NB: What is not stated is that what is being done with "AI", not yet
even using "true" AI on neurosynaptic hardware, is akin to releasing
nuclear weapons technology for general public use, including for
monetary gain. Now that the "genie is out of the bottle", and unlike
the manufacture of fissile material that requires significant mechanical
and civil infrastructure (with currently understood technology, a "home"
breeder reactor is not feasible save for the "homes" that are owned by
the ultra-wealthy psychopaths in some "James Bond" type motion
pictures), this form of AI will run on readily available hardware such
as used for crypto-currency "mining".
https://news.yahoo.com/big-tech-moving-cautiously-ai-212112653.html
Washington Post
Big Tech was moving cautiously on AI. Then came ChatGPT.
Nitasha Tiku
Fri, January 27, 2023 at 1:21 PM PST
Three months before ChatGPT debuted in November, Facebook's parent
company Meta released a similar chatbot. But unlike the phenomenon that
ChatGPT instantly became, with more than a million users in its first
five days, Meta's Blenderbot was boring, said Meta's chief artificial
intelligence scientist, Yann LeCun.
"The reason it was boring was because it was made safe," LeCun said last
week at a forum hosted by AI consulting company Collective[i]. He blamed
the tepid public response on Meta being "overly careful about content
moderation," like directing the chatbot to change the subject if a user
asked about religion. ChatGPT, on the other hand, will converse about
the concept of falsehoods in the Quran, write a prayer for a rabbi to
deliver to Congress and compare God to a flyswatter.
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ChatGPT is quickly going mainstream now that Microsoft - which recently
invested billions of dollars in the company behind the chatbot, OpenAI -
is working to incorporate it into its popular office software and
selling access to the tool to other businesses. The surge of attention
around ChatGPT is prompting pressure inside tech giants including Meta
and Google to move faster, potentially sweeping safety concerns aside,
according to interviews with six current and former Google and Meta
employees, some of whom spoke on the condition of anonymity because they
were not authorized to speak.
At Meta, employees have recently shared internal memos urging the
company to speed up its AI approval process to take advantage of the
latest technology, according to one of them. Google, which helped
pioneer some of the technology underpinning ChatGPT, recently issued a
"code red" around launching AI products and proposed a "green lane" to
shorten the process of assessing and mitigating potential harms,
according to a report in the New York Times.
ChatGPT, along with text-to-image tools such as DALL-E 2 and Stable
Diffusion, is part of a new wave of software called generative AI. They
create works of their own by drawing on patterns they've identified in
vast troves of existing, human-created content. This technology was
pioneered at big tech companies like Google that in recent years have
grown more secretive, announcing new models or offering demos but
keeping the full product under lock and key. Meanwhile, research labs
like OpenAI rapidly launched their latest versions, raising questions
about how corporate offerings, like Google's language model LaMDA, stack up.
Tech giants have been skittish since public debacles like Microsoft's
Tay, which it took down in less than a day in 2016 after trolls prompted
the bot to call for a race war, suggest Hitler was right and tweet "Jews
did 9/11." Meta defended Blenderbot and left it up after it made racist
comments in August, but pulled down another AI tool, called Galactica,
in November after just three days amid criticism over its inaccurate and
sometimes biased summaries of scientific research.
"People feel like OpenAI is newer, fresher, more exciting and has fewer
sins to pay for than these incumbent companies, and they can get away
with this for now," said a Google employee who works in AI, referring to
the public's willingness to accept ChatGPT with less scrutiny. Some top
talent has jumped ship to nimbler start-ups, like OpenAI and Stable
Diffusion.
Some AI ethicists fear that Big Tech's rush to market could expose
billions of people to potential harms - such as sharing inaccurate
information, generating fake photos or giving students the ability to
cheat on school tests - before trust and safety experts have been able
to study the risks. Others in the field share OpenAI's philosophy that
releasing the tools to the public, often nominally in a "beta" phase
after mitigating some predictable risks, is the only way to assess real
world harms.
"The pace of progress in AI is incredibly fast, and we are always
keeping an eye on making sure we have efficient review processes, but
the priority is to make the right decisions, and release AI models and
products that best serve our community," said Joelle Pineau, managing
director of Fundamental AI Research at Meta.
"We believe that AI is foundational and transformative technology that
is incredibly useful for individuals, businesses and communities," said
Lily Lin, a Google spokesperson. "We need to consider the broader
societal impacts these innovations can have. We continue to test our AI
technology internally to make sure it's helpful and safe."
Microsoft's chief of communications, Frank Shaw, said his company works
with OpenAI to build in extra safety mitigations when it uses AI tools
like DALLE-2 in its products. "Microsoft has been working for years to
both advance the field of AI and publicly guide how these technologies
are created and used on our platforms in responsible and ethical ways,"
Shaw said.
OpenAI declined to comment.
The technology underlying ChatGPT isn't necessarily better than what
Google and Meta have developed, said Mark Riedl, professor of computing
at Georgia Tech and an expert on machine learning. But OpenAI's practice
of releasing its language models for public use has given it a real
advantage.
"For the last two years they've been using a crowd of humans to provide
feedback to GPT," said Riedl, such as giving a "thumbs down" for an
inappropriate or unsatisfactory answer, a process called "reinforcement
learning from human feedback."
Silicon Valley's sudden willingness to consider taking more reputational
risk arrives as tech stocks are tumbling. When Google laid off 12,000
employees last week, CEO Sundar Pichai wrote that the company had
undertaken a rigorous review to focus on its highest priorities, twice
referencing its early investments in AI.
A decade ago, Google was the undisputed leader in the field. It acquired
the cutting edge AI lab DeepMind in 2014 and open-sourced its machine
learning software TensorFlow in 2015. By 2016, Pichai pledged to
transform Google into an "AI first" company.
The next year, Google released transformers - a pivotal piece of
software architecture that made the current wave of generative AI possible.
The company kept rolling out state-of-the-art technology that propelled
the entire field forward, deploying some AI breakthroughs in
understanding language to improve Google search. Inside big tech
companies, the system of checks and balances for vetting the ethical
implications of cutting-edge AI isn't as established as privacy or data
security. Typically teams of AI researchers and engineers publish papers
on their findings, incorporate their technology into the company's
existing infrastructure or develop new products, a process that can
sometimes clash with other teams working on responsible AI over pressure
to see innovation reach the public sooner.
Google released its AI principles in 2018, after facing employee protest
over Project Maven, a contract to provide computer vision for Pentagon
drones, and consumer backlash over a demo for Duplex, an AI system that
would call restaurants and make a reservation without disclosing it was
a bot. In August last year, Google began giving consumers access to a
limited version of LaMDA through its app AI Test Kitchen. It has not yet
released it fully to the general public, in spite of Google's plans to
do so at the end of 2022, according to former Google software engineer
Blake Lemoine, who told The Washington Post that he had come to believe
LaMDA was sentient.
But the top AI talent behind these developments grew restless.
In the past year or so, top AI researchers from Google have left to
launch start-ups around large language models, including Character.AI,
Cohere, Adept, Inflection.AI and Inworld AI, in addition to search
start-ups using similar models to develop a chat interface, such as
Neeva, run by former Google executive Sridhar Ramaswamy.
Character.AI founder Noam Shazeer, who helped invent the transformer and
other core machine learning architecture, said the flywheel effect of
user data has been invaluable. The first time he applied user feedback
to Character.AI, which allows anyone to generate chatbots based on short
descriptions of real people or imaginary figures, engagement rose by
more than 30 percent.
Bigger companies like Google and Microsoft are generally focused on
using AI to improve their massive existing business models, said Nick
Frosst, who worked at Google Brain for three years before co-founding
Cohere, a Toronto-based start-up building large language models that can
be customized to help businesses. One of his co-founders, Aidan Gomez,
also helped invent transformers when he worked at Google.
"The space moves so quickly, it's not surprising to me that the people
leading are smaller companies," said Frosst.
AI has been through several hype cycles over the past decade, but the
furor over DALL-E and ChatGPT has reached new heights.
Soon after OpenAI released ChatGPT, tech influencers on Twitter began to
predict that generative AI would spell the demise of Google search.
ChatGPT delivered simple answers in an accessible way and didn't ask
users to rifle through blue links. Besides, after a quarter of a
century, Google's search interface had grown bloated with ads and
marketers trying to game the system.
"Thanks to their monopoly position, the folks over at Mountain View have
[let] their once-incredible search experience degenerate into a
spam-ridden, SEO-fueled hellscape," technologist Can Duruk wrote in his
newsletter Margins, referring to Google's hometown.
On the anonymous app Blind, tech workers posted dozens of questions
about whether the Silicon Valley giant could compete.
"If Google doesn't get their act together and start shipping, they will
go down in history as the company who nurtured and trained an entire
generation of machine learning researchers and engineers who went on to
deploy the technology at other companies," tweeted David Ha, a renowned
research scientist who recently left Google Brain for the open source
text-to-image start-up Stable Diffusion.
AI engineers still inside Google shared his frustration, employees say.
For years, employees had sent memos about incorporating chat functions
into search, viewing it as an obvious evolution, according to employees.
But they also understood that Google had justifiable reasons not to be
hasty about switching up its search product, beyond the fact that
responding to a query with one answer eliminates valuable real estate
for online ads. A chatbot that pointed to one answer directly from
Google could increase its liability if the response was found to be
harmful or plagiarized.
Chatbots like OpenAI routinely make factual errors and often switch
their answers depending on how a question is asked. Moving from
providing a range of answers to queries that link directly to their
source material, to using a chatbot to give a single, authoritative
answer, would be a big shift that makes many inside Google nervous, said
one former Google AI researcher. The company doesn't want to take on the
role or responsibility of providing single answers like that, the person
said. Previous updates to search, such as adding Instant Answers, were
done slowly and with great caution.
Inside Google, however, some of the frustration with the AI safety
process came from the sense that cutting-edge technology was never
released as a product because of fears of bad publicity - if, say, an AI
model showed bias.
Meta employees have also had to deal with the company's concerns about
bad PR, according to a person familiar with the company's internal
deliberations who spoke on the condition of anonymity to discuss
internal conversations. Before launching new products or publishing
research, Meta employees have to answer questions about the potential
risks of publicizing their work, including how it could be
misinterpreted, the person said. Some projects are reviewed by public
relations staff, as well as internal compliance experts who ensure the
company's products comply with its 2011 Federal Trade Commission
agreement on how it handles user data.
To Timnit Gebru, executive director of the nonprofit Distributed AI
Research Institute, the prospect of Google sidelining its responsible AI
team doesn't necessarily signal a shift in power or safety concerns,
because those warning of the potential harms were never empowered to
begin with. "If we were lucky, we'd get invited to a meeting," said
Gebru, who helped lead Google's Ethical AI team until she was fired for
a paper criticizing large language models.
From Gebru's perspective, Google was slow to release its AI tools
because the company lacked a strong enough business incentive to risk a
hit to its reputation.
After the release of ChatGPT, however, perhaps Google sees a change to
its ability to make money from these models as a consumer product, not
just to power search or online ads, Gebru said. "Now they might think
it's a threat to their core business, so maybe they should take a risk."
Rumman Chowdhury, who led Twitter's machine-learning ethics team until
Elon Musk disbanded it in November, said she expects companies like
Google to increasingly sideline internal critics and ethicists as they
scramble to catch up with OpenAI.
"We thought it was going to be China pushing the U.S., but looks like
it's start-ups," she said.