Machine Learning models are being deployed as parts of real-world systems with the upsurge of interest in artificial intelligence. The design, implementation, and maintenance of such systems are challenged by real-world environments that produce larger amounts of heterogeneous data and users requiring increasingly faster responses with efficient resource consumption. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging concept that equips systems better for integrating ML models. DOA extends current architectures to create data-driven, loosely coupled, decentralised, open systems. Even though papers on deployed ML-based systems do not mention DOA, their authors made design decisions that implicitly follow DOA. The reasons why, how, and the extent to which DOA is adopted in these systems are unclear. Implicit design decisions limit the practitioners' knowledge of DOA to design ML-based systems in the real world. This paper answers these questions by surveying real-world deployments of ML-based systems. The survey shows the design decisions of the systems and the requirements these satisfy. Based on the survey findings, we also formulate practical advice to facilitate the deployment of ML-based systems. Finally, we outline open challenges to deploying DOA-based systems that integrate ML models.

Modern machine learning methods and the availability of large-scale data opened the door to accurately predict target quantities from large sets of covariates. However, existing prediction methods can perform poorly when the training and testing data are different, especially in the presence of hidden confounding. While hidden confounding is well studied for causal effect estimation (e.g., instrumental variables), this is not the case for prediction tasks. This work aims to bridge this gap by addressing predictions under different training and testing distributions in the presence of unobserved confounding. In particular, we establish a novel connection between the field of distribution generalization from machine learning, and simultaneous equation models and control function from econometrics. Central to our contribution are simultaneous equation models for distribution generalization (SIMDGs) which describe the data-generating process under a set of distributional shifts. Within this framework, we propose a strong notion of invariance for a predictive model and compare it with existing (weaker) versions. Building on the control function approach from instrumental variable regression, we propose the boosted control function (BCF) as a target of inference and prove its ability to successfully predict even in intervened versions of the underlying SIMDG. We provide necessary and sufficient conditions for identifying the BCF and show that it is worst-case optimal. We introduce the ControlTwicing algorithm to estimate the BCF and analyze its predictive performance on simulated and real world data.

Given a policy of a Markov Decision Process, we define a SafeZone as a subset of states, such that most of the policy's trajectories are confined to this subset. The quality of a SafeZone is parameterized by the number of states and the escape probability, i.e., the probability that a random trajectory will leave the subset. SafeZones are especially interesting when they have a small number of states and low escape probability. We study the complexity of finding optimal SafeZones, and show that in general, the problem is computationally hard. Our main result is a bi-criteria approximation learning algorithm with a factor of almost $2$ approximation for both the escape probability and SafeZone size, using a polynomial size sample complexity.

A United Nations forum on public policy issues regarding the internet began in Kyoto on Sunday with focus on artificial intelligence and measures against disinformation. The results of the discussions at the Internet Governance Forum scheduled through Thursday will be utilized for the Hiroshima AI Process, in which the Group of Seven industrialized nations will establish rules on AI-related topics. Digital Minister Taro Kono attended as a panelist for a discussion titled "Understanding'Data Free Flow with Trust (DFFT),'" where he emphasized the need for more nations to join the dialogue. About 6,000 people from government, business and educational facilities are expected to attend the over 300 scheduled talks about cybercrime and the information gap born from differences in internet availability, among other topics. One session will look at measures against fake AI-generated video and audio that may be disseminated on social media.

Republican members of congress reveal why they voted to oust former Speaker Kevin McCarthy from his seat, while one of his supporters reveals a contrasting perspective of the historic vote. An unseasonably warm, autumnal light bathed a corridor near the Hall of Columns on the first floor of the Capitol. But the well-trafficked hall was surprisingly empty when I encountered a lawmaker who was a friend and occasional ally of then-House Majority Leader Kevin McCarthy, R-Calif. Then-House Speaker John Boehner, R-Ohio, unexpectedly announced his retirement just a few days before. McCarthy was poised to assume the speakership. But there were already rumblings that the McCarthy lacked the votes.

One domain of interest is their use in algorithmic hiring, specifically in matching resumes with job categories. Yet, this introduces issues of bias on protected attributes like gender, race and maternity status. The seminal work of Bertrand & Mullainathan (2003) set the gold-standard for identifying hiring bias via field experiments where the response rate for identical resumes that differ only in protected attributes, e.g., racially suggestive names such as Emily or Lakisha, is compared. We replicate this experiment on state-of-art LLMs (GPT-3.5, Bard, Claude and Llama) to evaluate bias (or lack thereof) on gender, race, maternity status, pregnancy status, and political affiliation. We evaluate LLMs on two tasks: (1) matching resumes to job categories; and (2) summarizing resumes with employment relevant information. Overall, LLMs are robust across race and gender. They differ in their performance on pregnancy status and political affiliation. We use contrastive input decoding on open-source LLMs to uncover potential sources of bias.

Multi-armed bandits (MAB) (Auer, 2002) is a classic framework of sequential decision-making problems that has been extensively studied (Lattimore & Szepesvári, 2020; Slivkins et al., 2019). In each round, the learning agent selects one out of m arms and observes its reward feedback which follows an unknown reward distribution. The goal is to maximize the cumulative reward, which requires the agent to balance exploitation (selecting the arm with the highest average reward) and exploration (exploring arms that have high potential but have not been played enough). Combinatorial multi-armed bandits (CMAB) is a generalized setting of original MAB with many real-world applications such as online advertising, ranking, and influence maximization (Liu & Zhao, 2012; Kveton et al., 2015; Chen et al., 2016; Wang & Chen, 2017). In CMAB, the agent chooses a combinatorial action (called a super arm) over the m base arms in each round, and observes outcomes of base arms triggered by the action as feedback, known as the semi-bandit feedback.

Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible test scenarios for ADSs is infinite. Besides, the operating environment of ADSs is dynamic, continuously evolving, and full of uncertainties, which requires a testing approach adaptive to the environment. In addition, existing ADS testing techniques have limited effectiveness in ensuring the realism of test scenarios, especially the realism of weather conditions and their changes over time. Recently, reinforcement learning (RL) has demonstrated great potential in addressing challenging problems, especially those requiring constant adaptations to dynamic environments. To this end, we present DeepQTest, a novel ADS testing approach that uses RL to learn environment configurations with a high chance of revealing abnormal ADS behaviors. Specifically, DeepQTest employs Deep Q-Learning and adopts three safety and comfort measures to construct the reward functions. To ensure the realism of generated scenarios, DeepQTest defines a set of realistic constraints and introduces real-world weather conditions into the simulated environment. We employed three comparison baselines, i.e., random, greedy, and a state-of-the-art RL-based approach DeepCOllision, for evaluating DeepQTest on an industrial-scale ADS. Evaluation results show that DeepQTest demonstrated significantly better effectiveness in terms of generating scenarios leading to collisions and ensuring scenario realism compared with the baselines. In addition, among the three reward functions implemented in DeepQTest, Time-To-Collision is recommended as the best design according to our study.

Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the APIs by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Here, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and human operator monitors experiment progression in real-and feature space of the system and tunes the policies of the ML agent to steer the experiment towards specific objectives. One of the hallmarks of the meeting was the large number of presentations on machine learning (ML) in microscopy, ranging from denoising, unsupervised data analysis via variational autoencoders, and supervised learning applications for semantic segmentations and feature identification. Remarkably, by now most manufacturers offer or have plans to offer Python application programming interfaces (APIs), allowing the deployment of the codes on operational microscopes. From this perspective, the technical barriers for the broad implementation of automated microscopy in which ML algorithms analyze the data streaming from instrument detectors and make decisions based on this data are lower than ever.

Text editing, i.e., the process of modifying or manipulating text, is a crucial step in human writing process. In this paper, we study the problem of controlled text editing by natural language instruction. According to a given instruction that conveys the edit intention and necessary information, an original draft text is required to be revised into a target text. Existing automatically constructed datasets for this task are limited because they do not have informative natural language instruction. The informativeness requires the information contained in the instruction to be enough to produce the revised text. To address this limitation, we build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. We first preprocess the Wikipedia edit history database to extract the raw data (WikiIns-Raw). Then we crowdsource high-quality validation and test sets, as well as a small-scale training set (WikiIns-Gold). With the high-quality annotated dataset, we further propose automatic approaches to generate a large-scale ``silver'' training set (WikiIns-Silver). Finally, we provide some insightful analysis on our WikiIns dataset, including the evaluation results and the edit intention analysis. Our analysis and the experiment results on WikiIns may assist the ongoing research on text editing. The dataset, source code and annotation guideline are available at https://github.com/CasparSwift/WikiIns.