Category: Technology

Technical review of the newest machine intelligence research.

AI Technology

Get a Grip! Berkeley Targets Dexterous Manipulation Using Deep RL

UC Berkeley researchers have published a paper demonstrating how Deep Reinforcement Learning can be used to control dexterous robot hands for complicated tasks. Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations proposes a low-cost and high-efficiency control method that uses demonstration and simulation techniques to accelerate the learning process.

AI Technology

BigGAN: A New State of the Art in Image Synthesis

“Best GAN samples ever yet? Very impressive ICLR submission! BigGAN improves Inception Scores by >100.” The above Tweet is from renowned Google DeepMind research scientist Oriol Vinyals. It was retweeted last week by Google Brain researcher and “Father of Generative Adversarial Networks” Ian Goodfellow, and picked up momentum and praise from AI researchers on social media.

AI Technology

Jeff Dean’s 1990 Senior Thesis Is Better Than Yours

Google AI lead Jeff Dean recently posted a link to his 1990 senior thesis on Twitter, which set off a wave of nostalgia for the early days of machine learning in the AI community. Parallel Implementation of Neural Network Training: Two Back-Propagation Approaches may be almost 30 years old and only eight pages long, but the paper does a remarkable job of explaining the methods behind neural network training and the modern development of artificial intelligence.

AI Health Technology

The Eyes Have It: DeepMind’s AI 3D OCT Scans

Artificial intelligence can now match or outperform human experts in diagnosis and referral on eye diseases, suggests a new paper from DeepMind. The UK-based, Google-owned research institute today released joint research results with the UK’s Moorfields Eye Hospital and UCL Institute of Ophthalmology, which present a new AI technique in the context of OCT imaging. The paper was published on Nature Medicine’s website.

AI Technology

Harvard & University of Toronto Researchers Apply Deep Generative Models to Inverse Molecular Design

Benjamin Sanchez-Lengeling from Harvard University and Alán Aspuru-Guzik from the University of Toronto have successfully applied machine learning models to speed up the materials discovery process. Their paper Inverse molecular design using machine learning: Generative models for matter engineering was published July 27 in Science Vol. 361.

AI Technology

CycleGAN Bikini Fix for Nudes

The Internet is woven into our everyday lives. We access massive amounts of data through our laptops, smartphones and tablets. This free flow of information however has prompted attempts to filter content which may not be appropriate for example for young people. One such new effort from Brazil puts virtual bikinis on nudes.

AI Technology

SJTU & MIT Paper Reinvents Neural Architecture Search; Slashes Computational Resource Requirements

The dearth of AI talents capable of manually designing neural architecture such as AlexNet and ResNet has spurred research in automatic architecture design. Google’s Cloud AutoML is an example of a system that enables developers with limited machine learning expertise to train high quality models. The trade-off, however, is AutoML’s high computational costs.