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Scientists discover details of vision vary from person to person

Daily Mail - Science & tech

Two people looking at the exact same scene before them may perceive it differently as a result of a so-called'fingerprint of misperception'. Researchers at the University of California Berkeley found natural variation in the inherent visual ability to pinpoint the exact location and size of objects. A series of experiments on nine individuals found'dramatic differences' in the ability to resolve fine details as well as discrepancies in judging location and size. The differences are due to how the brain processes visual stimuli, the academics believe, but the exact neural network responsible for the variation remains unknown. 'We assume our perception is a perfect reflection of the physical world around us, but this study shows that each of us has a unique visual fingerprint,' study lead author Miss Zixuan Wang, a UC Berkeley doctoral student in psychology, told Berkeley News.


Introduction to AI & ML techniques in Drug Discovery

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A perfect course for Bachelors / Masters / PhD students who are getting started into Drug Discovery research. This course is specially designed keeping in view of beginner level knowledge on Artificial Intelligence, Machine learning and computational drug discovery applications for science students. By the end of this course participants will be equipped with the basic knowledge required to navigate their drug discovery project making use of the Artificial Intelligence and Machine learning based tools.Who this course is for:


How Artificial Intelligence Could Lead to Better Investment Decisions – IAM Network

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The decision to invest in a company can rely on a lot of guesswork, but Kim Polese, co-founder and chairman of CrowdSmart, is using artificial intelligence to turn qualitative information into quantitative data--and reduce bias along the way. "When we're talking about using collective intelligence, augmented collective intelligence, what we're really talking about is using a combination of human and machine intelligence to improve the way that diligence is done," Polese said this past Wednesday at a Barron'sInvesting in Tech panel. The founder of an artificial-intelligence platform designed to predict a company's potential for success, Polese detailed how the CrowdSmart platform works, and how it could help remove bias from the diligence process. The system draws on the insights of a group of 25 or more people, selected for their different levels of expertise, to evaluate prospective investments, explained Polese, who said her career in Silicon Valley began 30 years ago at the first artificial-intelligence company to go public. "Those people are able to access all of the full diligence materials, so that might be videos, live Q&As with …


Open-Source Process Mining Vendor Apromore Raises AU$6.84 Million in Series a Funding

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Apromore's CEO, Prof. Marcello La Rosa, highlighted that "Apromore inherits a decade of R&D in the field of business process management and process mining, and we have packed the most advanced algorithms for process mining into Apromore's products. This investment will allow us to maintain our competitive advantage, by incorporating the latest research and innovations in the field of AI-driven automated process improvement into our product. It will also allow us to continue pursuing our mission of democratising process mining via a commercial open-source business model." The investment is coupled with a strategic partnership with GBTEC. Commenting on the investment and the strategic partnership, GBTEC's CEO, Gregor Greinke, said: "The integration of Apromore into the BIC Platform will provide a one-stop shop for companies seeking to accelerate their digital transformation journey with full-lifecycle process optimisation. The analytics capabilities that Apromore brings into the BIC Platform are truly exceptional when it comes to identifying underperforming processes, rework loops, waste, and bottlenecks. By joining forces, we are certain to establish one of the best analytics and AI-driven process optimisation tools on the market."


Unlocking the power of machine learning for small molecule drug discovery

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Rick Wagner of ZebiAI and Patrick Riley of Google Accelerated Science (GAS) discuss the development and benefits of a new machine learning drug discovery platform. A collaborative study between ZebiAI, Google Accelerated Science (GAS) and X-Chem has used the power of machine learning to improve the drug discovery process. The paper, published in the Journal of Medicinal Chemistry, describes an effective machine learning platform with the ability to accelerate drug discovery based on DNA-encoded small molecule library (DEL) selection data. According to the researchers, their findings demonstrate the efficacy of the programme to predict highly potent small molecule inhibitors within a virtual library of compounds across three diverse protein targets. "We envision artificial intelligence (AI) and machine learning will be a leading source of novel, small molecule drug candidates. These technologies will become indispensable as a means for leveraging large datasets to understand disease biology and identify the best candidates to address intractable diseases," said Founder and Director of ZebiAI, Rick Wagner, when speaking to Drug Target Review.


Research reflects how AI sees through the looking glass

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Things are different on the other side of the mirror. Right hands become left hands. Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards – findings with implications for training machine learning models and detecting faked images. AI learns to pick up on unexpected clues to differentiate original images from their reflections, the researchers found.


AI is reinventing the way we invent

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Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.


Research reflects how AI sees through the looking glass

#artificialintelligence

Right hands become left hands. Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell University researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards -- findings with implications for training machine learning models and detecting faked images. "The universe is not symmetrical. If you flip an image, there are differences," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of the study, "Visual Chirality," presented at the 2020 Conference on Computer Vision and Pattern Recognition, held virtually June 14-19.


DARPA honors artificial intelligence expert

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Best listening experience is on Chrome, Firefox or Safari. The irony of artificial intelligence is how much human brainpower is required to build it. For three years, our next guest had been on loan from the University of Massachusetts, to the Defense Advanced Research Projects Agency. There's she headed up several DARPA artificial intelligence projects. Now she's been awarded a high honor, the Meritorious Public Service Medal.


Microsoft AI Grant Helps Mount Sinai Establish COVID-19 Data Science Center

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The Mount Sinai Health System has received an award from Microsoft AI for Health to support the work of a new data science center dedicated to COVID-19 research. The Mount Sinai COVID Informatics Center (MSCIC) brings together leaders from entities across Mount Sinai, including the Hasso Plattner Institute for Digital Health, the Department of Genetics and Genomic Sciences, and the BioMedical Engineering and Imaging Institute. "This partnership with Microsoft provides us with cloud resources that will accelerate our discovery, translation and implementation of digital tools in the fight against COVID-19," said Robbie Freeman, MSN, RN, vice president of Clinical Innovation at The Mount Sinai Hospital. "Through this collaboration with AI for Health, we are leveraging the expertise of the Mount Sinai Health System in delivering world-class patient care and the Azure cloud to bring our AI-enabled products from bench to bedside." The philanthropic Microsoft AI for Health Grant will support the care of patients with the coronavirus, enabling the Center to develop tools using artificial intelligence (AI) that enhance care and evidence-based medicine for treating COVID-19 patients.