There is little doubt that artificial intelligence technologies will be transformational. Breathtaking advances will be made, extraordinary wealth will be created, and many of our social and institutional structures will be transformed. However, we must ask: whose lives will be improved (or harmed) by these technologies? A key assertion of this paper, "Artificial Intelligence and Human Development" is that, if we continue blindly forward, we should expect to see increased inequality alongside economic disruption, social unrest, and in some cases, political instability, with the technologically disadvantaged and underrepresented faring the worst. This prediction stems from the interweaving of two elements: the nature of AI applications, and projections of the impacts of AI applications in the current global context.

Check out all the on-demand sessions from the Intelligent Security Summit here. Human intelligence and intuition are vital to training artificial intelligence (AI) and machine learning (ML) models to provide enterprises with hybrid cybersecurity at scale. Combining human intelligence and intuition with AI and ML models helps catch the nuances of attack patterns that elude numerical analysis alone. Experienced threat hunters, security analysts and data scientists help ensure that the data used to train AI and ML models enables a model to accurately identify threats and reduce false positives. Combining human expertise and AI and ML models with a real-time stream of telemetry data from enterprises' many systems and apps defines the future of hybrid cybersecurity.

With modern infotainment systems, drivers are increasingly tempted to engage in secondary tasks while driving. Since distracted driving is already one of the main causes of fatal accidents, in-vehicle touchscreen Human-Machine Interfaces (HMIs) must be as little distracting as possible. To ensure that these systems are safe to use, they undergo elaborate and expensive empirical testing, requiring fully functional prototypes. Thus, early-stage methods informing designers about the implication their design may have on driver distraction are of great value. This paper presents a machine learning method that, based on anticipated usage scenarios, predicts the visual demand of in-vehicle touchscreen interactions and provides local and global explanations of the factors influencing drivers' visual attention allocation. The approach is based on large-scale natural driving data continuously collected from production line vehicles and employs the SHapley Additive exPlanation (SHAP) method to provide explanations leveraging informed design decisions. Our approach is more accurate than related work and identifies interactions during which long glances occur with 68 % accuracy and predicts the total glance duration with a mean error of 2.4 s. Our explanations replicate the results of various recent studies and provide fast and easily accessible insights into the effect of UI elements, driving automation, and vehicle speed on driver distraction. The system can not only help designers to evaluate current designs but also help them to better anticipate and understand the implications their design decisions might have on future designs.

In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data set. This work introduces three graph-based models incorporating Merriman-Bence-Osher (MBO) techniques to tackle this challenge. Specifically, graph-based modifications of the MBO scheme are integrated with state-of-the-art techniques, including a home-made transformer and an autoencoder, in order to deal with scarcely-labeled data sets. In addition, a consensus technique is detailed. The proposed models are validated using five benchmark data sets. We also provide a thorough comparison to other competing methods, such as support vector machines, random forests, and gradient boosting decision trees, which are known for their good performance on small data sets. The performances of various methods are analyzed using residue-similarity (R-S) scores and R-S indices. Extensive computational experiments and theoretical analysis show that the new models perform very well even when as little as 1% of the data set is used as labeled data.

The research applies AI-driven code assistants to analyze a selection of influential computer code that has shaped modern technology, including email, internet browsing, robotics, and malicious software. The original contribution of this study was to examine half of the most significant code advances in the last 50 years and, in some cases, to provide notable improvements in clarity or performance. The AI-driven code assistant could provide insights into obfuscated code or software lacking explanatory commentary in all cases examined. We generated additional sample problems based on bug corrections and code optimizations requiring much deeper reasoning than a traditional Google search might provide. Future work focuses on adding automated documentation and code commentary and translating select large code bases into more modern versions with multiple new application programming interfaces (APIs) and chained multi-tasks. The AI-driven code assistant offers a valuable tool for software engineering, particularly in its ability to provide human-level expertise and assist in refactoring legacy code or simplifying the explanation or functionality of high-value repositories. NTRODUCTION The latest generation of artificial intelligence (AI) and chat applications [1-13] shows particular promise as software generators [4,11], presenting a new interactive way to learn complex coding principles [6], comment on existing code in multiple languages [8], and generally serve as coding assistants [8-12]. Recent efforts by OpenAI have put large language models (LLMs) into public access [1-2]. As an experimental platform, particularly for understanding software principles, its interactive chat [1] simulates a vast knowledge base, expert role-playing, and long-term memory spanning 8000 tokens, or approximately 20-25 pages of generated text. Several tests or benchmarks, such as QuixBugs [8] and HackerRank [12], have demonstrated the potential of generative coders as software assistants [10]. A recent review from the University of Washington and Microsoft Research [14] estimated that 1.2 million coders currently use OpenAI's copilot for tasks formerly requiring searches, such as code completion, commentary, or bug detection.

Conversational artificial intelligence (AI) disrupts how humans interact with technology. Recently, OpenAI introduced ChatGPT, a state-of-the-art dialogue model that can converse with its human counterparts with unprecedented capabilities. ChatGPT has witnessed tremendous attention from the media, academia, industry, and the general public, attracting more than a million users within days of its release. However, its explosive adoption for information search and as an automated decision aid underscores the importance to understand its limitations and biases. This paper focuses on one of democratic society's most important decision-making processes: political elections. Prompting ChatGPT with 630 political statements from two leading voting advice applications and the nation-agnostic political compass test in three pre-registered experiments, we uncover ChatGPT's pro-environmental, left-libertarian ideology. For example, ChatGPT would impose taxes on flights, restrict rent increases, and legalize abortion. In the 2021 elections, it would have voted most likely for the Greens both in Germany (B\"undnis 90/Die Gr\"unen) and in the Netherlands (GroenLinks). Our findings are robust when negating the prompts, reversing the order of the statements, varying prompt formality, and across languages (English, German, Dutch, and Spanish). We conclude by discussing the implications of politically biased conversational AI on society.

Logic-based machine learning [1, 2] learns interpretable knowledge expressed in the form of a logic program, called a hypothesis, that explains labelled examples in the context of (optional) background knowledge. Recent logic-based machine learning systems have demonstrated the ability to learn highly complex and noise-tolerant hypotheses in a data efficient manner (e.g., Learning from Answer Sets (LAS) [2]). However, they require labelled examples to be specified in a structured logical form, which limits their applicability to many real-world problems. On the other hand, differentiable learning systems, such as (deep) neural networks, are able to learn directly from unstructured data, but they require large amounts of training data and their learned models are difficult to interpret [3]. Within neural-symbolic artificial intelligence, many approaches aim to integrate neural and symbolic systems with the goal of preserving the benefits of both paradigms [4, 5]. Most neural-symbolic integrations assume the existence of pre-defined knowledge expressed symbolically, or logically, and focus on training a neural network to extract symbolic features from raw unstructured data [6-10]. In this paper, we introduce Feed-Forward Neural-Symbolic Learner (FFNSL), a neural-symbolic learning framework that assumes the opposite. Given a pre-trained neural network, FFNSL uses a logic-based machine learning system robust to noise to learn a logic-based hypothesis whose symbolic features are constructed from neural network predictions.

Autonomous vehicles are being deployed with a spectrum of capability, extending from driver assistance features for the highway in personal vehicles (SAE Level 2+) to fully autonomous fleet ride sharing services operating in complex city environments (SAE Level 4+). This spectrum of autonomy often operates in different physical environments with different degrees of assumed driver in-the-loop oversight and hence have very different system and subsystem requirements. At the heart of SAE Level 2 to 5 systems is localization and mapping, which ranges from road determination for feature geofencing or high-level routing, through lane determination for advanced driver assistance, to where-in-lane positioning for full vehicle control. We assess localization and mapping requirements for different levels of autonomy and supported features. This work provides a framework for system decomposition, including the level of redundancy needed to achieve the target level of safety. We examine several representative autonomous and assistance features and make recommendations on positioning requirements as well map georeferencing and information integrity.

While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.

Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: (show all) Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. It's 2022 and the near billion-dollar quantum computing sector has gone from a passion project for forward-thinking physicists to a thriving B2B industry. Experts predict the market for quantum technologies will quadruple in value by 2029. Simply put there's never been a better time than right now for potential jobseekers to get in on what, arguably, could be the greatest technological revolution since the advent of the internet.