Google-stablemate DeepMind is creating a blockchain-like system to show how sensitive medical data passing through its processors will be used, allowing healthcare professionals to check if data has been tampered with. Its healthcare arm, DeepMind Health, is working to improve medical diagnoses with machine learning tools. Large amounts of confidential data are required to develop these tools – something DeepMind hasn't always been trusted to handle. Last year, the company was criticized for gaining access to current and historic patient records for 1.6 million individuals across three London Royal Free NHS Trust hospitals, which extended beyond the scope of their research. The announcement of Verifiable Data Audit is an attempt to gain back some of the lost trust.

As the scale of deep learning networks increases, the computational complexity also increases accordingly, which severely limits its application to smart devices such as mobile phones. For example, the use of large-scale complex network models such as VGGNet and the residual network on an end device is not realistic. Therefore, we need a deep learning model to perform compression and acceleration. We have described two major compression algorithms below. The Low Bit model refers to compressing consecutive weights into discrete low-precision weights.

Pattern matching and predicting an exigent need in hospitals is a difficult task for skilled medical staffs, but not for AI and machine learning. Medical staffs do not have the luxury of observing each of their patients on a full-time basis. Although incredibly good at identifying the immediate needs of patients in obvious circumstances, nurses and medical staffs do not possess the capabilities of discerning the future from a complex array of patient symptoms exhibited over a reasonable period. Machine learning has the luxury of not only observing and analyzing patient data 24/7, but also combining information collected from multiple sources, i.e. historical records, daily evaluations by medical staff, and real-time measurements of vitals such as heart rate, oxygen usage and blood pressure. The application of AI in the assessment and prediction of imminent heart attacks, falls, strokes, sepsis and complications is currently underway all over the world.

Prostate cancer is expected to be the leading source of new cancer for men and the second most frequent cause of death after lung cancer. It is also cancer that is very hard to detect, and small lesions can comprise just a fraction of 1% of the tissue surface. To help solve the problem, researchers from Cornell University and the Memorial Sloan Kettering Cancer Center, a cancer treatment and research institution in New York City, developed a deep learning-based approach that more accurately detects cancer. Using the center's biopsy dataset, the team developed a state-of-the-art system that can be considered clinically relevant, the researchers said. "Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset," the researchers stated in their research paper.

That's how quickly brain damage happen when the cells get no oxygen in a stroke or in some brain injuries. Both can have tragic consequences -- paralysis, memory loss, speech difficulties and even death. But doctors can't start treatment without an initial diagnosis, and that requires reading a CT scan as soon as the test's completed. Unfortunately, that's not what usually happens, said Prashant Warier, co-founder of Qure.ai, a member of our Inception startup accelerator program. "Radiologists typically have a backlog of cases," he said.

Welcome to this series on reinforcement learning! We'll first start out by introducing the absolute basics to build a solid ground for us to run. We'll then progress onto more advanced and sophisticated topics that integrate artificial neural networks and deep learning into reinforcement learning. We'll also be getting our hands dirty by implementing some super cool reinforcement learning projects in code! Without further ado, let's get to it!

A self-learning optimal control algorithm for sequential manufacturing processes with time-discrete control actions is proposed and evaluated with simulated deep drawing processes. The necessary control model is built during consecutive process executions under optimal control via Reinforcement Learning, using the measured product quality as reward after each process execution. Prior model formation, which is required by state-of-the-art algorithms like Model Predictive Control and Approximate Dynamic Programming, is therefore obsolete. This avoids the difficulties in system identification and accurate modelling, which arise with processes subject to non-linear dynamics and stochastic influences. Also runtime complexity problems of these approaches are avoided, which arise when more complex models and larger control prediction horizons are employed. Instead of using pre-created process- and observation-models, Reinforcement Learning algorithms build functions of expected future reward during processing, which are then used for optimal process control decisions. The learning of such expectation functions is realized online by interacting with the process. The proposed algorithm also takes stochastic variations of the process conditions into consideration and is able to cope with partial observability. A method for the adaptive optimal control of partially observable fixed-horizon manufacturing processes, based on Q-learning is developed and studied. The resulting algorithm is instantiated and then evaluated by application to a time-stochastic optimal control problem in metal sheet deep drawing, where the experiments use FEM-simulated processes. The Reinforcement Learning based control shows superior results over the model-based Model Predictive Control and Approximate Dynamic Programming approaches.

Abstract--Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timingdependent plasticity (STDP) mechanism for learning. Existing hardware implementations of SNN are limited in scale or do not have in-hardware learning capability. In this work, we propose a low-cost scalable Network-on-Chip (NoC) based SNN hardware architecture with fully distributed in-hardware STDP learning capability. All hardware neurons work in parallel and communicate through the NoC. This enables chip-level interconnection, scalability and reconfigurability necessary for deploying different applications. The hardware is applied to learn MNIST digits as an evaluation of its learning capability. We explore the design space to study the tradeoffs between speed, area and energy. How to use this procedure to find optimal architecture configuration is also discussed. In the field of deep learning, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are developed to perform a series of human-level cognitive applications [1] [2]. However, the tremendous computation and memory requirement have been seriously challenging the processing efficiency of deep learning systems [3] [4]. The limitations of Von Neumann architecture coupled with increasing power demands due to Dennard scaling and the approaching end of Moore's Law have motivated multiple research efforts into low-power, highly parallel and distributed computing architecture [5] [6] [7] [8] and brain-inspired computing architecture [9] [10]. Brain as a source of inspiration is not surprising given its ability to process massive amounts of real-time information while consuming less than 20 W of power [11]. The goal of neuromorphic hardware design is to explore the bio-inspired architecture to achieve cognitive functions in real time utilizing lower power and smaller footprint than the traditional Von Neumann architectures.

Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there is still more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. In this work, we address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor's surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, as well as closed course tests, the method was successfully deployed to a fleet of SDVs.

Models that attempt to extract insight from this firehose of information must face the torrential covariate shift that is endemic to the Twitter platform. While regularly-retrained algorithms can maintain performance in the face of this shift, fixed model features that fail to represent new trends and tokens can quickly become stale, resulting in performance degradation. To mitigate this problem we employ learned features, or embedding models, that can efficiently represent the most relevant aspects of a data distribution. Sharing these embedding models across teams can also reduce redundancy and multiplicatively increase cross-team modeling productivity. In this paper, we detail the commoditized tools, algorithms and pipelines that we have developed and are developing at Twitter to regularly generate high quality, up-to-date embeddings and share them broadly across the company.