A recent partnership between BrainChip Holdings and NaNose Medical has yielded a new COVID-19 testing solution boasting higher accuracy than RT-PCR screening. The Nano Artificial Nose is said to actively analyze patient breath samples while remaining highly portable. How exactly does it work? Officially dubbed the DiaNose, NaNose's technology has actually existed since 2017. Based on the Technion Israeli Institute of Technology's artificial nose, the device has since screened numerous patients for Parkinson's disease, cancer, kidney failure, and MS.
The intestines and their bacteria are sometimes called our'second brain', but studying these bacteria in their natural environment is difficult. Now researchers from the University of Copenhagen have developed a method that uses artificial intelligence to map intestinal bacteria using faeces. The researchers thus hope to gain more knowledge of the role played by these bacteria in various diseases. Both past and present-day scientists have suspected the intestines of playing a role in various diseases. Present-day studies focus on the intestinal flora's role in physical diseases such as diabetes and overweight, while others seek to establish a connection between the intestinal flora and e.g.
What if you could interact with a therapist, learn new skills to improve your well-being and gain access to information that would normally be learned in therapy for a fraction of the cost, or for free, without leaving your home? The prospect is certainly alluring, and with Artificial Intelligence (AI) technology, there is reason to take this prospect seriously. AI technology for mental health was first developed in the 1960s with ELIZA, a simple computer program. AI has since been developed for use in other areas, including assisting therapists with diagnosing depression and PTSD in the US armed forces. Recently, the integration of AI with smartphone technology had opened up opportunities to provide mental health support that is not possible with traditional in-person therapy.
Before she was a published author, Divya was an engineer with a background in computational neuroscience and data science, as well as computing hardware and software. She talked to Fast Company about how her work has shaped her writing, the not-quite-dystopian world she envisions in Machinehood, and why she's still optimistic about the future. The interview has been edited for length and clarity. How did your tech career inform your writing? I actually started college intending to go into astrophysics, and after a couple of years I got sideswiped by a really interesting new department at Caltech at the time, which was computational neuroscience.
Elon Musk is renowned for his innovative mind and unceasing desire to improve multiple facets of life such as transportations, space exploration, cities, and now, the human brain. The famed CEO and inventor announced the Neuralink brain microchip that could give humans equal footing with AI technology. Elon Musk is well known for his multiple ventures. Recently the tech genius announced plans to construct a Starbase--a new town in Southern Texas that will function as a miniature Cape Canaveral. Garry Kitchen, a pioneer gamer and engineer, tells The Post.
Skin cancer is one of the most deadly cancers worldwide. Yet, it can be reduced by early detection. Recent deep-learning methods have shown a dermatologist-level performance in skin cancer classification. Yet, this success demands a large amount of centralized data, which is oftentimes not available. Federated learning has been recently introduced to train machine learning models in a privacy-preserved distributed fashion demanding annotated data at the clients, which is usually expensive and not available, especially in the medical field.
Scientists from NVIDIA and Harvard have made a huge breakthrough in genetic research. They developed a deep-learning toolkit that is able to significantly cut down the time and cost needed to run rare and single-cell experiments. According to a study published in Nature Communications, the AtacWorks toolkit can run inference on a whole genome, a process that normally takes a little over two days, in just half an hour. It's able to do so thanks to NVIDIA's Tensor Core GPUs. AtacWorks works with ATAC-seq, a well-established method designed to find open areas in the genome of healthy and diseased cells. These "open areas" are subsections of a person's DNA that are used to determine and activate specific functions (think liver, blood or skin cells).
A new machine learning approach to COVID-19 testing has produced encouraging results in Greece. The technology, named Eva, dynamically used recent testing results collected at the Greek border to detect and limit the importation of asymptomatic COVID-19 cases among arriving international passengers between August and November 2020, which helped contain the number of cases and deaths in the country. The findings of the project are explained in a paper titled "Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border," authored by Hamsa Bastani, a Wharton professor of operations, information and decisions and affiliated faculty at Analytics at Wharton; Kimon Drakopoulos and Vishal Gupta from the University of Southern California; Jon Vlachogiannis from investment advisory firm Agent Risk; Christos Hadjicristodoulou from the University of Thessaly; and Pagona Lagiou, Gkikas Magiorkinis, Dimitrios Paraskevis and Sotirios Tsiodras from the University of Athens. The analysis showed that Eva on average identified 1.85 times more asymptomatic, infected travelers than what conventional, random surveillance testing would have achieved. During the peak travel season of August and September, the detection of infection rates was up to two to four times higher than random testing.
We've discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP's accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Fifteen years ago, Quiroga et al. discovered that the human brain possesses multimodal neurons. These neurons respond to clusters of abstract concepts centered around a common high-level theme, rather than any specific visual feature. The most famous of these was the "Halle Berry" neuron, a neuron featured in both Scientific American and The New York Times, that responds to photographs, sketches, and the text "Halle Berry" (but not other names).
This article was originally published February 23, 2021 on PSQH by Matt Phillion. An aging population, a shortage of clinicians, and an abundance of data--treating patients grows more and more complicated all the time. Leveraging available and emerging technology to maximize efficiency, however, offers a chance to improve care in innovative ways. "The population is aging, and more and more people are suffering from cardiac issues. Expertise is expensive, and there is limited access to those experts," says Jia Li, co-founder of Cardiologs, a medical technology company developing medical-grade artificial intelligence (AI) and cloud technology to improve cardiac diagnoses.