Weak ontic necessity is the ontic necessity expressed by ``should'' or ``ought to'' in English. An example of it is ``I should be dead by now''. A feature of this necessity is whether it holds does not have anything to do with whether its prejacent holds. In this paper, we present a logical theory for conditional weak ontic necessity based on context update. A context is a set of ordered defaults, determining expected possible states of the present world. Sentences are evaluated with respect to contexts. When evaluating the conditional weak ontic necessity with respect to a context, we first update the context with the antecedent, then check whether the consequent holds with respect to the updated context. The logic is complete. Our theory combines premise semantics and update semantics for conditionals.

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.

Widely used deep learning models are found to have poor robustness. Little noises can fool state-of-the-art models into making incorrect predictions. While there is a great deal of high-performance attack generation methods, most of them directly add perturbations to original data and measure them using L_p norms; this can break the major structure of data, thus, creating invalid attacks. In this paper, we propose a black-box attack, which, instead of modifying original data, modifies latent features of data extracted by an autoencoder; then, we measure noises in semantic space to protect the semantics of data. We trained autoencoders on MNIST and CIFAR-10 datasets and found optimal adversarial perturbations using a genetic algorithm. Our approach achieved a 100% attack success rate on the first 100 data of MNIST and CIFAR-10 datasets with less perturbation than FGSM.

When the MIT Lincoln Laboratory Supercomputing Center (LLSC) unveiled its TX-GAIA supercomputer in 2019, it provided the MIT community a powerful new resource for applying artificial intelligence to their research. Anyone at MIT can submit a job to the system, which churns through trillions of operations per second to train models for diverse applications, such as spotting tumors in medical images, discovering new drugs, or modeling climate effects. But with this great power comes the great responsibility of managing and operating it in a sustainable manner -- and the team is looking for ways to improve. "We have these powerful computational tools that let researchers build intricate models to solve problems, but they can essentially be used as black boxes. What gets lost in there is whether we are actually using the hardware as effectively as we can," says Siddharth Samsi, a research scientist in the LLSC.

More than fifty years after a research group at Dartmouth University launched work on a new field called "Artificial Intelligence," the United States still lacks a national strategy on artificial intelligence (AI) policy. The growing urgency of this endeavor is made clear by the rapid progress of both U.S. allies and adversaries. The European Union is moving forward with two initiatives of far-reaching consequence. The EU Artificial Intelligence Act will establish a comprehensive, risk-based approach for the regulation of AI when it is adopted in 2023. Many anticipate that the EU AI Act will extend the "Brussels Effect" across the AI sector as the earlier European data privacy law, the General Data Privacy Regulation, did for much of the tech industry.

Four years ago, Alexandria Ocasio-Cortez's shock victory in a low-turnout midterm primary election in New York changed the shape of American politics. On Tuesday, the state held low-turnout midterm primaries with no such results. Instead, what the most-watched races offered was the latest glimpse of the ongoing fight between progressive insurgents and Democratic Party loyalists in New York. Loyalists claimed the day's biggest victories, thanks in large part to the state's new political maps--a consequence of a 2020 redistricting process that some of those same Party loyalists, led by then Governor Andrew Cuomo, botched so badly that a state judge ultimately outsourced the job to a postdoctoral fellow at Carnegie Mellon. The race that got the most attention, and which had the closest outcome, was for an open seat in the newly redrawn Tenth Congressional District, where the attorney Dan Goldman--who served as the House Democrats' lawyer during Donald Trump's first impeachment--squeaked out a victory in a crowded field.

MACAU, August 24 - Rostam J Neuwirth, head of the Department of Global Legal Studies of the University of Macau (UM) Faculty of Law, has published a new book titled'The EU Artificial Intelligence Act: Regulating Subliminal AI Systems'. Through exploring legal, ethical, and scientific issues related to artificial intelligence (AI), the book aims to show how cognitive, technological, and legal questions are intrinsically interwoven and to stimulate a transdisciplinary and transnational global debate between students, academics, practitioners, policymakers, and citizens. The book has been published by the British publisher Routledge. It contextualises the future regulation of AI as proposed by the European Union, specifically addressing the regulatory challenges relating to the planned prohibition of the use of AI systems that deploy subliminal techniques to manipulate the human mind and alter human behaviour. Subliminal perception usually refers to perception received below the threshold of awareness, such as images flashed quickly before the eyes or background music embedded with hidden messages, and these external stimuli can affect people without their being aware of it. In this respect, Prof Neuwirth points out that the convergence of AI with various related technologies, such as brain–computer interfaces, functional magnetic resonance imaging, robotics, and big data, already allows for'mind reading' or'dream hacking' through brain spyware, as well as other practices that intrude on cognition and the right to freedom of thought.

The United States has announced nearly $3bn in new military aid to Ukraine, with President Joe Biden saying the assistance aims to help the country defend against Russia's invasion "over the long term" as the war entered its seventh month. In a statement on Wednesday, as Ukraine marked its independence from the Soviet Union, Biden said the $2.98bn package would allow Kyiv to acquire air defence systems, "artillery systems and munitions, counter-unmanned aerial systems, and radars". This is the single largest US aid package for Ukraine since Russian forces began their full-scale military invasion of the country in February. "I know this independence day is bittersweet for many Ukrainians as thousands have been killed or wounded, millions have been displaced from their homes, and so many others have fallen victim to Russian atrocities and attacks," Biden said in the statement. "But six months of relentless attacks have only strengthened Ukrainians' pride in themselves, in their country, and in their thirty-one years of independence."

This is my weekly newsletter, with a dose of insights into the future. The topic of this newsletter is the exponential times we live in, hence the title of f(x) e x, which is the (natural) exponential function. You can visit TheDigitalSpeaker.com to book me as a keynote speaker for your next event or to hire me as a future tech coach for your CEO. I have also published my Trend Prediction for 2022. Tech is expanding, and so are the number of threats and vulnerabilities.

Artificial intelligence (AI) may be a distant and little-known subject for some people, but the reality is that it is much closer than many people believe. Through Artificial Intelligence, it is possible to combat violence against women; assist lawyers, law firms, and departments with document analysis and monitoring of changes in legislation; assist clients with financial matters; make farmers have better productivity; help the elderly to have a better quality of life, among thousands of other things. AI advancements will be no less significant. For example, AI will soon be able to accelerate drug discovery and green energy research. According to Andrew Ng, one of the world's leading AI experts, AI's advancement can add more than $10 trillion to the global economy by 2030. Many people fail to recognize that this isn't necessarily a bad thing or something to be afraid of.