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) …
Despite more than 50 years of experimentation with artificial general intelligence (AGI), mankind still hasn't achieved the goal of creating a single machine that can perform all cognitive tasks as well as humans can. But what if there was a way to attain AGI-level artificial intelligence (AI) using today's technology? The fact is that artificial narrow intelligence (ANI) systems are already available that can approximate the capabilities of AGI by employing multiple cognitive engines in a parallel-processing architecture. And with the number of commercially-available cognitive engines expanding exponentially, this approach will enable ANI to rapidly overtake and even surpass human capabilities. Moreover, with the AI market set to expand by nearly a factor of 60 from 2016 to 2022, this method represents the best way for companies to capitalize on the industry's growth potential.
Good back in the mid-1960s introduced what he called the intelligence explosion, which in essence was the same as the concept that Vernor Vinge later introduced and Ray Kurzweil adopted and called the technological singularity. Good said was the first intelligent machine will be the last invention that humanity needs to make. Good wasn't thinking about a system like AlphaGo that could beat Go but couldn't walk down the street or add five plus five. In the modern vernacular what we can say is the first human level AGI, the first human level artificial general intelligence, will be the last invention that humanity needs to make. And the reason for that is once you get a human level AGI you can teach this human level AGI math and programming and AI theory and cognitive science and neuroscience.
The researchers add, "[I]t is possible that in the future algorithms will write many if not most algorithms." That future looks pretty close. MIT Technology Review rounds up several recent milestones in machine learning software that is making machine learning software. There are lots of implications here, but one obvious one is that some of the hottest tech job categories of 2017 may be at just as much risk of being automated away as truck drivers. "If self-starting AI techniques become practical, they could increase the pace at which machine-learning software is implemented across the economy," writes Tom Simonite, the magazine's San Francisco Bureau Chief. "Companies must currently pay a premium for machine-learning experts, who are in short supply."
Hephaestus, the Greek god of craftsmen and blacksmiths, was believed to have created automatons to work for him. Another mythological figure, Pygmalion, carved a statue of a beautiful woman from ivory, who he proceeded to fall in love with. Aphrodite then imbued the statue with life as a gift to Pygmalion, who then married the now living woman. Throughout history, myths and legends of artificial beings that were given intelligence were common. These varied from having simple supernatural origins (such as the Greek myths), to more scientifically-reasoned methods as the idea of alchemy increased in popularity. In fiction, particularly science fiction, artificial intelligence became more and more common beginning in the 19th century.
Experts at the University of Oslo, Norway have discovered a new way for robots to design, evolve and manufacture themselves, without input from humans, using a form of artificial evolution called "Generative design," and 3D printers – although admittedly the team, for now at least, still has to assemble the final product, robot, when it's printed. Generative design is something we've talked about several times before and it's where artificial intelligence programs – creative machines, if you will – not humans, innovate new products – such as chairs and even Under Armour's new Architech sneakers. The labs latest robot, "Number Four," which is made up of sausage like plastic parts linked together with servo motors, is trying out different gaits, attempting to figure out the best way to move from one end of the floor to the other. And while you might look at this video and think it's weird, or funny remember that this is just the start. Today it's evolving, trying to learn how to move from A to B in the most efficient manner, but tomorrow – well, it could be "evolving" anything, and all at a much faster rate than humans.
Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce.
Every step forward in artificial intelligence (AI) challenges assumptions about what machines can do. Myriad opportunities for economic benefit have created a stable flow of investment into AI research and development, but with the opportunities come risks to decision-making, security and governance. Increasingly intelligent systems supplanting both blue- and white-collar employees are exposing the fault lines in our economic and social systems and requiring policy-makers to look for measures that will build resilience to the impact of automation. Leading entrepreneurs and scientists are also concerned about how to engineer intelligent systems as these systems begin implicitly taking on social obligations and responsibilities, and several of them penned an Open Letter on Research Priorities for Robust and Beneficial Artificial Intelligence in late 2015.1 Whether or not we are comfortable with AI may already be moot: more pertinent questions might be whether we can and ought to build trust in systems that can make decisions beyond human oversight that may have irreversible consequences. By providing new information and improving decision-making through data-driven strategies, AI could potentially help to solve some of the complex global challenges of the 21st century, from climate change and resource utilization to the impact of population growth and healthcare issues.
Until recently, artificial intelligence (AI) was similar to nuclear fusion in unfulfilled promise. It had been around a long time but had not reached the spectacular heights foreseen in its infancy. AI is swiftly becoming the foundational technology in areas as diverse as self-driving cars and financial trading. Self- learning algorithms are now routinely embedded in mobile and online services. Researchers have leveraged massive gains in processing power and the data streaming from digital devices and connected sensors to improve AI performance.
The term "artificial intelligence" was beaten to semantic death in 2016. The term has been used and abused before, but perhaps never like it was during a year of self-driving cars and home assistants, anyone and everyone is trying to associate the "AI" acronym with their startup like it were the 1991 marketing blitz for Terminator 2. From that perspective, one might be quick to forgive anyone who dismissed the latest acquisition by Microsoft of Maluuba. Based in Montreal, University of Waterloo graduates Sam Pasupalak and Kaheer Suleman founded the company in 2010. Maluuba is currently focused on applying deep learning and "reinforcement learning" to language comprehension by machines. To that end, they have published several research papers though, haven't publicly released a product.
Ever since we were classmates in our AI course (CS 486) at the University of Waterloo, way back in the summer of 2010, our vision has been to solve artificial general intelligence by creating literate machines that could think, reason and communicate like humans. Understanding human language is an extremely complex task and, ultimately, the holy grail in the field of AI. In early 2014, we observed great leaps in the fields of computer vision and speech recognition and pondered the potential of Deep Learning and Reinforcement Learning to enable our mission of creating literate machines. We realized that a great opportunity lay ahead, where machines could learn to model the intelligence and decision-making capabilities of the human brain. This meant more than simple pattern matching on text, but building systems that can actually comprehend, synthesize, infer and make logical decisions like humans.