Earth Week: AI Helps Build A Sustainable Future

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SyncedReview

4 min readApr 28, 2019

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AI for Earth Can Be A Game-Changer for Our Planet
Microsoft announced that they are broadening their AI for Earth program with an expanded strategic plan and committing $50 million over the next five years to put artificial intelligence technology in the hands of individuals and organizations around the world who are working to protect our planet.
(Microsoft)

The AI for Climate Change Bootcamp
Stanford ML group launched The AI for Climate Change Bootcamp provides Stanford students an opportunity to do cutting-edge research at the intersection of AI and climate change. Students receive training from PhD students and bootcamp faculty to do high impact work.
(Stanford ML Group)

National Science Foundation Awards Litterati Grant to Advance Its AI for A Cleaner Planet
On Earth Day, 2019, the National Science Foundation has awarded Litterati a second grant in the amount of $750,000 to advance its machine learning and further its mission to create a litter-free world by using data to plot and identify litter across the planet.
(Forbes)

AI May Help Develop Clean, Limitless Fusion Energy
A team, including researchers from Princeton University and Harvard University, is applying deep learning to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks or apparatus that house the reactions.
(TheWeek)

Technology

OpenAI Created Musenet, A Deep Neural Network
OpenAI has created Musenet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files.
(OpenAI)

Speech Synthesis from Neural Decoding of Spoken Sentences
Researchers designed a neural decoder that explicitly leverages kinematic and sound representations encoded in human cortical activity to synthesize audible speech. Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics.
(UCSF & UC Berkeley)

SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition
Researchers from Google AI take a new approach to augmenting audio data, treating it as a visual problem rather than an audio one. Instead of augmenting the input audio waveform as is traditionally done, SpecAugment applies an augmentation policy directly to the audio spectrogram (i.e., an image representation of the waveform).
(Google AI)

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The 10 Biggest Semiconductor M&A Deals in 2018
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