Synced is proud to present Gary Marcus as the last installment in our Lunar New Year Project — a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. (Read the previous articles on Clarifai CEO Matt Zeiler and Google Brain Researcher Quoc Le.)
In 2017 Google introduced Federated Learning (FL), “a specific category of distributed machine learning approaches which trains machine learning models using decentralized data residing on end devices such as mobile phones.” A new Google paper has now proposed a scalable production system for federated learning to enable increasing workload and output through the addition of resources such as compute, storage, bandwidth, etc.
The San Francisco-based AI non-profit however has raised eyebrows in the research community with its unusual decision to not release the language model’s code and training dataset. In a statement sent to Synced, OpenAI explained the choice was made to prevent malicious use: “it’s clear that the ability to generate synthetic text that is conditioned on specific subjects has the potential for significant abuse.”
Papers With Code is a unique and useful resource that presents trending ML research along with the code to implement it. The site was created by Atlas ML CEO Robert Stojnic, aka “rstoj” on Reddit’s machine learning board. The latest version of Papers With Code has added 950+ unique machine learning tasks, 500+ State-of-the-Art result leaderboards and 8500+ papers with code.
To make ML-based solutions available for a wider variety of deployment scenarios, Waymo’s autonomous driving team has collaborated with Google AI Brain Team researchers on a system that automates the creation of high quality and low latency neural networks on existing AutoML architectures.
It all started with a tweet from Google Japan Data Project Manager Suzana Ilić: “Yesterday someone (ML, CS PhD, Stanford) said he would not hire a person who is online educated in Machine Learning. Who here agrees and who thinks differently?” The question triggered a long and occasionally heated discussion that spread from Ilić’s twitter across the machine learning community.
It did not take long to see the first major AI talent reshuffling of 2019. Multiple sources are now confirming that reputed AI researcher Dr. Tong Zhang left his position as Executive Director of Tencent AI Lab effective December 31. Rumours suggest Zhang might return to teaching.
The number of AI-related research papers has skyrocketed in recent years, outpacing papers from all other academic topics since 2000. This has, not unsurprisingly, resulted in a shortage of qualified peer reviewers in the machine learning community, particularly when it comes to conference paper submissions.
Synthesizing peptides — the chains of amino acids that conduct various functions within cells — has long been a research area of interest for scientists and engineers. There has however been little success thus far, as existing methods for synthesizing peptides have been prohibitively expensive and time-consuming.
Photos of an artificial intelligence textbook for Chinese preschoolers have gone viral. Artificial Intelligence Experiment Materials is a 33-volume textbook series aimed at Chinese students from kindergarten to high school that was published this July by Henan People’s Publishing House.
Amazon Web Services has unveiled two chips and 13 machine learning capabilities and services at its AWS re:Invent conference in Las Vegas. The releases reflect Amazon’s determination to attract more developers to AWS by broadening its range of tools and services.
One of the top minds in machine learning, Andrew Ng is having an increasingly profound impact on AI education. Ng’s machine learning course at Stanford University remains the most popular on Coursera, the world-leading online education platform he co-founded in 2012.
A founding member of Google Brain and the mind behind AutoML, Quoc Le is an AI natural: he loves machine learning and loves automating things. Le used millions of YouTube thumbnails to develop an unsupervised learning system that recognized cats when he was a Stanford University PhD in 2011.
Peer review is an essential process that subjects new research to the scrutiny of other experts in the same field. Today’s top Machine Learning (ML) conferences are heavily reliant on peer review as it allows them to gauge submitted academic papers’ quality and suitability.
DeepMind announced today that it has opened its Graph Nets (GN) library to the public, enabling the use of graph networks in TensorFlow and Sonnet. Graph Nets is a machine learning framework that was published by DeepMind, Google Brain, MIT and University of Edinburgh on Jun 15.
The company made a series of AI-related announcements today at the Huawei Connect 2018 Conference in Shanghai, introducing two AI chips and a machine learning framework. Huawei’s AI push is expected to intensify its battle with domestic rivals Alibaba, Tencent and Baidu in the AI market.
Georgia Tech and Google Brain researchers have introduced the new interactive tool GAN Lab, which visually presents the training process of complex machine learning model Generative Adversarial Networks (GANs). Even machine learning newbs can now experiment with GAN models using only a common web browser.
Apple has unveiled the latest iteration of its smartphone chip: the A12 Bionic SoC (system-on-a-chip). The company made the announcement yesterday at its annual product showcase event in Cupertino, California, hailing the A12 as the industry’s first ever 7nm chip (the smallest current transistor scale). It will be embedded in Apple’s new XR, XS, and XS Max iPhones.
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.
Tencent AI Lab has announced that it will open source its multi-label image dataset ML-Images and deep residual network ResNet-101 by the end of September. ML-Images contains 18 million images and more than 11,000 common object categories; while ResNet-101 has reached the highest precision level in the industry.
Registration opened at 8:00 a.m. PDT today for December’s NIPS 2018 (Conference on Neural Information Processing Systems) in Montreal. The early birds were the fortunate ones this year — as tickets for the main conference were all snapped up less than a dozen minutes later.