Computing power, big data, and advancement of algorithms have led to a renewed interest in artificial intelligence (AI), especially in deep learning (DL). The success of DL largely lies on data representation because different representations can indicate to a degree the different explanatory factors of variation behind the data. In the last few year, the most successful story in DL is supervised learning. However, to apply supervised learning, one challenge is that data labels are expensive to get, noisy, or only partially available. With consideration that we human beings learn in an unsupervised way; self-supervised learning methods have garnered a lot of attention recently. A dominant force in self-supervised learning is the autoencoder, which has multiple uses (e.g., data representation, anomaly detection, denoise). This research explored the application of an autoencoder to learn effective data representation of helicopter flight track data, and then to support helicopter track identification. Our testing results are promising. For example, at Phoenix Deer Valley (DVT) airport, where 70% of recorded flight tracks have missing aircraft types, the autoencoder can help to identify twenty-two times more helicopters than otherwise detectable using rule-based methods; for Grand Canyon West Airport (1G4) airport, the autoencoder can identify thirteen times more helicopters than a current rule-based approach. Our approach can also identify mislabeled aircraft types in the flight track data and find true types for records with pseudo aircraft type labels such as HELO. With improved labelling, studies using these data sets can produce more reliable results.

Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. Small sophisticated noise in the input images can accumulate along the network inference path and produce wrong decisions at the network output. On the other hand, human eyes recognize objects based on their global structure and semantic cues, instead of local image textures. Because of this, human eyes can still clearly recognize objects from images which have been heavily damaged by adversarial attacks. This leads to a very interesting approach for defending deep neural networks against adversarial attacks. In this work, we propose to develop a structure-preserving progressive low-rank image completion (SPLIC) method to remove unneeded texture details from the input images and shift the bias of deep neural networks towards global object structures and semantic cues. We formulate the problem into a low-rank matrix completion problem with progressively smoothed rank functions to avoid local minimums during the optimization process. Our experimental results demonstrate that the proposed method is able to successfully remove the insignificant local image details while preserving important global object structures. On black-box, gray-box, and white-box attacks, our method outperforms existing defense methods (by up to 12.6%) and significantly improves the adversarial robustness of the network.

We argue that the attempt to build morality into machines is subject to what we call the Interpretation problem, whereby any rule we give the machine is open to infinite interpretation in ways that we might morally disapprove of, and that the interpretation problem in Artificial Intelligence is an illustration of Wittgenstein's general claim that no rule can contain the criteria for its own application. Using games as an example, we attempt to define the structure of normative spaces and argue that any rule-following within a normative space is guided by values that are external to that space and which cannot themselves be represented as rules. In light of this problem, we analyse the types of mistakes an artificial moral agent could make and we make suggestions about how to build morality into machines by getting them to interpret the rules we give in accordance with these external values, through explicit moral reasoning and the presence of structured values, the adjustment of causal power assigned to the agent and interaction with human agents, such that the machine develops a virtuous character and the impact of the interpretation problem is minimised.

We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.

Artificial intelligence algorithms are increasingly adopted as decisional aides by public organisations, with the promise of overcoming biases of human decision-makers. At the same time, the use of algorithms may introduce new biases in the human-algorithm interaction. A key concern emerging from psychology studies regards human overreliance on algorithmic advice even in the face of warning signals and contradictory information from other sources (automation bias). A second concern regards decision-makers inclination to selectively adopt algorithmic advice when it matches their pre-existing beliefs and stereotypes (selective adherence). To date, we lack rigorous empirical evidence about the prevalence of these biases in a public sector context. We assess these via two pre-registered experimental studies (N=1,509), simulating the use of algorithmic advice in decisions pertaining to the employment of school teachers in the Netherlands. In study 1, we test automation bias by exploring participants adherence to a prediction of teachers performance, which contradicts additional evidence, while comparing between two types of predictions: algorithmic v. human-expert. We do not find evidence for automation bias. In study 2, we replicate these findings, and we also test selective adherence by manipulating the teachers ethnic background. We find a propensity for adherence when the advice predicts low performance for a teacher of a negatively stereotyped ethnic minority, with no significant differences between algorithmic and human advice. Overall, our findings of selective, biased adherence belie the promise of neutrality that has propelled algorithm use in the public sector.

Knowledge graph embedding (KGE) models learn to project symbolic entities and relations into a continuous vector space based on the observed triplets. However, existing KGE models cannot make a proper trade-off between the graph context and the model complexity, which makes them still far from satisfactory. In this paper, we propose a lightweight framework named LightCAKE for context-aware KGE. LightCAKE explicitly models the graph context without introducing redundant trainable parameters, and uses an iterative aggregation strategy to integrate the context information into the entity/relation embeddings. As a generic framework, it can be used with many simple KGE models to achieve excellent results. Finally, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework.

Tom Cruise has gone viral on the popular video-sharing app TikTok, but the clips featuring the'Mission Impossible' star are deepfakes that experts are calling the'most alarmingly lifelike examples' of the technology. An account appeared on the app last week, dubbed'deeptomcruise,' which shows a number of videos depicting Cruise doing a magic trick, playing golf and reminiscing about the time he met the former President of the Soviet Union. The series of clips have been seen more than 11 million times on TikTok as of Tuesday, with many millions more on other social media platforms. Although the clips are for entertainment, experts warn that such content'should worry us'. 'Seeing is no longer believing' rhetoric undermines real video.' An account appeared on the app last week, dubbed'deeptomcruise,' which shows a number of videos that have been viewed more than 11 million times.

Artificial intelligence, which can generate astonishingly realistic false images and videos, is increasingly being used to detect them. Distinguishing between fact and fakery has become an everyday part of our online lives. During the U.S. election campaign, a manipulated video appearing to show Joe Biden forget which state he was in went viral, receiving more than a million views before it was debunked. The doctoring of visual material for political mischief-making is nothing new. Josef Stalin notoriously erased undesirable companions from photographs during the Great Purge in 1930s Russia.

The U.S., which once had a dominant head start in artificial intelligence, now has just a few years' lead on China and risks being overtaken unless government steps in, according to a new report to Congress and the White House. Why it matters: Former Google CEO Eric Schmidt, who chaired the committee that issued the report, tells Axios that the U.S. risks dire consequences if it fails to both invest in key technologies and fully integrate AI into the military. Driving the news: The National Security Commission on Artificial Intelligence approved its 750-page report on Monday, following a 2-year effort. Schmidt chaired the 15-member commission, which also included Oracle's Safra Catz, Microsoft's Eric Horvitz and Amazon's Andy Jassy. "We don't have to go to war with China," Schmidt said.

Artificial Intelligence (AI) is one of the main weapons by which companies or medium-sized corporations can combat numerous cyber threats successfully. According to Warren Buffet, "Cyber-attack is the biggest threat to mankind, even more of a bigger threat than the nuclear weapon." Therefore, organizations should consider applying the concepts of AI within their workplaces if they want to prosper in the future without compromising their digital anonymity. Continue reading this post to know what is AI and how it is transforming cybersecurity for all the right reasons. Artificial Intelligence (AI) is a modern branch of computer science.