Ways to Change the destiny of the world:
The Future of Artificial Intelligence
Employing machine learning and computer vision for detection and classification of various “safety events,” the shoebox-sized device doesn’t see all, but it sees plenty. Like which way the driver is looking as he operates the vehicle, how fast he’s driving, where he’s driving, locations of the people around him and how other forklift operators are maneuvering their vehicles. IFM’s software automatically detects safety violations (for example, cell phone use) and notifies warehouse managers so they can take immediate action. The main goals are to prevent accidents and increase efficiency. The mere knowledge that one of IFM’s devices is watching, Gyongyosi claims, has had “a huge effect.”
The Evolution of AI
IFM is just one of countless AI innovators in a field that’s hotter than ever and getting more so all the time. Here’s a good indicator: Of the 9,100 patents received by IBM inventors in 2018, 1,600 (or nearly 18 percent) were AI-related. Here’s another: Tesla founder and tech titan Elon Musk recently donated $10 million to fund ongoing research at the non-profit research company OpenAI — a mere drop in the proverbial bucket if his $1 billion co-pledge in 2015 is any indication. And in 2017, Russian president Vladimir Putin told school children that “Whoever becomes the leader in this sphere [AI] will become the ruler of the world.” He then tossed his head back and laughed maniacally.
OK, that last thing is false. This, however, is not: After more than seven decades marked by hoopla and sporadic dormancy during a multi-wave evolutionary period that began with so-called “knowledge engineering,” progressed to model- and algorithm-based machine learning and is increasingly focused on perception, reasoning and generalization, AI has re-taken center stage as never before. And it won’t cede the spotlight anytime soon.
Preparing for the Future of AI
Helpful or Homicidal: The Fantastical Possibilities of Artificial General Intelligence
Speaking at London’s Westminster Abbey in late November of 2018, internationally renowned AI expert Stuart Russell joked (or not) about his “formal agreement with journalists that I won’t talk to them unless they agree not to put a Terminator robot in the article.” His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic. What Russell referred to as “human-level AI,” also known as artificial general intelligence, has long been fodder for fantasy. But the chances of its being realized anytime soon, or at all, are pretty slim. The machines almost certainly won’t rise (sorry, Dr. Russell) during the lifetime of anyone reading this story.
“There are still major breakthroughs that have to happen before we reach anything that resembles human-level AI,” Russell explained. “One example is the ability to really understand the content of language so we can translate between languages using machines… When humans do machine translation, they understand the content and then express it. And right now machines are not very good at understanding the content of language. If that goal is reached, we would have systems that could then read and understand everything the human race has ever written, and this is something that a human being can't do... Once we have that capability, you could then query all of human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer because they haven't read and been able to put together and join the dots between things that have remained separate throughout history.”
That’s a mouthful. And a mind full. On the subject of which, emulating the human brain is exceedingly difficult and yet another reason for AGI’s still-hypothetical future. Longtime University of Michigan engineering and computer science professor John Laird has conducted research in the field for several decades.
“The goal has always been to try to build what we call the cognitive architecture, what we think is innate to an intelligence system,” he says of work that’s largely inspired by human psychology. “One of the things we know, for example, is the human brain is not really just a homogenous set of neurons. There’s a real structure in terms of different components, some of which are associated with knowledge about how to do things in the world.”
That’s called procedural memory. Then there’s knowledge based on general facts, a.k.a. semantic memory, as well as knowledge about previous experiences (or personal facts) that’s called episodic memory. One of the projects at Laird’s lab involves using natural language instructions to teach a robot simple games like Tic-Tac-Toe and puzzles. Those instructions typically involve a description of the goal, a rundown of legal moves and failure situations. The robot internalizes those directives and uses them to plan its actions. As ever, though, breakthroughs are slow to come — slower, anyway, than Laird and his fellow researchers would like.
“Every time we make progress,” he says, “we also get a new appreciation for how hard it is.”
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