Story by Big Think
We live in strange times, when the technology we depend on the most is also that which we fear the most. We celebrate cutting-edge achievements even as we recoil in fear at how they could be used to hurt us. From genetic engineering and AI to nuclear technology and nanobots, the list of awe-inspiring, fast-developing technologies is long.
However, this fear of the machine is not as new as it may seem. Technology has a longstanding alliance with power and the state. The dark side of human history can be told as a series of wars whose victors are often those with the most advanced technology. (There are exceptions, of course.) Science, and its technological offspring, follows the money.
This fear of the machine seems to be misplaced. The machine has no intent: only its maker does. The fear of the machine is, in essence, the fear we have of each other — of what we are capable of doing to one another.
How AI changes things
Sure, you would reply, but AI changes everything. With artificial intelligence, the machine itself will develop some sort of autonomy, however ill-defined. It will have a will of its own. And this will, if it reflects anything that seems human, will not be benevolent. With AI, the claim goes, the machine will somehow know what it must do to get rid of us. It will threaten us as a species.
Well, this fear is also not new. Mary Shelley wrote Frankenstein in 1818 to warn us of what science could do if it served the wrong calling. In the case of her novel, Dr. Frankenstein’s call was to win the battle against death — to reverse the course of nature. Granted, any cure of an illness interferes with the normal workings of nature, yet we are justly proud of having developed cures for our ailments, prolonging life and increasing its quality. Science can achieve nothing more noble. What messes things up is when the pursuit of good is confused with that of power. In this distorted scale, the more powerful the better. The ultimate goal is to be as powerful as gods — masters of time, of life and death.
Should countries create a World Mind Organization that controls the technologies that develop AI?
Back to AI, there is no doubt the technology will help us tremendously. We will have better medical diagnostics, better traffic control, better bridge designs, and better pedagogical animations to teach in the classroom and virtually. But we will also have better winnings in the stock market, better war strategies, and better soldiers and remote ways of killing. This grants real power to those who control the best technologies. It increases the take of the winners of wars — those fought with weapons, and those fought with money.
A story as old as civilization
The question is how to move forward. This is where things get interesting and complicated. We hear over and over again that there is an urgent need for safeguards, for controls and legislation to deal with the AI revolution. Great. But if these machines are essentially functioning in a semi-black box of self-teaching neural nets, how exactly are we going to make safeguards that are sure to remain effective? How are we to ensure that the AI, with its unlimited ability to gather data, will not come up with new ways to bypass our safeguards, the same way that people break into safes?
The second question is that of global control. As I wrote before, overseeing new technology is complex. Should countries create a World Mind Organization that controls the technologies that develop AI? If so, how do we organize this planet-wide governing board? Who should be a part of its governing structure? What mechanisms will ensure that governments and private companies do not secretly break the rules, especially when to do so would put the most advanced weapons in the hands of the rule breakers? They will need those, after all, if other actors break the rules as well.
As before, the countries with the best scientists and engineers will have a great advantage. A new international détente will emerge in the molds of the nuclear détente of the Cold War. Again, we will fear destructive technology falling into the wrong hands. This can happen easily. AI machines will not need to be built at an industrial scale, as nuclear capabilities were, and AI-based terrorism will be a force to reckon with.
So here we are, afraid of our own technology all over again.
What is missing from this picture? It continues to illustrate the same destructive pattern of greed and power that has defined so much of our civilization. The failure it shows is moral, and only we can change it. We define civilization by the accumulation of wealth, and this worldview is killing us. The project of civilization we invented has become self-cannibalizing. As long as we do not see this, and we keep on following the same route we have trodden for the past 10,000 years, it will be very hard to legislate the technology to come and to ensure such legislation is followed. Unless, of course, AI helps us become better humans, perhaps by teaching us how stupid we have been for so long. This sounds far-fetched, given who this AI will be serving. But one can always hope.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
