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) …
We own a full exclusive full license to this photo.. In the world of the rising numbers of Machine Learning (ML) related projects, simplified ML frameworks and environments, and prepackaged ML solutions in the cloud -- the voice of disappointment can be heard more and more often. This ever growing voice is coming from the top experts in the matter, so we at Avenga Tech would like to take a moment to share our opinion about whether deep learning is really hitting the wall. Is the current state of enterprise AI in need of another major breakthrough, or can we use current techniques and not worry? Avenga has extensive expertise in data science and deep learning in particular. So, here we are to help you understand the reasons and practical implications of the current situation. AI has improperly, but surely, become synonymous with Machine Learning, and machine learning is almost always related to deep learning (also false, because there are more techniques), and deep learning usually relates to Convolutional Neural Network (CNN, a type of artificial neural networks that learns how to recognize and classify the patterns in input data). For a given set of problems, usually pattern recognition, deep learning enables very high accuracy, relatively quick learning, and the fast and low resources model execution including mobile battery powered devices.
Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games just as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to today's deep learning-powered bots that can beat even the best players in the world at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older. As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern.
Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to today's deep learning-powered bots that can beat even the best players at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older. As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern.
The workshop marked the official beginning of AI history. But as the two-month effort--and many others that followed--only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it. That is why, despite six decades of research and development, we still don't have AI that rivals the cognitive abilities of a human child, let alone one that can think like an adult. What we do have, however, is a field of science that is split into two different categories: artificial narrow intelligence (ANI), what we have today, and artificial general intelligence (AGI), what we hope to achieve. Defining artificial general intelligence is very difficult.
Tesla and SpaceX CEO Elon Musk tweeted that "Facebook sucks" after an argument over AI on Twitter. Mr Musk made the comments to the company's head of artificial intelligence Jerome Pesenti after Pesenti criticised Musk's knowledge of artificial intelligence. The comments came after a CNBC report that said multiple anonymous AI researchers said they saw Musk's views on the technology as inappropriate. Musk has previously warned that AI will become as intelligent as humans and could threaten humanity's very existence, saying that "there's a five to 10 percent chance of success [of making AI safe]". An AI executive speaking to CNBC, who asked to remain anonymous because their company may work for one of Musk's businesses, said that "A large proportion of the community think he's a negative distraction."
Elon Musk gave a simple response to the vice president of artificial intelligence at Facebook after being questioned about his knowledge of the technology – 'Facebook sucks.' The blunt response was to a tweet posted by Jerome Pesenti, who criticized the billionaire's warnings about the dangers of artificial general intelligence (AGI). 'I believe a lot of people in the AI community would be ok saying it publicly,' Pesenti wrote on Twitter. '[Musk] has no idea what he is talking about when he talks about AI. There is no such thing as AGI and we are nowhere near matching human intelligence.'
Artificial General Intelligence (AGI) - a hypothetical machine capable of any and all of the intellectual tasks performed by humans - is considered by many to be a pipe dream. A long-standing feature of science fiction, AGI has achieved a cultural reputation of both reverence and fear, but above all an appreciation for the possibilities it presents. However, despite what the movies might suggest, there is still considerable debate around what constitutes general intelligence in humans, let alone machines. Before diving into AGI, it's worth establishing what has become the accepted meaning of'general intelligence'. The term'general intelligence' is an evolving term.
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. From ancient mythology to modern science fiction, humans have been dreaming of creating artificial intelligence for millennia. But the endeavor of synthesizing intelligence only began in earnest in the late 1950s, when a dozen scientists gathered in Dartmouth College, NH, for a two-month workshop to create machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves." The workshop marked the official beginning of AI history. But as the two-month effort--and many others that followed--only proved that human intelligence is very complicated, and the complexity becomes more evident as you try to replicate it.
To differentiate themselves from researchers solving narrow AI problems, a few research teams have claimed an almost proprietary interest in producing human-level intelligence (or more) under the name "artificial general intelligence." Some have adopted the term "super-intelligence" to describe AGI systems that by themselves could rapidly design even more capable systems, with those systems further evolving to develop capabilities that far exceed any possessed by humans.
Self-supervised learning could lead to the creation of AI that's more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online. Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it'll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data's parts, a step believed to be critical to achieving human-level intelligence. "Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It's basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way," said LeCun.