AIoT is the catalyst for the advent of the smart era, and edge computing devices lie at the core of real-time processing and analysis in the field. The Cincoze GOLD series is a range of GPU computers designed from the ground up to meet the needs of large-scale image processing, machine vision, and machine learning applications in AIoT. The series includes the GP-3000 and GM-1000, which are selectable according to application requirements like size, performance, I/O, functionality, and future upgradeability. Whether it is smart manufacturing, smart transportation, smart cities, or even national defense, the GOLD series is an excellent choice for building smart applications for AIoT. The GP-3000 series is a top-of-the-line GPU edge computer that supports 720W total system power.

Today, national security is defined by tech superiority – but despite Britain's best efforts, our adversaries are seizing the edge. Imagine the threats we could face if adversarial states managed to take advantage of emerging "metaverse" technologies: from disinformation, political subversion, money laundering, fraud and extortion, to surveillance, industrial espionage, the exploitation of vulnerable people and cyber attacks. China has already developed a deliberate strategy to employ artificial intelligence to increase the pace and potency of these threats. There is now evidence that China is harnessing various emerging technologies centred around the metaverse (a network of increasingly realistic online worlds), such as simulation, artificial intelligence, blockchain, social media and virtual reality – to further enhance its technological arsenal and develop a metaverse for war. Imagine if a nation state could faithfully recreate reality in a virtual world; where detailed computer models of the physical, human, information, economic and industrial domains are brought together, made available to up to thousands of interested stakeholders from scientists to government officials to military leaders in the same persistent, immersive and secure virtual space, to collaborate and ask important "what if" questions.

Predictive Oncology Inc (NASDAQ: POAI) is set to be a first mover in the artificial intelligence (AI) powered drug discovery market that the company estimates will grow to $20 billion in the next three years. The Minneapolis, Minnesota-based company outlined its strategy for 2022 and revealed that it plans to leverage its existing pharma relationships to market PeDAL in a move that will take the proprietary platform out of the research lab into the pipelines of oncology drug discovery companies. Predictive Oncology recently completed its Discovery 21 evaluation, which is the proof-of-concept for PeDAL. CoRE, the company's AI program, together with tumor profile data, human tumor samples and active machine learning, power PeDAL to determine the most effective drug treatment for a specific cancer type. Investors responded well, sending shares of Predictive Oncology nearly 5% higher to $0.84 in the pre-market trading session.

First, financial regulators need to ensure that regulatory oversight delivers on the inclusion and intermediation-enhancing benefits of digital finance without compromising traditional regulatory goals such as financial stability, adequate competition, consumer protection and market integrity. Second, there is a pressing need for a system of data governance that allows consumers and business to exercise control over their data through the granting and withholding of consent to the use and transfer of their data. Developing a user-friendly granular consent-based data governance system with low transaction costs is a challenge that, when successfully addressed, will promote the development of virtual banking worldwide. Hong Kong SAR offers one example of an integrated regulatory framework for virtual banks. The licensing and regulatory regime – also applicable to incumbent banks – aims to manage the full spectrum of risks arising from any source, including the ownership structure, without compromising development objectives that often rest on technological innovation.

When researchers at Oregon State University created new tools to evaluate the decision-making algorithms of an advanced artificial intelligence system, study participants assigned to use them did, indeed, find flaws in the AI's reasoning. But once investigators instructed participants to use the tools in a more structured and rigorous way, the number of bugs they discovered increased markedly. "That surprised us a bit, and it showed that having good tools for visualizing and interfacing with AI systems is important, but it's only part of the story," said Alan Fern, professor of computer science at Oregon State. Since 2017, Fern has led a team of eight computer scientists funded by a four-year, $7.1 million grant from the Defense Advanced Research Projects Agency to develop explainable artificial intelligence, or XAI -- algorithms through which humans can understand, build trust in, and manage the emerging generation of artificial intelligence systems. Dramatic advancements in the artificial neural networks, or ANNs, at the heart of advanced AI have created a wave of powerful applications for transportation, defense, security, medicine, and other fields.

In November, the Internal Revenue Service launched an online security system that uses face recognition to confirm a person's identity. Public attention to the project last week triggered an outcry. The ACLU called the project "deeply troubling," saying face recognition "has been shown to be less accurate for people of color." Some IRS functions, like scheduling payments--but not filing taxes--now require first-time users to verify their identity with Virginia startup ID.me, which also works with 27 state employment agencies and the Veterans Administration. The process involves photographing a government-issued ID and uploading a video selfie so algorithms can match face and document.

The 1990s might have a lot to teach us about how we should tackle harm from artificial intelligence in the 2020s. Back then, some companies found they could actually make themselves safer by incentivizing the work of independent "white hat" security researchers who would hunt for issues and disclose them in a process that looked a lot like hacking with guardrails. That's how the practice of bug bounties became a cornerstone of cybersecurity today. In a research paper unveiled Thursday, researchers Josh Kenway, Camille François, Sasha Costanza-Chock, Inioluwa Deborah Raji and Joy Buolamwini argue that companies should once again invite their most ardent critics in -- this time, by putting bounties on harms that might originate in their artificial intelligence systems. François, a Fulbright scholar who has advised the French CTO and who played a key role in the U.S. Senate's probe of Russia's attempts to influence the 2016 election, published the report through the Algorithmic Justice League, which was founded in 2016 and "combines art and research to illuminate the social implications and harms of artificial intelligence."

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen--or shouldn't happen--unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur--and it's inevitable they will. That's one reason Nvidia's car is still experimental.

Locally interpretable model agnostic explanations (LIME) method is one of the most popular methods used to explain black-box models at a per example level. Although many variants have been proposed, few provide a simple way to produce high fidelity explanations that are also stable and intuitive. In this work, we provide a novel perspective by proposing a model agnostic local explanation method inspired by the invariant risk minimization (IRM) principle -- originally proposed for (global) out-of-distribution generalization -- to provide such high fidelity explanations that are also stable and unidirectional across nearby examples. Our method is based on a game theoretic formulation where we theoretically show that our approach has a strong tendency to eliminate features where the gradient of the black-box function abruptly changes sign in the locality of the example we want to explain, while in other cases it is more careful and will choose a more conservative (feature) attribution, a behavior which can be highly desirable for recourse. Empirically, we show on tabular, image and text data that the quality of our explanations with neighborhoods formed using random perturbations are much better than LIME and in some cases even comparable to other methods that use realistic neighbors sampled from the data manifold. This is desirable given that learning a manifold to either create realistic neighbors or to project explanations is typically expensive or may even be impossible. Moreover, our algorithm is simple and efficient to train, and can ascertain stable input features for local decisions of a black-box without access to side information such as a (partial) causal graph as has been seen in some recent works.

Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research. Prior work has tended to evaluate cooperative AI performance under the restrictive paradigms of self-play (teams composed of agents trained together) and cross-play (teams of agents trained independently but using the same algorithm). Recent work has indicated that AI optimized for these narrow settings may make for undesirable collaborators in the real-world. We formalize an alternative criteria for evaluating cooperative AI, referred to as inter-algorithm cross-play, where agents are evaluated on teaming performance with all other agents within an experiment pool with no assumption of algorithmic similarities between agents. We show that existing state-of-the-art cooperative AI algorithms, such as Other-Play and Off-Belief Learning, under-perform in this paradigm. We propose the Any-Play learning augmentation -- a multi-agent extension of diversity-based intrinsic rewards for zero-shot coordination (ZSC) -- for generalizing self-play-based algorithms to the inter-algorithm cross-play setting. We apply the Any-Play learning augmentation to the Simplified Action Decoder (SAD) and demonstrate state-of-the-art performance in the collaborative card game Hanabi.