Lifeboat Foundation

NASA just launched a new citizen science project — it wants the public’s help to find and identify brand new exoplanets.

Human Touch

This is the sort of work that technically could be automated with an algorithm trained to spot new worlds, Space.com reports. But it turns out that in this case, there’s no substitute for human judgment.

Continue reading “New Website Lets You Help NASA Find Alien Worlds” »

Researchers at the Allen Institute for Artificial Intelligence (AI2) have created a machine learning algorithm that can produce images using only text captions as its guide. The results are somewhat terrifying… but if you can look past the nightmare fuel, this creation represents an important step forward in the study of AI and imaging.

Unlike some of the genuinely mind-blowing machine learning algorithms we’ve shared in the past—see here, here, and here —this creation is more of a proof-of-concept experiment. The idea was to take a well-established computer vision model that can caption photos based on what it “sees” in the image, and reverse it: producing an AI that can generate images from captions, instead of the other way around.

This is a fascinating area of study and, as MIT Technology Review points out, it shows in real terms how limited these computer vision algorithms really are. While even a small child can do both of these things readily—describe an image in words, or conjure a mental picture of an image based on those words—when the Allen Institute researchers tried to generate a photo from a text caption using a model called LXMERT, it generated nonsense in return.

Frost & Sullivan’s recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move…

Pharmaceutical companies and hospitals are adopting data science rapidly, and its application is going to be established in all branches of healthcare

SANTA CLARA, Calif., Sept. 29, 2020 /PRNewswire/ — Frost & Sullivan’s recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move to the next phase of advancements. Hence, they are expected to witness adoption by pharma and biotech companies in the next four to five years. Further, with the COVID-19 pandemic, AI and machine learning (ML) can be used for drug research and clinical trials against the coronavirus to screen large databases and perform docking studies to identify existing potential drugs or design new drugs using advanced learning algorithms.

Continue reading “Data Science to Accelerate Drug Discovery with Artificial Intelligence and Machine Learning, Says Frost & Sullivan” »

David Sinclair wants to slow down and ultimately reverse aging. Sinclair sees aging as a disease and he is convinced aging is caused by epigenetic changes, abnormalities that occur when the body’s cells process extra or missing pieces of DNA. This results in the loss of the information that keeps our cells healthy. This information also tells the cells which genes to read. David Sinclair’s book: “Lifespan, why we age and why we don’t have to”, he describes the results of his research, theories and scientific philosophy as well as the potential consequences of the significant progress in genetic technologies.

At present, researchers are only just beginning to understand the biological basis of aging even in relatively simple and short-lived organisms such as yeast. Sinclair however, makes a convincing argument for why the life-extension technologies will eventually offer possibilities of life prolongation using genetic engineering.

Continue reading “Harvard Professor Wants to Slow Down & Reverse Aging: David Sinclair’s Approach For a Longer Life” »

In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time.

Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference.

“Our lab has made significant progress in understanding how anesthetic medications affect neural activity and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings,” Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore. “In our recent study, we trained a neural network using the cross-entropy method, by repeatedly letting it run on simulated patients and encouraging actions that led to good outcomes.”

Microsoft has made several quirky and useful apps that can help you with daily problems and its new app seeks to help you with math.

Microsoft Math Solver — available on both iOS and Android — can solve various math problems including quadratic equations, calculus, and statistics. The app can also show graphs for the equation to enhance your understanding of the subject.

Ira Pastor, ideaXme life sciences ambassador and founder of Bioquark interviews Dr Vitaly Vanchurin, PhD, Associate Professor, Theoretical Physics and Cosmology, Swenson College of Science and Engineering, at the University of Minnesota (UMN).

Dr Vanchurin’s big questions and the tools we need to answer them:

Continue reading “Big Questions: The Multiverse, Cosmological Neural Networks and ‘Space Noodles’” »

While critically ill patients experience a life-threatening illness, they commonly contract ventilator-associated pneumonia. This nosocomial infection increases morbidity and likely mortality as well as the cost of health care. This article reviews the literature with regard to diagnosis, treatment, and prevention. It provides conclusions that can be implemented in practice as well as an algorithm for the bedside clinician and also focuses on the controversies with regard to diagnostic tools and approaches, treatment plans, and prevention strategies.

Patients in the intensive care unit (ICU) are at risk for dying not only from their critical illness but also from secondary processes such as nosocomial infection. Pneumonia is the second most common nosocomial infection in critically ill patients, affecting 27% of all critically ill patients (170). Eighty-six percent of nosocomial pneumonias are associated with mechanical ventilation and are termed ventilator-associated pneumonia (VAP). Between 250,000 and 300,000 cases per year occur in the United States alone, which is an incidence rate of 5 to 10 cases per 1,000 hospital admissions (134, 170). The mortality attributable to VAP has been reported to range between 0 and 50% (10, 41, 43, 96, 161).

How much control do you have over your thoughts? What if you were specifically told not to think of something—like a pink elephant?

A recent study led by UNSW psychologists has mapped what happens in the brain when a person tries to suppress a thought. The neuroscientists managed to ‘decode’ the complex brain activity using functional brain imaging (called fMRI) and an imaging algorithm.

The findings suggest that even when a person succeeds in ignoring a thought, like the pink elephant, it can still exist in another part of the brain—without them being aware of it.