The researcher asked the bot to write a 500 word academic thesis and submitted it to an academic journal. The paper is now being peer reviewed.
Andrew Camarata built his dream house by stacking shipping containers on each other for months, and now he has put the final touches by painting the new metalwork on the container castle.
For the function of many biomolecules, their three-dimensional structure is crucial. Researchers are therefore not only interested in the sequence of the individual building blocks of biomolecules, but also in their spatial structure. With the help of artificial intelligence (AI), bioinformaticians can already reliably predict the three-dimensional structure of a protein from its amino acid sequence. For RNA molecules, however, this technology is still in its infancy. Researchers at Ruhr-Universität Bochum (RUB) describe a way to use AI to reliably predict the structure of certain RNA molecules from their nucleotide sequence in the journal PLOS Computational Biology on July 7, 2022.
For the work, the teams led by Vivian Brandenburg and Professor Franz Narberhaus from the RUB Chair of Biology of Microorganisms cooperated with Professor Axel Mosig from the Bioinformatics Competence Area of the Bochum Center for Protein Diagnostics.
Deepmind enters into a partnership with the renowned British research institute “The Crick”. Together, the organizations aim to advance the use of artificial intelligence in biology and biomedicine.
Artificial intelligence is already having a direct impact on our everyday lives, for example in autonomous driving, through generative AI systems such as DALL-E 2 and Alphacode or hand tracking for VR headsets.
Beyond these direct application scenarios, AI can be a tool that accelerates science – indirectly impacting our future, but possibly on a much larger scale.
Google Engineer Blake Lemoine joins Emily Chang to talk about some of the experiments he conducted that lead him to think that LaMDA was a sentient AI, and to explain why he is now on administrative leave.
The basic design of the toothbrush hasn’t changed in a thousand years — sure, there are motors, different materials, and funky shapes, but they’re all still sticks with bristles attached. A team from the University of Pennsylvania believes it’s time to shake things up. In a new study, the researchers have shown that shapeshifting nanoparticles can successfully clean teeth, replacing all the manual labor with a nano-scale robotic dance. Not only can these particles be transformed into tooth-cleaning shapes, but their action can have antimicrobial effects that destroy plaque-causing bacteria.
This project came together quite by accident. A group from the Penn School of Dental Medicine under professor Hyun (Michel) Koo was interested in leveraging the catalytic activity of nanoparticles to release free radicals that could kill microbes on the teeth. Meanwhile, senior engineering researcher Edward Steagar was spearheading work at the Penn School of Engineering and Applied Sciences on assembling nanoparticles into robots. Bringing these projects together gave us the sci-fi gray goo toothbrush.
The combined team used magnetic fields to manipulate iron oxide nanoparticles, testing them first on a slab of tooth-like material. Next, the team moved to 3D-printed copies of teeth. Finally, they tested the nanoparticle brushes on real teeth that were mounted in a realistic way to simulate a human mouth. The tests show these nanoparticles can form brush-like shapes capable of scrubbing off the biofilms that lead to tooth decay. They can also flow between teeth like floss. All the while, the nanoparticles promote the production of free radicals that further eliminate bacteria.
Circa 2021
Finding and fixing bugs in code is a time-consuming, and often frustrating, part of everyday work for software developers. Can deep learning address this problem and help developers deliver better software, faster? In a new paper, Self-Supervised Bug Detection and Repair, presented at the 2021 Conference on Neural Information Processing Systems (NeurIPS 2021), we show a promising deep learning model, which we call BugLab can be taught to detect and fix bugs, without using labelled data, through a “hide and seek” game.
To find and fix bugs in code requires not only reasoning over the code’s structure but also understanding ambiguous natural language hints that software developers leave in code comments, variable names, and more. For example, the code snippet below fixes a bug in an open-source project in GitHub.
Here the developer’s intent is clear through the natural language comment as well as the high-level structure of the code. However, a bug slipped through, and the wrong comparison operator was used. Our deep learning model was able to correctly identify this bug and alert the developer.
The right temperature ensures the success of technical processes, the quality of food and medicines, or affects the lifetime of electronic components and batteries. Temperature indicators enable to detect (un)desired temperature exposures and irreversibly record them by changing their signal for a readout at any later time.
Of particular interest are small-sized temperature indicators that can be easily integrated into any arbitrary object and subsequently monitor the objects’ temperature history autonomously, i.e. without power supply. Accordingly, the indicators’ signal readout permits to verify successful bonding processes, to uncover temperature peaks in global supply chains, or to localize hot spots in electronic devices.
Prof. Dr. Karl Mandel (Professorship for Inorganic Chemistry) and his research group have succeeded in developing a new type of temperature indicator in the form of a micrometer-sized particle, which differs from previously established, mostly optical indicators mainly due to its innovative magnetic readout method. The results of the research work have now been published in the journal Advanced Materials (“Recording Temperature with Magnetic Supraparticles”).