Israel has sought to increase its operational success on the battlefield through a major push for digitization in the Israel Defense Forces. The importance of this transformation was apparent in the recent conflict in Gaza that Israeli officials have called the first "artificial intelligence war." Chief of Staff Aviv Kochavi has made employing digital potential a central feature of his command, according to Col. Eli Birenbaum, head of the IDF Digital Transformation Division's Architecture Department. "The IDF had a few shortcomings to increase our lethality on the battlefield," said Birenbaum in an interview. While the IDF looks like one organization from the outside, for years its different services, including the air force, navy and ground forces, were balkanized in their use of their own networks for data services, he said.

The one glaring gap in the Commonwealth government's AI strategy and action plan is a process to develop a coordinated governance framework around the development, use and procurement of AI services within commonwealth government agencies. This is where the NSW Government has taken a clear lead, setting out a mandatory customer service circular which all NSW Government agencies need to adhere to. There is practical guidance on adhering to principles, assessing risk, managing data, sourcing AI solutions, meeting legal obligations and more.

If an agent, or a coalition of agents, has a strategy, knows that she has a strategy, and knows what the strategy is, then she has a know-how strategy. Several modal logics of coalition power for know-how strategies have been studied before. The contribution of the article is three-fold. First, it proposes a new class of know-how strategies that depend on the intelligence information about the opponents' actions. Second, it shows that the coalition power modality for the proposed new class of strategies cannot be expressed through the standard know-how modality. Third, it gives a sound and complete logical system that describes the interplay between the coalition power modality with intelligence and the distributed knowledge modality in games with imperfect information.

Variational Autoencoders (VAEs) have been shown to be remarkably effective in recovering model latent spaces for several computer vision tasks. However, currently trained VAEs, for a number of reasons, seem to fall short in learning invariant and equivariant clusters in latent space. Our work focuses on providing solutions to this problem and presents an approach to disentangle equivariance feature maps in a Lie group manifold by enforcing deep, group-invariant learning. Simultaneously implementing a novel separation of semantic and equivariant variables of the latent space representation, we formulate a modified Evidence Lower BOund (ELBO) by using a mixture model pdf like Gaussian mixtures for invariant cluster embeddings that allows superior unsupervised variational clustering. Our experiments show that this model effectively learns to disentangle the invariant and equivariant representations with significant improvements in the learning rate and an observably superior image recognition and canonical state reconstruction compared to the currently best deep learning models.

All the sessions from Transform 2021 are available on-demand now. There is a significant gap between an organization's ambitions for using artificial intelligence (AI) and the reality of how those projects turn out, Intel chief data scientist Dr. Melvin Greer said in a conversation with VentureBeat founder and CEO Matt Marshall at last week's Transf0rm 2021 virtual conference. One of the key areas is emotional intelligence and mindfulness. The pandemic highlighted this gap: The way people had to juggle home and work responsibilities meant their ability to stay focused and mindful could be compromised, Greer said. This could be a problem when AI is used in a cyberattack, like when someone is trying to use a chatbot or some other adversarial machine learning technique against us. "Our ability to get to the heart of what we're trying to achieve can be compromised when we are not in an emotional state and mindful and present," Greer said.

As the US continues to deal with the economic fallout of the pandemic, many states are requiring residents to submit to a facial-recognition software program to collect unemployment benefits. Currently 25 states are using ID.me, a Virginia-based online identity network, CNN reports. Two more have signed contracts and at least seven others are in discussions. To register with ID.me, clients verify their identity online--comparing a valid photo ID with a video selfie taken on their phone. State agencies say they are trying to trim processing time and address the rising tide of benefits fraud that's developed during the pandemic.

Shield AI announced on 22 July that it has acquired Heron Systems, bringing together two US software companies that are developing artificial intelligence (AI) pilots for military aviation. "Together, Shield AI and Heron will accelerate the deployment of advanced AI pilots to legacy and future military aircraft – an urgent and necessary step towards achieving national security priorities and remaining credible in the face of sophisticated peer countries," the announcement says. Heron Systems made headlines last year when its AI software defeated a human US Air Force F-16 pilot 5-0, and five other AI pilots, during the US Defense Advanced Research Projects Agency's (DARPA's) AlphaDogfight Trials. "Heron has developed the most advanced AI pilot for fighter aircraft in the United States," Shield AI co-founder and CEO Ryan Tseng said. Heron general manager Brett Darcey said that joining a larger company like Shield AI will provide "the opportunity and scale to accelerate the integration of our AI pilot on a next-generation fighter" and unmanned aircraft systems (UASs).

My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.

The Research Brief is a short take about interesting academic work. In collaboration with the United States Navy's Underwater Archaeology Branch, I taught a computer how to recognize shipwrecks on the ocean floor from scans taken by aircraft and ships on the surface. The computer model we created is 92% accurate in finding known shipwrecks. The project focused on the coasts of the mainland U.S. and Puerto Rico. It is now ready to be used to find unknown or unmapped shipwrecks.

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.