The Philosophical Foundations of Artificial Intelligence
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Artificial General Intelligence and Classical Neural Network
Artificial General Intelligence and Classical Neural Network Pei Wang Department of Computer and Information Sciences, Temple University Room 1000X, Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122 Web: http://www.cis.temple.edu/∼pwang/ Email: [email protected] Abstract— The research goal of Artificial General Intelligence • Capability. Since we often judge the level of intelligence (AGI) and the notion of Classical Neural Network (CNN) are of other people by evaluating their problem-solving capa- specified. With respect to the requirements of AGI, the strength bility, some people believe that the best way to achieve AI and weakness of CNN are discussed, in the aspects of knowledge representation, learning process, and overall objective of the is to build systems that can solve hard practical problems. system. To resolve the issues in CNN in a general and efficient Examples: various expert systems. way remains a challenge to future neural network research. • Function. Since the human mind has various cognitive functions, such as perceiving, learning, reasoning, acting, I. ARTIFICIAL GENERAL INTELLIGENCE and so on, some people believe that the best way to It is widely recognized that the general research goal of achieve AI is to study each of these functions one by Artificial Intelligence (AI) is twofold: one, as certain input-output mapping. Example: various intelligent tools. • As a science, it attempts to provide an explanation of • Principle. Since the human mind seems to follow certain the mechanism in the human mind-brain complex that is principles of information processing, some people believe usually called “intelligence” (or “cognition”, “thinking”, that the best way to achieve AI is to let computer systems etc.). -
Artificial Intelligence: Distinguishing Between Types & Definitions
19 NEV. L.J. 1015, MARTINEZ 5/28/2019 10:48 AM ARTIFICIAL INTELLIGENCE: DISTINGUISHING BETWEEN TYPES & DEFINITIONS Rex Martinez* “We should make every effort to understand the new technology. We should take into account the possibility that developing technology may have im- portant societal implications that will become apparent only with time. We should not jump to the conclusion that new technology is fundamentally the same as some older thing with which we are familiar. And we should not hasti- ly dismiss the judgment of legislators, who may be in a better position than we are to assess the implications of new technology.”–Supreme Court Justice Samuel Alito1 TABLE OF CONTENTS INTRODUCTION ............................................................................................. 1016 I. WHY THIS MATTERS ......................................................................... 1018 II. WHAT IS ARTIFICIAL INTELLIGENCE? ............................................... 1023 A. The Development of Artificial Intelligence ............................... 1023 B. Computer Science Approaches to Artificial Intelligence .......... 1025 C. Autonomy .................................................................................. 1026 D. Strong AI & Weak AI ................................................................ 1027 III. CURRENT STATE OF AI DEFINITIONS ................................................ 1029 A. Black’s Law Dictionary ............................................................ 1029 B. Nevada ..................................................................................... -
24.00 Recitation Handout 6A: a Brief Review of Materialist Theories Of
Problems of Philosophy Handout #6 A Brief Review of Materialist Theories of Mind The question these theories are trying to answer is what the mind is, what consciousness consists of, what experiences are (for example, what the experience of seeing red is, or the experience of being in pain is), and what beliefs and desires are. In sum, they are trying to give an analysis of mental states (where a mental state can be an experience, a belief, a desire, etc…). I. Non Materialist Theories of The Mind 1. Dualism: the view that (at least some) mental states are not physical states. Problems: (a) dualism is inconsistent with physicalism – the view that everything that there is, is physical, which many people are sympathetic to. (b) Even if you’re not a physicalist, a lot of people think that the physical world is causally closed – that is, that every physical state can be explained fully by appeal to other physical events. But, it seems as if mental states are the sorts of things that cause physical states (for example, my desire to eat ice cream causes me to eat ice cream). Unless we give up on the claim that our mental states can cause physical states (as Jackson does), it looks like we have to give up on causal closure of the physical world. II. Materialist Theories of the Mind Some general issues with materliaism: the Mary argument (also called “the knowledge argument – it looks like maybe you can know all the physical facts without knowing facts about what it’s like to see red), subjectivity (facts about our mental life seem to essentially involve a subjective or first person point of view; facts about physics seem to essentially involve an objective or third person point of view). -
Artificial Intelligence/Artificial Wisdom
Artificial Intelligence/Artificial Wisdom - A Drive for Improving Behavioral and Mental Health Care (06 September to 10 September 2021) Department of Computer Science & Information Technology Central University of Jammu, J&K-181143 Preamble An Artificial Intelligence is the capability of a machine to imitate intelligent human behaviour. Machine learning is based on the idea that machines should be able to learn and adapt through experience. Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. Artificial intelligence (AI) technology holds both great promises to transform mental healthcare and potential pitfalls. Artificial intelligence (AI) is increasingly employed in healthcare fields such as oncology, radiology, and dermatology. However, the use of AI in mental healthcare and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental healthcare providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. According to the publication Spectrum News, a form of AI called "deep learning" is sometimes better able than human beings to spot relevant patterns. This five days course provides an overview of AI approaches and current applications in mental healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. The proposed workshop is envisaged to provide opportunity to our learners to seek and share knowledge and teaching skills in cutting edge areas from the experienced and reputed faculty. -
John Mccarthy – Father of Artificial Intelligence
Asia Pacific Mathematics Newsletter John McCarthy – Father of Artificial Intelligence V Rajaraman Introduction I first met John McCarthy when he visited IIT, Kanpur, in 1968. During his visit he saw that our computer centre, which I was heading, had two batch processing second generation computers — an IBM 7044/1401 and an IBM 1620, both of them were being used for “production jobs”. IBM 1620 was used primarily to teach programming to all students of IIT and IBM 7044/1401 was used by research students and faculty besides a large number of guest users from several neighbouring universities and research laboratories. There was no interactive computer available for computer science and electrical engineering students to do hardware and software research. McCarthy was a great believer in the power of time-sharing computers. John McCarthy In fact one of his first important contributions was a memo he wrote in 1957 urging the Director of the MIT In this article we summarise the contributions of Computer Centre to modify the IBM 704 into a time- John McCarthy to Computer Science. Among his sharing machine [1]. He later persuaded Digital Equip- contributions are: suggesting that the best method ment Corporation (who made the first mini computers of using computers is in an interactive mode, a mode and the PDP series of computers) to design a mini in which computers become partners of users computer with a time-sharing operating system. enabling them to solve problems. This logically led to the idea of time-sharing of large computers by many users and computing becoming a utility — much like a power utility. -
Machine Guessing – I
Machine Guessing { I David Miller Department of Philosophy University of Warwick COVENTRY CV4 7AL UK e-mail: [email protected] ⃝c copyright D. W. Miller 2011{2018 Abstract According to Karl Popper, the evolution of science, logically, methodologically, and even psy- chologically, is an involved interplay of acute conjectures and blunt refutations. Like biological evolution, it is an endless round of blind variation and selective retention. But unlike biological evolution, it incorporates, at the stage of selection, the use of reason. Part I of this two-part paper begins by repudiating the common beliefs that Hume's problem of induction, which com- pellingly confutes the thesis that science is rational in the way that most people think that it is rational, can be solved by assuming that science is rational, or by assuming that Hume was irrational (that is, by ignoring his argument). The problem of induction can be solved only by a non-authoritarian theory of rationality. It is shown also that because hypotheses cannot be distilled directly from experience, all knowledge is eventually dependent on blind conjecture, and therefore itself conjectural. In particular, the use of rules of inference, or of good or bad rules for generating conjectures, is conjectural. Part II of the paper expounds a form of Popper's critical rationalism that locates the rationality of science entirely in the deductive processes by which conjectures are criticized and improved. But extreme forms of deductivism are rejected. The paper concludes with a sharp dismissal of the view that work in artificial intelligence, including the JSM method cultivated extensively by Victor Finn, does anything to upset critical rationalism. -
How Empiricism Distorts Ai and Robotics
HOW EMPIRICISM DISTORTS AI AND ROBOTICS Dr Antoni Diller School of Computer Science University of Birmingham Birmingham, B15 2TT, UK email: [email protected] Abstract rently, Cog has no legs, but it does have robotic arms and a head with video cameras for eyes. It is one of the most im- The main goal of AI and Robotics is that of creating a hu- pressive robots around as it can recognise various physical manoid robot that can interact with human beings. Such an objects, distinguish living from non-living things and im- android would have to have the ability to acquire knowl- itate what it sees people doing. Cynthia Breazeal worked edge about the world it inhabits. Currently, the gaining of with Cog when she was one of Brooks’s graduate students. beliefs through testimony is hardly investigated in AI be- She said that after she became accustomed to Cog’s strange cause researchers have uncritically accepted empiricism. appearance interacting with it felt just like playing with a Great emphasis is placed on perception as a source of baby and it was easy to imagine that Cog was alive. knowledge. It is important to understand perception, but There are several major research projects in Japan. A even more important is an understanding of testimony. A common rationale given by scientists engaged in these is sketch of a theory of testimony is presented and an appeal the need to provide for Japan’s ageing population. They say for more research is made. their robots will eventually act as carers for those unable to look after themselves. -
Artificial Intelligence for Robust Engineering & Science January 22
Artificial Intelligence for Robust Engineering & Science January 22 – 24, 2020 Organizing Committee General Chair: Logistics Chair: David Womble Christy Hembree Program Director, Artificial Intelligence Project Support, Artificial Intelligence Oak Ridge National Laboratory Oak Ridge National Laboratory Committee Members: Jacob Hinkle Justin Newcomer Research Scientist, Computational Sciences Manager, Machine Intelligence and Engineering Sandia National Laboratories Oak Ridge National Laboratory Frank Liu Clayton Webster Distinguished R&D Staff, Computational Distinguished Professor, Department of Sciences and Mathematics Mathematics Oak Ridge National Laboratory University of Tennessee–Knoxville Robust engineering is the process of designing, building, and controlling systems to avoid or mitigate failures and everything fails eventually. This workshop will examine the use of artificial intelligence and machine learning to predict failures and to use this capability in the maintenance and operation of robust systems. The workshop comprises four sessions examining the technical foundations of artificial intelligence and machine learning to 1) Examine operational data for failure indicators 2) Understand the causes of the potential failure 3) Deploy these systems “at the edge” with real-time inference and continuous learning, and 4) Incorporate these capabilities into robust system design and operation. The workshop will also include an additional session open to attendees for “flash” presentations that address the conference theme. AGENDA Wednesday, January 22, 2020 7:30–8:00 a.m. │ Badging, Registration, and Breakfast 8:00–8:45 a.m. │ Welcome and Introduction Jeff Nichols, Oak Ridge National Laboratory David Womble, Oak Ridge National Laboratory 8:45–9:30 a.m. │ Keynote Presentation: Dave Brooks, General Motors Company AI for Automotive Engineering 9:30–10:00 a.m. -
Artificial Intelligence and National Security
Artificial Intelligence and National Security Artificial intelligence will have immense implications for national and international security, and AI’s potential applications for defense and intelligence have been identified by the federal government as a major priority. There are, however, significant bureaucratic and technical challenges to the adoption and scaling of AI across U.S. defense and intelligence organizations. Moreover, other nations—particularly China and Russia—are also investing in military AI applications. As the strategic competition intensifies, the pressure to deploy untested and poorly understood systems to gain competitive advantage could lead to accidents, failures, and unintended escalation. The Bipartisan Policy Center and Georgetown University’s Center for Security and Emerging Technology (CSET), in consultation with Reps. Robin Kelly (D-IL) and Will Hurd (R-TX), have worked with government officials, industry representatives, civil society advocates, and academics to better understand the major AI-related national and economic security issues the country faces. This paper hopes to shed more clarity on these challenges and provide actionable policy recommendations, to help guide a U.S. national strategy for AI. BPC’s effort is primarily designed to complement the work done by the Obama and Trump administrations, including President Barack Obama’s 2016 The National Artificial Intelligence Research and Development Strategic Plan,i President Donald Trump’s Executive Order 13859, announcing the American AI Initiative,ii and the Office of Management and Budget’s subsequentGuidance for Regulation of Artificial Intelligence Applications.iii The effort is also designed to further June 2020 advance work done by Kelly and Hurd in their 2018 Committee on Oversight 1 and Government Reform (Information Technology Subcommittee) white paper Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S. -
I Am an Integrated Information System, Therefore I Am NO
I am an integrated information system, therefore I am NO. 160 // THEME 03 // WEEK 34 // AUGUST 24, 2018 THEME 03 I am an integrated infor- mation system, therefore I am A.I. BRAIN-COMPUTER Concepts like general artificial intelligence, brain-computer interfaces, brain uploading, the internet hive-mind and full- immersive virtual reality have been the bread and butter of science fiction. However, against the backdrop of recent developments in digital technology, these concepts are being taken more seriously. Consequently, age-old discussions about the nature of consciousness are now refuelled. Interestingly, this coincides with a new trend of that considers consciousness to be a fundamental part of our universe and one worthy of academic inquiry. Our observations • A new study published in the journal Nature Communications, reports that scientists from the Neuroimaging Unit Optical Neuroimaging Unit at the Okinawa Institute of Science and Technology Graduate University have developed an imaging technique that allows for the mapping of electrical activity in single neurons in animals that are wide awake. This has been impossible due to now, due to the delicate structure of neurons and the fast changes in their voltages. • Philosopher David Chalmers notes in a few interviews (e.g. Quartz, New Scientist) that panpsychism is being taken more seriously by the academic community as it elegantly takes up some of the problems in the debate around consciousness. • In the article “Are we already living in virtual reality?” in The New Yorker Joshua Rothman writes about a fourteen-partner E.U.-funded project called Virtual Embodiment and Robotic Re-Embodiment that investigates how virtual reality can be used to manipulate the mental model of the participant’s own body. -
HUMAN-TYPE COMMON SENSE NEEDS EXTENSIONS to LOGIC John Mccarthy, Stanford University
HUMAN-TYPE COMMON SENSE NEEDS EXTENSIONS TO LOGIC John McCarthy, Stanford University • Logical AI (artificial intelligence) is based on programs that represent facts about the world in languages of mathematical logic and decide what actions will achieve goals by logical rea- soning. A lot has been accomplished with logic as is. • This was Leibniz’s goal, and I think we’ll eventually achieve it. When he wrote Let us calculate, maybe he imagined that the AI prob- lem would be solved and not just that of a logical language for expressing common sense facts. We can have a language adequate for expressing common sense facts and reasoning before we have the ideas needed for human- level AI. 1 • It’s a disgrace that logicians have forgotten Leibniz’s goal, but there’s an excuse. Non- monotonic reasoning is needed for common sense, but it can yield conclusions that aren’t true in all models of the premises—just the preferred models. • Almost 50 years work has gone into logical AI and its rival, AI based on imitating neuro- physiology. Both have achieved some success, but neither is close to human-level intelligence. • The common sense informatic situation, in contrast to bounded informatic situations, is key to human-level AI. • First order languages will do, especially if a heavy duty axiomatic set theory is included, e.g. A × B, AB, list operations, and recur- sive definition are directly included. To make reasoning as concise as human informal set- theoretic reasoning, many theorems of set the- ory need to be taken as axioms. -
Toward a Well-Innervated Philosophy of Mind
4 Toward a Well-Innervated Philosophy of Mind It might be thought that when I have been talking about ‘philoso- phers of mind’, collectively, as failing to appreciate our new folk neuroscience, in which the PNS is as important to conceptual issues as the brain (or more generally the CNS), I have neglected the new- est developments in philosophy of mind and cognitive science, a group of proposals commonly referred to as embodied and embed- ded cognition (EE from now on). It is not a homogeneous group of theories, yet, all the proposals have in common a critique of cogni- tivism, that is, cognitive science that focuses on either abstract computational-symbolic phenomena in the brain, or on distributed, nonsymbolic, but still brain-confi ned patterns of nerve activation, and reduces or confi nes mental activity to these. The various EE proposals, starting from early 1990 (e.g., Varela, Thompson, and Rosch 1991), 1 have developed into a considerable literature by now, where the common point is to reestablish the mind-body-world con- nection that classical philosophy of mind and cognitive science have severed. There is not much space here to enter the details of EE, and though there is today a well-deserved sympathy for this approach, given its novelty and force, I will just point out that EE itself fails to 1 Although some of the ideas emerged earlier, as pointed out by EE theorists, especially in the tradition of phenomenology established by Husserl and Merleau-Ponty, but also in the Heideggerian (or pop-Heideggerian, as the sarcastic critics sometimes refer to it) one.