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) …
Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different laws of nature? We consider the theory that predictive coding is such a general rule, and falsify it for one specific neural architecture known for high-performance predictions on natural videos and replication of human visual illusions: PredNet. Our results show that PredNet's high performance generalizes without retraining on a completely different natural video dataset. Yet PredNet cannot be trained to reach even mediocre accuracy on an artificial video dataset created with the rules of the Game of Life (GoL). We also find that a submodule of PredNet, a Convolutional Neural Network trained alone, reaches perfect accuracy on the GoL while being mediocre for natural videos, showing that PredNet's architecture itself is responsible for both the high performance on natural videos and the loss of performance on the GoL. Just as humans cannot predict the dynamics of the GoL, our results suggest that there might be a trade-off between high performance on sensory inputs with different sets of rules.
As artificial intelligence systems take on more tasks and solve more problems, it's hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that "By 2029, computers will have emotional intelligence and be convincing as people." We don't know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they're conscious, does that mean they actually are?
The goal of creating thinking machines is not a new one. It has been theorized and fantasized about for almost as long as humans have been capable of attributing intelligence to the non-living. From Frankenstein's monster to Alan Turing's famous "Imitation Game," we have dreamed about various entities that can think and reason as we can. Let's break down what we mean by "Artificial General Intelligence," and separate it from the more commonplace terms of "artificial intelligence" and "machine learning." For our purposes, we imagine an Artificial General Intelligence (AGI) as a machine (or network of machines) that is capable of understanding, rationalizing and acting.
In this lecture, I will offer you a definition of artificial intelligence, or AI, and give you a brief overview of its history from its inception in the 1950s. Let's start by saying what AI isn't. AI is not machines that think, or even computers that work the way the brain works. AI is what machines do, not how they do it. The authors of a leading textbook on AI have offered eight possible definitions of the term.
For many months, artificial intelligence has been in my peripheral vision, just sitting there, ignored by me because it seemed too far in the future to be interesting now. And then, there were all these terms -- Big Data, machine learning, data science -- which circled the subject and, frankly, gave me a bit of a headache. Artificial intelligence is upon us, unleashed and unbridled in its ability to transform the world. If in the previous technological revolution, machines were invented to do the physical work, then in this revolution, machines are being invented to do the thinking work. And no field involves more thinking than medicine.
An Artificial General Intelligence is coming, and we have no clue how homo sapiens might be impacted. When we think of preparing for our future, we used to think about going to good college and moving for a good job that would put us on a relatively good career trajectory for a stable life where we will prosper in a free market meritocracy where we compete against fellow humans. However, over the course of the next few decades homo sapiens including generation GenZ and Alpha, may be among the last people to grow up in a pre automation and pre AGI world. Considering the exponential levels of technological progress expected in the next 30 years, that's hard to put into words or even historical context. Namely, because there's no historical precedent and no words to describe what the next-gen AI might become.
As we head into 2019, the IT industry can expect to see some very disruptive changes throughout the new year, with domination from artificial intelligence, followed by the cloud and innovative digital platforms. As one of the key technologies, 5G will gain in importance for the implementation of Industry 4.0. IT trends are developments in information technology, which quickly gain ground and are commonly used by many. For example, the internet is one of the most successful and lasting IT trends of the past decades. Some IT trends have a disruptive character and sometimes lead to profound social and economic changes.
In 2017, we predicted that artificial general intelligence will gain traction in research and certain areas will aid towards AGI systems. The prediction was made in a set of other AI predictions in an article titled 18 striking AI Trends to watch in 2018. Let's see how 2018 went for AGI research. Artificial general intelligence or AGI is an area of AI in which efforts are made to make machines have intelligence closer to the complex nature of human intelligence. Such a system could possibly, in theory, perform tasks that a human can with the ability to learn as it progresses through tasks, collects data/sensory input.
Reinforcement learning is recently one of the potential research field of data scientists, it makes feasible to outdo processes what we have so far, and makes imaginable to reach the so called artificial general intelligence (AGI). In our previous blog we described and made the theory of reinforcement learning familiar to you. This following blog requires the knowledge of it and introduces the process basics of reinforcement learning through a practical example. We have to mention OpenAi, they are one of the lead researchers on the reinforcement learning field and on the artificial general intelligence topic. They developed a toolkit called Gym which is a free and easy to use tool to the artificial intelligence community.
Marx argued that under capitalism, everyone must work to live. We have some freedom to chose what type of work we do. But few of us have the choice not to work at all. Most of us need to find some particular task(s) we can do in exchange for a wage. And we cannot just walk away if we do not like it.