Flying into Dallas Fort Worth International Airport from Mexico in December, I queued in the immigration line for US citizens and was taken aback when – rather than request my passport – the Customs and Border Protection (CBP) agent simply instructed me to look at the camera and then pronounced my first name: "Maria?" Feeling an abrupt violation of my entire bodily autonomy, I nodded – and reckoned that it was perhaps easy to lose track of the rapid dystopian devolution of the world when one had spent the past two years hanging out on a beach in Oaxaca. A CBP poster promoting the transparent infringement on privacy was affixed to the airport wall, and featured a grey-haired man smiling suavely into the camera along with the text: "Our policies on privacy couldn't be more transparent. In my case, the process was not so fast, as I had to hand over my passport for physical scrutiny after I raised the agent's suspicions by being unable to answer in any remotely coherent fashion the ...

In industries from healthcare to education to finance to manufacturing, quarantine and extended work-from-home forced companies to use technology to reimagine nearly every facet of their operations. As the world reopens in fits and starts, we analyze the industries poised to thrive in a post-Covid world. As the Covid-19 pandemic has charted its unprecedented path around the world, it's carried with it the question: What will Covid-19's legacy be? From healthcare to education to entertainment to manufacturing, technology innovators are stepping forward to help answer that question. "Crisis can be… a catalyst or can speed up changes that are on the way -- it almost can serve as an accelerant." In the wake of the outbreak, everything from doctors appointments to schooling to workouts went online. As more people have worked, learned, banked, exercised, relaxed, and even sought medical care from home during Covid-19, they have gotten a crash course in just how much can be accomplished at ...

A police officer is at the scene of a murder. No obvious suspects or motives. DNA from the cells of one strand is copied and compared against a database. No match comes back, and the case goes cold. Corsight AI, a facial recognition subsidiary of the Israeli AI company Cortica, purports to be devising a solution for that sort of situation by using DNA to create a model of a face that can then be run through a facial recognition system.

Welcome to our first monthly digest of 2022! This is the place where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we cover our new series New voices in AI, hear from an ACML award winner, and celebrate an arXiv milestone. We're excited to announce the launch of a new series for AIhub: New voices in AI. Hosted by Joe Daly, this series will highlight the work of PhD students, early career researchers, and those in the field of AI with a fresh perspective.

Dubai has emerged as a leading centre for research and development in emerging industries with a primary focus on embracing new technologies. Digital transformation in the government and private sector across the United Arab Emirates (UAE) is further strengthening Dubai's position as a global tech hub. The Emirate is building momentum as a knowledge-based economy by large-scale adoption of Artificial Intelligence (AI) which is benefiting businesses in multiple ways. "AI is a game-changer for businesses since it improves productivity, lowers cost, unlocks job expansion and creates growth opportunities," states Omar Bin Sultan Al Olama, the UAE Minister of State for Artificial Intelligence. Al Olama, in fact, is the first minister dedicated to AI by any government in the world.

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated learning (FL) addresses these limitations by creating federated and privacy-preserving models. However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data falsification (SSDF) attacks on spectrum sensors has not been studied. To address this challenge, the first contribution of this work is the creation of a novel dataset suitable for FL and modeling the behavior (usage of CPU, memory, or file system, among others) of resource-constrained spectrum sensors affected by different SSDF attacks. The second contribution is a pool of experiments analyzing and comparing the robustness of federated models according to i) three families of spectrum sensors, ii) eight SSDF attacks, iii) four scenarios dealing with unsupervised (anomaly detection) and supervised (binary classification) federated models, iv) up to 33% of malicious participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms to increase the models robustness.

Encoding facts as representations of entities and binary relationships between them, as learned by knowledge graph representation models, is useful for various tasks, including predicting new facts, question answering, fact checking and information retrieval. The focus of this thesis is on (i) improving knowledge graph representation with the aim of tackling the link prediction task; and (ii) devising a theory on how semantics can be captured in the geometry of relation representations. Most knowledge graphs are very incomplete and manually adding new information is costly, which drives the development of methods which can automatically infer missing facts. The first contribution of this thesis is HypER, a convolutional model which simplifies and improves upon the link prediction performance of the existing convolutional state-of-the-art model ConvE and can be mathematically explained in terms of constrained tensor factorisation. The second contribution is TuckER, a relatively straightforward linear model, which, at the time of its introduction, obtained state-of-the-art link prediction performance across standard datasets. The third contribution is MuRP, first multi-relational graph representation model embedded in hyperbolic space. MuRP outperforms all existing models and its Euclidean counterpart MuRE in link prediction on hierarchical knowledge graph relations whilst requiring far fewer dimensions. Despite the development of a large number of knowledge graph representation models with gradually increasing predictive performance, relatively little is known of the latent structure they learn. We generalise recent theoretical understanding of how semantic relations of similarity, paraphrase and analogy are encoded in the geometric interactions of word embeddings to how more general relations, as found in knowledge graphs, can be encoded in their representations.

In domains ranging from computer vision to natural language processing, machine learning models have been shown to exhibit stark disparities, often performing worse for members of traditionally underserved groups. One factor contributing to these performance gaps is a lack of representation in the data the models are trained on. It is often unclear, however, how to operationalize representativeness in specific applications. Here we formalize the problem of creating equitable training datasets, and propose a statistical framework for addressing this problem. We consider a setting where a model builder must decide how to allocate a fixed data collection budget to gather training data from different subgroups. We then frame dataset creation as a constrained optimization problem, in which one maximizes a function of group-specific performance metrics based on (estimated) group-specific learning rates and costs per sample. This flexible approach incorporates preferences of model-builders and other stakeholders, as well as the statistical properties of the learning task. When data collection decisions are made sequentially, we show that under certain conditions this optimization problem can be efficiently solved even without prior knowledge of the learning rates. To illustrate our approach, we conduct a simulation study of polygenic risk scores on synthetic genomic data -- an application domain that often suffers from non-representative data collection. We find that our adaptive sampling strategy outperforms several common data collection heuristics, including equal and proportional sampling, demonstrating the value of strategic dataset design for building equitable models.

Tensor completion aims at imputing missing entries from a partially observed tensor. Existing tensor completion methods often assume either multi-linear or nonlinear relationships between latent components. However, real-world tensors have much more complex patterns where both multi-linear and nonlinear relationships may coexist. In such cases, the existing methods are insufficient to describe the data structure. This paper proposes a Joint mUlti-linear and nonLinear IdentificAtion (JULIA) framework for large-scale tensor completion. JULIA unifies the multi-linear and nonlinear tensor completion models with several advantages over the existing methods: 1) Flexible model selection, i.e., it fits a tensor by assigning its values as a combination of multi-linear and nonlinear components; 2) Compatible with existing nonlinear tensor completion methods; 3) Efficient training based on a well-designed alternating optimization approach. Experiments on six real large-scale tensors demonstrate that JULIA outperforms many existing tensor completion algorithms. Furthermore, JULIA can improve the performance of a class of nonlinear tensor completion methods. The results show that in some large-scale tensor completion scenarios, baseline methods with JULIA are able to obtain up to 55% lower root mean-squared-error and save 67% computational complexity.

AI is also taking biometrics to a new level. For example, facial recognition metrics always vary – there can be a different angle of head tilt, different distance between features, lighting, or facial expression. The same applies to voice recognition and other biometrics. It is impossible to set fixed rules of recognition, so AI analyzes these deviations of metrics within the specified ranges, makes decisions accordingly and learns continously. AI can also help to identify users and differentiate them from bots and automatic attacks, by analysing unique behavioral biometrics such as keystroke, scroll and swipe patterns.