Everyone seems to be worried about the potential impact of artificial intelligence (AI) these days. Even technology leaders including Elon Musk and the Apple co-founder Steve Wozniak have signed a public petition urging OpenAI, the makers of the conversational chatbot ChatGPT, to suspend development for six months so it can be "rigorously audited and overseen by independent outside experts". Their concerns about the impact AI may have on humanity in the future are justified – we are talking some serious Terminator stuff, without a Schwarzenegger to save us. Unfortunately, there's AI that's being used right now which is already starting to have a big impact – even financially destroy – businesses and individuals. So much so that the US Federal Trade Commission (FTC) felt the need to issue a warning about an AI scam which, according to this NPR report "sounds like a plot from a science fiction story".

Outsourcing has become a popular business tool for companies across the U.S. looking for ways to ... [ ] increase profit margins and cut costs. Johnny Taylor Jr., CEO of the Society of Human Resource Management, offshored the job of a tech employee to India, saving 40% in the labor-cost arbitrage, when she asked to relocate from Arlington, Virginia, where the HR membership group is based, to North Carolina, according to the Wall Street Journal. Companies are accelerating their efforts to send jobs to lower-cost countries in response to the challenge of finding workers and inflation driving up wages. A recent Federal Reserve Bank of Atlanta survey found that 7.3% of leadership in the United States plans to move more jobs offshore as the next step from remote work within America. Richard Baldwin, an economics professor at the Graduate Institute in Geneva who studied the "offshoreability" of teleworking jobs, gave a warning at the European-based Center for Economic Policy Research last year, "If you can do your job from home, be scared."

March 31, 2023 - Artificial Intelligence suddenly seems to be everywhere. ChatGPT is writing human-sounding sermons, news updates, and answers to law school exam questions, while Dall·E is generating images ranging from the lifelike to the surreal in response to virtually any prompt. With much less fanfare, AI has already become ubiquitous in myriad ways. AI curates social media feeds and generates purchasing suggestions to fill internet shopping carts. AI saves lives by identifying potential pharmaceutical compounds and by quickly and accurately interpreting medical scans and images.

China's top digital regulator proposed bold new guidelines this week that prohibit ChatGPT-style large language models from spitting out content believed to subvert state power or advocate for the overthrow of the country's communist political system. Experts speaking with Gizmodo said the new guidelines mark the clearest signs yet of Chinese authorities' eagerness to extend its hardline online censorship apparatus to the emerging world of generative artificial intelligence. "We should be under no illusions. The Party will wield the new Generative AI Guidelines to carry out the same function of censorship, surveillance, and information manipulation it has sought to justify under other laws and regulations," Michael Caster, Asia Digital Programme Manager for Article 19, a human rights organization focused on online free expression, told Gizmodo. The draft guidelines, published by the Cyberspace Administration of China, come hot on the heels of new generative AI products from Baidu, Alibaba, and other Chinese tech giants.

This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms. Our studies discover a new attack pattern that negatively impact the forecasting of a target time series via making strategic, sparse (imperceptible) modifications to the past observations of a small number of other time series. To mitigate the impact of such attack, we have developed two defense strategies. First, we extend a previously developed randomized smoothing technique in classification to multivariate forecasting scenarios. Second, we develop an adversarial training algorithm that learns to create adversarial examples and at the same time optimizes the forecasting model to improve its robustness against such adversarial simulation. Extensive experiments on real-world datasets confirm that our attack schemes are powerful and our defense algorithms are more effective compared with baseline defense mechanisms. Understanding the robustness for time-series models has been a long-standing issue with applications across many disciplines such as climate change (Mudelsee, 2019), financial market analysis (Andersen et al., 2005; Hallac et al., 2017), down-stream decision systems in retail (Böse et al., 2017), resource planning for cloud computing (Park et al., 2019; 2020), and optimal control of vehicles (Kim et al., 2020). In particular, the notion of robustness defines how sensitive the model output is when authentic data is (potentially) perturbed with noises. In practice, as observation data are often corrupted by measurement noises, it is important to develop statistical forecasting models that are less sensitive to such noises (Brown, 1957; Brockwell & Davis, 2009; Taylor & Letham, 2018) or more stable against outliers that might arise from such corruption (Connor et al., 1994; Gelper et al., 2010; Liu & Zhang, 2021; Wang & Tsay, 2021).

Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse PINNs are formulated as soft-constrained multi-objective optimization problems with several hyperparameters. In this work, we demonstrate that inverse PINNs can be framed in terms of maximum-likelihood estimators (MLE) to allow explicit error propagation from interpolation to the physical model space through Taylor expansion, without the need of hyperparameter tuning. We explore its application to high-dimensional coupled ODEs constrained by differential algebraic equations that are common in transient chemical and biological kinetics. Furthermore, we show that singular-value decomposition (SVD) of the ODE coupling matrices (reaction stoichiometry matrix) provides reduced uncorrelated subspaces in which PINNs solutions can be represented and over which residuals can be projected. Finally, SVD bases serve as preconditioners for the inversion of covariance matrices in this hyperparameter-free robust application of MLE to ``kinetics-informed neural networks''.

While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks. Tremendous research efforts have been dedicated to mitigating the threats of adversarial attacks, but the essential trait of adversarial examples is not yet clear, and most existing methods are yet vulnerable to hybrid attacks and suffer from counterattacks. In light of this, in this paper, we first reveal a gradient-based correlation between sensitivity analysis-based DNN interpreters and the generation process of adversarial examples, which indicates the Achilles's heel of adversarial attacks and sheds light on linking together the two long-standing challenges of DNN: fragility and unexplainability. We then propose an interpreter-based ensemble framework called X-Ensemble for robust adversary defense. X-Ensemble adopts a novel detection-rectification process and features in building multiple sub-detectors and a rectifier upon various types of interpretation information toward target classifiers. Moreover, X-Ensemble employs the Random Forests (RF) model to combine sub-detectors into an ensemble detector for adversarial hybrid attacks defense. The non-differentiable property of RF further makes it a precious choice against the counterattack of adversaries. Extensive experiments under various types of state-of-the-art attacks and diverse attack scenarios demonstrate the advantages of X-Ensemble to competitive baseline methods.

In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed by a factorized temporo-spatial encoder. We argue, that in contrast to natural images, a temporal-then-spatial factorization is more intuitive for SITS processing and present experimental evidence for this claim. Additionally, we enhance the model's discriminative power by introducing two novel mechanisms for acquisition-time-specific temporal positional encodings and multiple learnable class tokens. The effect of all novel design choices is evaluated through an extensive ablation study. Our proposed architecture achieves state-of-the-art performance, surpassing previous approaches by a significant margin in three publicly available SITS semantic segmentation and classification datasets. All model, training and evaluation codes are made publicly available to facilitate further research.

This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10). In both iterations the task consists of three subtasks: first detect whether the current turn is knowledge seeking, second select a relevant knowledge document, and third generate a response grounded on the selected document. For DSTC9 we proposed different approaches to make the selection task more efficient. The best method, Hierarchical Selection, actually improves the results compared to the original baseline and gives a speedup of 24x. In the DSTC10 iteration of the task, the challenge was to adapt systems trained on written dialogs to perform well on noisy automatic speech recognition transcripts. Therefore, we proposed data augmentation techniques to increase the robustness of the models as well as methods to adapt the style of generated responses to fit well into the proceeding dialog. Additionally, we proposed a noisy channel model that allows for increasing the factuality of the generated responses. In addition to summarizing our previous contributions, in this work, we also report on a few small improvements and reconsider the automatic evaluation metrics for the generation task which have shown a low correlation to human judgments.

Objective: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: i) develop a natural language processing (NLP) information extraction model to capture detailed SDOH information and ii) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. Materials and Methods: We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225,089 patients and 430,406 notes with social history sections and compared the extracted SDOH information with existing structured data. Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. Conclusions: Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.