Overview The goal of artificial intelligence is to enable the development of computers to do things normally done by people -- in particular, things associated with people acting intelligently. In the case of cybersecurity, its most practical application has been automating human intensive tasks to keep pace with attackers! Progressive organizations have begun using artificial intelligence in cybersecurity applications to defend against attackers. However, on it's own, artificial intelligence is best designed to identify "what is wrong." What today's enterprise needs to know is not only "what is wrong" in the face of a breach, but to understand "why it's wrong" and "how to fix it!"

Could Apple be about to release the HomePod 2, a smaller version of its Siri smart speaker? That's a question we've been asking ourselves for a while now now – and while Apple's iPhone X launch event on September 12, 2018 didn't reveal what the next HomePod will look like, the HomePod 2 could finally be on the horizon. Nearly a year has passed since then, and the iPhone 11 launch saw no mention of the new Apple HomePod Mini – so, everything is pointing to a 2020 release date. The speculation is that the next version of the HomePod, the Apple HomePod 2, may be a more compact version of the original, with the name Apple HomePod Mini being rumored. According to a Bloomberg report in July 2018, Apple may have been looking to release the HomePod 2 sometime in early 2019, which would make sense based on the release date of the original HomePod – of course, it never actually materialized.

Foundation today announces general availability of the newest, fastest, most powerful BeagleBoard.org Built on our proven open source Linux approach, BeagleBone AI fills the gap between small single board computers (SBCs) and more powerful industrial computers. Leveraging the Texas Instruments Sitara AM5729 processor, developers have access to powerful machine learning capabilities with the ease of the BeagleBone Black header and mechanical compatibility. BeagleBone AI makes it easy to explore how artificial intelligence (AI) and machine learning can be used in everyday life. Through BeagleBone AI, developers can take advantage of the TI C66x digital-signal-processor (DSP) cores and embedded-vision-engine (EVE) cores on the Sitara AM5729 processor.

The professional world has changed a lot in the last decades. Now, many workers are remote or freelance contractors that get hired and paid on a per-project basis. Thanks to the nomadic nature of the digital workforce, the technology that these employees use is entirely different from the last generation's -- and the evolution of this technology does not seem to be slowing down any time soon. Below, 15 members of Forbes Technology Council explore some of the cutting-edge technology trends that already are or will soon be transforming the workplace, and how companies can adapt to make the most of these changes. Embracing a "remote-first" culture at workplaces will be a key factor in companies' success in the coming decade.

Last month data scientists, analysts, executives, engineers, developers, and AI researchers from a wide range of industries flew in the city of seven hills, San Francisco, California. Each one of over 1000 attendees was super pumped to share and learn emerging trends that are transforming data and businesses. I was one of them and I would like to share some key takeaways with the data science community around the globe. In my opinion, at Strata Data Conferences one could see a perfect intersection of cutting-edge science and evolving business models. The conference featured more than 300 speakers, 10 keynotes, 10 tutorials, and 150 technical sessions.

AI could improve operational consistencies and enhance equipment utilization, Navis survey shows. Global container terminals are expected to embrace automated decision making powered by artificial intelligence (AI) as they pursue ways to improve operational consistencies and enhance equipment utilization, a new survey shows. The findings indicate that container terminals, regardless of their AI maturity, are increasingly aware of the possibilities of automated decision-making, according to supply chain technology provider Navis LLC. The Oakland, California-based firm said its TechValidate customer survey included responses from nearly 60 Navis customers, representing a cross-section of container terminals around the world using various degrees of automation. In addition to the 86% who cited operational consistency and equipment utilization as the most important benefits of automated decision-making, port operators also named other goals.

The AI market is possibly one of the toughest to keep track of, as the pace of change is relentless. Making predictions is even harder for this market, as one tends to follow what is known as Amara's Law, overestimating the potential of a technology in the short term and underestimating it in the longer term. Nevertheless, Tractica has identified 10 key predictions that cover various aspects of the ever-evolving AI market, based on our ongoing research and analysis including extensive primary research and interviews. Key trends to watch in the AI market in 2019 will include the commercialization of promising technologies, the growth of new application markets, changes in hardware architectures and infrastructure to support AI deployments, and continuing evolution of business models and public policy issues. Tractica's 10 key predictions for AI in 2019 include the following: This Tractica white paper identifies 10 key predictions for the AI market in 2019 and provides supporting details and examples behind each prediction. These trends draw from ongoing research and analysis that form part of Tractica's Artificial Intelligence advisory service.

Swiping is no longer the only way to find matches on Tinder. In a choose-your-own-adventure style series set to be rolled out next month, users will be able to match with other dating hopefuls by clicking their way through an interactive narrative. 'Swipe Night,' as Tinder is calling it, will air on October 6 and is designed to match users based on the choices they make during a short ''first-person apocalyptic adventure.' All of the episodes will be'live', so-to-speak, with each being available for viewing only between the hours of 6 pm and midnight during a respective users' local time. The series will consist of short five-minute videos during which users are periodically given seven seconds to choose what happens next.

In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed controllers can be learned with sub-linear sample complexity and computed with near-linear time complexity, both measured with respect to the dimension of the system. In particular, we provide sharp end-to-end guarantees on the stability and the performance of the designed distributed controller and prove that for sparse systems, the number of samples needed to guarantee robust and near optimal performance of the designed controller can be significantly smaller than the dimension of the system. Finally, we show that the proposed optimization problem can be solved to global optimality with near-linear time complexity by iteratively solving a series of small quadratic programs.

Optimal engine operation during a transient driving cycle is the key to achieving greater fuel economy, engine efficiency, and reduced emissions. In order to achieve continuously optimal engine operation, engine calibration methods use a combination of static correlations obtained from dynamometer tests for steady-state operating points and road and/or track performance data. As the parameter space of control variables, design variable constraints, and objective functions increases, the cost and duration for optimal calibration become prohibitively large. In order to reduce the number of dynamometer tests required for calibrating modern engines, a large-scale simulation-driven machine learning approach is presented in this work. A parallel, fast, robust, physics-based reduced-order engine simulator is used to obtain performance and emission characteristics of engines over a wide range of control parameters under various transient driving conditions (drive cycles). We scale the simulation up to 3,906 nodes of the Theta supercomputer at the Argonne Leadership Computing Facility to generate data required to train a machine learning model. The trained model is then used to predict various engine parameters of interest. Our results show that a deep-neural-network-based surrogate model achieves high accuracy for various engine parameters such as exhaust temperature, exhaust pressure, nitric oxide, and engine torque. Once trained, the deep-neural-network-based surrogate model is fast for inference: it requires about 16 micro sec for predicting the engine performance and emissions for a single design configuration compared with about 0.5 s per configuration with the engine simulator. Moreover, we demonstrate that transfer learning and retraining can be leveraged to incrementally retrain the surrogate model to cope with new configurations that fall outside the training data space.