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Blockdrop to Accelerate Neural Network training by IBM Research


IBM Research, with the help of the University of Texas Austin and the University of Maryland, has created a technology, called BlockDrop, that promises to speed convolutional neural network operations without any loss of fidelity. This could further excel the use of neural nets, particularly in places with limited computing capability. Increase in accuracy level have been accompanied by increasingly complex and deep network architectures. This presents a problem for domains where fast inference is essential, particularly in delay-sensitive and realtime scenarios such as autonomous driving, robotic navigation, or user-interactive applications on mobile devices. Further research results show regularization techniques for fully connected layers, is less effective for convolutional layers, as activation units in these layers are spatially correlated and information can still flow through convolutional networks despite dropout.

Adoption Picking Up for AI in Cybersecurity; More Skilled Humans Needed Too


AI is increasingly being put to use in the technology stacks of cybersecurity companies, but not at the expense of human experts who guide the rollout and work alongside the smart tools. Before 2019, one in five cybersecurity software and service providers were employing AI, according to a study last year by Capgemini Research Institute, in a review of recent research published in DarkReading. Adoption was found to be "poised to skyrocket" by the end of 2020, with 63% of the firms planning to deploy AI in their solutions. Planned use in IT operations and the Internet of Things are predicted to see the most uptick. Increased adoption of AI does not mean that security professionals on IT staffs are ready to hand off their responsibilities.

Human Resource Management in the Age of AI


You don't have to be a prophet to foresee that artificial intelligence will also play an essential role in the field of human resource management. It will have a decisive impact on the way we connect people in the future. Using human-machine partnerships to improve the process of connecting people to the right job is relatively new to how most organizations hire. While there are many favorable advancements and novel solutions that promote more inclusive hiring, there are several risks to consider. First and foremost, we must challenge the assumption that hiring managers know what constitutes an ideal employee.

Prepare for Artificial Intelligence to Produce Less Wizardry


Early last year, a large European supermarket chain deployed artificial intelligence to predict what customers would buy each day at different stores, to help keep shelves stocked while reducing costly spoilage of goods. The company already used purchasing data and a simple statistical method to predict sales. With deep learning, a technique that has helped produce spectacular AI advances in recent years--as well as additional data, including local weather, traffic conditions, and competitors' actions--the company cut the number of errors by three-quarters. It was precisely the kind of high-impact, cost-saving effect that people expect from AI. But there was a huge catch: The new algorithm required so much computation that the company chose not to use it.

New predictive capabilities in Google Analytics


Google Analytics helps you measure the actions people take across your app and website. By applying Google's machine learning models, Analytics can analyze your data and predict future actions people may take. Today we are introducing two new predictive metrics to App Web properties. The first is Purchase Probability, which predicts the likelihood that users who have visited your app or site will purchase in the next seven days. And the second, Churn Probability, predicts how likely it is that recently active users will not visit your app or site in the next seven days.

CSRIO's Data61 alumni Emesent is flying drones beyond line-of-sight


Brisbane-based drone company Emesent has launched what it has dubbed as the "first plug-and-play payload" that enables industrial drones to fly beyond communications range and into unmapped areas. Built on Emesent's Hovermap simultaneous localisation and mapping (SLAM) autonomous flight system, the autonomy level 2 (AL2) technology was designed to enable companies to map, navigate, and collect data in challenging environments, such as mines, civil construction works, telecommunications infrastructure, and areas hit by natural disasters. "With the intelligence to navigate environments without a prior map, customers can use the system to carry out complex missions, secure the safety of personnel, and drive greater efficiency in their operations," Emesent co-founder and CEO Stefan Hrabar said. Emesent added that using AL2 would mean the drone processes data on-board in real-time to stream a 3D map of the environment back to the operator's tablet. It also touted that the ability for a drone to fly beyond line of sight allows workers to avoid hazardous environments while also enhancing visibility.

Hackmakers on LinkedIn: #artificialintelligence #datascience #machinelearning


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USA Artificial Intelligence Products Market Report 2019


For the region, type and application, the sales, revenue and their market share, growth rate are key research objects; we can research the manufacturers' sales, price, revenue, cost and gross profit and their changes. What's more, we will display the main consumers, raw material manufacturers, distributors, etc.Geographically, this report split USA into several key Regions, with sales (K Units), revenue (Million USD), market share and growth rate of Artificial Intelligence Products for these regions, from 2012 to 2023 (forecast), including Northeast Midwest South West USA Artificial Intelligence Products market competition by top manufacturers/players, with Artificial Intelligence Products sales volume, price, revenue (Million USD) and market share for each manufacturer/player; the top players including Open AI IBM NEC Nuance's Google Microsoft Corp Ipsoft Google Rocket Fuel Inc Fingenius Ltd On the basis of product, this report displays the sales volume (K Units), revenue (Million USD), product price (USD/Unit), market share and growth rate of each type, primarily split into Computer/GPU Chip Hardware Cloud Hardware Other On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, sales volume (K Units), market share and growth rate of Artificial Intelligence Products for each application, including Media & Advertising Healthcare Automotive & Transportation Other If you have any special requirements, please let us know and we will offer you the report as you want.

Nvidia brings Ampere A100 GPUs to Google Cloud


Just over a month after announcing its latest generation Ampere A100 GPU, Nvidia said this week that the powerhouse processor system is now available on Google Cloud. The A100 Accelerator Optimized VM A2 instance family is designed for enormous artificial intelligence workloads and data analytics. Nvidia says users can expect substantive improvements over previous processing models, in this instance up to a 20-fold performance boost. The Nvidia Ampere is the largest 7 nanometer chip ever constructed. It sports 54 billion transistors and offers innovative features such as multi-instance GPU, automatic mixed precision, an NVLink that doubles GPU-to-GPU direct bandwidth and faster memory reaching 1.6 terabytes per second.

IBM Buys RPA firm WDG Automation to Bolster AI-based Automation - EnterpriseTalk


IBM has acquired robotic process automation firm WDG Automation. The financial terms of the deal were not disclosed. The Brazil-based company will help advance IBM's AI-backed automation efforts, including Watson AIOps and Cloud Pak for Multicloud Management. WDG Automation's portfolio includes RPA, automation, interactive voice response, and chatbots. The acquisition will also help IBM to use WDG Automation's services for client digital transformation efforts and artificial intelligence workloads.