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.
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.
Advances in artificial intelligence (AI) deep learning, genomics, and computing hardware is accelerating life sciences research and discovery. In a new study published today in Nature Communications, researchers from NVIDIA Corporation (NASDAQ: NVDA) and Harvard University's Department of Stem Cell and Regenerative Biology create an AI deep learning tool called AtacWorks that denoises genomic sequencing data and find areas with accessible DNA that may help speed up new diagnostics, de novo drugs, and treatments for diseases in the future. Early intervention and treatment of cancer and genetic diseases may make the difference in outcomes and requires early intervention. The challenge is that sample size of cell data may be small and the data itself may contain extraneous "noise." Having a way to filter and reduce the non-relevant data, or noise, and to boost the relevant data, or signal, in those cases can help speed up research.
To celebrate International Women's Day, we take a look back over the past year of AIhub content and highlight some of our favourite articles, interviews, podcasts and videos, by, or featuring, women in the field. Falaah Arif Khan is an engineer/scientist by training and an artist by nature. She is currently Artist-in-Residence at the Center for Responsible AI at New York University. When we interviewed Falaah in 2020 she had just completed her first comic book, Meet AI. She has since teamed up with other AI researchers on other exciting projects.
Researchers have developed a method based on Artificial Intelligence (AI) that rapidly identifies currently available medications that may treat Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action. "Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment -- but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," said researcher Artem Sokolov from Harvard Medical School. "We therefore built a framework for prioritising drugs, helping clinical studies to focus on the most promising ones," Sokolov added.
When Amazon envisioned Alexa, an AI-powered, voice-activated customer recommendation system, it was a feat that required machine learning and massive amounts of data to provide answers to conversational queries quickly, even in a noisy environment. Now, the same data analysis capabilities that enable Amazon to become hyper-familiar with consumer purchasing patterns could hold the key to reducing waste in healthcare. Think about the similarities between healthcare and retail. Both industries revolve around the consumer, and they use data to gain context into behavior and draw meaningful conclusions. In healthcare, this includes the ability to predict which consumers could develop type 2 diabetes with 95% accuracy or to pinpoint where and when the Covid-19 virus will spread and how to protect those most vulnerable.
The researchers examined the subjects' word usage with an artificial intelligence program that looked for subtle differences in language. It identified one group of subjects who were more repetitive in their word usage at that earlier time when all of them were cognitively normal. These subjects also made errors, such as spelling words wrongly or inappropriately capitalizing them, and they used telegraphic language, meaning language that has a simple grammatical structure and is missing subjects and words like "the," "is" and "are." The members of that group turned out to be the people who developed Alzheimer's disease. The A.I. program predicted, with 75 percent accuracy, who would get Alzheimer's disease, according to results published recently in The Lancet journal EClinicalMedicine.