Tag: learning

A new way to think about machine learning

network
Credit: CC0 Public Domain

Deep neural networks, multilayered systems built to process images and other data through the use of mathematical modeling, are a cornerstone of artificial intelligence.

They are capable of seemingly sophisticated results, but they can also be fooled in ways that range from relatively harmless—misidentifying one animal as another—to potentially deadly if the network guiding a self-driving car misinterprets a stop sign as one indicating it is safe to proceed.

A philosopher with the University of Houston suggests in a paper published in Nature Machine Intelligence that common assumptions about the cause behind these supposed malfunctions may

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Machine learning innovation to develop chemical library for drug discovery

Machine learning innovation to develop chemical library
Purdue University scientists are using machine learning models to create new options for drug discovery pipelines. Credit: Purdue University/Gaurav Chopra

Machine learning has been used widely in the chemical sciences for drug design and other processes.


The models that are prospectively tested for new reaction outcomes and used to enhance human understanding to interpret chemical reactivity decisions made by such models are extremely limited.

Purdue University innovators have introduced chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions. The work is published in Organic Letters.

“Developing

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Machine learning for making machines: Applying visual search to mechanical parts

WEST LAFAYETTE, Ind. — Computer vision researchers use machine learning to train computers in visually recognizing objects – but very few apply machine learning to mechanical parts such as gearboxes, bearings, brakes, clutches, motors, nuts, bolts and washers.

A team of Purdue University mechanical engineers has created the first comprehensive open-source annotated database of more than 58,000 3D mechanical parts, designed to help researchers apply machine learning to those parts in actual machines.

“We are in the deep learning era, using computers to search for things visually,” said Karthik Ramani, Purdue’s Donald W. Feddersen Distinguished Professor of Mechanical Engineering. “But

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New technology exploits fluorescence and machine learning to speedily scan thousands of images

New technology exploits fluorescence and machine learning to speedily scan thousands of images
Becky Hess, a biomedical scientist, leads the OmniScreen project for PNNL. In OmniScreen, human cells glow if exposed to a pathogen. Credit: Andrea Starr | Pacific Northwest National Laboratory

Any biological sample—dirt, water, or food, for example—contains billions of bacteria. Only a few are harmful to humans, or pathogenic. But those few pathogens can mean the difference between a reliable supply of meat or lettuce, for example, and an outbreak of food poisoning—or worse, a pandemic.

Discovering deadly pathogens before they strike is one goal of the Defense Advanced Research Projects Agency (DARPA). The agency, part of the U.S. Department

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Researchers at the forefront of developing machine learning methods for chemical discovery

chemical
Credit: CC0 Public Domain

The discovery and formulation of new drugs, antivirals, antibiotics and in general chemicals with tailored properties is a long and painstaking process. Interdisciplinary research at the crossroads of biochemistry, physics and computer science can change this. The development of machine learning (ML) methods, combined with first principles of quantum and statistical mechanics and trained on increasingly available molecular big datasets, has the potential to revolutionize the process of chemical discovery.


“Chemical discovery and machine learning are bound to evolve together, but achieving true synergy between them requires solving many outstanding challenges,” says Alexandre Tkatchenko, Professor of

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