🔹Neural networks facilitate optimization in the search for new materials

Sorting through millions of possibilities, a search for battery materials delivered results in five weeks instead of 50 years. When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once.

📌Via: @cedeeplearning

link: http://news.mit.edu/2020/neural-networks-optimize-materials-search-0326

#MIT
#deeplearning
#neuralnetworks
#imagedetection

🔻Newly discovered enzyme “square dance” helps generate #DNA building blocks

MIT #biochemists can trap and visualize an enzyme as it becomes active — an important development that may aid in future #drug development.

How do you capture a cellular process that transpires in the blink of an eye? Biochemists at #MIT have devised a way to trap and #visualize a vital enzyme at the moment it becomes active — informing drug development and revealing how biological systems store and transfer energy.

link: http://news.mit.edu/2020/enzyme-square-dance-helps-generate-dna-building-blocks-0330

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#python
#statistics
#bioinformatics

🔹Predicting people's driving personalities

System from #MIT CSAIL sizes up drivers as selfish or selfless. Could this help self-driving cars navigate in traffic?
#Self_driving cars are coming. But for all their fancy sensors and intricate data-crunching abilities, even the most #cutting_edge cars lack something that (almost) every 16-year-old with a learner’s permit has: social awareness.

While autonomous technologies have improved substantially, they still ultimately view the drivers around them as obstacles made up of ones and zeros, rather than human beings with specific intentions, motivations, and personalities.
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link: http://news.mit.edu/2019/predicting-driving-personalities-1118

📌Via: @cedeeplearning

#deeplearning
#neuralnetworks
#machinelearning

🔹Computing and artificial intelligence: Humanistic perspectives from MIT

"The advent of artificial intelligence presents our species with an historic opportunity — disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world?"

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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: https://shass.mit.edu/news/news-2019-computing-and-ai-humanistic-perspectives-mit-foreword-dean-melissa-nobles

#MIT
#AI
#machinelearning
#computing

🔻Detecting patients’ pain levels via their brain signals

System could help with diagnosing and treating #noncommunicative patients.

Researchers from #MIT and elsewhere have developed a system that measures a patient’s pain level by analyzing brain activity from a portable #neuroimaging device. The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2019/detecting-pain-levels-brain-signals-0912

#deeplearning
#neuralnetworks
#machinelearning
#computerscience

🔻Reducing risk, empowering resilience to disruptive global change
by Mark Dwortzan

The MIT Joint Program on the Science of Global Change launched in 2019 its Adaptation-at-Scale initiative (AS-MIT), which seeks evidence-based solutions to global change-driven risks. Using its Integrated Global System Modeling (IGSM) framework, as well as a suite of resource and infrastructure assessment models, AS-MIT targets, diagnoses, and projects changing risks to life-sustaining resources under impending societal and environmental stressors, and evaluates the effectiveness of potential risk-reduction measures.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2020/reducing-risk-empowering-resilience-disruptive-global-change-0123

#deeplearning #math
#neuralnetworks
#machinelearning
#datascience
#globalchange
#MIT #research

🔻🔻Using AI to predict breast cancer and personalize care

MIT/MGH's image-based deep learning model can predict breast cancer up to five years in advance.

A team from MIT’s #Computer_Science and #Artificial_Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) has created a new deep-learning model that can predict from a mammogram if a patient is likely to develop breast cancer as much as five years in the future. Trained on mammograms and known outcomes from over 60,000 MGH patients, the model learned the subtle patterns in breast tissue that are precursors to malignant tumors.
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📌Via: @cedeeplearning

http://news.mit.edu/2019/using-ai-predict-breast-cancer-and-personalize-care-0507

#deeplearning
#neuralnetworks
#machinelearning
#datascience
#MIT #math
#prediction
#computervision

Gift will allow MIT researchers to use artificial intelligence in a biomedical device

🔹by Maria Iacobo

Researchers in the MIT Department of Civil and Environmental Engineering (CEE) have received a gift to advance their work on a device designed to position living cells for growing human organs using acoustic waves. The Acoustofluidic Device Design with Deep Learning is being supported by Natick, Massachusetts-based MathWorks, a leading developer of mathematical computing software.
“One of the fundamental problems in growing cells is how to move and position them without damage,” says John R. Williams, a professor in CEE. “The devices we’ve designed are like acoustic tweezers.”
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📌Via: @cedeeplearning

http://news.mit.edu/2020/gift-to-mit-cee-artificial-intelligence-biomedical-device-0129

#deeplearning
#MIT #math
#machinelearning
#AI #datascience
#biomedical

🔻 Deep learning accurately stains digital biopsy slides

Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.

🔹 This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

A Good Read 👌
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📌Via: @cedeeplearning

http://news.mit.edu/2020/deep-learning-provides-accurate-staining-digital-biopsy-slides-0522

#deeplearning #machinelearning
#neuralnetworks
#MIT #math #AI

⚪️ Visualizing the world beyond the frame

🔹Researchers test how far artificial intelligence models can go in dreaming up varied poses and colors of objects and animals in photos.

🔹To give computer vision models a fuller, more imaginative view of the world, researchers have tried feeding them more varied images. Some have tried shooting objects from odd angles, and in unusual positions, to better convey their real-world complexity. Others have asked the models to generate pictures of their own, using a form of artificial intelligence called GANs, or generative adversarial networks. In both cases, the aim is to fill in the gaps of image datasets to better reflect the three-dimensional world and make face- and object-recognition models less biased.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2020/visualizing-the-world-beyond-the-frame-0506

#deeplearning #GANs #math
#machinelearning #visualization
#AI #MIT #datascience

⭕️ A foolproof way to shrink deep learning models

​Researchers unveil a pruning algorithm to make artificial intelligence applications run faster.

🖋By Kim Martineau

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models.
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📌Via: @cedeeplearning

http://news.mit.edu/2020/foolproof-way-shrink-deep-learning-models-0430

#deeplearning #AI #model
#MIT #machinelearning
#datascience #neuralnetworks
#algorithm #research

🔋 Machine-learning tool could help develop tougher materials

Engineers develop a rapid screening system to test fracture resistance in billions of potential materials.

🖊 By David L. Chandler

For engineers developing new materials or protective coatings, there are billions of different possibilities to sort through. Lab tests or even detailed computer simulations to determine their exact properties, such as toughness, can take hours, days, or more for each variation. Now, a new artificial intelligence-based approach developed at MIT could reduce that to a matter of milliseconds, making it practical to screen vast arrays of candidate materials.
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📌Via: @cedeeplearning

http://news.mit.edu/2020/machine-learning-develop-materials-0520

#machinelearning #deeplearning
#neuralnetworks #material #AI
#datascience #MIT #engineering

⭕️ OpenAI’s new language generator GPT-3 is shockingly good—and completely mindless

📌Via: @cedeeplearning


https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/

#deeplearning #gp3 #machinelearning #math
#neuralnetworks #AI #MIT