AI Work Research Articles

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.

Artificial Intelligence and the Marginalization of the Poor

Artificial Intelligence aims at recreating human intelligence through a computer program. AI programmers tend to see intelligence as what they have, so it reflects the vision of wealthy, able-bodied, white men. This paper examines the experience of poverty in the first world as a challenge to AI research. The poor tend to have their interests dominated and denigrated by the upper classes, make choices good and ill out of desperation for survival, and experience a fractured consciousness of their class status as a result of their situation and exploitation. These epistemic realities are foreign to an AI whose aim is optimal choice selection. As a result, the more common place AI algorithms become for making decisions, the more marginalized we should expect the epistemologies of the poor, and consequently the poor themselves, to be. A Catholic ethic based in the “option for the poor” must prioritize the epistemology of the poor over current AI work and resist definitions of intelligence that fail to account for “the least of these.”

How a shopping mall trip inspired me to work in neuro-symbolic AI

No abstract available.

Knowledge Graphs: Introduction, History and, Perspectives

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.

Knowledge graphs: Introduction, history, and perspectives

AbstractKnowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.

SignNet II: A Transformer-Based Two-Way Sign Language Translation Model.

The role of a sign interpreting agent is to bridge the communication gap between the hearing-only and Deaf or Hard of Hearing communities by translating both from sign language to text and from text to sign language. Until now, much of the AI work in automated sign language processing has focused primarily on sign language to text translation, which puts the advantage mainly on the side of hearing individuals. In this work, we describe advances in sign language processing based on transformer networks. Specifically, we introduce SignNet II, a sign language processing architecture, a promising step towards facilitating two-way sign language communication. It is comprised of sign-to-text and text-to-sign networks jointly trained using a dual learning mechanism. Furthermore, by exploiting the notion of sign similarity, a metric embedding learning process is introduced to enhance the text-to-sign translation performance. Using a bank of multi-feature transformers, we analyzed several input feature representations and discovered that keypoint-based pose features consistently performed well, irrespective of the quality of the input videos. We demonstrated that the two jointly trained networks outperformed their singly-trained counterparts, showing noteworthy enhancements in BLEU-1 - BLEU-4 scores when tested on the largest available German Sign Language (GSL) benchmark dataset.

Rational verification: game-theoretic verification of multi-agent systems

We provide a survey of the state of the art of rational verification: the problem of checking whether a given temporal logic formula ϕ is satisfied in some or all game-theoretic equilibria of a multi-agent system – that is, whether the system will exhibit the behavior ϕ represents under the assumption that agents within the system act rationally in pursuit of their preferences. After motivating and introducing the overall framework of rational verification, we discuss key results obtained in the past few years as well as relevant related work in logic, AI, and computer science.

Legal Governance of Brain Data Derived from Artificial Intelligence

Legal Governance of Brain Data Derived from Artificial Intelligence

AI-Assisted Scientific Data Collection with Iterative Human Feedback

Although artificial intelligence has revolutionized data analysis, significantly less work has focused on using AI to improve scientific data collection. Past work in AI for data collection has typically assumed the objective function is well-defined by humans before starting an experiment; however, this is a poor fit for scientific domains where new discoveries and insights are made as data is being collected. In this paper we present a new framework to allow AI systems to work together with humans (e.g. scientists) to collect data more effectively in simple scientific domains. We present a novel algorithm, TESA, which seeks to achieve good performance by learning from past human behavior how to direct data to places that are likely to become scientifically interesting in the future. We analyze the problem theoretically, defining a novel notion of regret in this setting and showing that TESA is zero regret. Next, we show that TESA outperforms other related algorithms in simulations using real data drawn from three diverse domains (economics, mental health, and cognitive psychology). Finally, we run experiments with human subjects across these scientific domains to compare our iterative human-in-the-loop process to a (more standard) workflow in which information is communicated to the AI a priori.

Intelligent Behavior Depends on the Ecological Niche

This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment. We argue that a major limit of current work in AI is that it is missing this perspective, both theoretically and experimentally. Finally, we discuss the promising approach of developmental artificial intelligence, modeling infant development through multi-scale interaction between intrinsically motivated learning, embodiment and a fastly changing socio-cultural environment. This paper takes the form of an interview of Pierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI - K{\"{u}}nstliche Intelligenz special issue in developmental robotics.

Cobots in knowledge work: Human – AI collaboration in managerial professions

Current technological developments, as well as widespread application of artificial intelligence, will doubtlessly continue to impact how people live and work. In this research, we explored synergies between human workers and AI in managerial tasks. We hypothesized that human-AI collaboration will increase productivity. In the paper, several levels of proximity between AI and humans in a work setting are distinguished. The multi-stage study, covering the exploratory phase in which we conducted a study of preferences using 10-item Likert scale, was conducted with a sample of 366 respondents. The study focused on working with different types of AI. The second and third phase of the study, in which we primarily used qualitative methods (scenario-based design combined with semi-structured interviews with six participants), focused on researching modes of collaboration between humans and virtual assistants. The study results generally confirmed our hypothesis about increased productivity due to enhanced human-AI collaboration, proving that the future of AI in knowledge work needs to focus not on full automation but rather on collaborative approaches where humans and AI work closely together.

A Prologue to Artificial Intelligence, Mechanical Technology, and Research Streams to inspect the Financial and Authoritative results of related Advancements

This article gives a prologue to artificial intelligence, apply autonomy, and research streams that analyze the monetary and hierarchical outcomes of these and related innovations. We depict the beginning examination of human-made brainpower and mechanical technology in the financial matters and the board writing and sum up the principal methodologies taken by researchers around there. Investigations of AI and mechanical innovation based on their speculation and assessment on builds of computerization, apply self-rule and machine learning. In this array of composing, the use of robotization, human-made intellectual prowess, and AI developments can be used both as free and as destitute elements. While AI, mechanical autonomy, and motorization are related thoughts, it is fundamental to think about the separation between all of these developments. In this investigation, there is additionally creating writing in monetary issues, methods, and information systems that surveys the use of AI estimations in unique. A touch of the creator in this forming utilize truncated, little extension level information to attract experiences concerning how AI impacts firms or people contingent on their aptitudes part of this work examines whether and how the use of AI and AI mechanical assemblies affects solitary inclinations. Further in the exploration, progressively express to the board analysts, we need a point by point understanding about how AI and mechanical self-governance impact work. It fuses not precisely how AI and mechanized innovation change a given sort of work or occupation, yet also how modernized thinking and apply independence impact how individuals associate in the workplace. That is, we theorize that these headways will change the kind of work that we do, and how that work is arranged and made as a massive part of an increasingly conspicuous creation structure. We examine the ramifications of artificial intelligence, robotics, and computerization for the hierarchical plan and firm method, contend for more outstanding commitment with these points by authoritative and technique specialists, and diagram bearings for future research.

“If we do it wisely, AI can be the best thing ever for humanity”

Where once he sought to unmask the mysteries of the multiverse, Max Tegmark now has his sights on artificial intelligence. He talks to Richard Webb about cosmology, consciousness and how to make AI work for everyone

Augmenting Dementia Cognitive Assessment With Instruction-Less Eye-Tracking Tests.

Eye-tracking technology is an innovative tool that holds promise for enhancing dementia screening. In this work, we introduce a novel way of extracting salient features directly from the raw eye-tracking data of a mixed sample of dementia patients during a novel instruction-less cognitive test. Our approach is based on self-supervised representation learning where, by training initially a deep neural network to solve a pretext task using well-defined available labels (e.g. recognising distinct cognitive activities in healthy individuals), the network encodes high-level semantic information which is useful for solving other problems of interest (e.g. dementia classification). Inspired by previous work in explainable AI, we use the Layer-wise Relevance Propagation (LRP) technique to describe our network's decisions in differentiating between the distinct cognitive activities. The extent to which eye-tracking features of dementia patients deviate from healthy behaviour is then explored, followed by a comparison between self-supervised and handcrafted representations on discriminating between participants with and without dementia. Our findings not only reveal novel self-supervised learning features that are more sensitive than handcrafted features in detecting performance differences between participants with and without dementia across a variety of tasks, but also validate that instruction-less eye-tracking tests can detect oculomotor biomarkers of dementia-related cognitive dysfunction. This work highlights the contribution of self-supervised representation learning techniques in biomedical applications where the small number of patients, the non-homogenous presentations of the disease and the complexity of the setting can be a challenge using state-of-the-art feature extraction methods.

AI-Enabled Emotion Communication

With the development of AI technology, the application of AI will greatly change and influence people's daily lives. While AI technology brings great convenience to people's lives, people have shifted their focus from the physical world to the spiritual world, so there is an increasing demand for emotional services. As a result, emotional AI systems and emotional calculation are favored by many scholars nowadays. However, the existing emotional AI work mainly focuses on improving the accuracy of emotion recognition, lacking personalized emotional services for users. Therefore, in this article, the authors propose AI-EmoCom, which casts emotion as a communication medium in the network and makes the emotional communication system more intelligent by combining it with AI technology. We applied the AI-enabled emotional communication system to the field of unmanned driving, and proposed people-centered hybrid driving to reduce the incidence of traffic accidents to a greater extent. We also apply AI-enabled emotional communication to emotional social robots to provide users with personalized service emotion. Then the system architecture for the AI-enabled emotion communication is introduced in detail, and the no-tag learning model of dataset labeling and processing as well as the AI algorithm model for emotion recognition are elaborated in detail, and experiments are done to verify the interactive delay in the AI-enabled emotion communication system and accuracy of emotion recognition.

Improving Search with Supervised Learning in Trick-Based Card Games

In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values. While the evaluation component is vital, the accuracy of move value estimates is also fundamentally linked to how well the sampling distribution corresponds the true distribution. Despite this, recent work in trick-taking card game AI has mainly focused on improving evaluation algorithms with limited work on improving sampling. In this paper, we focus on the effect of sampling on the strength of a player and propose a novel method of sampling more realistic states given move history. In particular, we use predictions about locations of individual cards made by a deep neural network — trained on data from human gameplay — in order to sample likely worlds for evaluation. This technique, used in conjunction with Perfect Information Monte Carlo (PIMC) search, provides a substantial increase in cardplay strength in the popular trick-taking card game of Skat.

Fast and accurate medication identification

Much of the AI work in healthcare is focused around disease prediction in clinical settings, which is an important application that has yet to deliver in earnest. However, there are other fundamental aspects like helping patients and care teams interact and communicate in efficient and meaningful ways, which could deliver quadruple-aim improvements. After heart disease and cancer, preventable medical errors are the third leading cause of death in the United States. The largest subset of medical errors is medication error. Providing the right treatment plan for patients includes knowledge about their current medications and drug allergies, an often challenging task. The widespread growth of prescribing and consuming medications has increased the need for applications that support medication reconciliation. We show a deep-learning application that can help reduce avoidable errors with their attendant risk, i.e., correctly identifying prescription medication, which is currently a tedious and error-prone task. We demonstrate prescription-pill identification from mobile images in the NIH NLM Pill Image Recognition Challenge dataset. Our application recognizes the correct pill within the top-5 results at 94% accuracy, which compares favorably to the original competition winner at 83.3% for top-5 under comparable, though not identical configurations. The Institute of Medicine claims that better use of information technology can be an important step in reducing medication errors. Therefore, we believe that a more immediate impact of AI in healthcare will occur with a seamless integration of AI into clinical workflows, readily addressing the quadruple aim of healthcare.

Review of Existing Indicators for Illicit Financial Flows and Possible Alternative Indicators for African Countries

Review of Existing Indicators for Illicit Financial Flows and Possible Alternative Indicators for African Countries

South Korean university’s AI work for defense contractor draws boycott

South Korean university’s AI work for defense contractor draws boycott

AI and the future of the brain power society: When the descendants of Athena and Prometheus work together

AbstractTaking the great opportunity of visiting Crete, I have presented my freewheeling imagination about the future of the brain power society, bearing in mind the recent development of AI and robots. Based on Greek mythology, I consider that all brain‐power professionals in civil society are the descendants of Athena, whereas the rapidly developing AI and robots are the ultimate descendants of Prometheus. In this context, in order to foresee the possible future of economic research in the coming AI revolution, I introduced the recent attempt by Marcus Berliant and myself to develop a formal framework for modeling the operational process of economic research. Then, I have suggested that if real humans and AI work together through such a framework while amplifying mutual comparative advantages, creativity in economic research as a whole will be vastly enhanced.