If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
At its crudest, most reductive, we could sum up the future of artificial intelligence as being about robot butlers v killer robots. We have to get there eventually, so we might as well start with the killer robots. If we were to jump forward 50 years to see what artificial intelligence might bring us, would we – Terminator-style – step into a world of human skulls being crushed under the feet of our metal and microchip overlords? No, we're told by experts. It might be much worse.
Over the past two decades, the philosopher David Chalmers has established himself as a leading thinker on consciousness. He began his academic career in mathematics but slowly migrated toward cognitive science and philosophy of mind. He eventually landed at Indiana University working under the guidance of Douglas Hofstadter, whose influential book "Gödel, Escher, Bach: An Eternal Golden Braid" had earned him a Pulitzer Prize. Chalmers's dissertation, "Toward a Theory of Consciousness," grew into his first book, "The Conscious Mind" (1996), which helped revive the philosophical conversation on consciousness. Perhaps his best-known contribution to philosophy is "the hard problem of consciousness" -- the problem of explaining subjective experience, the inner movie playing in every human mind, which in Chalmers's words will "persist even when the performance of all the relevant functions is explained."
AI is applicable in a wide variety of areas--everything from agriculture to cybersecurity. However, most of our work has been on the short-term impact of AI in business. We're not talking about next quarter, or even next year, but in the decades to come. As AI becomes more powerful, we expect it to have a larger impact on our world, including your organization. So, we decided to do what we do best: a deep analysis of AI applications and implications.
Artificial general intelligence, or "AGI," the idea of a machine that can approach human levels of cognition, is a great topic to get people all worked up. Because no one can really define it, it serves as a Rorschach Test, onto which one can imprint whatever thoughts and feelings they care to. What is artificial general intelligence? Everything you need to know about the path to creating an AI as smart as a human. The result was a spirited discussion this past Friday night at John Jay College in Manhattan, site of the World Science Festival, now in its twelfth year.
These days, when you browse the internet for news on artificial intelligence, you'll find out about new AI that just managed to do something humans do, yet far better. Present day AI can detect cancers better than human doctors, build better AI algorithms than human developers, and beat the world champions at games like chess and Go. Instances like these may lead us to believe that perhaps, there's not a whole lot that artificial intelligence can not do better than us humans. The realization of AI's superior and ever-improving capabilities in different fields has evoked both hope and caution from the global tech community as well as the general public. While many believe the rise of artificial general intelligence can massively benefit humanity by raising our standard of living and status as a civilization, some believe the development may lead to global doom.
In What's the Future, Tim O'Reilly argues that our world is governed by automated systems that are out of our control. Alluding to The Terminator, he says we're already in a "Skynet moment," dominated by artificial intelligence that can no longer be governed by its "former masters." The systems that control our lives optimize for the wrong things: they're carefully tuned to maximize short-term economic gain rather than long-term prosperity. The "flash crash" of 2010 was an economic event created purely by the software that runs our financial systems going awry. However, the real danger of the Skynet moment isn't what happens when the software fails, but when it is working properly: when it's maximizing short-term shareholder value, without considering any other aspects of the world we live in.
Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality. Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future. Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market.
The question of whether an artificial general intelligence will be developed in the future--and, if so, when it might arrive--is controversial. One (very uncertain) estimate suggests 2070 might be the earliest we could expect to see such technology. Some futurists point to Moore's Law and the increasing capacity of machine learning algorithms to suggest that a more general breakthrough is just around the corner. Others suggest that extrapolating exponential improvements in hardware is unwise, and that creating narrow algorithms that can beat humans at specialized tasks brings us no closer to a "general intelligence." But evolution has produced minds like the human mind at least once.
With increase in capabilities of artificial intelligence, over the last decade, a significant number of researchers have realized importance in creating not only capable intelligent systems, but also making them safe and secure [1-6]. Unfortunately, the field of AI Safety is very young, and researchers are still working to identify its main challenges and limitations. Impossibility results are well known in many fields of inquiry [7-13], and some have now been identified in AI Safety [14-16]. In this paper, we concentrate on a poorly understood concept of unpredictability of intelligent systems , which limits our ability to understand impact of intelligent systems we are developing and is a challenge for software verification and intelligent system control, as well as AI Safety in general. In theoretical computer science and in software development in general, many well-known impossibility results are well established, some of them are strongly related to the subject of this paper, for example: Rice's Theorem states that no computationally effective method can decide if a program will exhibit a particular nontrivial behavior, such as producing a specific output .
In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans. But true artificial intelligence, as McCarthy conceived it, continues to elude us.