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) …
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.
This article was originally published by the World Economic Forum. The doomsday scenarios spun around this theme are so outlandish--like The Matrix, in which human-created artificial intelligence plugs humans into a simulated reality to harvest energy from their bodies--it's difficult to visualize them as serious threats. Meanwhile, artificially intelligent systems continue to develop apace. Self-driving cars are beginning to share our roads; pocket-sized devices respond to our queries and manage our schedules in real-time; algorithms beat us at Go; robots become better at getting up when they fall over. It's obvious how developing these technologies will benefit humanity. But, then, don't all the dystopian sci-fi stories start out this way?
Our world has been revolutionized by Artificial Intelligence. A subject hotly glorified by popular sci-fi movies, AI has now penetrated various spheres of our life. It is widely used in applications such as aerospace, bio-informatics, business intelligence, financial advisory systems, emergency response, homeland security, logistics and supply chain. In recent years, we have witnessed a rebirth of AI through the use of cloud, with technology firms such as Google leading the way in showing the power of data-driven computing. Artificial intelligence systems are extensively used by researchers at technology firms, universities and government labs.
Cybersecurity research involves publishing papers about malicious exploits as much as publishing information on how to design tools to protect cyber-infrastructure. It is this information exchange between ethical hackers and security experts, which results in a well-balanced cyber-ecosystem. In the blooming domain of AI Safety Engineering, hundreds of papers have been published on different proposals geared at the creation of a safe machine, yet nothing, to our knowledge, has been published on how to design a malevolent machine. Availability of such information would be of great value particularly to computer scientists, mathematicians, and others who have an interest in AI safety, and who are attempting to avoid the spontaneous emergence or the deliberate creation of a dangerous AI, which can negatively affect human activities and in the worst case cause the complete obliteration of the human species. This paper provides some general guidelines for the creation of a Malevolent Artificial Intelligence (MAI).
The field of artificial intelligence has spawned a vast range of subset fields and terms: machine learning, neural networks, deep learning and cognitive computing, to name but a few. However here we will turn our attention to the specific term'artificial general intelligence', thanks to the Portland-based AI company Kimera Systems' (momentous) claim to have launched the world's first ever example, called Nigel. The AGI Society defines artificial general intelligence as "an emerging field aiming at the building of "thinking machines"; that is general-purpose systems with intelligence comparable to that of the human mind (and perhaps ultimately well beyond human general intelligence)". AGI would, in theory, be able to perform any intellectual feat a human can. You can now perhaps see why a claim to have launched the world's first ever AGI might be a tad ambitious, to say the least.