This chapter focuses on the evolution of Human-Centered Design (HCD) in aerospace systems over the last forty years. Human Factors and Ergonomics first shifted from the study of physical and medical issues to cognitive issues circa the 1980s. The advent of computers brought with it the development of human-computer interaction (HCI), which then expanded into the field of digital interaction design and User Experience (UX). We ended up with the concept of interactive cockpits, not because pilots interacted with mechanical things, but because they interacted using pointing devices on computer displays. Since the early 2000s, complexity and organizational issues gained prominence to the point that complex systems design and management found itself center stage, with the spotlight on the role of the human element and organizational setups. Today, Human Systems Integration (HSI) is no longer only a single-agent problem, but a multi-agent research field. Systems are systems of systems, considered as representations of people and machines. They are made of statically and dynamically articulated structures and functions. When they are at work, they are living organisms that generate emerging functions and structures that need to be considered in evolution (i.e., in their constant redesign). This chapter will more specifically, focus on human factors such as human-centered systemic representations, life critical systems, organizational issues, complexity management, modeling and simulation, flexibility, tangibility and autonomy. The discussion will be based on several examples in civil aviation and air combat, as well as aerospace.

Facial images disclose many hidden personal traits such as age, gender, race, health, emotion, and psychology. Understanding these traits will help to classify the people in different attributes. In this paper, we have presented a novel method for classifying images using a pretrained transformer model. We apply the pretrained transformer for the binary classification of facial images in criminal and non-criminal classes. The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images. During the finetuning process with images, most of the layers of GT-2 are frozen during backpropagation and the model is frozen pretrained transformer (FPT). The FPT acts as a universal image classifier, and this paper shows the application of FPT on facial images. We also use our FPT on encrypted images for classification. Our FPT shows high accuracy on both raw facial images and encrypted images. We hypothesize the meta-learning capacity FPT gained because of its large size and trained on a large size with theory and experiments. The GPT-2 trained to generate a single word token at a time, through the autoregressive process, forced to heavy-tail distribution. Then the FPT uses the heavy-tail property as its meta-learning capacity for classifying images. Our work shows one way to avoid bias during the machine classification of images.The FPT encodes worldly knowledge because of the pretraining of one text, which it uses during the classification. The statistical error of classification is reduced because of the added context gained from the text.Our paper shows the ethical dimension of using encrypted data for classification.Criminal images are sensitive to share across the boundary but encrypted largely evades ethical concern.FPT showing good classification accuracy on encrypted images shows promise for further research on privacy-preserving machine learning.

As the costs of sensors and associated IT infrastructure decreases - as exemplified by the Internet of Things - increasing volumes of observational data are becoming available for use by environmental scientists. However, as the number of available observation sites increases, so too does the opportunity for data quality issues to emerge, particularly given that many of these sensors do not have the benefit of official maintenance teams. To realise the value of crowd sourced 'Internet of Things' type observations for environmental modelling, we require approaches that can automate the detection of outliers during the data modelling process so that they do not contaminate the true distribution of the phenomena of interest. To this end, here we present a Bayesian deep learning approach for spatio-temporal modelling of environmental variables with automatic outlier detection. Our approach implements a Gaussian-uniform mixture density network whose dual purposes - modelling the phenomenon of interest, and learning to classify and ignore outliers - are achieved simultaneously, each by specifically designed branches of our neural network. For our example application, we use the Met Office's Weather Observation Website data, an archive of observations from around 1900 privately run and unofficial weather stations across the British Isles. Using data on surface air temperature, we demonstrate how our deep mixture model approach enables the modelling of a highly skilled spatio-temporal temperature distribution without contamination from spurious observations. We hope that adoption of our approach will help unlock the potential of incorporating a wider range of observation sources, including from crowd sourcing, into future environmental models.

We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a response and a set of possibly high dimensional covariates, and (ii) a much larger unlabeled data set where only the covariates are observed. We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets, to improve the estimation accuracy compared to the supervised estimator, i.e., the sample quantile from the labeled data. These estimators use a flexible imputation strategy applied to the estimating equation along with a debiasing step that allows for full robustness against misspecification of the imputation model. Further, a one-step update strategy is adopted to enable easy implementation of our method and handle the complexity from the non-linear nature of the quantile estimating equation. Under mild assumptions, our estimators are fully robust to the choice of the nuisance imputation model, in the sense of always maintaining root-n consistency and asymptotic normality, while having improved efficiency relative to the supervised estimator. They also attain semi-parametric optimality if the relation between the response and the covariates is correctly specified via the imputation model. As an illustration of estimating the nuisance imputation function, we consider kernel smoothing type estimators on lower dimensional and possibly estimated transformations of the high dimensional covariates, and we establish novel results on their uniform convergence rates in high dimensions, involving responses indexed by a function class and usage of dimension reduction techniques. These results may be of independent interest. Numerical results on both simulated and real data confirm our semi-supervised approach's improved performance, in terms of both estimation and inference.

A new initiative to shape international standards for Artificial Intelligence (AI) was launched last week by the UK government, as part of its strategy to become a global AI power. The "AI Standards Hub" will focus on governance and guidance and falls under the National AI Strategy that aims to increase Britain's contribution to development of global AI technical standards. The Alan Turing Institute, the London-based data science and AI organisation, has been selected to lead the pilot with support from the British Standards Institution and National Physical Laboratory. "The new AI Standard Hub will create practical tools for businesses, bring the UK's AI community together through a new online platform, and develop educational materials to help organisations develop and benefit from global standards," the government announced, adding that the move puts the country at the "forefront" of a rapidly developing industry. "On the face of it, the AI Standards Hub offers some substance to the government's claims of Britain being a tech power and paves the way for it to play a leadership role in shaping AI at the global level," London-based political risk analyst Mikhail Sebastian told TRT World.

The US Department of Energy (DOE) has long stood out as one of the most science, technology, and innovation-focused US federal agencies. It should come as little surprise then that the DOE continues to invest in transformative technology such as artificial intelligence and machine learning. The DOE established the Artificial Intelligence and Technology (AITO) office to help transform the DOE into a world leading Artificial Intelligence (AI) enterprise by accelerating the research, development, delivery, and adoption of AI. Pamela Isom, the new Director of the AITO, will be presenting at the February 2022 AI in Government event to share how they are maximizing the impacts of AI through strategic coordination, planning, and customer service excellence. In this interview article Ms. Isom goes into greater detail about how the DOE is leveraging data, and transformative technologies to help advance the agency's core missions.

Under the theme "Ensuring AI as a Common Good to Transform Education", the 2021 International Forum on Artificial Intelligence (AI) and Education convened policy-makers and practitioners from around the world on 7 and 8 December 2021. The goal was to share knowledge on how governance can be aligned to direct AI towards the common good for education and humanity, and how countries are leveraging AI to deliver the unfulfilled promises and enable the futures of learning. The Forum was co-organized by UNESCO and China with the support of the Inter-UN-Agency Working Group on Artificial Intelligence. It convened approximately 74 speakers including 17 Ministers or Vice Ministers, from UN agencies, international organizations and more than 40 countries around the world. During the two-day event, the Forum attracted more than 9,000 real-time participants and viewers from more than 100 countries.

I feel sorry for Tom Cruise. The next Top Gun movie will probably star an Apple chip designer and a ... [ ] team of LISP programmers. In August this year, eight teams gathered for the three-day final of DARPA's AlphaDogfight trials. The teams had developed Artificial Intelligence (AI) pilots to control F-16 fighter aircraft in simulated dogfights. The winner beat the human USAF pilot in five dogfights out of five.

To many, Artificial Intelligence (AI), is still in the realm of science fiction: decades away, and far removed from our current reality. However, increasingly, we are interfacing with platforms and systems powered by AI, such as smart watches and virtual assistants, such as Siri and Alexa. More importantly, in the coming decade, AI will drive and underpin a broad range of processes, and consequently change the ways we live and work. This episode is also available on SoundCloud, Apple iTunes, Google Play Music, Spotify, Amazon Music (NEW!) and on Stitcher! Over the past few months, there have been some new developments in the AI space that highlight the fact that AI is developing rapidly.

In the ABA/ABA FinCEN (Financial Crime Enforcement Network) conference 2019, the acting director Ken Blanco discussed the introduction of new divisions for transforming the current AML/CFT regimes in the UK. There were entire new divisions of enforcement and compliance along with global investigations to restrict financial crimes. Until now the current Anti-Money Laundering (AML) landscape was struggling to gain some form of momentum. However, with the implementation of the recent Anti-Money Laundering Act 2020 and AML solutions with integration of AI and ML, hopefully, 2022 will be remembered as a year proven to be a turning point for financial institutes. It is surprising to note that in the year 2020, banks from all over the world paid a total of $15.13 billion dollars and the US held the first rank in those AML fines, a sum of $11.11 billion was paid.