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) …
Early I brought up Howard Gardner's theory of multiple intelligences. That is, humans exhibit strengths in different kinds of intelligences. Specifically these are interpersonal, intrapersonal, verbal, logical, spatial, rhythmic, naturalistic and kinaesthetic intelligence. Clearly there are many kinds of ways of thinking, each with their own strengths. Therefore, one may ask if we can use this notion of multiple intelligences to explore the different ways that AGI research may evolve.
A common incorrect assumption about the evolution of Artificial General Intelligence (AGI), that is self-aware sentient automation, will follow the path of ever more intelligent machines and thus accelerate towards a super intelligence once human level sentient automation is created. I'm writing this article to argue that this likely will not be the case and that there will be an initial divergence of two kinds of artificial intelligences. First, let us establish here that the starting point will come from present day Deep Learning technology. More specifically, I refer these as intuition machines (see: Intuition Machines a Cognitive Breakthrough). There will be a fork in the evolution of more intelligent machines.
You could argue that artificial intelligence (AI) got started many years ago with a grand challenge. That, of course, was Alan Turing's "Imitation Game," which he presented in his seminal 1950 paper "Computing Machinery and Intelligence." Essentially, it involved building machines that can behave indistinguishably from the way a thinker behaves. In the example that Turing provides, that behavior is entirely focused on human (or humanlike) conversation. In many ways, that grand challenge is already history.
This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn't necessary to have prior knowledge of them to gain value from this series. Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic. The rate of acceleration is already astounding.
Everywhere you look nowadays, it seems that artificial intelligence is making enormous leaps and bounds. It's gotten smart enough that it can trounce humans in a growing number of tasks -- winning games like chess, Go and poker, as well as engaging in creative endeavors such as writing novels and music -- all once thought of as unassailable by machines. We're also seeing an emerging trend of AI-powered automation in industries like medicine, sales, retail and hotel management -- making us wonder what will happen once the machines take all the jobs. Nevertheless, despite these recent, high-profile achievements, AI still has some ways to go before it even comes close to truly imitating and even surpassing the complex mystery that epitomizes human intelligence. While there have been advances in getting machines to learn how to learn and reason like humans, current AI models are still relatively narrow in their scope, and have yet to embody the full range of cognitive abilities that humans use daily in solving a wide range of problems.
Designers today most likely have been designing for products that use some level of AI for automation. We have been designing in the first stage of AI, artificial narrow intelligence. To get to the second stage of AI, artificial general intelligence, we need user data. How do we get this information? To better understand how design makes AI smarter, you can first take a quick look at the different stages of AI in You can be an AI designer.
Designers today most likely have been designing for products that use some level of AI for automation. We have been designing in the first stage of AI, artificial narrow intelligence. To get to the second stage of AI, artificial general intelligence, we need user data. How do we get this information? To better understand how design makes AI smarter, first take a quick look at the different stages of AI in You can be an AI designer.
Artificial Intelligence has become a very popular term today. There is sure to be at least one article in the newspaper daily on the revolutionary advancements made in the field. But, there seems to be some confusion about what AI really is. Will the Terminator movie actually come true? Or is it something that has crept into our daily lives without us even realizing it? This article will give you a broad understanding on the buzzwords associated with AI, its applications, the careers & opportunities it has and its future.
We present an alternative methodology for the analysis of algorithms, based on the concept of expected discounted reward. This methodology naturally handles algorithms that do not always terminate, so it can (theoretically) be used with partial algorithms for undecidable problems, such as those found in artificial general intelligence (AGI) and automated theorem proving. We mention an approach to self-improving AGI enabled by this methodology. Aug 2017 addendum: This article was originally written with multiple audiences in mind. It is really best put in the following terms. Goertzel, Hutter, Legg, and others have developed a definition of an intelligence score for a general abstract agent: expected lifetime reward in a random environment. AIXI is generally the optimal agent according to this score, but there may be reasons to analyze other agents and compare score values. If we want to use this definition of intelligence in practice, perhaps we can start by analyzing some simple agents. Common algorithms can be thought of as simple agents (environment is input, reward is based on running time) so we take the goal of applying the agent intelligence score to algorithms. That is, we want to find, what are the IQ scores of algorithms? We can do some very simple analysis, but the real answer is that even for simple algorithms, the intelligence score is too difficult to work with in practice.
– Blockchain is a mystery story or provides the foundation for cryptocurrencies like Bitcoin. What's different about blockchains compared to traditional big-data distributed databases like MongoDB. Its like featuring a product that contains small blocks of brain in form of dust but consider that the innovation efforts of several publicly traded asset managers and banks are also on this brain block dust quest. Computers start simulating the brain's sensation, action, interaction, perception and cognition abilities. Blockchain is a new approach to manage/monitor financial and other transactions, Guarding an innovation department or powerhouse lab is a smart setup without inbuilt component of artificial intelligence is like an effort of joining blocks without reference of previous block.