Lifeboat Foundation

Fascinating interview with Dutch astronomer Anthony Brown on ESA’s Gaia satellite and what it’s telling us about our own Milky Way Galaxy.

Dutch astronomer Anthony Brown of Leiden University explains how the European Space Agency’s GAIA satellite is revolutionizing what we know about the Milky Way. This all-sky survey mission revisits each target 70 times over the course of the years-long mission to give astronomers a real 3D map of a large swath of our galaxy. The next big data drop is scheduled by year’s end.

Continue reading “Episode 9 --- How ESA’s GAIA Satellite Is Revolutionizing Our Understanding of the Milky Way” »

A new AI algorithm developed by the University of Pittsburgh has achieved the highest accuracy to date in identifying prostate cancer, with 98% sensitivity and 97% specificity.

Quantum computers have enormous potential for calculations using novel algorithms and involving amounts of data far beyond the capacity of today’s supercomputers. While such computers have been built, they are still in their infancy and have limited applicability for solving complex problems in materials science and chemistry. For example, they only permit the simulation of the properties of a few atoms for materials research.

Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago (UChicago) have developed a method paving the way to using quantum computers to simulate realistic molecules and complex materials, whose description requires hundreds of atoms.

The research team is led by Giulia Galli, director of the Midwest Integrated Center for Computational Materials (MICCoM), a group leader in Argonne’s Materials Science division and a member of the Center for Molecular Engineering at Argonne. Galli is also the Liew Family Professor of Electronic Structure and Simulations in the Pritzker School of Molecular Engineering and a Professor of Chemistry at UChicago. She worked on this project with assistant scientist Marco Govoni and graduate student He Ma, both part of Argonne’s Materials Science division and UChicago.

The most reliable predictor of a relationship’s success is partners’ belief that the other person is fully committed, a Western University-led international research team has found.

Other important factors in a successful relationship include feeling close to, appreciated by, and sexually satisfied with your partner, says the study—the first-ever systematic attempt at using machine-learning algorithms to predict people’s relationship satisfaction.

“Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity,” Western Psychology professor Samantha Joel said. “But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.”

A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program.

“Humans are good at recognizing anomalies, but they have their own biases or past experience,” said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. “Machines are detached from the whole story. There’s definitely an element of standardizing care.”

To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer.

In a new paper, DeepMind describes an AI algorithm that was able to discover a critical programming rule in deep reinforcement learning from scratch.

Note: This article was originally published on May 29, 2017, and updated on July 24, 2020 Overview Neural Networks is one of the most popular machine learning algorithms Gradient Descent forms the basis of Neural networks Neural networks can be implemented in both R and Python using certain libraries and packages.

Proteins are essential to the life of cells, carrying out complex tasks and catalyzing chemical reactions. Scientists and engineers have long sought to harness this power by designing artificial proteins that can perform new tasks, like treat disease, capture carbon, or harvest energy, but many of the processes designed to create such proteins are slow and complex, with a high failure rate.

In a breakthrough that could have implications across the healthcare, agriculture, and energy sectors, a team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago has developed an artificial intelligence-led process that uses big data to design new proteins.

By developing machine-learning models that can review protein information culled from genome databases, the researchers found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they found that they performed chemistries so well that they rivaled those found in nature.

Yann LeCun, the chief AI scientist at Facebook, helped develop the deep learning algorithms that power many artificial intelligence systems today. In conversation with head of TED Chris Anderson, LeCun discusses his current research into self-supervised machine learning, how he’s trying to build machines that learn with common sense (like humans) and his hopes for the next conceptual breakthrough in AI.

This talk was presented at an official TED conference, and was featured by our editors on the home page.