You Could Make The Next Exoplanet Discovery With This New Game

International Business Times

When it comes to finding exoplanets in photos, hundreds or even thousands of eyes are better than an algorithm. The University of Geneva is creating a game to help researchers identify exoplanets with the help of gamers, it will be one of the largest citizen science projects ever, according to the MIT Technology Review. The game will operate within EVE Online servers under the title "Project Discovery." EVE is an online gaming platform based in space, developed by the video game company CCP. There is already one Project Discovery game that involves identifying patterns of protein to help advance the science and expansion of the Human Protein Atlas database.

Can we trust scientific discoveries made using machine learning?


Rice University statistician Genevera Allen says scientists must keep questioning the accuracy and reproducibility of scientific discoveries made by machine-learning techniques until researchers develop new computational systems that can critique themselves. Allen, associate professor of statistics, computer science and electrical and computer engineering at Rice and of pediatrics-neurology at Baylor College of Medicine, will address the topic in both a press briefing and a general session today at the 2019 Annual Meeting of the American Association for the Advancement of Science (AAAS). "The question is, 'Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?'" "The answer in many situations is probably, 'Not without checking,' but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions." Machine learning (ML) is a branch of statistics and computer science concerned with building computational systems that learn from data rather than following explicit instructions. Allen said much attention in the ML field has focused on developing predictive models that allow ML to make predictions about future data based on its understanding of data it has studied.

Prehistoric shark discovery

FOX News

Researchers have discovered a new species of prehistoric shark, and at about 13 feet long, it was comparable to the size of the great white sharks of today. The new predator, called Megalolamna paradoxodon, lived about 20 million years ago and is now extinct. The scientists based their discovery on just a handful of teeth from the shark, describing five of the prehistoric chompers (which originated from three different countries) in a new study in the journal Historical Biology. Like great whites, the shark is a member of the lamniformes group, and it lived during the Miocene epoch, which spans about 23 million to five million years in the past. Kenshu Shimada, the lead author of the new paper and a professor at DePaul University, described the species as "exceptionally rare."


AAAI Conferences

Discovery informatics focuses on intelligent systems aimed at accelerating discovery, particularly in science but also from any data-rich domain. It is a generalization of scientific informatics work (for example, medical-, bio-, eco-, or geoinformatics) that seeks to apply principles of intelligent computing and information systems in order to understand, automate, improve, and innovate any aspects of discovery processes. A range of AI research is directly relevant including process representation and workflows; intelligent interfaces; causal reasoning; machine learning; knowledge representation and engineering; semantic web; advanced visualization toolkits and social computing.Thee application of AI approaches to assist in scientific discovery is an open ended knowledge-driven challenge with a very high potential impact. This is especially true in this era of big data, which provides the theme of this symposium.

U of T startup raises US$45 million to build AI-powered drug discovery business


A University of Toronto startup using artificial intelligence to speed drug discovery has raised US$45 million to fund growth – yet another example of how efforts to commercialize the university's AI expertise are making waves in the business world. San Francisco-based Atomwise, which helps screen millions of potential drug candidates in a fraction of the time of traditional methods, said this week its latest funding round was led by Monsanto Growth Ventures, Data Collective (DCVC) and B Capital Group. That brings the total amount of capital Atomwise has raised to more than US$51 million. "With our initial work in 2012, Atomwise became the first startup to commercialize deep neural networks for drug discovery," Abraham Heifets, the company's co-founder and chief executive, said in a statement. "It seemed to many like science fiction then, but now in 2018 Atomwise has the commercial traction with a host of customers to demonstrate our leadership in AI for drug discovery."