The researchers worked with epilepsy patients undergoing brain surgery. Neuroengineers have crafted a breakthrough device that uses machine-learning neural networks to read brain activity and translate it into speech. An article, published Tuesday in the journal Scientific Reports, details how the team at Columbia University's Zuckerman Mind Brain Behavior Institute used deep-learning algorithms and the same type of tech that powers devices like Apple's Siri and the Amazon Echo to turn thought into "accurate and intelligible reconstructed speech." The research was reported earlier this month but the journal article goes into far greater depth. The human-computer framework could eventually provide patients who have lost the ability to speak an opportunity to use their thoughts to verbally communicate via a synthesized robotic voice.

Over the past nine months, the input pipeline part of deep learning training jobs in TensorFlow has become significantly more efficient. In this post, we investigate the performance impact of those TensorFlow changes and discuss futures on the horizon that will continue to impact full-stack performance. In Part 1 of this blog, we discussed the performance benefits of switching to lower precision and higher batch size during training. Both precision and batch size have "optimal" values for various workloads which have evolved over time. Beyond those major, well-known parameters, the entire software stack is evolving to improve training job throughput.

In 2015, Google's DeepMind AI was tasked with learning to play Atari video games. It was quite successful too, becoming as good at Video Pinball as a human player. But beyond the simple arcade games it began to struggle, notoriously failing to even collect the first key in the legendary 1980s adventure game Montezuma's Revenge, due to the game's complexity. However, a new approach has resulted in an AI algorithm that learnt from mistakes and identified intermediate steps 10 times faster, succeeding where Google failed and successfully autonomously playing Montezuma's Revenge. The work was carried out by Fabio Zambetta and his team from RMIT University in Melbourne, Australia.

Overall, the model detected and correctly localized 91 percent (310/340 images) and 96 percent (236/245 images) of all radius and ulna fractures on the frontal and lateral views, respectively. On a per-image basis the CNN achieved a sensitivity, specificity and AUC of 96 percent, 83 percent and 0.92, respectively for the frontal view. For the lateral views those numbers were 97 percent, 86 percent and 0.93, respectively. The per-study sensitivity, specificity and AUC were 98 percent, 73 percent and 0.89, respectively.

Download our new free report, "Evolving Data Infrastructure," to learn how companies are using, building, or evaluating data infrastructure in the cloud. In 2017, we published "How Companies Are Putting AI to Work Through Deep Learning," a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning. We found companies were planning to use deep learning over the next 12-18 months. In 2018, we decided to run a follow-up survey to determine whether companies' machine learning (ML) and AI initiatives are sustainable--the results of which are in our recently published report, "Evolving Data Infrastructure." The current generation of AI and ML methods and technologies rely on large amounts of data--specifically, labeled training data.

Predicting 3D human poses might not fall within most people's purview, but robotics, computer graphics, and other fields chiefly concerned with kinematics -- the branch of mechanics concerned with the motion of objects -- stand to benefit from systems that can do just that. Pose prediction is a task to which artificial intelligence (AI) has been applied before, somewhat recently by Google, but some prior work hit a roadblock: It stretched digital joints and bones in unnatural directions, particularly when the joints rotated. New research by Facebook's AI Research division, Google Brain, and ETH Zurich promises to address the problem, fortunately. In a paper ("Modeling Human Motion with Quaternion-based Neural Networks") published on the preprint server Arxiv.org this week, researchers describe an AI system -- QuaterNet -- that improves pose generation by representing joint rotations as complex number systems called quaternions, and by penalizing joint position errors. As the coauthors of the paper explain, recurrent neural networks -- a type of AI algorithm capable of learning long-term dependencies -- have been historically used to perform both short- and long-term pose prediction, while convolutional neural networks -- algorithms highly adept at analyzing visual imagery -- have been successfully applied to long-term generation of locomotion (movement from one place to another).

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow-on mission, GRACE-FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country-average correlation coefficient of 0.94 and Nash-Sutcliff efficient of 0.87, or 14\% and 52\% improvement respectively over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.

An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and time-consuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequately. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change.

We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots. Some key takeaways from the competition are: (i) pretrained Transformer variants are currently the best performing models on this task, (ii) but to improve performance on multi-turn conversations with humans, future systems must go beyond single word metrics like perplexity to measure the performance across sequences of utterances (conversations) in terms of repetition, consistency and balance of dialogue acts (e.g. The Conversational Intelligence Challenge aims at finding approaches to creating highquality dialogue agents capable of meaningful open domain conversation. Today, the progress in the field is significantly hampered by the absence of established benchmark tasks for non-goal-oriented dialogue systems (chatbots) and solid evaluation criteria for automatic assessment of dialogue quality. The aim of this competition was therefore to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable, including open source datasets, evaluation code (both automatic evaluations and code to run the human evaluation on Mechanical Turk), model baselines and the winning model itself. Taking into account the results of the previous edition, this year we improved the task, the evaluation process, and the human conversationalists' experience. We did this in part by making the setup simpler for the competitors, and in part by making the conversations more engaging for humans. We provided a dataset from the beginning, Persona-Chat, whose training set consists of conversations between crowdworkers who were randomly paired and asked to act the part of a given provided persona (randomly assigned, and created by another set of crowdworkers). The paired workers were asked to chat naturally and to get to know each other during the conversation. This produces interesting and engaging conversations that learning agents can try to mimic.