Sila Inc., a fintech software platform that provides payment infrastructure as a service, announced that it has rolled out Instant ACH, a new service that uses state-of-the-art risk management to avail customers of funds from ACH debit transactions instantly. After passing a risk analysis, funds are immediately available to the end-user for ACH debits. "Our team at Sila is always looking for ways to push the envelope to provide real value to customers" To qualify for Instant ACH on Sila's platform, additional criteria for end-user onboarding as well as support for multiple Plaid services are required. Once these prerequisites are met, if an ACH debit request is made, Sila's risk scoring engine examines the onboarding data, details from the transaction, and other signals, to guarantee the transaction. Sila also sends an SMS to the verified phone of the end-user to confirm all transactions as part of the approval process.

Australia on Friday shrugged off Chinese anger over its decision to acquire US nuclear-powered submarines, while vowing to defend the rule of law in airspace and waters where Beijing has staked hotly contested claims. US President Joe Biden announced the new Australia-US-Britain defence alliance on Wednesday, extending US nuclear submarine technology to Australia as well as cyber defence, applied artificial intelligence and undersea capabilities. Beijing described the new alliance as an "extremely irresponsible" threat to regional stability, questioning Australia's commitment to nuclear non-proliferation and warning the Western allies that they risked "shooting themselves in the foot". China has its own "very substantive programme of nuclear submarine building", Australian Prime Minister Scott Morrison argued Friday in an interview with radio station 2GB. "They have every right to take decisions in their national interests for their defence arrangements and of course so does Australia and all other countries," he said.

Artificial intelligence is changing the game for cybersecurity across several industries by providing cutting-edge security technologies that analyze massive quantities of data. AI technology uses its ability to improve network security over time. Today, several organizations are increasingly implementing AI-powered intelligent security solutions & services to understand and reuse threat patterns to identify new coercions. AI technology provides wider security solutions and simplifies complete recognition and acknowledgment procedures related to cyberattacks. Thus, there is a growing demand for AI-based solutions in the end-use industry for cybersecurity.

Australia on Friday shrugged off Chinese anger over its decision to acquire US nuclear-powered submarines and vowed to defend the rule of law in airspace and waters where Beijing has staked multiple hotly contested claims. US President Joe Biden announced the new Australia-US-Britain defence alliance on Wednesday, extending US nuclear submarine technology to Australia as well as cyber defence, applied artificial intelligence and undersea capabilities. China's government described the alliance as an "extremely irresponsible" threat to regional stability, questioning Australia's commitment to nuclear non-proliferation and warning the Western allies that they risked "shooting themselves in the foot". China has its own "very substantive programme of nuclear submarine building", Australian Prime Minister Scott Morrison said Friday in an interview with radio station 2GB. "They have every right to take decisions in their national interests for their defence arrangements and of course so does Australia and all other countries," he said.

The existential threat of COVID-19 has highlighted an acute need to develop working therapeutics against emerging health threats. One of the luxuries deep learning has afforded us is the ability to modify the landscape as it unfolds -- so long as we can keep up with the viral threat, and access the right data. As with all new medical maladies, oftentimes the data needs time to catch up, and the virus takes no time to slow down, posing a difficult challenge as it can quickly mutate and become resistant to existing drugs. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) to ask: how can we identify the right synergistic drug combinations for the rapidly spreading SARS-CoV-2? Typically, data scientists use deep learning to pick out drug combinations with large existing datasets for things like cancer and cardiovascular disease, but, understandably, they can't be used for new illnesses with limited data.

This paper looks at solving collaborative planning problems formalized as Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e., situations where each agent's policy is a best response to the other agents' (fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP) algorithm does this in the finite-horizon setting relying on policy trees, we propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state controller (FSC) policy representations. In this article, we (1) explain how to turn a Dec-POMDP with $N-1$ fixed FSCs into an infinite-horizon POMDP whose solution is an $N^\text{th}$ agent best response; (2) propose a JESP variant, called \infJESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce heuristic initializations for JESP aiming at leading to good solutions; and (4) conduct experiments on state-of-the-art benchmark problems to evaluate our approach.

Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This paper presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.

Research on the detection of AI-generated videos has focused almost exclusively on face videos, usually referred to as deepfakes. Manipulations like face swapping, face reenactment and expression manipulation have been the subject of an intense research with the development of a number of efficient tools to distinguish artificial videos from genuine ones. Much less attention has been paid to the detection of artificial non-facial videos. Yet, new tools for the generation of such kind of videos are being developed at a fast pace and will soon reach the quality level of deepfake videos. The goal of this paper is to investigate the detectability of a new kind of AI-generated videos framing driving street sequences (here referred to as DeepStreets videos), which, by their nature, can not be analysed with the same tools used for facial deepfakes. Specifically, we present a simple frame-based detector, achieving very good performance on state-of-the-art DeepStreets videos generated by the Vid2vid architecture. Noticeably, the detector retains very good performance on compressed videos, even when the compression level used during training does not match that used for the test videos.

Many mathematical algorithms that we use in data science and machine learning require numeric data. And many algorithms tend to be very complex to implement (such as Support Vector Machines or Local Linear Embedding, which we previously discussed). But, association rule mining is perfect for categorical (non-numeric) data and it involves nothing more than simple counting! What we have here is a simple algorithm with not so simplistic results! The ratio of actionable insights discovery potential (high) to algorithm complexity (low) is quite large and atypical, IMHO.

In terms of fatal use of force, drones help tackle and even eliminate missiles, reducing collateral damage in comparison to other weapons systems. Drones provide a pin-prick, limited, covert strike to avert widening the war zone. In addition, the removal of pilots from combat zones completely eliminates the threat to pilots' lives. Drones are operated from facilities that are far away from the combat location, which helps their operators make better targeting decisions, as they do not have to fear for their own safety. Drones also help diminish the number of civilian casualties, a factor that helps drive the market.