The United States Patent and Trademark Office (USPTO) is in the process of developing artificial intelligence (AI) capabilities to add to the tool …

Road maintenance planning is an integral part of road asset management. One of the main challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance type and timing. This research proposes a framework using Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP) database to determine the type and timing of M&R practices. A predictive DNN model is first developed in the proposed algorithm, which serves as the Environment for the RL algorithm. For the Policy estimation of the RL model, both DQN and PPO models are developed. However, PPO has been selected in the end due to better convergence and higher sample efficiency. Indicators used in this study are International Roughness Index (IRI) and Rutting Depth (RD). Initially, we considered Cracking Metric (CM) as the third indicator, but it was then excluded due to the much fewer data compared to other indicators, which resulted in lower accuracy of the results. Furthermore, in cost-effectiveness calculation (reward), we considered both the economic and environmental impacts of M&R treatments. Costs and environmental impacts have been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical case study of a six-lane highway with 23 kilometers length located in Texas, which has a warm and wet climate. The results propose a 20-year M&R plan in which road condition remains in an excellent condition range. Because the early state of the road is at a good level of service, there is no need for heavy maintenance practices in the first years. Later, after heavy M&R actions, there are several 1-2 years of no need for treatments. All of these show that the proposed plan has a logical result. Decision-makers and transportation agencies can use this scheme to conduct better maintenance practices that can prevent budget waste and, at the same time, minimize the environmental impacts.

In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal dismissed an asylum appeal basing the decision on the output of a biometric system, alongside other discrepancies. The fingerprints of the asylum seeker were found in a biometric database which contradicted the appellant's account. The Tribunal found this evidence unequivocal and denied the asylum claim. Nowadays, the proliferation of biometric systems is shaping public debates around its political, social and ethical implications. Yet whilst concerns towards the racialised use of this technology for migration control have been on the rise, investment in the biometrics industry and innovation is increasing considerably. Moreover, fairness has also been recently adopted by biometrics to mitigate bias and discrimination on biometrics. However, algorithmic fairness cannot distribute justice in scenarios which are broken or intended purpose is to discriminate, such as biometrics deployed at the border. In this paper, we offer a critical reading of recent debates about biometric fairness and show its limitations drawing on research in fairness in machine learning and critical border studies. Building on previous fairness demonstrations, we prove that biometric fairness criteria are mathematically mutually exclusive. Then, the paper moves on illustrating empirically that a fair biometric system is not possible by reproducing experiments from previous works. Finally, we discuss the politics of fairness in biometrics by situating the debate at the border. We claim that bias and error rates have different impact on citizens and asylum seekers. Fairness has overshadowed the elephant in the room of biometrics, focusing on the demographic biases and ethical discourses of algorithms rather than examine how these systems reproduce historical and political injustices.

Artificial intelligence is a misnomer. AI is neither artificial nor intelligent. The implication is that AI is analogous to human intelligence, but AI requires extensive human training to function, and it exhibits completely different logic from humans in terms of recognizing, understanding, and classifying objects or scenes. AI often lacks any semblance of common sense, can be easily fooled or corrupted, and can fail in unexpected and unpredictable ways. In other words, proceed with caution.

Artificial intelligence is making its presence felt in more and more areas of our lives. But what impact is it going to have on digital transformation projects, legislation and more? Industry experts gave us their views. AI and 5G will converge thinks EdgeQ CEO Vinay Ravuri, "The convergence of 5G and Artificial Intelligence will become more indispensable and value oriented. Enterprises will expect 5G and AI to be seamlessly integrated as a key value center of 5G deployment. This will include the use of AI for advanced network automation and for intelligent analytics to streamline operations, optimize network performance and drive greater efficiencies overall. These capabilities will be important in the deployment, management, and operation of 5G." Nick Thomson, general manager of AI at iManage believes there needs to be greater transparency, "Increasingly, there's a need for greater explainability with AI. Users need to be able to say how and why the AI arrived at a particular decision rather than just writing off AI as a'black box' whose thought process is totally opaque. Because AI is being used to assist with both process automation (extracting clauses from mountains of contracts, for example) as well as decision-making (applying rules and logic to the information that's been extracted), there needs to be more explainability at that decision-making level. Making sure that the rules and the logic behind a decision are not just fully explainable to a human being but also defensible are key to ensuring that AI isn't inadvertently making bad decisions based on poorly trained models, unintentional bias, or other faulty logic."

Forms of automation such as artificial intelligence increasingly inform decisions about who gets hired, is arrested, or receives health care. Examples from around the world articulate that the technology can be used to exclude, control, or oppress people and reinforce historic systems of inequality that predate AI. Now teams of sociologists and computer science researchers say the builders and deployers of AI models should consider race more explicitly, by leaning on concepts such as critical race theory and intersectionality. Critical race theory is a method of examining the impact of race and power first developed by legal scholars in the 1970s that grew into an intellectual movement influencing fields including education, ethnic studies, and sociology. Intersectionality acknowledges that people from different backgrounds experience the world in different ways based on their race, gender, class, or other forms of identity.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

The EU's proposed artificial intelligence act fails to fully take into account the recent rise of an ultra-powerful new type of AI, meaning the legislation will rapidly become obsolete as the technology is deployed in novel and unexpected ways. Foundation models trained on gargantuan amounts of data by the world's biggest tech companies, and then adapted to a wide range of tasks, are poised to become the infrastructure on which other applications are built. That means any deficits in these models will be inherited by all uses to which they are put. The fear is that foundation models could irreversibly embed security flaws, opacity and biases into AI. One study found that a model trained on online text replicated the prejudices of the internet, equating Islam with terrorism, a bias that could pop up unexpectedly if the model was used in education, for example.

Certain advanced driving assistance features can promote safety by helping drivers avoid crashes and mitigate the severity of crashes that occur, but as with all technologies and equipment on motor vehicles, drivers must use them correctly and responsibly,

The use of artificial intelligence ("AI") and machine learning in the workplace is growing exponentially – and specifically in hiring. Over the last two decades, web-based applications and questionnaires have made paper applications nearly obsolete. As employers seek to streamline recruitment and control costs, they have jumped to use computer-based screening tools such as "chatbots" to communicate with job applicants, to schedule interviews, ask screening questions, and even conduct video conference interviews and presentations in the selection process. Employers of all sizes are creating their own systems, or hiring vendors who will design and implement keyword searches, predictive algorithms and even facial recognition algorithms to find the best-suited candidates. The algorithms in these computer models make inferences from data about people, including their identities, their demographic attributes, their preferences, and their likely future behaviors.