advanced music generation model and two new AI experiments, designed to open a new playground for creativity

Announcing our most advanced music generation model and two new AI experiments, designed to open a new playground for creativity

3D printing is advancing rapidly, and the range of materials that can be used has expanded considerably. While the technology was previously limited to fast-curing plastics, it has now been made suitable for slow-curing plastics as well. These have decisive advantages as they have enhanced elastic properties and are more durable and robust.

An artificial sensory system that is able to recognize fine textures—such as twill, corduroy and wool—with a high resolution, similar to a human finger, is reported in a Nature Communications paper. The findings may help improve the subtle tactile sensation abilities of robots and human limb prosthetics and could be applied to virtual reality in the future, the authors suggest.

Amazon is deploying a new robot to take on the extra-heavy lifting in its fulfillment centers. The new technology called Titan is a mobile robot, which means it will help carry products across our fulfillment centers, supporting safety and efficiency in our operations.

Experience AI's course and resources are expanding on a global scale

Experience AI's course and resources are expanding on a global scale

Researchers at Carnegie Mellon University and Google DeepMind recently developed RoboTool, a system that can broaden the capabilities of robots, allowing them to use tools in more creative ways. This system, introduced in a paper published on the arXiv preprint server, could soon bring a new wave of innovation and creativity to the field of robotics.

We introduce GraphCast, a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracy

We introduce GraphCast, a state-of-the-art AI model able to make medium-range weather forecasts with unprecedented accuracy

Princeton researchers have developed a flexible, lightweight and energy efficient soft robot that moves without the use of any legs or rotary parts. Instead, the device uses actuators that convert electrical energy into vibrations that allow it to wiggle from point to point using only a single watt.

One of the key elements to consider when selecting LiDAR technology is whether you need safe LiDAR or LiDAR. But what makes LiDAR safe? And when do you need a safety-rated device? Well, look no further, we’ve got the answers for you right here!

The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text.

Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual errors, so wary readers may want to know if generative AI tools have been used to ghostwrite news articles or other sources before trusting them.

What can teachers and consumers do? Existing tools to detect AI-generated text sometimes do poorly on data that differs from what they were trained on. In addition, if these models falsely classify real human writing as AI-generated, they can jeopardize students whose genuine work is called into question.

Our recent paper introduces Ghostbuster, a state-of-the-art method for detecting AI-generated text. Ghostbuster works by finding the probability of generating each token in a document under several weaker language models, then combining functions based on these probabilities as input to a final classifier. Ghostbuster doesn’t need to know what model was used to generate a document, nor the probability of generating the document under that specific model. This property makes Ghostbuster particularly useful for detecting text potentially generated by an unknown model or a black-box model, such as the popular commercial models ChatGPT and Claude, for which probabilities aren’t available. We’re particularly interested in ensuring that Ghostbuster generalizes well, so we evaluated across a range of ways that text could be generated, including different domains (using newly collected datasets of essays, news, and stories), language models, or prompts.

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