Across the world, companies are in a fierce battle to assemble the right mix of hardware and software technology to deliver autonomous vehicles. With dozens of companies working on one of the hardest problems, driverless vehicles will be here in the not too distant future. So how is Australia getting ready to facilitate their introduction and allow businesses and citizens to take advantage of the technology? Last Friday, the 16th meeting of Infrastructure and Transport Ministers was held and they have made a really important determination, available in the now-public documents at infrastruture.gov.au Automated vehicles Ministers agreed that the future Automated Vehicle Safety Law will be implemented through Commonwealth law.

BE PART OF BUILDING THE FUTURE. What do NASA and emerging space companies have in common with COVID vaccine R&D teams or with Roblox and the Metaverse? The answer is data, -- all fast moving, fast growing industries rely on data for a competitive edge in their industries. And the most advanced companies are realizing the full data advantage by partnering with Pure Storage. Pure's vision is to redefine the storage experience and empower innovators by simplifying how people consume and interact with data.

In the right hands, artificial intelligence can do a lot more than beat chess masters or drive a vehicle. Four years ago, Dr. Nidhal C. Bouaynaya founded MRIMath, LLC, with Dr. Hassan Fathallah-Shaykh, a neuro-oncologist and mathematician at the University of Alabama at Birmingham - School of Medicine. Together, they developed an AI platform to help physicians detect brain tumor growth about three years earlier than the standard of care. In one example, Bouaynaya said MRIMath detected tumor growth in a patient whose doctors believed had been stabilized. "We showed them that, according to our AI, the tumor was growing and they had better do something about it," Bouaynaya said.

Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable--and more trustworthy--than real faces. Artificial intelligence (AI)–powered audio, image, and video synthesis--so-called deep fakes--has democratized access to previously exclusive Hollywood-grade, special effects technology. From synthesizing speech in anyone's voice (1) to synthesizing an image of a fictional person (2) and swapping one person's identity with another or altering what they are saying in a video (3), AI-synthesized content holds the power to entertain but also deceive. Generative adversarial networks (GANs) are popular mechanisms for synthesizing content.

The Department of Defense's Joint Artificial Intelligence Center recently launched new AI education pilots for thousands of DOD employees that range from executive education for general officers to in-depth coding bootcamps. The most recent cohort of participants started taking an "AI 101" course in early February through a partnership with the Massachusetts Institute of Technology while another recently entered an AI coding bootcamp. The range of educational offerings from the AI-accelerator is designed to eventually be transitioned to other DOD institutions for tens or even hundreds of thousands of people to learn about AI, Greg Allen, the JAIC's head of policy and strategy, told FedScoop. "We are running training pilots to really test," Allen said. "We partner with the broader department of defense … to help them deliver education materiel at scale."

We address a three-tier data-driven approach to solve the inverse problem in complex systems modelling from spatio-temporal data produced by microscopic simulators using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic dimension of the manifold where the emergent dynamics evolve and for feature selection over the parametric space. In the second step, based on the selected features, we learn the right-hand-side of the effective partial differential equations (PDEs) using two machine learning schemes, namely shallow Feedforward Neural Networks (FNNs) with two hidden layers and single-layer Random Projection Networks(RPNNs) which basis functions are constructed using an appropriate random sampling approach. Finally, based on the learned black-box PDE model, we construct the corresponding bifurcation diagram, thus exploiting the numerical bifurcation analysis toolkit. For our illustrations, we implemented the proposed method to construct the one-parameter bifurcation diagram of the 1D FitzHugh-Nagumo PDEs from data generated by $D1Q3$ Lattice Boltzmann simulations. The proposed method was quite effective in terms of numerical accuracy regarding the construction of the coarse-scale bifurcation diagram. Furthermore, the proposed RPNN scheme was $\sim$ 20 to 30 times less costly regarding the training phase than the traditional shallow FNNs, thus arising as a promising alternative to deep learning for solving the inverse problem for high-dimensional PDEs.

Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection systems and ensure MCS systems security. However, adversaries that aim to clog the sensing front-end and MCS back-end leverage intelligent techniques, which are challenging for MCS platform and service providers to develop appropriate detection frameworks against these attacks. Generative Adversarial Networks (GANs) have been applied to generate synthetic samples, that are extremely similar to the real ones, deceiving classifiers such that the synthetic samples are indistinguishable from the originals. Previous works suggest that GAN-based attacks exhibit more crucial devastation than empirically designed attack samples, and result in low detection rate at the MCS platform. With this in mind, this paper aims to detect intelligently designed illegitimate sensing service requests by integrating a GAN-based model. To this end, we propose a two-level cascading classifier that combines the GAN discriminator with a binary classifier to prevent adversarial fake tasks. Through simulations, we compare our results to a single-level binary classifier, and the numeric results show that proposed approach raises Adversarial Attack Detection Rate (AADR), from $0\%$ to $97.5\%$ by KNN/NB, from $45.9\%$ to $100\%$ by Decision Tree. Meanwhile, with two-levels classifiers, Original Attack Detection Rate (OADR) improves for the three binary classifiers, with comparison, such as NB from $26.1\%$ to $61.5\%$.

Machine learning algorithms, models, strategies, and other influential features are assisting us in unlocking a wide range of applications. These computer systems are capable of self-learning and making business decisions, as well as assisting research and improving technology. As machine learning finds new applications across various sectors, the demand for professionals in the field is growing. According to the US Bureau of Labor Statistics, the job outlook will rise 22 percent until 2030 for computer and information research scientists. Whichever area of machine learning interests you more, you must first familiarize yourself with machine learning terminology.

All it took was public outrage, a widespread campaign, and political condemnation for the IRS to reverse its plans to require facial recognition for access to certain online services. In abandoning its intention to require tax-payers to upload images of their government-issued IDs and video selfies to controversial third-party company ID.me, the IRS has acknowledged that Americans shouldn't have to sacrifice their privacy for security. But the controversy around ID.me has somewhat eclipsed the broader and more concerning context of biometric identification technologies. Coverage of the IRS's announcement has in many cases not addressed the fact that millions of less advantaged individuals in the United States have already been forced to have their faces scanned by ID.me to access government services. ID.me has contracts with 10 federal agencies and has been verifying identities for the IRS's Child Tax Credit Update Portal since last year.

The stakes are now higher because bad actors are engaging in organized crime, akin to cyberwarfare by nation-states. We've seen hospitals targeted during COVID-19 outbreaks, pipelines unable to deliver fuel, and other highly targeted attacks. The bad actors' new paradigm is to present two extortion threats on stolen enterprise data: holding the data hostage and threatening to leak sensitive information, including customer records and intellectual property. Such threats are especially salient for large organizations, which have the money and data desired by cybercriminals. Moreover, the attack surface for such crimes is ever-expanding as trends such as the adoption of 5G mobile networks and work-from-home policies push enterprise technology beyond its traditional borders.