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) …
Why job security won't exist in the age of'superintelligence' and that's OK Artificial intelligence (AI) is a repeatedly misused term. Coined in 1956 by John McCarthy, it was originally intended to define an independent machine agent that can take actions to maximize success toward a particular goal, with human-like functions such as learning and problem solving. AI can be broadly categorized as ANI (artificial narrow intelligence), AGI (artificial general intelligence) and ASI (artificial superintelligence). Almost all of the AI systems we see today align under ANI -- e.g., IBM Watson, Deep Blue, a calculator, even the device you're reading this from all fall into that category. All are built to perform specific functions, but are not quite at a human level.
Artificial Intelligence (AI) has had a major resurgence in the past few years. If we look at the history of AI, we see a repeated cycle – it was at the forefront in the 80's too – it has been going through cycles of over-promise, investment, under delivery and investment reduction. However this time around, the technology advancements, scale and attention being paid to AI are much larger than before. The advances in big data technologies combined with cheap massively scalable infrastructure and storage is now helping us tremendously to tackle bigger and bolder problems in Artificial Intelligence. However, we are really just at the tip of the iceberg with AI.
In May, the White House Office of Science and Technology Policy (OSTP) announced "a new series of workshops and an interagency working group to learn more about the benefits and risks of artificial intelligence." They hosted a June Workshop on Safety and Control for AI (videos), along with three other workshops, and issued a general request for information on AI (see MIRI's primary submission here). The OSTP has now released a report summarizing its conclusions, "Preparing for the Future of Artificial Intelligence," and the result is very promising. The OSTP acknowledges the ongoing discussion about AI risk, and recommends "investing in research on longer-term capabilities and how their challenges might be managed": General AI (sometimes called Artificial General Intelligence, or AGI) refers to a notional future AI system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks. A broad chasm seems to separate today's Narrow AI from the much more difficult challenge of General AI. Attempts to reach General AI by expanding Narrow AI solutions have made little headway over many decades of research.
With Uber recently launching a trial of self-driving cars in Pittsburgh, it's the question everyone, not just attorneys, is now asking, "In the case of an accident, who's the legally responsible'driver' in a driver-less car?" Artificial Intelligence (AI) and the Internet of Things (IoT) are beginning to learn on their own and make independent decisions based on that learning, triggering new questions of responsibility and accountability. Among AI and IoT's many challenges in becoming mainstream technologies, the most important ones may be around building a legal framework for when the responsible party is no longer an easily identifiable person or company. To start this discussion on the legal questions to be answered in a world increasingly populated by autonomous drones, robots, and vehicles, we reached out to three leaders in the AI space – Stanford's Sudha Jamthe, CityMD's Ramu Kannan, and Kimera Systems' Mounir Shita (we've included their bios and contact information at the end of this article). Here's what we asked them, and their striking responses: AI means different things to different people. There are people who think of AI as a sensationalized topic that will build robots who will take over the world.
When writing the screenplay for 1968's 2001, Arthur C. Clarke and Stanley Kubrick were confident that something resembling the sentient, humanlike HAL 9000 computer would be possible by the film's namesake year. That's because the leading AI experts of the time were equally confident. Clarke and Kubrick took the scientific community's predictions to their logical conclusion, that an AI could have not only human charm but human frailty as well: HAL goes mad and starts offing the crew. But HAL was put in an impossible situation, forced to hide critical information from its (his?) coworkers and ordered to complete the mission to Jupiter no matter what. "I'm afraid, Dave," says the robot as it's being dismantled by the surviving astronaut.
Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.
Microsoft is quickly pivoting to position itself as a leader in artificial intelligence. In his second keynote on the subject this year, CEO Satya Nadella yesterday (Sept. Quartz caught up with Nadella after he hopped off stage, to talk about the progress of his quest to make machines that assist humanity in a transparent way. You started talking earlier in the year saying we need to create transparent machines, ethical machines, accountable machines. What has been done since then, what is concrete?
This intelligence, the Artificial General Intelligence, shall solve all our problems and the entire world is working on it. But the current Artificial Intelligence is mostly at the level of robotics. Fact is, that problem awareness is increased, we have recognized that we need to bundle our capacities to create something that helps mankind to find solutions against the most urgent problems, resource scarcity, world hunger, diseases, and social turmoil. In search of that Super Computer, the Super Intelligence, we forget that our planet has 7 billion super intelligences walking around and they are all waiting to give a meaning to their lives and bring in their cognitive-emotional talents: Humans. We try to walk with crutches whilst we already sit in a Formula 1 racing car.
The media is to blame for the hysteria around Artificial Intelligence (AI), according to senior strategic designer at BCG Digital Ventures, Chris Naylor. Human intelligences exhibited by machines is changing the way consumers interact with businesses and can be taken advantage of right now- without the world coming to a fiery end. Naylor explained to the audience at yesterday's Daze of Disruption the three main types of AI and how businesses, like Spotify and ANZ, are using artificial intelligence to successfully power customer interaction and drive greater value. "Artificial Intelligence (AI) is expected to boom into a 70 billion dollar industry, it's easy to see why it's touted as the next big thing. But the hype is a shrouded with mystery, AI is often seen as this futuristic technology out of reach for most companies and limited to powering autonomous cars and useless chatbots.
Simply put, any algorithm that has the ability to learn on its own, given a set of data, without having to program the rules of the domain explicitly, falls under the ambit of Machine Learning. This is different from Data Analytics or Expert systems where, rules, logic, propositions or activities has to be manually coded by an expert programmer. Systems which has ability to learn on its own and progress towards a pre-defined goal, without much of human intervention can be broadly termed as Intelligent Systems. The quality of intelligence can range from an amoeba, algae, ant, armadillo all the way to chimps, humans or beyond. As an example, systems which interact with humans in natural language cannot be built by coding the rules and conversational logic of human language.