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. Read More
Tag Archives: Human
Twitter billionaire Jack Dorsey: Automation will even put tech jobs in jeopardy
The rise of artificial intelligence will make even software engineers less sought after.
That’s because artificial intelligence will soon write its own software, according to Jack Dorsey, the tech billionaire boss of Twitter and Square. And that’s going to put some beginning-level software engineers in a tough spot. Read More
Is the Brain a Useful Model for Artificial Intelligence?
IN THE SUMMER of 2009, the Israeli neuroscientist Henry Markram strode onto the TED stage in Oxford, England, and made an immodest proposal: Within a decade, he said, he and his colleagues would build a complete simulation of the human brain inside a supercomputer. They’d already spent years mapping the cells in the neocortex, the supposed seat of thought and perception. “It’s a bit like going and cataloging a piece of the rain forest,” Markram explained. “How many trees does it have? What shapes are the trees?” Now his team would create a virtual rain forest in silicon, from which they hoped artificial intelligence would organically emerge. If all went well, he quipped, perhaps the simulated brain would give a follow-up TED talk, beamed in by hologram. …
What computer scientists and neuroscientists are after is a universal theory of intelligence—a set of principles that holds true both in tissue and in silicon. What they have instead is a muddle of details. Read More
Neuro-symbolic AI seen as evolution of artificial intelligence
Symbolic AI algorithms have played an important role in AI’s history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by deep learning.
The power of neural networks is that they help automate the process of generating models of the world. This has led to several significant milestones in artificial intelligence, giving rise to deep learning models that, for example, could beat humans in progressively complex games, including Go and StarCraft. But it can be challenging to reuse these deep learning models or extend them to new domains. Read More
Algorithm and Blues
Why your brain is not a computer
For decades it has been the dominant metaphor in neuroscience. But could this idea have been leading us astray all along?
We are living through one of the greatest of scientific endeavours – the attempt to understand the most complex object in the universe, the brain. Scientists are accumulating vast amounts of data about structure and function in a huge array of brains, from the tiniest to our own. Tens of thousands of researchers are devoting massive amounts of time and energy to thinking about what brains do, and astonishing new technology is enabling us to both describe and manipulate that activity. Read More
How neuro-symbolic AI might finally make machines reason like humans
If you want a machine to learn to do something intelligent you either have to program it or teach it to learn.
For decades, engineers have been programming machines to perform all sorts of tasks — from software that runs on your personal computer and smartphone to guidance control for space missions.
But although computers are generally much faster and more precise than the human brain at sequential tasks, such as adding numbers or calculating chess moves, such programs are very limited in their scope. Something as trivial as identifying a bicycle among a crowded pedestrian street or picking up a hot cup of coffee from a desk and gently moving it to the mouth can send a computer into convulsions, never mind conceptualizing or abstraction (such as designing a computer itself).
The gist is that humans were never programmed (not like a digital computer, at least) — humans have become intelligent through learning. Read More
The Link Between Sleep and Deep Learning
The cognitive purpose of sleep is an open question. Recently, however, scientists have a new conjecture (“How memory replay in sleep boosts creative problem solving“) as to the purpose of two important phases of sleep.
According to the research, the brain goes through several 90-minute cycles of REM and Non-REM sleep. Non-REM sleep involves the sequential replay of acquired memories. In contrast, REM sleep involves a more random associate game involving disparate memories. In deep learning, this is analogous to the search algorithms of optimization and exploration respectively. Read More
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