Since March, Congress has held at least 10 hearings about A.I. across eight different committees or subcommittees. The Senate Judiciary Committee grilled the CEO of OpenAI, the Senate Armed Services Committee explored A.I. and defense, and the House Science Committee wanted to know about the latest A.I. innovations. In other words, it's been a bit of a mess--largely because, unlike agriculture, financial services, and other crucial areas of American life, technology doesn't have a committee dedicated solely to its regulation. Even committees like the House Committee on Science, Space, and Technology or the Senate Judiciary's Subcommittee on Privacy, Technology, and the Law do not have exclusive jurisdiction over tech. As a result, several different committees are throwing spaghetti against the wall in a real-time demonstration that Congress is simply not structured or resourced to do its job on A.I., or the other technologies that are shaping its constituents' lives.

A farmer in Niger tends to a tree sprout growing among his millet crop.Tony Rinaudo/World Vision Australia This story was originally published by Yale Environment 360 and is reproduced here as part of the Climate Desk collaboration. For decades, there have been reports of the deforestation in Africa. And they are true--the continent's forests are disappearing, lost mainly to expanding agriculture, logging, and charcoal-making. Maybe not, according to new satellite data analyzed by artificial intelligence and a growing body of on-the-ground studies. This new research is finding ever more trees outside forests, many of them nurtured by farmers and sprouting on their previously treeless fields.

The paper studies the capabilities of Recurrent-Neural-Network sequence to sequence (RNN seq2seq) models in learning four transduction tasks: identity, reversal, total reduplication, and quadratic copying. These transductions are traditionally well studied under finite state transducers and attributed with increasing complexity. We find that RNN seq2seq models are only able to approximate a mapping that fits the training or in-distribution data, instead of learning the underlying functions. Although attention makes learning more efficient and robust, it does not overcome the out-of-distribution generalization limitation. We establish a novel complexity hierarchy for learning the four tasks for attention-less RNN seq2seq models, which may be understood in terms of the complexity hierarchy of formal languages, instead of string transductions. RNN variants also play a role in the results. In particular, we show that Simple RNN seq2seq models cannot count the input length.

Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e.g., replacing key question entities or shuffling table columns. To systematically study the robustness of Table QA models, we propose a benchmark called RobuT, which builds upon existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question. Our results indicate that both state-of-the-art Table QA models and large language models (e.g., GPT-3) with few-shot learning falter in these adversarial sets. We propose to address this problem by using large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. Our data and code is publicly available at https://github.com/yilunzhao/RobuT.

The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition with radio-frequency sensing. In this work, we focus on Joint Communication and Sensing (JCS) systems where, unlike in dedicated radar sensing systems, a suitable tradeoff between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from sparse and noisy channel estimates obtained from communication packets, limiting as much as possible the transmission of additional probing signals for the purpose of sensing. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case in real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. Here, we present STAR, a lightweight neural network that combines a single unrolled iterative hard-thresholding layer with an attention mechanism. Our new approach exploits the temporal correlation of the micro-Doppler to accurately reconstruct microDoppler sequences from human movement even from very sparse channel measurements. In doing so, it combines model-based and data-driven approaches into an interpretable and low-complexity architecture, which is amenable to real-time implementations. We evaluate STAR on a public JCS dataset of 60 GHz IEEE 802.11ay channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art solutions in terms of the reconstructed microDoppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90%-sparse channel measurements, for which existing techniques fail.

Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced, researchers have endeavored to devise FL systems that protect privacy or ensure fair results, with most research focusing on one or the other. As two crucial ethical notions, the interactions between privacy and fairness are comparatively less studied. However, since privacy and fairness compete, considering each in isolation will inevitably come at the cost of the other. To provide a broad view of these two critical topics, we presented a detailed literature review of privacy and fairness issues, highlighting unique challenges posed by FL and solutions in federated settings. We further systematically surveyed different interactions between privacy and fairness, trying to reveal how privacy and fairness could affect each other and point out new research directions in fair and private FL.

Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence; (2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity.

The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted significant attention on foundation models. It is widely believed that foundation models will serve as the fundamental building blocks for future AI systems. As foundation models are in their early stages, the design of foundation model based systems has not yet been systematically explored. There is little understanding about the impact of introducing foundation models in software architecture. Therefore, in this paper, we propose a taxonomy of foundation model based systems, which classifies and compares the characteristics of foundation models and design options of foundation model based systems. Our taxonomy comprises three categories: foundation model pretraining and fine-tuning, architecture design of foundation model based systems, and responsible-AI-by-design. This taxonomy provides concrete guidance for making major design decisions when designing foundation model based systems and highlights trade-offs arising from design decisions.

A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

Complex Query Answering (CQA) is an important and fundamental task for knowledge graph (KG) reasoning. Query encoding (QE) is proposed as a fast and robust solution to CQA. In the encoding process, most existing QE methods first parse the logical query into an executable computational direct-acyclic graph (DAG), then use neural networks to parameterize the operators, and finally, recursively execute these neuralized operators. However, the parameterization-and-execution paradigm may be potentially over-complicated, as it can be structurally simplified by a single neural network encoder. Meanwhile, sequence encoders, like LSTM and Transformer, proved to be effective for encoding semantic graphs in related tasks. Motivated by this, we propose sequential query encoding (SQE) as an alternative to encode queries for CQA. Instead of parameterizing and executing the computational graph, SQE first uses a search-based algorithm to linearize the computational graph to a sequence of tokens and then uses a sequence encoder to compute its vector representation. Then this vector representation is used as a query embedding to retrieve answers from the embedding space according to similarity scores. Despite its simplicity, SQE demonstrates state-of-the-art neural query encoding performance on FB15k, FB15k-237, and NELL on an extended benchmark including twenty-nine types of in-distribution queries. Further experiment shows that SQE also demonstrates comparable knowledge inference capability on out-of-distribution queries, whose query types are not observed during the training process.