Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google’s automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. However, because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does. The brain, and biological systems in general, are able to perform high-performance calculations much more efficiently than computers, and they do it quickly and with minimal energy consumption
Building artificial neural networks is an emerging field of research in bio-inspired computing. Read More
Tag Archives: Human
Artificial intelligence: How to measure the “I” in AI
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI.
Last week, Lee Se-dol, the South Korean Go champion who lost in a historical matchup against DeepMind’s artificial intelligence algorithm AlphaGo in 2016, declared his retirement from professional play.
“With the debut of AI in Go games, I’ve realized that I’m not at the top even if I become the number one through frantic efforts,” Lee told the Yonhap news agency. “Even if I become the number one, there is an entity that cannot be defeated.” Read More
On the Measure of Intelligence
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems,as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches,while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates to-wards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks, such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to “buy” arbitrary levels of skills for a system, in a way that masks the system’s own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope,generalization difficulty,priors, and experience, as critical pieces to be accounted for in characterizing intelligent systems. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like.Finally, we present a new benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans. Read More
Robot debates humans about the dangers of artificial intelligence
An artificial intelligence has debated with humans about the the dangers of AI – narrowly convincing audience members that AI will do more good than harm.
Project Debater, a robot developed by IBM, debated on both sides of the argument, with two human team mates for each side helping it out. Speaking in a female American voice to a crowd at the University of Cambridge Union on Thursday evening, the AI gave each side’s opening statements, using arguments drawn from more than 1100 human submissions ahead of time. Read More
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. Read More
Computers Evolve a New Path Toward Human Intelligence
In 2007, Kenneth Stanley, a computer scientist at the University of Central Florida, was playing with Picbreeder, a website he and his students had created, when an alien became a race car and changed his life. On Picbreeder, users would see an array of 15 similar images, composed of geometric shapes or swirly patterns, all variations on a theme. On occasion, some might resemble a real object, like a butterfly or a face. Users were asked to select one, and they typically clicked on whatever they found most interesting. Once they did, a new set of images, all variations on their choice, would populate the screen. From this playful exploration, a catalog of fanciful designs emerged. Read More
13 Mind-Blowing Things Artificial Intelligence Can Already Do Today
By now, most of us are aware of artificial intelligence (AI) being an increasingly present part of our everyday lives. But, many of us would be quite surprised to learn of some of the skills AI already knows how to do. Here are 13 mind-blowing skills artificial intelligence can already do today.
— Read
— Write
— See
— Hear and Understand
— Speak
— Smell
— Touch
— Move
— Understand Emotions
— Play Games
— Debate
— Create
— Read Your Mind
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The Man & Machine Issue: Artificial Intelligence vs. Human Behavior
The world is currently discussing if artificial systems are good or bad, will help us or destroy us, and if they will ever function or not, and by doing that people make the mistake of actually trying to answer the wrong question. As of today, the biggest question about artificial intelligence is not the system itself, but the biggest challenge is the interface consequences between the human and the machine, or to be more precise the system existent out of two elements — a carbon and a silicon body. Read More
A will to survive might take AI to the next level
…In real life robots have no more feelings than a rock submerged in novocaine.
There might be a way, though, to give robots feelings, say neuroscientists Kingson Man and Antonio Damasio. Simply build the robot with the ability to sense peril to its own existence. It would then have to develop feelings to guide the behaviors needed to ensure its own survival. Read More
Evolving Neural Networks through Augmenting Topologies
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex. Read More
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