Artificial intelligence (AI) is all around us. I didn't make that statement because I read it somewhere from a popular news website or from Twitter. We don't have to go far for AI services; while writing this article, I've likely spoiled the first trend-that people don't talk about something that has been an invisible guiding hand. I unlock my phone through my face because of AI. A sleeping app might use the data on how I sleep to organize my nighttime routine.

Refugee rights are cosmological, binding, blended, co-dependent, and interconnected and constitute the basic structure of international custom [BASIC] encapsulating the national jurisdictions across the world. BASIC thrives on dignity; therefore, the word "refugee rights" can be delineated and defined in a single word–as per my understanding–called "dignity," as it is the issue of human dignity that we address in refugee rights. Therefore, refugee rights mean dignity, but the same has been further convoluted with the ascendance of Artificial Intelligence (AI) has presented new challenges to human equality in all walks of life. AI has reduced humanity in algorithmic calculations contrary to global human rights norms. AI does not recognize the significance of humanitarianism in its current form. It has envisioned a world of dynamic numerals that do not protect humanity and mitigate human sufferings in the Refugee Status Determination (RSD) procedures.

A computer using artificial intelligence can't be listed as an inventor on patents because only a human can be an inventor under U.S. law, a federal judge ruled in the first American decision that's part of a global debate over how to handle computer-created innovation. Federal law requires that an "individual" take an oath that he or she is the inventor on a patent application, and both the dictionary and legal definition of an individual is a natural person, ruled U.S. District Judge Leonie Brinkema in Alexandria, Virginia.

The previous post on "Technology Regulation: Risk-based approaches to Artificial Intelligence governance, Part I" discussed recent advancements in AI technologies that have led to new commercial applications with potentially adverse social implications. We also considered the challenges of AI governance and discussed the role of technical benchmarks for evaluating AI systems. In this post, we will explore the different AI risk assessment approaches that can underpin AI regulation. This post will conclude with a discussion on the next steps for national AI governance initiatives. Risk assessments can help identify the AI systems that need to be regulated.

North Carolina Agricultural and Technical State University is playing a critical role in the creation of Athena, a new artificial intelligence (AI) research center led by Duke University and including Yale, Massachusetts Institute of Technology and other prominent institutional partners. Funded by the National Science Foundation and the Department of Homeland Security, Athena is part of a $220 million investment in 11 AI research institutes engaging campuses in 40 states nationwide. Daniel Limbrick, Ph.D., an associate professor in the Department of Electrical and Computer Engineering, is the principal investigator for North Carolina A&T's sub-award from Duke University. Limbrick will contribute expertise in microprocessing and autonomous vehicles by creating small-scale testbeds of autonomous vehicle scenarios. The testbeds offer physical platforms to test and research the capabilities and limitations of edge computing and expand the research beyond computational modeling.

Best listening experience is on Chrome, Firefox or Safari. Everyone agrees artificial intelligence is powerful technology. That's why agencies across the government are looking at it to speed things up and improve mission delivery. But AI can be misused. Just look at the super-surveillance society that is communist China.

Should an artificially intelligent machine be able to patent its own inventions? For a US federal judge, the larger implications of that question were irrelevant. In April 2020, the US Patent and Trademark Office (USPTO) ruled that only "natural persons" could be credited as the inventor of a patent, and a US court decided Thursday that yes, that's what the law technically says (via Bloomberg). Not every country agrees with that direction. South Africa and Australia decided to go the other direction, granting one patent and reinstating a second patent application filed by AI researcher Steven Thaler, whose AI system DABUS reportedly came up with a flashing light and a new type of food container.

Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the so-called false negative (FN) problem. Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures. To address this issue, we propose a two-stage approach. First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. Experiments on two wildly-used benchmark datasets demonstrate the effectiveness of our approach.

Weighting methods are a common tool to de-bias estimates of causal effects. And though there are an increasing number of seemingly disparate methods, many of them can be folded into one unifying regime: causal optimal transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between a source and target population. Our approach is model-free but can also incorporate moments or any other important functions of covariates that the researcher desires to balance. We find that the causal optimal transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control study examining the effect of misoprostol versus oxytocin for treatment of post-partum hemorrhage.

The United States Securities and Exchange Commission (SEC) signed a deal with blockchain analytics firm AnChain.AI to help its efforts in monitoring the decentralized finance (DeFi) space. What Happened: According to a Forbes report, AnChain.AI CEO and Co-Founder Victor Fang explained that the collaboration started because "the SEC is very keen on understanding what is happening in the world of smart contract-based digital assets," and the firm can help with that. The company's service focuses on tracking illicit activity across crypto exchanges, DeFi protocols, and traditional financial institutions. The contract between the blockchain analysis firm and the SEC started in May and probably played a role in AnChain.AI securing a $10 million Series A round led by Susquehanna Group affiliate SIG Asia Investments LLP. Why It Matters: The regulator is seemingly leveraging the contractor to monitor the DeFi ecosystem more closely, as expected after recent remarks by SEC Chairman Gary Gensler.