AI gets smarter, more capable, and more world-transforming every day. Here’s why that might not be a good thing.
In 2018 at the World Economic Forum in Davos, Google CEO Sundar Pichai had something to say: “AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire.” Pichai’s comment was met with a healthy dose of skepticism. But nearly five years later, it’s looking more and more prescient.
AI translation is now so advanced that it’s on the brink of obviating language barriers on the internet among the most widely spoken languages. College professors are tearing their hair out because AI text generators can now write essays as well as your typical undergraduate — making it easy to cheat in a way no plagiarism detector can catch. AI-generated artwork is even winning state fairs. A new tool called Copilot uses machine learning to predict and complete lines of computer code, bringing the possibility of an AI system that could write itself one step closer. DeepMind’s AlphaFold system, which uses AI to predict the 3D structure of just about every protein in existence, was so impressive that the journal Science named it 2021’s Breakthrough of the Year.
HANDING OVER HUGE SECTORS OF OUR SOCIETY TO BLACK-BOX ALGORITHMS WE BARELY UNDERSTAND CREATES A LOT OF PROBLEMS
Of course, handing over huge sectors of our society to black-box algorithms that we barely understand creates a lot of problems, which has already begun to help spark a regulatory response around the current challenges of AI discrimination and bias. But given the speed of development in the field, it’s long past time to move beyond a reactive mode, one where we only address AI’s downsides once they’re clear and present. We can’t only think about today’s systems, but where the entire enterprise is headed.
The systems we’re designing are increasingly powerful and increasingly general, with many tech companies explicitly naming their target as artificial general intelligence (AGI) — systems that can do everything a human can do. But creating something smarter than us, which may have the ability to deceive and mislead us — and then just hoping it doesn’t want to hurt us — is a terrible plan. We need to design systems whose internals we understand and whose goals we are able to shape to be safe ones. However, we currently don’t understand the systems we’re building well enough to know if we’ve designed them safely before it’s too late.
There are people working on developing techniques to understand powerful AI systems and ensure that they will be safe to work with, but right now, the state of the safety field is far behind the soaring investment in making AI systems more powerful, more capable, and more dangerous. As the veteran video game programmer John Carmack put it in announcing his new investor-backed AI startup, it’s “AGI or bust, by way of Mad Science!”
This particular mad science might kill us all. Here’s why.
Computers that can think
The human brain is the most complex and capable thinking machine evolution has ever devised. It’s the reason why human beings — a species that isn’t very strong, isn’t very fast, and isn’t very tough — sit atop the planetary food chain, growing in number every year while so many wild animals careen toward extinction.
It makes sense that, starting in the 1940s, researchers in what would become the artificial intelligence field began toying with a tantalizing idea: What if we designed computer systems through an approach that’s similar to how the human brain works? Our minds are made up of neurons, which send signals to other neurons through connective synapses. The strength of the connections between neurons can grow or wane over time. Connections that are used frequently tend to become stronger, and ones that are neglected tend to wane. Together, all those neurons and connections encode our memories and instincts, our judgments and skills — our very sense of self.
“IT WOULD BE POSSIBLE TO BUILD BRAINS THAT COULD REPRODUCE THEMSELVES ... AND BE CONSCIOUS OF THEIR EXISTENCE”
So why not build a computer that way? In 1958, Frank Rosenblatt pulled off a proof of concept: a simple model based on a simplified brain, which he trained to recognize patterns. “It would be possible to build brains that could reproduce themselves on an assembly line and which would be conscious of their existence,” he argued. Rosenblatt wasn’t wrong, but he was too far ahead of his time. Computers weren’t powerful enough, and data wasn’t abundant enough, to make the approach viable.
It wasn’t until the 2010s that it became clear that this approach could work on real problems and not toy ones. By then computers were as much as 1 trillion times more powerful than they were in Rosenblatt’s day, and there was far more data on which to train machine learning algorithms.
This technique — now called deep learning — started significantly outperforming other approaches to computer vision, language, translation, prediction, generation, and countless other issues. The shift was about as subtle as the asteroid that wiped out the dinosaurs, as neural network-based AI systems smashed every other competing technique on everything from computer vision to translation to chess.
AI IS DANGEROUS PRECISELY BECAUSE THE DAY COULD COME WHEN IT IS NO LONGER IN OUR CONTROL AT ALL
From there, Russell has a rather technical description of what will go wrong: “A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.”
So a powerful AI system that is trying to do something, while having goals that aren’t precisely the goals we intended it to have, may do that something in a manner that is unfathomably destructive. This is not because it hates humans and wants us to die, but because it didn’t care and was willing to, say, poison the entire atmosphere, or unleash a plague, if that happened to be the best way to do the things it was trying to do. As Russell puts it: “This is essentially the old story of the genie in the lamp, or the sorcerer’s apprentice, or King Midas: you get exactly what you ask for, not what you want.”
“You’re probably not an evil ant-hater who steps on ants out of malice,” the physicist Stephen Hawking wrote in a posthumously published 2018 book, “but if you’re in charge of a hydroelectric green-energy project and there’s an anthill in the region to be flooded, too bad for the ants. Let’s not place humanity in the position of those ants.”
The CEOs and researchers working on AI vary enormously in how much they worry about safety or alignment concerns. (Safety and alignment mean concerns about the unpredictable behavior of extremely powerful future systems.) Both Google’s DeepMind and OpenAI have safety teams dedicated to figuring out a fix for this problem — though critics of OpenAI say that the safety teams lack the internal power and respect they’d need to ensure that unsafe systems aren’t developed, and that leadership is happier to pay lip service to safety while racing ahead with systems that aren’t safe.
DeepMind founder Demis Hassabis, in a recent interview about the promise and perils of AI, offered a note of caution. “I think a lot of times, especially in Silicon Valley, there’s this sort of hacker mentality of like ‘We’ll just hack it and put it out there and then see what happens.’ And I think that’s exactly the wrong approach for technologies as impactful and potentially powerful as AI. … I think it’s going to be the most beneficial thing ever to humanity, things like curing diseases, helping with climate, all of this stuff. But it’s a dual-use technology — it depends on how, as a society, we decide to deploy it — and what we use it for.”
NEARLY HALF OF RESEARCHERS SAY THERE’S A 10 PERCENT CHANCE THEIR WORK WILL LEAD TO HUMAN EXTINCTION
The problem is that progress in AI has happened extraordinarily fast, leaving regulators behind the ball. The regulation that might be most helpful — slowing down the development of extremely powerful new systems — would be incredibly unpopular with Big Tech, and it’s not clear what the best regulations short of that are.
“THE REALITY IS WE’RE CREATING GOD.”
But as the potential of AI grows, the perils are becoming much harder to ignore. Former Google executive Mo Gawdat tells the story of how he became concerned about general AI like this: robotics researchers had been working on an AI that could pick up a ball. After many failures, the AI grabbed the ball and held it up to the researchers, eerily humanlike. “And I suddenly realized this is really scary,” Gawdat said. “It completely froze me. … The reality is we’re creating God.”
For Blake Lemoine, the eccentric Google engineer who turned whistleblower when he came to believe Google’s LaMDA language model was sentient, it was when LaMDA started talking about rights and personhood. For some people, it’s the chatbot Replika, whose customer service representatives are sick of hearing that the customers think their Replika is alive and sentient. For others, that moment might come from DALL-E or Stable Diffusion, or the systems released next year, or next month, or next week that are more powerful than any of these.
For a long time, AI safety faced the difficulty of being a research field about a far-off problem, which is why only a small number of researchers were even trying to figure out how to make it safe. Now, it has the opposite problem: The challenge is here, and it’s just not clear if we’ll solve it in time.
Credits/HELP - https://www.vox.com/
Related #tags - #AI #AI2022 #AIrisks #DALLE #LaMDA #stablediffusion #deepmind #trendingnow #technews #trendingtoday
If you're looking to augment your staff using, IT outsourcing services companies in USA Or IT solutions company in USA visit Reliable Group.
0 Response to "A Guide to why advanced AI could destroy the world - AI updates 2022"
Post a Comment