College of Image Arts and Sciences Tojiin, Kitamachi, Kita-ku, Kyoto, Japan, Abstract - The human face plays a central role in most forms of natural human interaction so we may expect that computational methods for analysis of facial information, modeling of internal emotional states, and methods for graphical synthesis of faces and facial expressions will play a growing role in human-computer and human-robot interaction. However, certain areas of face-based HCI, such as facial expression recognition and robotic facial display have lagged others, such as eye-gaze tracking, facial recognition, and conversational characters. Our goal in this paper is to review the situation in HCI with regards to the human face, and to discuss strategies, which could bring more slowly developing areas up to speed.
The goal of this research is to produce new AI knowledge and technologies that benefit society while minimizing negative impacts.
This report prioritizes strategies for future federal investment in research areas where private investment is unlikely. Benefits are described in terms of increased economic prosperity, improved quality of life, and strengthened national security as applied to particular industry applications expected to benefit from advances in AI.
The report states that increased economic prosperity can be realized through AI developments in applications that include manufacturing, logistics, finance, transportation, agriculture, marketing, communication, and science and technology; improved educational opportunities and quality of life will come from AI contributions to education, medicine, law and personal services; and enhanced national and homeland security will be achieved through AI advances applied to security and law enforcement and safety and prediction.
Make Long-Term Investments in AI Research While the report notes that an important component of long-term research is incremental research with predictable outcomes, it argues that long-term sustained investments in high-risk research can lead to high-reward payoffs.
Areas with potential long-term payoffs include: These aims can be achieved specifically by: Seeking new algorithms for AI that enable intuitive interaction with users and seamless machine-human collaborations; Developing techniques for AI that can improve the thinking and functioning of humans; Developing techniques for how AI effectively presents information to users in real-time in formats that are easy to interpret; and Developing more effective language processing systems to allow AI machines to interpret written or verbal commands regardless of the clarity of the commands.
Understand and Address the Ethical, Legal, and Societal Implications of AI The Report points out that research needs to account for the ethical, legal, and social implications of AI, as well as developing methods for AI that align with ethical, legal, and social principles.
Improving fairness, transparency, and accountability in AI design to avoid bias; Building ethical AI functions that reflect an appropriate value system, developed through examples that indicate preferred behavior when presented with difficult moral issues or with conflicting values; and Designing computer architectures that incorporate ethical reasoning.
This aim can be achieved specifically by: According to the Report, these aims can be achieved specifically by: Developing and making available a wide variety of datasets to meet the needs of AI interests and applications; Making training and testing resources responsive to commercial and public interests; and Developing and distributing software libraries and toolkits.
Developing requirements, specifications, guidelines, or characteristics that can be used consistently to ensure that AI technologies meet critical objectives for functionality and interoperability, and that the technologies perform reliably and safely; Establishing AI technology quantitative benchmarks to objectively measure AI accuracy, complexity, operator trust and competency, risk, uncertainty, transparency, unintended bias, performance, and economic impact; Increasing the availability of AI testbeds across all aspects of AI, including providing limited access to sensitive information for improving AI systems designed to protect confidential data; and Engaging the AI community e.
Furthermore, while this report does not explicitly address the appropriate scope or application of AI technologies, NITRD recognizes the necessity of addressing these issues and identifies relevant reports to that effect.
The report concludes by offering two recommendations to the Federal government for strengthening and promoting the success of this strategic plan: Relevant Science There is currently no universally agreed-upon definition of AI. However, there are different perspectives on how to define and categorize AI.
Ina foundational textbook classified AI in four categories: Ones that think like humans; Ones that think rationally; Ones that act like humans; and Ones that act rationally. Most of the progress seen in AI has been considered "narrow," having addressed specific problem domains like playing games, driving cars, or recognizing faces in images.
In recent years, AI applications have surpassed human abilities in some narrow tasks, and rapid progress is expected to continue, opening up new opportunities in critical areas such as health, education, energy, and the environment. Experts involved with the NSTC Committee on Technology believe that it will take decades before society advances to artificial "general" intelligence.
Key AI applications include the following: Machine learning is the basis for many of the recent advances in AI.
Machine learning is a method of data analysis that attempts to find structure or a pattern within a data set without human intervention. Machine learning systems search through data to look for patterns and adjust program actions accordingly, a process defined as training the system.
To perform this process, an algorithm called a model is given a training set or teaching set of data, which it uses to answer a question.Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.
In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
AI is a form of intelligence -> Specifically AI is a synthetic intelligence -> intelligence of a man-made yet real quality. AI is a type of technology -> A type of emerging computer technology -> A computer technology that performs some intellectual function.
The following paragraphs will categorize current goals and limitations of artificial intelligence, elucidate brain hemispheres and functioning relative to artificial intelligence, and generate criteria to classify what is a thinking machine.
The National Science and Technology Council (NSTC) and the Subcommittee on Networking and Information Technology Research and Development (NITRD) issued the report, The National Artificial Intelligence Research and Development Strategic Plan to establish a set of objectives for federally-funded AI research conducted within and .
Categorize The Current Goals And Limitations Of Artificial Intelligence. Artificial Intelligence: A Review Somya Khandelwal [email protected] Abstract-This paper examines the current and future roles of Artificial Intelligence.
Artificial intelligence augments and empowers human intelligence. So as long we are successful in keeping technology beneficial, we will be able to help this human civilization. Everything that has been created in this world and in our individual societies is the continuous result of intelligence.