artificial general intelligence

AI vs. Machine Learning vs. Deep Learning


Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality. Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future. Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market.

Tomorrow's 'general' AI revolution will grow from today's technology


During his closing remarks at the I/O 2019 keynote last week, Jeff Dean, Google AI's lead, noted that the company is looking at "AI that can work across disciplines," suggesting the Silicon Valley giant may soon pursue artificial general intelligence, a technology that eventually could match or exceed human intellect. In today's pop culture, machines with artificial general intelligence (AGI) are typically portrayed as walking, talking human analogs replete with personalities -- from the Terminator's murderous intent to Vision's noble heroism. In reality, self-aware robots are a long way off. Nathan Michael, associate research professor and the director of the Resilient Intelligent Systems Lab at Carnegie Mellon University, argues that generalized AI systems will grow out from today's single-purpose "narrow" AIs. "General AI is representative of this concept of bringing together many different kinds of specialized AI," he explained.

All that glitters is not quantum AI


Why hasn't the field of artificial intelligence created the equivalent of human intelligence? Is it because the problem, "artificial general intelligence," isn't well understood, or is because we just need much faster computers, specifically quantum computers? The latter view is the source of a vibrant field of research, "Quantum Machine Learning," or QML. But a bit of skepticism is warranted. "We need to look through a skeptical eye at the idea that quantum makes things faster and therefore can make machine learning advances," says Jennifer Fernick, the head of engineering at NCC Group, a cyber-security firm based in Manchester, U.K. Fernick was a keynote speaker a week ago at the O'Reilly A.I. conference in New York.

Towards a framework for the evolution of artificial general intelligence Artificial Intelligence

In this work, a novel framework for the emergence of general intelligence is proposed, where agents evolve through environmental rewards and learn throughout their lifetime without supervision, i.e., self-supervised learning through embodiment. The chosen control mechanism for agents is a biologically plausible neuron model based on spiking neural networks. Network topologies become more complex through evolution, i.e., the topology is not fixed, while the synaptic weights of the networks cannot be inherited, i.e., newborn brains are not trained and have no innate knowledge of the environment. What is subject to the evolutionary process is the network topology, the type of neurons, and the type of learning. This process ensures that controllers that are passed through the generations have the intrinsic ability to learn and adapt during their lifetime in mutable environments. We envision that the described approach may lead to the emergence of the simplest form of artificial general intelligence.

DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset Machine Learning

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.

What Would It Mean for AI to Become Conscious?


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?

How Artificial General Intelligence might be created


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.

AI explained


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.

The truth about artificial intelligence in medicine


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.

We're living in the Last Era Before Artificial General Intelligence


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.