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health & medicine

Defying the odds!


The phrase "overcoming the odds" is an understatement for 24-year-old Joshua Burgess. Though born with congenital rubella syndrome, which has caused him to suffer from a number of health challenges over the years, he continues to break barriers. On September 28, Burgess participated in the prestigious UNESCO Information for All Programme's (IFAP) Second Artificial Intelligence for Information Accessibility (AI4IA) Conference, where he spoke about'Openness and Inclusivity for the Disabled Community in a New Era'. "My presentation reflected my views as a young, blind Jamaican also living with chronic hearing loss. It was important for me to note that, while I have benefited from artificial intelligence's (AI) ability to help me integrate into society, it is also important for us to recognise that it is not a one-size-fits-all. We must collaborate with key stakeholders to ensure openness, inclusivity, fairness, and accessibility for everyone," said Burgess.

Genetic algorithms: Biologically inspired, fast-converging optimization


As you can see, beyond the details and the actual exact probability, the chances of any individual (but the first) are decreasing exponentially with k (while polynomially with m). It goes without saying that we need to apply tournament selection twice to get the pair of parents we need to generate a single element in the new population. Roulette wheel selection is definitely more complicated to implement than tournament selection, but the high-level idea is the same: higher-fitness individuals must have more chances to be selected. As we have seen, in tournament selection the probability that an element with low fitness is chosen decreases polynomially with the rank of the element (its position in the list of organisms sorted by fitness); in particular, since the probability will be O([(n-m)/n]k) the decrease will be super-linear, because k is certainly greater than 1. If, instead, we would like for lower-fitness elements to get a real chance of being selected, we could resort to a fairer selection method.

COVID-19: Implications for business


The Delta variant of the coronavirus spread to more countries in recent weeks, and the total number of cases officially logged soared past half a million per day. The global number of deaths is now about two-thirds as high as it was at the peak of the previous wave, in April of this year. As the virus spreads, the potential rises for a vaccine-resistant strain to emerge. Meanwhile, in poorer countries, vaccines are scarce, and most populations are little protected (exhibit).

Artificial intelligence in healthcare? 'Don't focus solely on technology' - Innovation Origins


Tech expert Jarno Duursma sees both advantages and disadvantages when it comes to using AI in healthcare. First the advantages: Scientists at Life Lines, a large-scale study into the onset of chronic diseases among 165 thousand people in the northern Netherlands, make use of artificially intelligent software. Duursma: "This research has been going on since 2006. A huge database is being compiled from all those studies and questionnaires. With the help of AI, doctors are able to identify connections that they would otherwise never have spotted, like improving the diagnosis of depression or the prediction of cancer."

Deep Learning Enhances Cancer Diagnostic Tools


Yi "Edwin" Sun, a Ph.D. candidate in electrical and computer engineering at the University of Illinois Urbana-Champaign and member of the Beckman Institute's Biophotonics Imaging Laboratory headed by Stephen Boppart, explored how deep learning methods can make polarization-sensitive optical coherence tomography, or PS-OCT, more cost-effective and better equipped to diagnose cancer in biological tissues. The paper, titled "Synthetic polarization-sensitive optical coherence tomography by deep learning," was published in npj Digital Medicine. OCT systems are common clinically and are used to generate high-resolution cross-sectional images of regions in the human body. Sun and his team developed a groundbreaking method of applying software to the OCT tool to provide polarization-sensitive capabilities -- without the cost and complexity that accompany hardware-based PS-OCT imaging systems. "We're trying to replace the hardware associated with PS-OCT," Sun said.

AI and simulation tools to fight COVID-19


In its on-going campaign to reveal the inner workings of the SARS-CoV-2 virus, the U.S. Department of Energy's (DOE) Argonne National Laboratory is leading efforts to couple artificial intelligence (AI) and cutting-edge simulation workflows to better understand biological observations and accelerate drug discovery. Argonne collaborated with academic and commercial research partners to achieve near real-time feedback between simulation and AI approaches to understand how two proteins in the SARS-CoV-2 viral genome, nsp10 and nsp16, interact to help the virus replicate and elude the host's immune system. The team achieved this milestone by coupling two distinct hardware platforms: Cerebras CS-1, a processor-packed silicon wafer deep learning accelerator; and ThetaGPU, an AI- and simulation-enabled extension of the Theta supercomputer, housed at the Argonne Leadership Computing Facility, a DOE Office of Science User Facility. To enable this capability, the team developed Stream-AI-MD, a novel application of the AI method called deep learning to drive adaptive molecular dynamics (MD) simulations in a streaming manner. Data from simulations is streamed from ThetaGPU onto the Cerebras CS-1 platform to simultaneously analyze how the two proteins interact.

Decision Tree -- Explained


In this blog we are going to talk about decision tree algorithm. Yeah, you read it right. It is a tree, or it looks like a tree (upside down tree) which helps to take decision. How come a tree helps us to take decision? So how do we take any decision?

A "New Nobel" – Computer Scientist Wins $1 Million Artificial Intelligence Prize


Duke professor becomes second recipient of AAAI Squirrel AI Award for pioneering socially responsible AI. Whether preventing explosions on electrical grids, spotting patterns among past crimes, or optimizing resources in the care of critically ill patients, Duke University computer scientist Cynthia Rudin wants artificial intelligence (AI) to show its work. Especially when it's making decisions that deeply affect people's lives. While many scholars in the developing field of machine learning were focused on improving algorithms, Rudin instead wanted to use AI's power to help society. She chose to pursue opportunities to apply machine learning techniques to important societal problems, and in the process, realized that AI's potential is best unlocked when humans can peer inside and understand what it is doing.

How AI is helping to make breast cancer history – TechCrunch


Every October for the last four decades, Breast Cancer Awareness Month has helped to raise visibility of the most prevalent cancer on Earth -- one that takes almost three-quarters of a million lives every year. Despite recorded cases stretching back to ancient Egypt, breast cancer was considered an "unspeakable" condition for millennia. Women were expected to suffer in silence and "dignity." This stigma fueled academic ignorance, with breast cancer languishing as a relatively unstudied disease until just a few decades ago. For most of the last century, a woman suffering from breast cancer would be offered radiation therapy and/or surgery -- often radical surgery, leaving them disfigured for little benefit -- while the treatment of other cancers progressed.

Japan planning ¥100 billion tech fund for economic security

The Japan Times

Economic revitalization minister Daishiro Yamagiwa on Sunday revealed a plan to set up a fund to support the development of cutting-edge technologies crucial for the country's economic security. "The government will fully support private-sector companies' research and development activities for advanced technologies, and their efforts to prepare a business environment for such technologies," he said during a television program. The fund will likely be worth about ¥100 billion. The government will include the planned fund in a package of economic measures to be drawn up after the Oct. 31 Lower House election. The fund is expected to help Japanese companies and universities develop artificial intelligence, quantum and robot technologies, biotechnology and other important tech, and put them into practical use.