Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification. However, previous works usually address one of two aspects: i) how to extract accurate rationales for explainability while being beneficial to prediction; ii) how to make the predictive model robust to different types of adversarial attacks. Intuitively, a model that produces helpful explanations should be more robust against adversarial attacks, because we cannot trust the model that outputs explanations but changes its prediction under small perturbations. To this end, we propose a joint classification and rationale extraction model named AT-BMC. It includes two key mechanisms: mixed Adversarial Training (AT) is designed to use various perturbations in discrete and embedding space to improve the model's robustness, and Boundary Match Constraint (BMC) helps to locate rationales more precisely with the guidance of boundary information. Performances on benchmark datasets demonstrate that the proposed AT-BMC outperforms baselines on both classification and rationale extraction by a large margin. Robustness analysis shows that the proposed AT-BMC decreases the attack success rate effectively by up to 69%. The empirical results indicate that there are connections between robust models and better explanations.

In previous research, we showed that 'texts that tell a story' exhibit a statistical structure that is not Maxwell-Boltzmann but Bose-Einstein. Our explanation is that this is due to the presence of 'indistinguishability' in human language as a result of the same words in different parts of the story being indistinguishable from one another. In the current article, we set out to provide an explanation for this Bose-Einstein statistics. We show that it is the presence of 'meaning' in 'stories' that gives rise to the lack of independence characteristic of Bose-Einstein, and provides conclusive evidence that 'words can be considered the quanta of human language', structurally similar to how 'photons are the quanta of light'. Using several studies on entanglement from our Brussels research group, we also show that it is also the presence of 'meaning' in texts that makes the von Neumann entropy of a total text smaller relative to the entropy of the words composing it. We explain how the new insights in this article fit in with the research domain called 'quantum cognition', where quantum probability models and quantum vector spaces are used in human cognition, and are also relevant to the use of quantum structures in information retrieval and natural language processing, and how they introduce 'quantization' and 'Bose-Einstein statistics' as relevant quantum effects there. Inspired by the conceptuality interpretation of quantum mechanics, and relying on the new insights, we put forward hypotheses about the nature of physical reality. In doing so, we note how this new type of decrease in entropy, and its explanation, may be important for the development of quantum thermodynamics. We likewise note how it can also give rise to an original explanatory picture of the nature of physical reality on the surface of planet Earth, in which human culture emerges as a reinforcing continuation of life.

Not a day passes without a fascinating snippet on the ethical challenges created by "black box" artificial intelligence systems. These use machine learning to figure out patterns within data and make decisions--often without a human giving them any moral basis for how to do it. Classics of the genre are the credit cards accused of awarding bigger loans to men than women, based simply on which gender got the best credit terms in the past. Or the recruitment AIs that discovered the most accurate tool for candidate selection was to find CVs containing the phrase "field hockey" or the first name "Jared." More seriously, former Google CEO Eric Schmidt recently partnered with Henry Kissinger to publish The Age of AI: And Our Human Future, a book warning of the dangers of machine-learning AI systems so fast that they could react to hypersonic missiles by firing nuclear weapons before any human got into the decision-making process. In fact, autonomous AI-powered weapons systems are already on sale and may in fact have been used.

We'll divide this story into four sections: These are those data samples that look like normal samples but are perturbed in a certain way to fool the machine learning systems. For instance, in a given image not all the pixels are of the same importance, if you could identify the most important pixels (for the ML system to make a classification decision) and change them, your algorithm decision will also change and your sample will still appear to be normal. Why does the adversarial attack happen? Before we point out the reason as to why they happen, we just want to let you know that no ML algorithm is safe from adversarial attacks be it Logistic regression, softmax regression, SVM, Decision tree, Nearest neighbors, or deep learning models. Adversarial examples happen because of the excessive linearity in the systems.

Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient's genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen. These are some of the promises that physicians and researchers look to fulfill using artificial intelligence -- promises poised to transform clinical care, lead to better patient outcomes, and, ultimately, improve human lives. Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place. Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.

This may be remembered as the year when the world learned that lethal autonomous weapons had moved from a futuristic worry to a battlefield reality. It's also the year when policymakers failed to agree on what to do about it. On Friday, 120 countries participating in the United Nations' Convention on Certain Conventional Weapons could not agree on whether to limit the development or use of lethal autonomous weapons. Instead, they pledged to continue and "intensify" discussions. "It's very disappointing, and a real missed opportunity," says Neil Davison, senior scientific and policy adviser at the International Committee of the Red Cross, a humanitarian organization based in Geneva.

Shortly before 3 a.m. on July 19, 2016, U.S. Special Operations forces bombed what they believed were three Islamic State (IS) group "staging areas" on the outskirts of Tokhar, a riverside hamlet in northern Syria. They reported 85 fighters killed. In fact, they hit houses far from the front line, where farmers, their families and other local people sought nighttime sanctuary from bombing and gunfire. More than 120 villagers were killed. In early 2017 in Iraq, an American war plane struck a dark-colored vehicle, believed to be a car bomb, stopped at an intersection in the Wadi Hajar neighborhood of West Mosul. Actually, the car had been bearing not a bomb but a man named Majid Mahmoud Ahmed, his wife and their two children, who were fleeing the fighting nearby. They and three other civilians were killed. In November 2015, after observing a man dragging an "unknown heavy object" into an IS "defensive fighting position," U.S. forces struck a building in Ramadi, Iraq. A military review found that the object was actually "a person of small stature" -- a child -- who died in the strike. None of these deadly failures resulted in a finding of wrongdoing. These cases are drawn from a hidden Pentagon archive of the American air war in the Middle East since 2014. The trove of documents -- the military's own confidential assessments of more than 1,300 reports of civilian casualties, obtained by The New York Times -- lays bare how the air war has been marked by deeply flawed intelligence, rushed and often imprecise targeting and the deaths of thousands of civilians, many of them children, a sharp contrast to the U.S. government's image of war waged by all-seeing drones and precision bombs. The documents show, too, that despite the Pentagon's highly codified system for examining civilian casualties, pledges of transparency and accountability have given way to opacity and impunity. In only a handful of cases were the assessments made public. Not a single record provided includes a finding of wrongdoing or disciplinary action. Fewer than a dozen condolence payments were made, even though many survivors were left with disabilities requiring expensive medical care. Documented efforts to identify root causes or lessons learned are rare. The air campaign represents a fundamental transformation of warfare that took shape in the final years of the Obama administration, amid the deepening unpopularity of the forever wars that had claimed more than 6,000 American service members. The United States traded many of its boots on the ground for an arsenal of aircraft directed by controllers sitting at computers, often thousands of kilometers away. President Barack Obama called it "the most precise air campaign in history." This was the promise: America's "extraordinary technology" would allow the military to kill the right people while taking the greatest possible care not to harm the wrong ones. The IS caliphate ultimately crumbled under the weight of American bombing.

Two Katyusha rockets have struck Baghdad's heavily fortified Green Zone, which houses several Western embassies, the Iraqi military has said. One rocket was destroyed in the air by the C-RAM defence system and the other landed near a national monument and damaged two cars, the military said in a statement on Sunday. Security forces started an investigation to detect the launch site. There was no immediate claim of responsibility for the attack. A United States military official told the Reuters news agency that the C-RAM system brought down one of the rounds and none of them landed on the US embassy.

This decoupling had baleful economic and social consequences: low paid, insecure jobs held by non-college workers; low participation rates in the labor force; weak upward mobility across generations; and festering earnings and employment disparities among races that have not substantially improved in decades. While new technologies have contributed to these poor results, these outcomes were not an inevitable consequence of technological change, nor of globalization, nor of market forces. Similar pressures from digitalization and globalization affected most industrialized countries, and yet their labor markets fared better."

IRVINE, Calif., Dec. 16, 2021 (GLOBE NEWSWIRE) -- Seoul Robotics, the 3D perception solution company using deep learning AI to power the future of mobility, today announced that in 2021 the company more than doubled the number of partnerships and global employees. In July alone, Seoul Robotics closed more business than in the entirety of 2020. Since launching in North America in January 2021, Seoul Robotics has introduced three plug-and-play 3D perception systems, Discovery, Voyage, and Endeavor, as well as the most advanced version of its 3D perception platform, SENSR 2.2. SENSR 2.2 is the only 3D perception software on the market leveraging deep learning AI and is compatible with over 75 different makes and models of 3D sensors, including LiDAR. Over the past year, Seoul Robotics has secured and scaled solutions with several top-tier companies and government entities, including multiple Tier-1 original equipment manufacturers (OEMs) and Departments of Transportation.