BOSTON--(BUSINESS WIRE)--O'Reilly, the premier source for insight-driven learning on technology and business, today announced the lineup of speakers presenting at the O'Reilly Artificial Intelligence Conference, presented with Intel. The event will take place from September 9-12 in San Jose, Calif. at the San Jose McEnery Convention Center. Through detailed case studies, technical sessions and trainings, the AI Conference will offer a unique opportunity to tap into the leading minds in AI and network with thousands of innovative researchers, data scientists, engineers, senior developers and executives across industries. Together, Conference Chairs Ben Lorica (O'Reilly), Julie Shin Choi (Intel) and Roger Chen (Computable Labs), along with Honorary Co-Chairs Tim O'Reilly (O'Reilly) and Peter Norvig (Google), have created a conference program designed to help organizations successfully apply AI from both a business and technical perspective, covering emerging AI techniques and technologies. In advance of the conference, O'Reilly will release a report, "How Organizations are Sharpening their Skills to Better Understand and Use AI," that explores the data- and AI-related topics technology experts are most interested in.

Researchers at UCLA and NantWorks have developed an artificial intelligence-powered device that detects cancer cells in a few milliseconds -- hundreds of times faster than previous methods. With that speed, the invention could make it possible to extract cancer cells from blood immediately after they are detected, which could in turn help prevent the disease from spreading in the body. A paper about the advance was published in the journal Nature Scientific Reports. The approach relies on two core technologies: deep learning and photonic time stretch. Deep learning is a type of machine learning, an artificial intelligence technique in which algorithms are "trained" to perform tasks using large volumes of data.

Pharmaceutical companies have traditionally kept their data close to the vest because collaboration's side effects may include compromising intellectual property and losing the edge over competitors. But sharing data has major perks: The more data a pharma company has at its disposal, the better equipped its researchers are to quickly identify and develop promising new drugs. This can ultimately improve drug candidate success rates and reduce treatment costs. Bringing a drug to market takes on average 13 years and close to $2 billion, said Hugo Ceulemans, project leader of MELLODDY -- a new drug-discovery consortium that hopes to eliminate the tradeoff between data sharing and security. The project will use cloud-based NVIDIA GPUs and a distributed approach known as federated learning to train AI models on data from multiple pharmaceutical companies while preserving IP.

Enterprise search has been stuck in the 1990s for two decades now. It's hard to believe search hasn't kept pace with the explosion of data. The two must go hand in hand. Yet many of us go to work every day at big companies with complex knowledge management systems and fat IT budgets. We sit down, make a coffee, and before we know it, we're transported back to 1997 whenever we need to find a document.

Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles. Over the years many anomaly detection technologies have been explored for detecting combustor faults, however, the performance (detection rate) of anomaly detection solutions fielded is still inadequate. Advanced technologies that can improve detection performance are in great need. Aiming for improving anomaly detection performance, in this paper we introduce recently-developed deep learning (DL) in machine learning into the combustors anomaly detection application. Specifically, we use deep learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures. And we then use the learned features as the input to a neural network classifier for performing combustor anomaly detection. Since such deep learned features potentially better capture complex relations among all sensor measurements and the underlying combustor behavior than handcrafted features do, we expect the learned features can lead to a more accurate and robust anomaly detection. Using the data collected from a real-world gas turbine combustion system, we demonstrated that the proposed deep learning based anomaly detection significantly indeed improved combustor anomaly detection performance.

MLPerf, an emerging machine learning benchmark suite strives to cover a broad range of applications of machine learning. We present a study on its characteristics and how the MLPerf benchmarks differ from some of the previous deep learning benchmarks like DAWNBench and DeepBench. We find that application benchmarks such as MLPerf (although rich in kernels) exhibit different features compared to kernel benchmarks such as DeepBench. MLPerf benchmark suite contains a diverse set of models which allows unveiling various bottlenecks in the system. Based on our findings, dedicated low latency interconnect between GPUs in multi-GPU systems is required for optimal distributed deep learning training. We also observe variation in scaling efficiency across the MLPerf models. The variation exhibited by the different models highlight the importance of smart scheduling strategies for multi-GPU training. Another observation is that CPU utilization increases with increase in number of GPUs used for training. Corroborating prior work we also observe and quantify improvements possible by compiler optimizations, mixed-precision training and use of Tensor Cores.

Preterm birth is the most common cause of neonatal death. Current diagnostic methods that assess the risk of preterm birth involve the collection of maternal characteristics and transvaginal ultrasound imaging conducted in the first and second trimester of pregnancy. Analysis of the ultrasound data is based on visual inspection of images by gynaecologist, sometimes supported by hand-designed image features such as cervical length. Due to the complexity of this process and its subjective component, approximately 30% of spontaneous preterm deliveries are not correctly predicted. Moreover, 10% of the predicted preterm deliveries are false-positives. In this paper, we address the problem of predicting spontaneous preterm delivery using machine learning. To achieve this goal, we propose to first use a deep neural network architecture for segmenting prenatal ultrasound images and then automatically extract two biophysical ultrasound markers, cervical length (CL) and anterior cervical angle (ACA), from the resulting images. Our method allows to estimate ultrasound markers without human oversight. Furthermore, we show that CL and ACA markers, when combined, allow us to decrease false-negative ratio from 30% to 18%. Finally, contrary to the current approaches to diagnostics methods that rely only on gynaecologist's expertise, our method introduce objectively obtained results.

Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and evaluate signals about such temporal context into prediction models. Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves. Therefore, here we propose a Gaussian Process model to both address these data challenges and combine blood glucose and latent feature representations of contextual data for a novel multi-signal blood glucose prediction task. We find this approach outperforms common methods for multi-variate data, as well as using the blood glucose values in isolation. Given a robust evaluation across two blood glucose datasets with different forms of contextual information, we conclude that multi-signal Gaussian Processes can improve blood glucose prediction by using contextual information and may provide a significant shift in blood glucose prediction research and practice.

Although GAN-based methods have received many achievements in the last few years, they have not been such successful in generating discrete data. The most important challenge of these methods is the difficulty of passing the gradient from the discriminator to the generator when the generator outputs are discrete. Despite several attempts done to alleviate this problem, none of the existing GAN-based methods has improved the performance of text generation (using measures that evaluate both the quality and the diversity of generated samples) compared to a generative RNN that is simply trained by the maximum likelihood approach. In this paper, we propose a new framework for generating discrete data by an adversarial approach in which we do not need to pass the gradient to the generator. In the proposed method, the update of either the generator or the discriminator can be accomplished straightforwardly. Moreover, we leverage the discreteness of data to explicitly model the data distribution and ensure the normalization of the generated distribution and consequently the convergence properties of the proposed method. Experimental results generally show the superiority of the proposed DGSAN method compared to the other GAN-based approaches for generating discrete sequential data.

--The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with Heterogeneous Multi-Processor Systems on Chips (HMPSoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. These different components are capable of independently performing inference but with very different power-performance characteristics. In this article, we provide a quantitative evaluation of the inference capabilities of the different components on HMPSoCs. Finally, we explore the performance limit of the HMPSoCs by synergistically engaging all the components concurrently. The tremendous popularity of neural-network (NN) based machine learning applications in recent years has been fuelled partly by the increased capability of the compute engines, in particular, the GPUs. Traditionally, both the network training and inference were performed on the cloud with mobile devices only acting as user interfaces. However, enriched user experience now demands inference to be performed on the mobile devices themselves with high accuracy and throughput. In this article, we look at NN-enabled vision applications on mobile devices. These applications extract high-level semantic information from real-time video streams and predominately use Convolutional Neural Networks (CNNs).