It may soon become common to
encounter a tweet, essay, or news article and wonder if it was written by
artificial intelligence software. There could be questions over the authorship
of a given piece of writing, like in academic settings, or the veracity of its
content, in the case of an article.
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There could also be questions about
authenticity: If a misleading idea suddenly appears in posts across the
internet, is it spreading organically, or have the posts been generated by AI
to create the appearance of real traction?
Tools to identify whether a piece of
text was written by AI have started to emerge in recent months, including one
created by OpenAI, the company behind ChatGPT. That tool uses an AI model
trained to spot differences between generated and human-written text.
When OpenAI tested the tool, it
correctly identified AI text in only about half of the generated writing
samples it analyzed. The company said at the time that it had released the
experimental detector “to get feedback on whether imperfect tools like this one
are useful”.
Identifying generated text, experts
say, is becoming increasingly difficult as software like ChatGPT continues to
advance and turns out text that is more convincingly human. OpenAI is now
experimenting with a technology that would insert special words into the text
that ChatGPT generates, making it easier to detect later. The technique is
known as watermarking.
The watermarking method that OpenAI
is exploring is similar to one described in a recent paper by researchers at
the University of Maryland, said Jan Leike, the head of alignment at OpenAI.
If someone tried to remove a
watermark by editing the text, they would not know which words to change. And
even if they managed to change some of the special words, they would most
likely only reduce the total percentage by a couple of points.
Tom Goldstein, a professor at the
University of Maryland and co-author of the watermarking paper, said a
watermark could be detected even from “a very short text fragment,” such as a
tweet. By contrast, the detection tool OpenAI released requires a minimum of
1,000 characters.
Like all approaches to detection,
however, watermarking is not perfect, Goldstein said. OpenAI’s current
detection tool is trained to identify text generated by 34 different language
models, while a watermark detector could only identify text that was produced
by a model or chatbot that uses the same list of special words as the detector
itself.
That means that unless companies in
the AI field agree on a standard watermark implementation, the method could
lead to a future where questionable text must be checked against several
different watermark detection tools.
To make watermarking work well every
time in a widely used product like ChatGPT, without reducing the quality of its
output, would require a lot of engineering, Goldstein said.
Leike of OpenAI said the company was
still researching watermarking as a form of detection, and added that it could
complement the current tool, since the two “have different strengths and
weaknesses”.
Still, many experts believe a one-stop
tool that can reliably detect all AI text with total accuracy may be out of
reach. That is partly because tools could emerge that could help remove
evidence that a piece of text was generated by AI. And generated text, even if
it is watermarked, would be harder to detect in cases where it makes up only a
small portion of a larger piece of writing.
Experts also say that detection
tools, especially those that do not use watermarking, may not recognize
generated text if a person has changed it enough.
Identifying generated text, experts say, is becoming increasingly difficult as software like ChatGPT continues to advance and turns out text that is more convincingly human.
“I think the idea that there’s going
to be a magic tool, either created by the vendor of the model or created by an
external third party, that’s going to take away doubt — I don’t think we’re
going to have the luxury of living in that world,” said David Cox, a director
of the MIT-IBM Watson AI Lab.
‘Impossible
to make perfect’Sam Altman, CEO of OpenAI, shared a
similar sentiment in an interview with StrictlyVC last month.
“Fundamentally, I think it’s
impossible to make it perfect,” Altman said. “People will figure out how much
of the text they have to change. There will be other things that modify the
outputted text.”
Part of the problem, Cox said, is
that detection tools themselves present a conundrum, in that they could make it
easier to avoid detection. A person could repeatedly edit generated text and
check it against a detection tool until the text is identified as human-written
— and that process could potentially be automated. Detection technology, Cox
added, will always be a step behind as new language models emerge, and as
existing ones advance.
“This is always going to have an
element of an arms race to it,” he said. “It’s always going to be the case that
new models will come out and people will develop ways to detect that it’s a
fake.”
Before not afterSome experts believe that OpenAI and
other companies building chatbots should come up with solutions for detection
before they release AI products, rather than after. OpenAI launched ChatGPT at
the end of November, for example, but did not release its detection tool until
about two months later, at the end of January.
By that time, educators and
researchers had already been calling for tools to help them identify generated
text. Many signed up to use a new detection tool, GPTZero, which was built by a
Princeton University student over his winter break and was released January 1.
“We’ve heard from an overwhelming
number of teachers,” said Edward Tian, the student who built GPTZero. As of
mid-February, more than 43,000 teachers had signed up to use the tool, Tian
said.
“Generative AI is an incredible
technology, but for any new innovation we need to build the safeguards for it
to be adopted responsibly, not months or years after the release, but
immediately when it is released,” Tian said.
How AI generates
textWhen artificial intelligence
software like ChatGPT writes, it considers many options for each word, taking
into account the response it has written so far and the question being asked.
It assigns a score to each option on
the list, which quantifies how likely the word is to come next, based on the
vast amount of human-written text it has analyzed.
ChatGPT, which is built on what is
known as a large language model, then chooses a word with a high score, and
moves on to the next one.
The model’s output is often so
sophisticated that it can seem like the chatbot understands what it is saying —
but it does not.
Every choice it makes is determined
by complex math and huge amounts of data. So much so that it often produces
text that is both coherent and accurate.
But when ChatGPT says something that
is untrue, it inherently does not realize it.
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