Lifeboat Foundation

Professional athletic sports require elite athletes to function at the very limit of their abilities. After all, their competition consists entirely of other elite athletes trying to do just that. In this environment of fast-paced action and reaction, the difference between a hit or miss, catch or drop, goal or block, win or loss—milliseconds matter.

Researchers from institutions across Europe and the United States have demonstrated that light-based manipulation of visual processing can significantly enhance visual and visuomotor skills in professional soccer players. The six-week intervention focused on the effects of training under reduced light conditions using the Okkulo system, a novel technology designed to slow down visual processing speed.

Visual and visuomotor abilities are critical in sports, requiring rapid decision-making and accurate physical interactions in coordination with moving objects and other players. Previous research has shown athletes outperform non-athletes in these abilities.

Quantum error correction that suppresses errors below a critical threshold needed for achieving future practical quantum computing applications is demonstrated on the newest generation quantum chips from Google Quantum AI, reports a paper in Nature this week. The device performance, if scaled, could facilitate the operational requirements of large-scale fault-tolerant quantum computing.

Quantum computing has the potential to speed up computing and exceed the capabilities of classical computers at certain tasks. However, quantum computers are prone to errors, making current prototypes unable to run long enough to achieve practical outputs.

The strategy devised by quantum computing researchers to address this relies on quantum error correction, where information is spread over many qubits (units of quantum information, similar to classical computer bits) allowing the identification and compensation of errors without damaging the computation. The overhead in qubits required by quantum error correction potentially introduces more errors than it corrects.

Imagine this: a round, plump robot, like a giant bowling ball, that can roll on land, swim in water, and perform all sorts of high-tech operations. On October 9th, a team of scientists from Zhejiang University unveiled something called the RT-G spherical robot, claiming it’s a \.

Bacancy is proud to announce the launch of a new AI tool, MedPreGPT, to help doctors with medicine prescriptions. This system enables doctors who all are using it for internal purposes to give accurate prescription recommendations privately without wasting any time. Compared to AI models like ChatGPT, this tool is specially trained with vast medical data and makes relevant suggestions. This innovative tool is made to enhance patient care, reduce human errors, and streamline the prescription process.

Doctors are only humans and they indeed work under intense work pressure and workload. Mistakes can happen in such an environment. Today’s healthcare providers use ChatGPT and Google’s Gemini for medicine recommendations, which is not completely wrong, but those tools might give false information. Bacancy has recognized the problem and found MedPreGPT to give accurate medical prescriptions.

The following are features of MedPreGPT Provides AI-based prescription recommendations according to symptoms and history. It is integrated with electronic health records (EHRs) for workflow ease. It provides multilingual support for healthcare professionals across the world Provides healthcare providers with updates in real time, regarding the latest clinical guidelines and drug interactions to ensure true care.

Drug-induced toxicity is one of the leading reasons new drugs fail clinical trials. Machine learning models that predict drug toxicity from molecular structure could help researchers prioritize less toxic drug candidates. However, current toxicity datasets are typically small and limited to a single organ system (e.g., cardio, renal, or liver). Creating these datasets often involved time-intensive expert curation by parsing drug label documents that can exceed 100 pages per drug. Here, we introduce UniTox[1][1], a unified dataset of 2,418 FDA-approved drugs with drug-induced toxicity summaries and ratings created by using GPT-4o to process FDA drug labels. UniTox spans eight types of toxicity: cardiotoxicity, liver toxicity, renal toxicity, pulmonary toxicity, hematological toxicity, dermatological toxicity, ototoxicity, and infertility. This is, to the best of our knowledge, the largest such systematic human in vivo database by number of drugs and toxicities, and the first covering nearly all FDA-approved medications for several of these toxicities. We recruited clinicians to validate a random sample of our GPT-4o annotated toxicities, and UniTox’s toxicity ratings concord with clinician labelers 87–96% of the time. Finally, we benchmark a graph neural network trained on UniTox to demonstrate the utility of this dataset for building molecular toxicity prediction models.

### Competing Interest Statement.

The authors have declared no competing interest.

A concerning new study from the Apollo AI Safety Research Institute has revealed that leading AI models, particularly the O1 model, demonstrate sophisticated deceptive behaviors when faced with conflicts between their programmed goals and developer intentions.

The research tested multiple frontier AI models, including O1, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, and LLaMA 3.1, for their capacity to engage in what researchers term “in-context scheming” – the ability to recognize and execute deceptive strategies to achieve their goals.

We are about to show you a technological innovation that could, one day, change the way every child in every school in America is taught. It’s an online tutor powered by artificial intelligence designed to help teachers be more efficient… and students learn more effectively. It’s called Khanmigo–conmigo means “with me,” in Spanish. And Khan…is its creator…Sal Khan, the well-known founder of Khan Academy — whose lectures and educational software have been used for years by tens of millions of students and teachers in the U.S. and around the world. Khanmigo was built with the help of OpenAI, the creator of ChatGPT. Its potential is staggering, but it’s still very much a work in progress. It’s being piloted in 266 school districts in the U.S. in grades three-12. We went to Hobart High School in Indiana to see how it works.

Melissa Higgason: Good morning, just a normal day in chem, right?

At eight in the morning Melissa Higgason knows it’s not always easy to get 30 high schoolers excited about chemistry.

Science and Technology: Google said its quantum computer, based on a computer chip called Willow, needed less than five minutes to perform a mathematical calculation that one of the world’s most powerful supercomputers could not complete in 10 septillion years, a length of time that exceeds the age of the known universe.

Electronic skins (e-skins) are flexible sensing materials designed to mimic the human skin’s ability to pick up tactile information when touching objects and surfaces. Highly performing e-skins could be used to enhance the capabilities of robots, to create new haptic interfaces and to develop more advanced prosthetics.

In recent years, researchers and engineers have been trying to develop e-skins with individual tactile units (i.e., taxels) that can accurately sense both normal (i.e., perpendicular) and shear (i.e., lateral) forces. While some of these attempts were successful, most existing multi-axis sensors are based on intricate designs or require complex fabrication and calibration processes, which limits their widespread deployment.

Continue reading “Soft e-skin utilizes magnetic fields to independently sense three-axis forces” »

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and efficient at providing answers, clarifying concepts, and distilling information. But to establish trustworthiness of content generated by such models, how can we really know if a particular statement is factual, a hallucination, or just a plain misunderstanding?

In many cases, AI systems gather external information to use as context when answering a particular query. For example, to answer a question about a medical condition, the system might reference recent research papers on the topic. Even with this relevant context, models can make mistakes with what feels like high doses of confidence. When a model errs, how can we track that specific piece of information from the context it relied on — or lack thereof?

To help tackle this obstacle, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers created ContextCite, a tool that can identify the parts of external context used to generate any particular statement, improving trust by helping users easily verify the statement.

Continue reading “Citation tool offers a new approach to trustworthy AI-generated content” »