Here’s how science works: You have a question about some infinitesimal sliver of the universe. You form a hypothesis, test it, and eventually gather enough data to support or disprove what you thought was going on. That’s the fun part. The next bit is less glamorous: You write a manuscript, submit it to an academic journal, and endure the gauntlet of peer review, where a small group of anonymous experts in your field scrutinize the quality of your work.
Peer review has its flaws. Human beings (even scientists) are biased, lazy, and self-interested. Sometimes they suck at math (even scientists). So, perhaps inevitably, some people want to remove humans from the process—and replace them with artificial intelligence. Computers are, after all, unbiased, sedulous, and lack a sense of identity. They are also, by definition, good at math. And scientists aren’t just waiting around for some binary brain to manifest a set of protocols for identifying experimental excellence. Journal publishers are already building this stuff, piecemeal.
Recently, a competition called ScienceIE challenged teams to create programs that could extract the basic facts out of sentences in scientific papers, and compare those to the basic facts from sentences in other papers. “The broad goal of my project is to help scientists and practitioners gain more knowledge about a research area more quickly,” says Isabelle Augenstein, a post-doctoral AI researcher at University College of London, who devised the challenge.
That’s a tiny part of artificial intelligence’s biggest challenge: processing natural human language. Competitors designed programs to tackle three subtasks: reading each paper and identifying its key concepts, organizing key words by type, and identifying relationships between different key phrases. And it’s not just an academic exercise: Augenstein is on a two-year contract with Elsevier, one of the world’s largest publishers of scientific research, to develop computational tools for their massive library of manuscripts.
She has her work cut out for her. Elsevier publishes over 2,5001 different journals. Each has an editor, who has to find the right reviewer for each manuscript. (In 2015, 700,000 peer reviewers reviewed over 1.8 million manuscripts across Elsevier’s journals; 400,000 were eventually published.) “The number of humans capable of reviewing a proposal is generally limited to the specialists in that field,” says Mike Warren, AI veteran and CTO/co-founder of Descartes Labs, a digital mapping company that uses AI to parse satellite images. “So, you’ve got this small set of people with PhDs, and you keep dividing them into disciplines and sub-disciplines, and when you’re done there might only be 100 people on the planet qualified to review a certain manuscript.” Augenstein’s work is part of Elseviers work to automatically suggest the right reviewers for each manuscript.
Elsevier has developed a suite of automated tools, called Evise, to aid in peer review. The program checks for plagiarism (although that’s not really AI, just a search and match function), clears potential reviewers for things like conflicts of interest, and handles workflow between authors, editors, and reviewers. Several other major publishers have automated software to aid peer review—Springer-Nature, for instance, is currently trialing an independently-developed software package called StatReviewer that ensures that each submitted paper has complete and accurate statistical data.
But none seem as open about their capabilities or aspirations as Elsevier. “We are investigating more ambitious tasks,” says Augenstein. “Say you have a question about a paper: A machine learning model reads the paper and answers your question.”
Thank you very much, Dr. Roboto, but no thanks
Not everyone is charmed by the prospect of Dr. Roboto, PhD. Last month, Janne Hukkinen, professor of environmental policy at University of Helsinki, Finland, and editor of the Elsevier journal Ecological Economics wrote a cautionary op-ed for WIRED, premised on a future where AI peer review became fully autonomous:
I dont see why learning algorithms couldnt manage the entire review from submission to decision by drawing on publishers databases of reviewer profiles, analyzing past streams of comments by reviewers and editors, and recognizing the patterns of change in a manuscript from submission to final editorial decision. Whats more, disconnecting humans from peer review would ease the tension between the academics who want open access and the commercial publishers who are resisting it.
By Hukkinens logic, an AI that could do peer review could also write manuscripts. Eventually, people become a legacy system within the scientific method—redundant, inefficient, obsolete. His final argument: “New knowledge which humans no longer experience as something they themselves have produced would shake the foundations of human culture.”
COMPUTER: I can render the shimmering fantasy worlds of your dreams 60 times a second
ME: search some emails for a string
COMPUTER: oh no
— Paul Kilduff-Taylor (@mode7games) February 9, 2017
But Hukkinens dark vision of machines capable of outthinking human scientists is, at the very least, decades away. “AI, despite its big successes in games like chess, Go, and poker, still cant understand most normal English sentences, let alone scientific text,” says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence. Consider this: The winning team from Augensteins ScienceIE competition scored 43 percent across the three subtasks.
And even non-computer brains have a hard time comprehending the passive-voiced mumbo jumbo common in scientific manuscripts; it is not uncommon for inscriptions within the literature to be structured such that the phenomenon being discussed is often described, after layers of prepositional preamble, and in vernacular that is vague, esoteric, and exorbitant, as being acted upon by causative factors. Linguists call anything written by humans, for humans, natural language. Computer scientists call natural language a hot mess.
“One large category of problems in natural language for AI is ambiguity,” says Ernest Davis, a computer scientist at NYU who studies common sense processing. Lets take a classic example of ambiguity, illustrated in this sentence by Stanford University emeritus computer scientist Terry Winograd:
The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
To you and me, the verbs give away who they refers to: the city council fears; the demonstrators advocate. But a computer brain would have a hell of a time figuring out which verb indicates which pronoun. And that type of ambiguity is just one thread in the tangled knot of natural language—from simple things like understanding homographs to unraveling the logic of narratives.
That’s not even touching on the specific issues in scientific papers, like connecting a written argument to some pattern in the data. This is even the case in pure mathematics papers. “Going from English to the formal logic of mathematics is not something we can automate,” says Davis. “And that would be one of the easiest things to work on because it’s highly restrictive and we understand the targets.” Disciplines that aren’t rooted in mathematics, like psychology, will be even more difficult. “In psychology papers, were nowhere near being able to check the reasonableness of arguments, says Davis. We dont know how to express the experiment in a way that a computer could use it.
And of course, a fully autonomous AI peer reviewer doesnt just have to outread humans, it has to outthink them. “When you think about AI problems, peer review is probably among the very hardest you can come up with, since the most important part of peer review is determining that research is novel, its something that has not been done before by someone else,” says Warren. A computer program might be able to survey the literature and figure out which questions remain, but would it be able to pick out research of Einsteinian proportions—some new theory that completely upends previous assumptions about how the world works?
Then again, what if everyone—AI advocates and critics alike—are looking at the problem backwards? “Maybe we just need to change the way we do scientific publishing,” says Tom Dietterich, AI researcher at Oregon State University. So, rather than writing our research as a story in English, we link our claims and evidence into a formalized structure, like a database, containing all the things that are known about a problem people are working on. Computerize the process of peer review, in other words, rather than its solution. But at that point its not computers youre reprogramming: Youre reprogramming human behavior.
1 UPDATE: 2/22/2017 — Originally, this said Elsevier published 7,500 journals. This was due to either a typo or just poorly-transcribed information. Either way, it’s fixed now.