Although the image of the tech-confused Boomer is a deeply-rooted stereotype, TechRepublic has reported that this is, in fact, a myth: In actuality, a Dropbox survey found that "people over age 55 are actually less likely than their younger colleagues to find using tech in the workplace stressful." A new report from security advisors Exabeam--2020 Cybersecurity Professionals Salary, Skills and Stress Survey--emphasizes these findings, as well. The research shows that although a whopping 88% of cybersecurity professionals embrace new technology, confident that automation will help them in their roles, it is the younger generation that is skeptical: 53% of respondents under the age of 45 "agreed or strongly agreed that AI and ML are a threat to their job security," according to the report. The findings, part of an annual survey, looked at attitudes regarding salary, training, innovation, and emerging technologies like artificial intelligence (AI) and machine learning (ML), among 350 cybersecurity professionals worldwide, hailing from the US, Germany, Singapore, Australia, and the UK. Overall, the results were positive, and the findings show that cybersecurity professionals continue to be satisfied in their jobs.

Last month, NHSX published "A Buyer's Guide to AI in Health and Care" (the Guide). As Artificial Intelligence (AI) plays an increasingly important role in healthcare, the Guide is a timely reminder of steps manufacturers, insurers and hospitals can take to mitigate liability risks. NHSX has responsibility for setting policy concerning the use of technology in the NHS. It is alive to liability risks and wants AI products to meet the highest standards of safety and effectiveness. The Guide is aimed at purchasers of AI products in the NHS, such as senior managers and procurement departments, but those manufacturing and supplying such products will also find it a useful resource.

In the present scenario, techniques like AI and machine learning are involved in almost all sectors. These techniques help organisations by various means, starting from getting insights from raw data to predicting future outcomes, and more. Focussing all the benefits of AI and ML, the utilisation of machine learning techniques in cybersecurity has been started only a few years ago and still at a niche stage. AI in cybersecurity can help in various ways, such as identifying malicious codes, self-training and other such. Here is a list of top eight machine learning tools, in alphabetical order for cybersecurity.

Saturday deliveries of ordinary mail from Japan Post may soon be a thing of past. During a Diet session set to begin on Oct. 26, the government plans to submit a bill scrapping such deliveries, sources have said. If the bill is enacted during the session, Saturday deliveries are expected to be abolished as early as autumn next year, the sources said. The government has been refraining from submitting the bill to revise the postal law in order to prioritize responses to sales irregularities involving postal life insurance products. The postal law currently requires Japan Post Co. to deliver ordinary mail six days a week or more.

Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation methods, which, however, are computationally expensive and do not scale well to large datasets. Variational Bayes (VB), a method from machine learning, addresses the shortcomings of MCMC by casting Bayesian estimation as an optimisation problem instead of a simulation problem. Considering all these advantages of VB, a VB method is derived for posterior inference in negative binomial models with unobserved parameter heterogeneity and spatial dependence. P\'olya-Gamma augmentation is used to deal with the non-conjugacy of the negative binomial likelihood and an integrated non-factorised specification of the variational distribution is adopted to capture posterior dependencies. The benefits of the proposed approach are demonstrated in a Monte Carlo study and an empirical application on estimating youth pedestrian injury counts in census tracts of New York City. The VB approach is around 45 to 50 times faster than MCMC on a regular eight-core processor in a simulation and an empirical study, while offering similar estimation and predictive accuracy. Conditional on the availability of computational resources, the embarrassingly parallel architecture of the proposed VB method can be exploited to further accelerate its estimation by up to 20 times.

Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as "buying a car" can be used in the context of a new but analogous process such as "buying a house". Nevertheless, most event understanding work in NLP is still at the ground level and does not consider abstraction. In this paper, we propose an Analogous Process Structure Induction APSI framework, which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub-event sequence of previously unseen open-domain processes. As our experiments and analysis indicate, APSI supports the generation of meaningful sub-event sequences for unseen processes and can help predict missing events.

Deep learning (DL) algorithms are the state of the art in automated classification of wildlife camera trap images. The challenge is that the ecologist cannot know in advance how many images per species they need to collect for model training in order to achieve their desired classification accuracy. In fact there is limited empirical evidence in the context of camera trapping to demonstrate that increasing sample size will lead to improved accuracy. In this study we explore in depth the issues of deep learning model performance for progressively increasing per class (species) sample sizes. We also provide ecologists with an approximation formula to estimate how many images per animal species they need for certain accuracy level a priori. This will help ecologists for optimal allocation of resources, work and efficient study design. In order to investigate the effect of number of training images; seven training sets with 10, 20, 50, 150, 500, 1000 images per class were designed. Six deep learning architectures namely ResNet-18, ResNet-50, ResNet-152, DnsNet-121, DnsNet-161, and DnsNet-201 were trained and tested on a common exclusive testing set of 250 images per class. The whole experiment was repeated on three similar datasets from Australia, Africa and North America and the results were compared. Simple regression equations for use by practitioners to approximate model performance metrics are provided. Generalized additive models (GAM) are shown to be effective in modelling DL performance metrics based on the number of training images per class, tuning scheme and dataset. Key-words: Camera Traps, Deep Learning, Ecological Informatics, Generalised Additive Models, Learning Curves, Predictive Modelling, Wildlife.

Many businesses and industries require accurate forecasts for weekly time series nowadays. The forecasting literature however does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method that can be used as a strong baseline in this domain, leveraging state-of-the-art forecasting techniques, forecast combination, and global modelling. Our approach uses four base forecasting models specifically suitable for forecasting weekly data: a global Recurrent Neural Network model, Theta, Trigonometric Box-Cox ARMA Trend Seasonal (TBATS), and Dynamic Harmonic Regression ARIMA (DHR-ARIMA). Those are then optimally combined using a lasso regression stacking approach. We evaluate the performance of our method against a set of state-of-the-art weekly forecasting models on six datasets. Across four evaluation metrics, we show that our method consistently outperforms the benchmark methods by a considerable margin with statistical significance. In particular, our model can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset.

Technology companies provide much of the critical infrastructure of the modern state and develop products that affect fundamental rights. Search and social media companies, for example, have set de facto norms on privacy, while facial recognition and predictive policing software used by law enforcement agencies can contain racial bias. In this episode of Deep Tech, Marietje Schaake argues that national regulators aren't doing enough to enforce democratic values in technology, and it will take an international effort to fight back. Schaake--a Dutch politician who used to be a member of the European parliament and is now international policy director at Stanford University's Cyber Policy Center--joins our editor-in-chief, Gideon Lichfield, to discuss how decisions made in the interests of business are dictating the lives of billions of people. Also this week, we get the latest on the hunt to locate an air leak aboard the International Space Station--which has grown larger in recent weeks. Elsewhere in space, new findings suggest there is even more liquid water on Mars than we thought. It's located in deep underground lakes and there's a chance it could be home to Martian life. Space reporter Neel Patel explains how we might find out. Back on Earth, the US election is heating up. Data reporter Tate Ryan-Mosley breaks down how technologies like microtargeting and data analytics have improved since 2016. Check out more episodes of Deep Tech here. Gideon Lichfield: There's a situation playing out onboard the International Space Station that sounds like something out of Star Trek… But there is an air leak in the space station.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. What if an advancing Army armored unit were maneuvering through mountainous terrain to "close with an enemy" when it is suddenly hit and disabled by an incoming artillery attack ... because a small, hovering enemy drone finds its location and transmits an electronic signal back to an enemy firebase? With its location compromised, the unit is paralyzed by enemy fire and denied freedom of maneuver. However, what if the armored unit is able to change its location and obscure itself from enemy fire when an EW (Electronic Warfare) detection system finds the electronic signature emitting from the enemy drone, deconflicts it from friendly electromagnetic emissions and then "jams" the data link connecting the drone to its operators, immediately disrupting the enemies' ability to know the location, speed and direction of the attacking friendly force.