The models will be integrated into virtual assistant Meta
AI, which the company is pitching as the most sophisticated of its free-to-use
peers. The assistant will be given more prominent billing within Meta’s
Facebook, Instagram, WhatsApp and Messenger apps as well as a new standalone
website that positions it to compete more directly with Microsoft-backed
OpenAI’s breakout hit ChatGPT.
The announcement comes as Meta has been scrambling to push
generative AI products out to its billions of users to challenge OpenAI’s
leading position on the technology, involving an overhaul of computing
infrastructure and the consolidation of previously distinct research and
product teams.
The social media giant equipped Llama 3 with new computer
coding capabilities and fed it images as well as text this time, though for now
the model will output only text, Chris Cox, Meta’s chief product officer, said
in an interview.
More advanced reasoning, like the ability to craft longer
multi-step plans, will follow in subsequent versions, he added. Versions
planned for release in the coming months will also be capable of
“multimodality”, meaning they can generate both text and images, Meta said in
blog posts.
“The goal eventually is to help take things off your plate,
just help make your life easier, whether it’s interacting with businesses,
whether it’s writing something, whether it’s planning a trip,” Cox said.
Cox said the inclusion of images in the training of Llama 3
would enhance an update rolling out this year to the Ray-Ban Meta smart
glasses, a partnership with glasses maker EssilorLuxottica, enabling Meta AI to
identify objects seen by the wearer and answer questions about them.
Meta also announced a new partnership with Alphabet’s Google
to include real-time search results in the assistant’s responses, supplementing
an existing arrangement with Microsoft’s Bing.
The Meta AI assistant is expanding to more than a dozen
markets outside the US with the update, including Australia, Canada, Singapore,
Nigeria and Pakistan. Meta is “still working on the right way to do this in
Europe”, Cox said, where privacy rules are more stringent and the forthcoming
AI Act is poised to impose requirements like disclosure of models’ training
data.
Generative AI models’ voracious need for data has emerged as
a major source of tension in the technology’s development.
Meta has been releasing models like Llama 3 for free
commercial use by developers as part of its catch-up effort, as the success of
a powerful free option could stymie rivals’ plans to earn revenue off their
proprietary technology. The strategy has also elicited safety concerns from
critics wary of what unscrupulous developers may use the model to build.
Mark Zuckerberg, Meta CEO, nodded at that competition in a
video accompanying the announcement, in which he called Meta AI “the most
intelligent AI assistant that you can freely use”.
Zuckerberg said the biggest version of Llama 3 is currently
being trained with 400bn parameters and is already scoring 85 MMLU, citing
metrics used to convey the strength and performance quality of AI models. The
two smaller versions rolling out now have 8bn parameters and 70bn parameters,
and the latter scored around 82 MMLU, or Massive Multitask Language
Understanding, he said.
Developers have complained that the previous Llama 2 version
of the model failed to understand basic context, confusing queries on how to
“kill” a computer program with requests for instructions on committing murder.
Rival Google has run into similar problems and recently paused use of its
Gemini AI image generation tool after it drew criticism for churning out
inaccurate depictions of historical figures.
Meta said it cut down on those problems in Llama 3 by using
“high quality data” to get the model to recognize nuance. It did not elaborate
on the datasets used, although it said it fed seven times the amount of data
into Llama 3 than it used for Llama 2 and leveraged “synthetic”, or AI-created,
data to strengthen areas like coding and reasoning.
Cox said there was “not a major change in posture” in terms
of how the company sourced its training data.
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