How AI Knows Things No One Told It
Researchers are still struggling to understand how AI models trained to parrot Internet text can perform advanced tasks such as running code, playing games and trying to break up a marriage
www.scientificamerican.com
No one yet knows how ChatGPT and its artificial intelligence cousins will transform the world, and one reason is that no one really knows what goes on inside them. Some of these systems’ abilities go far beyond what they were trained to do — and even their inventors are baffled as to why. A growing number of tests suggest these AI systems develop internal models of the real world, much as our own brain does, though the machines’ technique is different.
This will no doubt be an issue - when such doesn't correspond with what most humans tend to believe. So will AI be on our side or against us? And how will it deal with all the various religious beliefs?
At one level, she and her colleagues understand GPT (short for generative pretrained transformer) and other large language models, or LLMs, perfectly well. The models rely on a machine-learning system called a neural network. Such networks have a structure modeled loosely after the connected neurons of the human brain. The code for these programs is relatively simple and fills just a few screens. It sets up an autocorrection algorithm, which chooses the most likely word to complete a passage based on laborious statistical analysis of hundreds of gigabytes of Internet text. Additional training ensures the system will present its results in the form of dialogue. In this sense, all it does is regurgitate what it learned — it is a “stochastic parrot,” in the words of Emily Bender, a linguist at the University of Washington. But LLMs have also managed to ace the bar exam, explain the Higgs boson in iambic pentameter, and make an attempt to break up their users’ marriage. Few had expected a fairly straightforward autocorrection algorithm to acquire such broad abilities.
So, not so easy to dismiss AI as some rubbish in, rubbish out algorithm perhaps.
That GPT and other AI systems perform tasks they were not trained to do, giving them “emergent abilities,” has surprised even researchers who have been generally skeptical about the hype over LLMs. “I don’t know how they’re doing it or if they could do it more generally the way humans do — but they’ve challenged my views,” says Melanie Mitchell, an AI researcher at the Santa Fe Institute. “It is certainly much more than a stochastic parrot, and it certainly builds some representation of the world — although I do not think that it is quite like how humans build an internal world model,” says Yoshua Bengio, an AI researcher at the University of Montreal.
At a conference at New York University in March, philosopher Raphaël Millière of Columbia University offered yet another jaw-dropping example of what LLMs can do. The models had already demonstrated the ability to write computer code, which is impressive but not too surprising because there is so much code out there on the Internet to mimic. Millière went a step further and showed that GPT can execute code, too, however. The philosopher typed in a program to calculate the 83rd number in the Fibonacci sequence. “It’s multistep reasoning of a very high degree,” he says. And the bot nailed it. When Millière asked directly for the 83rd Fibonacci number, however, GPT got it wrong: this suggests the system wasn’t just parroting the Internet. Rather it was performing its own calculations to reach the correct answer. Although an LLM runs on a computer, it is not itself a computer. It lacks essential computational elements, such as working memory. In a tacit acknowledgement that GPT on its own should not be able to run code, its inventor, the tech company OpenAI, has since introduced a specialized plug-in — a tool ChatGPT can use when answering a query — that allows it to do so. But that plug-in was not used in Millière’s demonstration. Instead he hypothesizes that the machine improvised a memory by harnessing its mechanisms for interpreting words according to their context — a situation similar to how nature repurposes existing capacities for new functions.
Perhaps a bit more worrying - as to giving any AI more power than necessary?
Although LLMs have enough blind spots not to qualify as artificial general intelligence, or AGI — the term for a machine that attains the resourcefulness of animal brains — these emergent abilities suggest to some researchers that tech companies are closer to AGI than even optimists had guessed. “They’re indirect evidence that we are probably not that far off from AGI,” Goertzel said in March at a conference on deep learning at Florida Atlantic University. OpenAI’s plug-ins have given ChatGPT a modular architecture a little like that of the human brain. “Combining GPT-4 [the latest version of the LLM that powers ChatGPT] with various plug-ins might be a route toward a humanlike specialization of function,” says M.I.T. researcher Anna Ivanova.
So perhaps AGI is not so far away as many imagine? Any thoughts?
And one can see why so many are worried as to AI being a threat to humans, even if many of those polled might not know too much as to relevant factual information:
AI threatens humanity’s future, 61% of Americans say: Reuters/Ipsos poll
According to the data, 61% of respondents believe that AI poses risks to humanity, while only 22% disagreed, and 17% remained unsure. Those who voted for Donald Trump in 2020 expressed higher levels of concern; 70% of Trump voters compared to 60% of Joe Biden voters agreed that AI could threaten humankind. When it came to religious beliefs, Evangelical Christians were more likely to "strongly agree" that AI presents risks to humanity, standing at 32% compared to 24% of non-Evangelical Christians.