The battery sector is turning to artificial intelligence for clues on how to improve recharging rates without increasing the degradation of lithium-ion batteries. Last month, a team from Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute published findings from battery testing aimed at cutting electric-vehicle charging times down to 10 minutes. The research, published in Nature, revealed how artificial intelligence could speed up the testing process required for novel charging techniques. The researchers wrote a program that predicted how batteries would respond to different charging approaches and was able to cut the testing process from almost two years to 16 days, Stanford reported. The technique was used to evaluate 224 possible high-cycle-life charging processes in just over two weeks, the researchers said.

Fortinet (NASDAQ: FTNT), a global leader in broad, integrated and automated cybersecurity solutions, announced FortiAI, a first-of-its-kind on-premises appliance that leverages self-learning Deep Neural Networks (DNN) to speed threat remediation and handle time consuming, manual security analyst tasks. FortiAI's Virtual Security Analyst embeds one of the industry's most mature cybersecurity artificial intelligence – developed by Fortinet's FortiGuard Labs – directly into an organization's network to deliver sub-second detection of advanced threats. While traditional cyber threats continue, sophistication of advanced attacks – often enabled by artificial intelligence, machine learning and open source communities – are increasing. As a result, organizations and their defenses are challenged to keep pace with threat evolution. Millions of new applications, growing cloud adoption and the increase in connected devices are creating billions of edges that security teams need to properly protect and manage.

Socionext Inc. has developed a prototype chip that incorporates newly-developed quantized Deep Neural Network (DNN) technology, enabling highly-advanced AI processing for small and low-power edge computing devices. The prototype is a part of a research project on "Updatable and Low Power AI-Edge LSI Technology Development" commissioned by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The chip features a "quantized DNN engine" optimized for deep learning inference processing at high speeds with low power consumption. Today's edge computing devices are based on conventional, general-purpose GPUs. These processors are not generally capable of supporting the growing demand for AI-based processing requirements, such as image recognition and analysis, which need larger devices at higher cost due to increases in power consumption and heat generation.

New data from Gartner Inc. suggests that the recruiting, management, and retention of artificial intelligence talent (AI) will be a strategic challenge globally for the foreseeable future. For the past four years, Gartner found, the strongest demand for talent with AI skills has come from non-IT departments. Departments recruiting AI talent in high volumes include marketing, sales, customer service, finance, and research and development, Gartner said in a press release. "These business units are using AI talent for customer churn modeling, customer profitability analysis, customer segmentation, cross-sell and upsell recommendations, demand planning, and risk management." Gartner TalentNeuron data released Wednesday shows the total AI jobs posted by non-IT departments in Top 12 countries by GDP, grew 74%, to 156,294 through March 2019, from 89,895 in July 2015.

An Austin, Texas based technology company is launching "artificially intelligent thermal cameras" that it claims will be able to detect fevers in people, and in turn send an alert that they may be carrying the coronavirus. Athena Security is pitching the product to be used in grocery stores, hospitals, and voting locations. It claims to be deploying the product at several customer locations over the coming weeks, including government agencies, airports, and large Fortune 500 companies. "Our Fever Detection COVID19 Screening System is now a part of our platform along with our gun detection system which connects directly to your current security camera system to deliver fast, accurate threat detection," Athena's website reads. Athena previously sold software that it claims can detect guns and knives in video feeds and then send alerts to an app or security system.

If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives. As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies are far from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19. Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented. There exists extensive literature on this subject in the context of natural images that can potentially be built upon. However, it cannot be assumed that conclusions about robustness will transfer from natural images to mammogram images, due to significant differences between the two image modalities. In order to determine whether conclusions will transfer, we measure the sensitivity of a radiologist-level screening mammogram image classifier to four commonly studied input perturbations that natural image classifiers are sensitive to. We find that mammogram image classifiers are also sensitive to these perturbations, which suggests that we can build on the existing literature. We also perform a detailed analysis on the effects of low-pass filtering, and find that it degrades the visibility of clinically meaningful features called microcalcifications. Since low-pass filtering removes semantically meaningful information that is predictive of breast cancer, we argue that it is undesirable for mammogram image classifiers to be invariant to it. This is in contrast to natural images, where we do not want DNNs to be sensitive to low-pass filtering due to its tendency to remove information that is human-incomprehensible.

Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR, like the ones listed above; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges. The paper first describes an ASR model, based on a deep neural network inspired by recent work in this domain, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.

Generalized from image and language translation, graph translation aims to generate a graph in the target domain by conditioning an input graph in the source domain. This promising topic has attracted fast-increasing attention recently. Existing works are limited to either merely predicting the node attributes of graphs with fixed topology or predicting only the graph topology without considering node attributes, but cannot simultaneously predict both of them, due to substantial challenges: 1) difficulty in characterizing the interactive, iterative, and asynchronous translation process of both nodes and edges and 2) difficulty in discovering and maintaining the inherent consistency between the node and edge in predicted graphs. These challenges prevent a generic, end-to-end framework for joint node and edge attributes prediction, which is a need for real-world applications such as malware confinement in IoT networks and structural-to-functional network translation. These real-world applications highly depend on hand-crafting and ad-hoc heuristic models, but cannot sufficiently utilize massive historical data. In this paper, we termed this generic problem "multi-attributed graph translation" and developed a novel framework integrating both node and edge translations seamlessly. The novel edge translation path is generic, which is proven to be a generalization of the existing topology translation models. Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges. Finally, extensive experiments on both synthetic and real-world application data demonstrated the effectiveness of the proposed method.

We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. Interestingly, we unveil the efficacy of randomization in improving detection accuracy and the generalization capability of certain deep neural network architectures with Bootstrap Aggregating (Bagging).