A new MIT research center promises to accelerate our journey to a future in which bionics help people everywhere overcome the challenges of disabilities -- and even enhance human potential. The future is near: Bionics replace or restore the function of missing or damaged body parts with electronic devices -- examples include leg exoskeletons and mind-controlled prosthetic arms. These devices can be life-changing, but many are still unique and experimental, meaning the only people to benefit from them are a handful of study participants. The faster we can advance bionics research, the sooner they'll be available to everyone who needs them. "We must continually strive towards a technological future in which disability is no longer a common life experience," MIT professor Hugh Herr, himself a double amputee, told MIT News.

The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports. Such information can help authorities understand the emergent situation and react accordingly. Moreover, social media plays a vital role in tackling misinformation and disinformation. This work presents TBCOV, a large-scale Twitter dataset comprising more than two billion multilingual tweets related to the COVID-19 pandemic collected worldwide over a continuous period of more than one year. More importantly, several state-of-the-art deep learning models are used to enrich the data with important attributes, including sentiment labels, named-entities (e.g., mentions of persons, organizations, locations), user types, and gender information. Last but not least, a geotagging method is proposed to assign country, state, county, and city information to tweets, enabling a myriad of data analysis tasks to understand real-world issues. Our sentiment and trend analyses reveal interesting insights and confirm TBCOV's broad coverage of important topics.

Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose "document-level natural language inference (NLI) for contracts", a novel, real-world application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as "Some obligations of Agreement may survive termination.") and a contract, and it is asked to classify whether each hypothesis is "entailed by", "contradicting to" or "not mentioned by" (neutral to) the contract as well as identifying "evidence" for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (1) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (2) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.

Significant advances in deep learning have obtained hallmark accuracy rates for various computer vision applications. However, advances in deep generative models have also led to the generation of very realistic fake content, also known as deepfakes, causing a threat to privacy, democracy, and national security. Most of the current deepfake detection methods are deemed as a binary classification problem in distinguishing authentic images or videos from fake ones using two-class convolutional neural networks (CNNs). These methods are based on detecting visual artifacts, temporal or color inconsistencies produced by deep generative models. However, these methods require a large amount of real and fake data for model training and their performance drops significantly in cross dataset evaluation with samples generated using advanced deepfake generation techniques. In this paper, we thoroughly evaluate the efficacy of deep face recognition in identifying deepfakes, using different loss functions and deepfake generation techniques. Experimental investigations on challenging Celeb-DF and FaceForensics++ deepfake datasets suggest the efficacy of deep face recognition in identifying deepfakes over two-class CNNs and the ocular modality. Reported results suggest a maximum Area Under Curve (AUC) of 0.98 and an Equal Error Rate (EER) of 7.1% in detecting deepfakes using face recognition on the Celeb-DF dataset. This EER is lower by 16.6% compared to the EER obtained for the two-class CNN and the ocular modality on the Celeb-DF dataset. Further on the FaceForensics++ dataset, an AUC of 0.99 and EER of 2.04% were obtained. The use of biometric facial recognition technology has the advantage of bypassing the need for a large amount of fake data for model training and obtaining better generalizability to evolving deepfake creation techniques.

This PhD thesis contains several contributions to the field of statistical causal modeling. Statistical causal models are statistical models embedded with causal assumptions that allow for the inference and reasoning about the behavior of stochastic systems affected by external manipulation (interventions). This thesis contributes to the research areas concerning the estimation of causal effects, causal structure learning, and distributionally robust (out-of-distribution generalizing) prediction methods. We present novel and consistent linear and non-linear causal effects estimators in instrumental variable settings that employ data-dependent mean squared prediction error regularization. Our proposed estimators show, in certain settings, mean squared error improvements compared to both canonical and state-of-the-art estimators. We show that recent research on distributionally robust prediction methods has connections to well-studied estimators from econometrics. This connection leads us to prove that general K-class estimators possess distributional robustness properties. We, furthermore, propose a general framework for distributional robustness with respect to intervention-induced distributions. In this framework, we derive sufficient conditions for the identifiability of distributionally robust prediction methods and present impossibility results that show the necessity of several of these conditions. We present a new structure learning method applicable in additive noise models with directed trees as causal graphs. We prove consistency in a vanishing identifiability setup and provide a method for testing substructure hypotheses with asymptotic family-wise error control that remains valid post-selection. Finally, we present heuristic ideas for learning summary graphs of nonlinear time-series models.

Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have unaligned time profiles. The PARAFAC2 model has been successfully used to analyze such data by allowing the underlying factor matrices in one mode (i.e., the evolving mode) to change across slices. The traditional approach to fit a PARAFAC2 model is to use an alternating least squares-based algorithm, which handles the constant cross-product constraint of the PARAFAC2 model by implicitly estimating the evolving factor matrices. This approach makes imposing regularization on these factor matrices challenging. There is currently no algorithm to flexibly impose such regularization with general penalty functions and hard constraints. In order to address this challenge and to avoid the implicit estimation, in this paper, we propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM). With numerical experiments on simulated data, we show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art. We also apply our model to a real-world chromatography dataset, and show that constraining the evolving mode improves the interpretability of the extracted patterns.

We are used to looking only at well-defined and delimited fields, where business thrives and goes on, and where economic resources and technological availability make the road to innovation more straightforward. However, in my opinion, we never stop to analyse what Shakir Mohamed, in his "Decolonial AI", defines as the "peripheries", shifting our "ictu oculi" from the centre towards new paradigms, still unexplored, if not ignored. Therefore, I found this study by Agunbiade Akintunde Ifeanyichukwu, whose name already says it all, since he signs himself Agunbiade A.I., which analyses the relationship between Artificial Intelligence (AI) and the Nigerian legal system, entitled "Artificial Intelligence and Law, a Nigerian Perspective", really interesting. The aim was to explore the ways in which they can influence each other, capturing new and half-known aspects of little-discussed legal systems. This book proposed the development of an indigenous AI system, coupled with ADR mechanisms, that would have the power to reduce the incidence of court congestion, while analysing a comprehensive legal framework of how it would work.

Predicting a landslide susceptibility map (LSM) is essential for risk recognition and disaster prevention. Despite the successful application of data-driven prediction approaches, current data-driven methods generally apply a single global model to predict the LSM for an entire target region. However, we argue that, in complex circumstances, especially in large-scale areas, each part of the region holds different landslide-inducing environments, and therefore, should be predicted individually with respective models. In this study, target scenarios were segmented into blocks for individual analysis using topographical factors. But simply conducting training and testing using limited samples within each block is hardly possible for a satisfactory LSM prediction, due to the adverse effect of \textit{overfitting}. To solve the problems, we train an intermediate representation by the meta-learning paradigm, which is superior for capturing information from LSM tasks in order to generalize proficiently. We chose this based on the hypothesis that there are more general concepts among LSM tasks that are sensitive to variations in input features. Thus, using the intermediate representation, we can easily adapt the model for different blocks or even unseen tasks using few exemplar samples. Experimental results on two study areas demonstrated the validity of our block-wise analysis in large scenarios and revealed the top few-shot adaption performances of the proposed methods.

The former Google X executive Mo Gawdat is beginning the rounds on his new book Scary Smart, which renders artificial intelligence to be as much a force of nature as Covid. Indeed, he sees AI as nothing less than the next evolutionary step on this planet. For Gawdat, it's clear: the capacity for learning from data and experience in these machines is on an exponential curve (which doesn't just gently ascend but shoots eventually into the sky). At some singular point – probably aided by the unimaginable calculating power of quantum computing, and apparently by the end of the decade – we will be in the presence of massively superior beings. READ MORE: Even Google's algorithm understands this one key fact about the Union Gawdat wants us – indeed, warns us – to think of them as "our children", with a voracious appetite for learning from their environment.

Artificial Intelligence (AI) has found a new expression in the art of Dutch photographer and digital designer Bas Uterwijk, who publishes faces "recreated" by machine learning technology from a software called Artbreeder. The operation of the application is simple to understand: it compiles all known information about a person - not only their physical facial structure, but also geographical and temporal information (place of birth and period of life, for example) - to create a figure that is more approximate than their technical conclusions can draw. Result is obvious: ultra-realistic figures with iconic names in history, such as the first president of the United States, George Washington, or even more contemporary and well-known celebrities, such as the rock star David Bowie, who died in January 2016. There is even Mike Ehrmantraut, a character lived by actor Johnathan Banks in the Breaking Bad series. Certainly, one figure that draws a lot of attention in Uterwijk's Instagram is the "picture" of Jesus Christ.