On a recent afternoon, Jonas Thiel,
a socioeconomics major at a college in northern Germany, spent more than an
hour chatting online with some of the left-wing political philosophers he had
been studying. These were not the actual philosophers but virtual re-creations,
brought to conversation, if not quite life, by sophisticated chatbots on a
website called Character.AI.
اضافة اعلان
Thiel’s favorite was a bot that imitated
Karl Kautsky, a Czech-Austrian socialist who died before World War II. When
Thiel asked Kautsky’s digital avatar to provide some advice for modern-day
socialists struggling to rebuild the workers’ movement in Germany, the
Kautsky-bot suggested that they publish a newspaper. “They can use it not only
as a means of spreading socialist propaganda, which is in short supply in
Germany for the time being, but also to organize working-class people,” the bot
said.
The Kautsky-bot went on to argue that the
working classes would eventually “come to their senses” and embrace a
modern-day Marxist revolution. “The proletariat is at a low point in their
history right now,” it wrote. “They will eventually realize the flaws in
capitalism, especially because of climate change.”
For the moment, these and other advanced chatbots are a source of entertainment. And they are quickly becoming a more powerful way of interacting with machines.
Over the course of several days, Thiel met
with other virtual scholars, including G.A. Cohen and Adolph Reed Jr. But he
could have picked almost anyone — living or dead, real or imagined. At
Character.AI, which emerged this summer, users can chat with reasonable
facsimiles of people as varied as Queen Elizabeth II, William Shakespeare,
Billie Eilish, or Elon Musk (there are several versions). Anyone you want to
invoke, or concoct, is available for conversation. The company and site,
founded by Daniel De Freitas and Noam Shazeer, two former Google researchers,
is among the many efforts to build a new kind of chatbot. These bots cannot
chat exactly like a human, but they often seem to.
In late November,
OpenAI, a San Francisco
artificial intelligence lab, unveiled a bot called
ChatGPT that left more than
1 million people feeling as if they were chatting with another human being.
Similar technologies are under development at Google, Meta, and other tech
giants. Some companies have been reluctant to share the technology with the
wider public. Because these bots learn their skills from data posted to the
internet by real people, they often generate untruths, hate speech, and
language that is biased against women and people of color. If misused, they
could become a more efficient way of running the kind of misinformation
campaign that has become commonplace in recent years.
“Without any additional guardrails in
place, they are just going to end up reflecting all the biases and toxic
information that is already on the web,” said Margaret Mitchell, a former AI
researcher at Microsoft and Google, where she helped start its Ethical AI team.
She is now with AI startup Hugging Face.
But other companies, including
Character.AI, are confident that the public will learn to accept the flaws of
chatbots and develop a healthy distrust of what they say. Thiel found that the
bots at Character.AI had both a talent for conversation and a knack for
impersonating real-life people. “If you read what someone like Kautsky wrote in
the 19th century, he does not use the same language we use today,” he said.
“But the AI can somehow translate his ideas into ordinary modern English.”
For the moment, these and other advanced
chatbots are a source of entertainment. And they are quickly becoming a more
powerful way of interacting with machines. Experts are still debating whether
the strengths of these technologies will outweigh their flaws and potential for
harm, but they agree on one point: The believability of make-believe
conversation will continue to improve.
The art of conversationIn 2015, De Freitas, then working as a
software engineer at Microsoft, read a research paper published by scientists
at Google Brain, the flagship artificial intelligence lab at Google. Detailing
what it called “A Neural Conversational Model”, the paper showed how a machine
could learn the art of conversation by analyzing dialogue transcripts from
hundreds of movies.
“You could tell this bot could generalize,” he said. “What it said did not look like what was in a movie script.”
The paper described what AI researchers
call a neural network, a mathematical system loosely modeled on the web of
neurons in the brain. This same technology also translates between Spanish and
English on services like Google Translate and identifies pedestrians and
traffic signs for self-driving cars navigating city streets.
A neural network learns skills by
pinpointing patterns in enormous amounts of digital data. By analyzing
thousands of cat photos, for instance, it can learn to recognize a cat.
When De Freitas read the paper, he was not
yet an AI researcher; he was a software engineer working on search engines. But
what he really wanted was to take Google’s idea to its logical extreme.
“You could tell this bot could generalize,”
he said. “What it said did not look like what was in a movie script.”
He moved to Google in 2017. Officially, he
was an engineer for YouTube, the company’s video-sharing site. But for his “20
percent time” project — a Google tradition that lets employees explore new
ideas alongside their daily obligations — he began building his own chatbot.
The idea was to train a neural network
using a much larger collection of dialogue: reams of chat logs culled from
social media services and other sites across the internet. The idea was simple,
but it would require enormous amounts of computer processing power. Even a
supercomputer would need weeks or even months to analyze all that data.
As a Google engineer, he held a few credits
that allowed him to run experimental software across the company’s vast network
of computer data centers. But these credits would grant only a small fraction
of the computing power needed to train his chatbot. So he started borrowing
credits from other engineers; as the system analyzed more data, its skills
would improve by leaps and bounds.
Initially, he trained his chatbot using
what is called an LSTM, for Long Short-Term Memory — a neural network designed
in the 1990s specifically for natural language. But he soon switched to a new
kind of neural network called a transformer, developed by a team of Google AI
researchers that included Noam Shazeer.
Unlike an LSTM, which reads text one word
at a time, a transformer can use multiple computer processors to analyze an entire
document in a single step.
Google, OpenAI and other organizations were
already using transformers to build what are called “large language models”,
systems suited for a wide range of language tasks, from writing Twitter
messages to answering questions. Still working on his own, De Freitas focused
the idea on conversation, feeding his transformer as much dialogue as possible.
It was an exceedingly simple approach. But
as De Freitas likes to say: “Simple solutions for incredible results.”
The result in this case was a chatbot that
he called Meena. It was so effective that Google Brain hired De Freitas and
turned his project into an official research effort. Meena became LaMDA, short
for Language Model for Dialogue Applications.
The project spilled into the public
consciousness early in the summer when another Google engineer, Blake Lemoine,
told The Washington Post that LaMDA was sentient. This assertion was an
exaggeration, to say the least. But the brouhaha showed how quickly chatbots were
improving inside top labs like Google Brain and
OpenAI.
“These systems are not designed for truth,” Shazeer said. “They are designed for plausible conversation.”
Google was reluctant to release the
technology, worried that its knack for misinformation and other toxic language
could damage the company brand. But by this time De Freitas and Shazeer had
left Google, determined to get this kind of technology into the hands of as
many people as possible through their new company, Character.AI.
“The technology is useful today — for fun,
for emotional support, for generating ideas, for all kinds of creativity,” Shazeer
said.
Designed for open-ended exchangesChatGPT, the bot released by OpenAI to much
fanfare in late November, was designed to operate as a new kind of
question-and-answer engine. It is pretty good in this role, but the user never
knows when the chatbot will just make something up. It may tell you that the
official currency of Switzerland is the euro (it is actually the Swiss franc)
or that Mark Twain’s Celebrated Jumping Frog of Calaveras County could not only
jump but talk. AI researchers call this generation of untruths “hallucination”.
In building Character.AI, De Freitas and
Shazeer had a different objective: open-ended conversation. They believe that
today’s chatbots are better suited to this kind of service, for now a means of
entertainment, factual or not. As the site notes, “Everything Characters say is
made up!”
“These systems are not designed for truth,”
Shazeer said. “They are designed for plausible conversation.”
De Freitas, Shazeer, and their colleagues
did not build one bot that imitates Musk, another that mimics Queen Elizabeth,
and a third that parrots Shakespeare. They built a single system that can
imitate all those people and others. It has learned from reams of dialogue,
articles, books, and digital text describing people like Musk, the queen, and
Shakespeare.
Sometimes, the chatbot gets things right.
Sometimes, it does not. When Thiel chatted with an avatar meant to imitate
Reed, the 20th-century American political thinker, it turned him into “some
kind of militant Maoist, which is definitely not right”.
Like with Google, OpenAI, and other top
labs, De Freitas, Shazeer, and their colleagues plan on training their system
with ever-larger amounts of digital data. This training could take months and
millions of dollars; it could also sharpen the skills of the artificial
conversationalist.
Researchers say that the rapid improvement
will last only so long. Richard Socher, former chief scientist in charge of AI
at Salesforce who now runs a startup called You.com, believes these exponential
improvements will begin to level off over the next few years, when language
models reach a point when they have analyzed virtually all the text on the
internet.
But Shazeer said the runway is much longer:
“There are billions of people in the world generating text all the time. People
will keep spending more and more money to train smarter and smarter systems. We
are nowhere near the end of that trend.”
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