AI Technology & Industry Review
A research team from Google Brain and New York University says the Natural Language Understanding (NLU) evaluation system is “broken” and proposes four criteria for improving NLU benchmarks.
Researchers from the University of Wisconsin-Madison, UC Berkeley, Google Brain and American Family Insurance propose Nyströmformer, an adaption of the Nystrom method that approximates standard self-attention with O(n) complexity.
A recent study by the Google Brain Team proposes a new way of programming automated machine learning (AutoML) based on symbolic programming.
Google Brain’s Switch Transformer language model packs a whopping 1.6 trillion parameters while effectively controlling computational cost. The model achieved a 4x pretraining speedup over a strongly tuned T5-XXL baseline.
Researchers from Vector Institute, University of Toronto and Google Brain recently studied three common types of intrinsic motivation across seven agents, three Atari games, and the 3D game Minecraft.
Google Brain ICLR 2021 submission analyzes learned optimizers’ performance advantage over well-tuned baseline optimizers.
Google Brain has improved the SOTA on the LibriSpeech automatic speech recognition task, with their score of 1.4 percent/ 2.6 percent word-error-rates.
Google Brain recently introduced a new open-sourced TensorFlow package, TensorFlow Recommenders designed to simplify the process of building, evaluating, and serving sophisticated recommender models.
Former Uber Chief Scientist and VP for AI Zoubin Ghahramani has joined Google Research as part of the Google Brain team leadership.
Google Brain in Zürich and DeepMind London researchers believe one of the world’s most popular image databases may need a makeover.
A team of researchers from Google Brain have proposed a rethink of the dominant computer vision paradigm of pre-training.
Researchers introduce the notion of deflecting adversarial attacks, which presents a step towards ending the battle between attacks and defenses.
Google Brain announced this week that it is open-sourcing its object detector EfficientDet, which achieves SOTA performance while requiring significantly less compute.
A Google-led research team has introduced a new method for optimizing neural network parameters that is faster than all common first-order methods on complex problems.
Synced Global AI Weekly February 23rd
Researchers have proposed a simple but powerful “SimCLR” framework for contrastive learning of visual representations.
A recent Google Brain paper looks into Google’s hugely successful transformer network — BERT — and how it represents linguistic information internally.
Researchers from Google Brain and Carnegie Mellon University have released models trained with a semi-supervised learning method called “Noisy Student” that achieve 88.4 percent top-1 accuracy on ImageNet.
In an effort to sustain RL’s momentum, a team of researchers from Machine Zone, Google Brain, and California Institute of Technology have introduced a new software framework and benchmark for reproducible reinforcement learning research.
A team of researchers from Mila and Google Brain believe simple pencil sketches could help AI models generalize to a better understanding of unseen images.
Although convolutional neural networks have reached Superman level on image classification tasks, adversarial examples remain the kryptonite that can mysteriously defeat even SOTA models.
In fact, the accuracy of results in few-shot learning, both with and without labeled data, is very high.
Researchers from Carnegie Mellon University and Google Brain have now proposed an unsupervised data augmentation (UDA) technique that significantly improves semi-supervised learning (SSL) by conducting data augmentation on unlabeled data.
A team of researchers from Carnegie Mellon University and Google Brain have now proposed XLNet, a new language model which outperforms BERT on 20 language tasks including SQuAD, GLUE, and RACE; and has achieved SOTA results on 18 of these tasks.
Synced Global AI Weekly June 2nd
A Google Brain research team led by Turing Award recipient Geoffrey Hinton recently published a paper that presents an effective method for measuring the similarity of representations learned by deep neural networks.
Current state-of-the-art convolutional architectures for object detection tasks are human-designed. In a recent paper, Google Brain researchers leveraged the advantages of Neural Architecture Search (NAS) to propose NAS-FPN, a new automatic search method for feature pyramid architecture.
The idea of using machine learning to teach programs how to automatically write or modify code has always been tempting for computer scientists.
Google Brain researchers have proposed LAMB (Layer-wise Adaptive Moments optimizer for Batch training), a new optimizer which reduces training time for its NLP training model BERT (Bidirectional Encoder Representations from Transformers) from three days to just 76 minutes.
Model-free reinforcement learning can be used to learn effective strategies for complex tasks such as Atari games, but it usually requires a large amount of interaction, which adds significant time and cost.
TensorFlow is the world’s most popular open source machine learning library. Since its initial release in 2015, the Google Brain product has been downloaded over 41 million times. At this week’s 2019 TensorFlow Dev Summit, Google announced a major upgrade on the framework, the TensorFlow 2.0 Alpha version.
Machine learning models based on deep neural networks have achieved unprecedented performance on many tasks. These models are generally considered to be complex systems and difficult to analyze theoretically. Also, since it’s usually a high-dimensional non-convex loss surface which governs the optimization process, it is very challenging to describe the gradient-based dynamics of these models during training.
Synced Global AI Weekly February 10th
The Synced Lunar New Year Project is a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this second installment (click here to read the previous article on Clarifai CEO Matt Zeiler), Synced speaks with Google Brain Researcher Quoc Le on his latest invention, AutoML, Google Brain’s pursuit of AI, and the secret of transforming lab technologies into real practices.
Magenta Studio is a Google Brain project “exploring the role of machine learning as a tool in the creative process.” The Google Brain team created the open-source music-making package using machine learning models.
DARCCC (Detecting Adversaries by Reconstruction from Class Conditional Capsules) is a technique which uses a similarity metric to compare reconstructed images with an original input image to identify whether it was an adversarial image, and further detects whether the system was attacked.
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.
A Google Brain research group has published a paper proposing a new method for evaluating generative models by using insights from competitive games between human players.
Google is looking to expand its AI research activities in the Japanese capital. The company’s deep learning and AI research team Google Brain yesterday posted a “Tokyo job listing seeking talented experts to participate in cutting edge research on machine learning”.
Google has announced the release of MusicVAE, a machine learning model that makes composing musical scores as easy as mixing paint on a palette. A breakthrough from Google Brain’s Magenta Project, MusicVAE generates and morphs melodies to output multi-instrumental passages optimized for expression, realism and smoothness which sound convincingly like human-composed music.