What are Foundation Models?

 What Are Foundation Models?

Foundation models are AI neural networks trained on massive unlabeled datasets to handle a wide variety of jobs from translating text to analyzing medical images.

March 13, 2023 by RICK MERRITT

The mics were live and tape was rolling in the studio where the Miles Davis Quintet was recording dozens of tunes in 1956 for Prestige Records.

When an engineer asked for the next song’s title, Davis shot back, “I’ll play it, and tell you what it is later.”

Like the prolific jazz trumpeter and composer, researchers have been generating AI models at a feverish pace, exploring new architectures and use cases. Focused on plowing new ground, they sometimes leave to others the job of categorizing their work.

A team of more than a hundred Stanford researchers collaborated to do just that in a 214-page paper released in the summer of 2021.

2021 paper reports on applications of foundation models

In a 2021 paper, researchers reported that foundation models are finding a wide array of uses.

They said transformer models, large language models (LLMs) and other neural networks still being built are part of an important new category they dubbed foundation models.

Foundation Models Defined

A foundation model is an AI neural network — trained on mountains of raw data, generally with unsupervised learning — that can be adapted to accomplish a broad range of tasks, the paper said.

“The sheer scale and scope of foundation models from the last few years have stretched our imagination of what’s possible,” they wrote.

Two important concepts help define this umbrella category: Data gathering is easier, and opportunities are as wide as the horizon.

No Labels, Lots of Opportunity

Foundation models generally learn from unlabeled datasets, saving the time and expense of manually describing each item in massive collections.

Earlier neural networks were narrowly tuned for specific tasks. With a little fine-tuning, foundation models can handle jobs from translating text to analyzing medical images.

Foundation models are demonstrating “impressive behavior,” and they’re being deployed at scale, the group said on the website of its research center formed to study them. So far, they’ve posted more than 50 papers on foundation models from in-house researchers alone.

“I think we’ve uncovered a very small fraction of the capabilities of existing foundation models, let alone future ones,” said Percy Liang, the center’s director, in the opening talk of the first workshop on foundation models.

AI’s Emergence and Homogenization

In that talk, Liang coined two terms to describe foundation models:

Emergence refers to AI features still being discovered, such as the many nascent skills in foundation models. He calls the blending of AI algorithms and model architectures homogenization, a trend that helped form foundation models. (See chart below.)

Timeline for AI and foundation modelsThe field continues to move fast.

A year after the group defined foundation models, other tech watchers coined a related term — generative AI. It’s an umbrella term for transformers, large language models, diffusion models and other neural networks capturing people’s imaginations because they can create text, images, music, software and more.

Generative AI has the potential to yield trillions of dollars of economic value, said executives from the venture firm Sequoia Capital who shared their views in a recent AI Podcast.

A Brief History of Foundation Models

“We are in a time where simple methods like neural networks are giving us an explosion of new capabilities,” said Ashish Vaswani, an entrepreneur and former senior staff research scientist at Google Brain who led work on the seminal 2017 paper on transformers.

That work inspired researchers who created BERT and other large language models, making 2018 “a watershed moment” for natural language processing, a report on AI said at the end of that year.

Google released BERT as open-source software, spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs. Then it applied the technology to its search engine so users could ask questions in simple sentences.

In 2020, researchers at OpenAI announced another landmark transformer, GPT-3. Within weeks, people were using it to create poems, programs, songs, websites and more.

“Language models have a wide range of beneficial applications for society,” the researchers wrote.

Their work also showed how large and compute-intensive these models can be. GPT-3 was trained on a dataset with nearly a trillion words, and it sports a whopping 175 billion parameters, a key

 https://blogs.nvidia.com/blog/2023/03/13/what-are-foundation-models/?nvid=nv-int-bnr-463583#new_tab

What Are Foundation Models?

Foundation models are AI neural networks trained on massive unlabeled datasets to handle a wide variety of jobs from translating text to analyzing medical images.

March 13, 2023 by RICK MERRITT

The mics were live and tape was rolling in the studio where the Miles Davis Quintet was recording dozens of tunes in 1956 for Prestige Records.

When an engineer asked for the next song’s title, Davis shot back, “I’ll play it, and tell you what it is later.”

Like the prolific jazz trumpeter and composer, researchers have been generating AI models at a feverish pace, exploring new architectures and use cases. Focused on plowing new ground, they sometimes leave to others the job of categorizing their work.

A team of more than a hundred Stanford researchers collaborated to do just that in a 214-page paper released in the summer of 2021.

2021 paper reports on applications of foundation models

In a 2021 paper, researchers reported that foundation models are finding a wide array of uses.

They said transformer models, large language models (LLMs) and other neural networks still being built are part of an important new category they dubbed foundation models.

Foundation Models Defined

A foundation model is an AI neural network — trained on mountains of raw data, generally with unsupervised learning — that can be adapted to accomplish a broad range of tasks, the paper said.

“The sheer scale and scope of foundation models from the last few years have stretched our imagination of what’s possible,” they wrote.

Two important concepts help define this umbrella category: Data gathering is easier, and opportunities are as wide as the horizon.

No Labels, Lots of Opportunity

Foundation models generally learn from unlabeled datasets, saving the time and expense of manually describing each item in massive collections.

Earlier neural networks were narrowly tuned for specific tasks. With a little fine-tuning, foundation models can handle jobs from translating text to analyzing medical images.

Foundation models are demonstrating “impressive behavior,” and they’re being deployed at scale, the group said on the website of its research center formed to study them. So far, they’ve posted more than 50 papers on foundation models from in-house researchers alone.

“I think we’ve uncovered a very small fraction of the capabilities of existing foundation models, let alone future ones,” said Percy Liang, the center’s director, in the opening talk of the first workshop on foundation models.

AI’s Emergence and Homogenization

In that talk, Liang coined two terms to describe foundation models:

Emergence refers to AI features still being discovered, such as the many nascent skills in foundation models. He calls the blending of AI algorithms and model architectures homogenization, a trend that helped form foundation models. (See chart below.)

Timeline for AI and foundation modelsThe field continues to move fast.

A year after the group defined foundation models, other tech watchers coined a related term — generative AI. It’s an umbrella term for transformers, large language models, diffusion models and other neural networks capturing people’s imaginations because they can create text, images, music, software and more.

Generative AI has the potential to yield trillions of dollars of economic value, said executives from the venture firm Sequoia Capital who shared their views in a recent AI Podcast.

A Brief History of Foundation Models

“We are in a time where simple methods like neural networks are giving us an explosion of new capabilities,” said Ashish Vaswani, an entrepreneur and former senior staff research scientist at Google Brain who led work on the seminal 2017 paper on transformers.

That work inspired researchers who created BERT and other large language models, making 2018 “a watershed moment” for natural language processing, a report on AI said at the end of that year.

Google released BERT as open-source software, spawning a family of follow-ons and setting off a race to build ever larger, more powerful LLMs. Then it applied the technology to its search engine so users could ask questions in simple sentences.

In 2020, researchers at OpenAI announced another landmark transformer, GPT-3. Within weeks, people were using it to create poems, programs, songs, websites and more.

“Language models have a wide range of beneficial applications for society,” the researchers wrote.

Their work also showed how large and compute-intensive these models can be. GPT-3 was trained on a dataset with nearly a trillion words, and it sports a whopping 175 billion parameters, a key