The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI wasn't formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined.
ai the tumultuous search for artificial intelligence pdf book
MIT cognitive scientist Marvin Minsky and others who attended the conference were extremely optimistic about AI's future. "Within a generation [...] the problem of creating 'artificial intelligence' will substantially be solved," Minsky is quoted as saying in the book "AI: The Tumultuous Search for Artificial Intelligence" (Basic Books, 1994). [Super-Intelligent Machines: 7 Robotic Futures]
But the accomplishment has been controversial, with artificial intelligence experts saying that only a third of the judges were fooled, and pointing out that the bot was able to dodge some questions by claiming it was an adolescent who spoke English as a second language.
Hawking, S., Russell, S., Tegmark, M., & Wilczek, F. (2014). Stephen H awking: Transcendence looks at the implications of artificial intelligence-but are we taking AI seriously enough? The Independent, 2014(05-01)
Kate Crawford is a leading academic focusing on the social and political implications of artificial intelligence. For over a decade, her work has centred on understanding large-scale data systems in the wider contexts of politics, history, labour, and the environment. Kate Crawford is a research professor at USC Annenberg, and the Visiting Chair of AI and Justice at the École Normale Supérieure. In 2020, she is the inaugural Visiting Chair for AI and Justice at the École Normale Supérieure in Paris. She co-founded the AI Now Institute at New York University, a university centre dedicated to researching the social implications of AI and related technologies. In 2019, she and Vladan Joler won the Beazley Design of the Year Award, for their Anatomy of an AI System project, which was recently acquired by MoMA for its permanent collection. Crawford was also jointly awarded the Ayrton Prize from the British Society for the History of Science for the project Excavating AI. Her new book Atlas of AI is forthcoming with Yale University Press in 2021.
Nigel Shadbolt is Principal of Jesus College, Professorial Research Fellow in Computer Science at the University of Oxford, and Chairman and Cofounder of the Open Data Institute. He is the author of The Digital Ape: How to Live (in Peace) with Smart Machines (2019) and The Spy in the Coffee Machine: The End of Privacy as We Know It (2008), as well numerous papers on artificial intelligence, human-centered computing, and computational neuroscience.
Artificial intelligence has a decades-long history that exhibits alternating enthusiasm and disillusionment for the field's scientific insights, technical accomplishments, and socioeconomic impact. Recent achievements have seen renewed claims for the transformative and disruptive effects of AI. Reviewing the history and current state of the art reveals a broad repertoire of methods and techniques developed by AI researchers. In particular, modern machine learning methods have enabled a series of AI systems to achieve superhuman performance. The exponential increases in computing power, open-source software, available data, and embedded services have been crucial to this success. At the same time, there is growing unease around whether the behavior of these systems can be rendered transparent, explainable, unbiased, and accountable. One consequence of recent AI accomplishments is a renaissance of interest around the ethics of such systems. More generally, our AI systems remain singular task-achieving architectures, often termed narrow AI. I will argue that artificial general intelligence-able to range across widely differing tasks and contexts-is unlikely to be developed, or emerge, any time soon.
Artificial intelligence surrounds us, both as a topic of debate and a deployed technology. AI technologists, engineers, and scientists add to an ever-growing list of accomplishments; the fruits of their research are everywhere. Voice recognition software now goes unremarked upon on our smartphones and laptops and is ever present in digital assistants like Alexa and Siri. Our faces, fingerprints, gait, voices, and the flight of our fingers across a keypad can all be used to identify each and every one of us following the application of AI machine learning methods. AI increasingly plays a role in every sector of our economy and every aspect of our daily lives. From driving our cars to controlling our critical infrastructure, from diagnosing our illnesses to recommending content for our entertainment, AI is ubiquitous.
The title of this essay draws on the closing sentence of Charles Darwin's magisterial On the Origin of Species. Darwin gave us the means to understand how all of life, including self-aware, natural intelligence, has evolved. Evolution works over deep time, producing diverse species within rich and varied ecosystems. It produces complex systems whose operating and organizational principles we struggle to decipher and decode. AI has begun to populate specialist niches of the cyber-physical ecosystem, and species of narrow AI are able to master specific tasks. However, we face challenges on the same scale as cognitive neuroscientists in our quest to realize artificial general intelligence (AGI): systems able to reflectively range across widely differing tasks and contexts. Such systems remain the stuff of Hollywood films.
Whatever its basis, a key property of human consciousness is that we have conceptual self-awareness: we have abstract concepts for our physical and mental selves; my body, my mind, and my thought processes as well as an integrated sense of myself-me. A construct replete with emotions, experience, history, goals, and relationships. We are possessed of theories of mind to understand other entities and motivations in context, to be able to make sense of their actions and to interact with them appropriately. None of this is in our AI systems at present. This is not to say such awareness will never be present in future species of AI. Our own cognitive and neural architectures, the rich layering of systems, present an existence proof. But our AI systems are not yet in the world in any interesting sense.25 When discussing the prospect of artificial general intelligence, we tend to reserve a special place for our own variety-possessed of experiential self-awareness- and we seem particularly drawn to the symbolic expression of that experience in our language, teleological understanding of the world, and imagined future possibilities. We need to continue to interrogate our understanding of the concept of intelligence. For the foreseeable future, no variety of AI will have a reasonable claim to a sufficient range of attributes for us to ascribe them general intelligence. But this cannot be an in-principle embargo.
AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go).[1]As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[2] For instance, optical character recognition is frequently excluded from things considered to be AI,[3] having become a routine technology.[4]
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[5][6] followed by disappointment and the loss of funding (known as an "AI winter"),[7][8] followed by new approaches, success and renewed funding.[6][9] AI research has tried and discarded many different approaches since its founding, including simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the first decades of the 21st century, highly mathematical-statistical machine learning has dominated the field, and this technique has proved highly successful, helping to solve many challenging problems throughout industry and academia.[9][10]
The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[b]This raised philosophical arguments about the mind and the ethical consequences of creating artificial beings endowed with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity.[13] Computer scientists and philosophers have since suggested that AI may become an existential risk to humanity if its rational capacities are not steered towards beneficial goals.[c]
Artificial beings with intelligence appeared as storytelling devices in antiquity,[14]and have been common in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R.[15] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[16]
By the 1950s, two visions for how to achieve machine intelligence emerged. One vision, known as Symbolic AI or GOFAI, was to use computers to create a symbolic representation of the world and systems that could reason about the world. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. Closely associated with this approach was the "heuristic search" approach, which likened intelligence to a problem of exploring a space of possibilities for answers. The second vision, known as the connectionist approach, sought to achieve intelligence through learning. Proponents of this approach, most prominently Frank Rosenblatt, sought to connect Perceptron in ways inspired by connections of neurons.[20] James Manyika and others have compared the two approaches to the mind (Symbolic AI) and the brain (connectionist). Manyika argues that symbolic approaches dominated the push for artificial intelligence in this period, due in part to its connection to intellectual traditions of Descartes, Boole, Gottlob Frege, Bertrand Russell, and others. Connectionist approaches based on cybernetics or artificial neural networks were pushed to the background but have gained new prominence in recent decades.[21] 2ff7e9595c
Comments