Awesome, not awesome.
#Awesome
“Artificial intelligence (AI) approaches have the potential to affect several facets of cancer therapy. These include drug discovery and development and how these drugs are clinically validated and ultimately administered at the point of care, among others. Currently, these processes are expensive and time-consuming. Moreover, therapies often result in variable treatment outcomes between patients. The convergence of AI and cancer therapy has resulted in multiple solutions to address these challenges. AI platforms ranging from machine learning to neural networks can accelerate drug discovery, harness biomarkers to accurately match patients to clinical trials, and truly personalize cancer therapy using only a patient’s own data. These advances are indicators that practice-changing cancer therapy empowered by AI may be on the horizon.” — Dean Ho, Professor Learn More from Science >
#Not Awesome
“In some ways, adversarial policies are more worrying than attacks on supervised learning models, because reinforcement learning policies govern an AI’s overall behavior. If a driverless car misclassifies input from its camera, it could fall back on other sensors, for example. But sabotage the car’s control system — governed by a reinforcement learning algorithm — and it could lead to disaster. “If policies were to be deployed without solving these problems, it could be very serious,” says Gleave. Driverless cars could go haywire if confronted with an arm-waving pedestrian.” — Will Douglas Heaven, Senior Editor Learn More from MIT Technology Review >
What we’re reading.
1/ Hospitals in China turn to machine learning software to detect pneumonia that may be caused by coronavirus in patients. Learn More from WIRED >
2/ Artificial intelligence will make life easier for air travelers as it is used more often to power “self-boarding gates” and reduce the amount of time airplanes sit idly at gates. Learn More from The New York Times >
3/ As climate change pushes more crops to be grown indoors, farmers turn to machine learning to automatically tweak the conditions of the enclosed systems. Learn More from Modern Farmer >
4/ MIT and Harvard researchers built a neural network that was able to come up with hundreds of antibiotics that could treat antibiotic-resistant strains of E coli and “look different” from today’s known treatments. Learn More from The Guardian >
5/ None of the canaries in the coal mine of AI — like the automatic formulation of learning problems or self-driving cars — suggest that we should be nervous about dramatic AI breakthroughs on the horizon. Learn More from MIT Technology Review >
6/ Two musicians develop an algorithm that generated every possible melody — and released them for public use — in hopes of protecting other musicians from copyright lawsuits in the future. Learn More from VICE >
7/ Manual labor jobs that can’t be fully automated yet are being “digitized” so that managers can gain leverage on employees. Learn More from WIRED >
Links from the community.
“I built a DIY license plate reader with a Raspberry Pi and machine learning” submitted by Samiur Rahman (@samiur1204). Learn More from Towards Data Science >
“Why use Python for AI and Machine Learning?” submitted by Avi Eisenberger (@aeisenberger). Learn More from Tech Native >
“Designing Faster Neural Networks” by Deepak Mangla. Learn More from Noteworthy >
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