science

Meta AI

Meta AI is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Meta AI rather than just read about it. In short: Meta AI is a research division of Meta (formerly Facebook) that develops artificial intelligence and augmented reality technologies. It has workspaces in Menlo Park, London, New York City, Paris, Seattle, Pittsburgh, Tel Aviv, and Montreal as of 2025.

Key takeaways

Meta AI belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Meta AI to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Meta AI from memory before moving on to harder problems.

Reference excerpt

Meta AI is a research division of Meta (formerly Facebook) that develops artificial intelligence and augmented reality technologies. It has workspaces in Menlo Park, London, New York City, Paris, Seattle, Pittsburgh, Tel Aviv, and Montreal as of 2025.

History Meta AI was founded in 2013 as Facebook AI Research (FAIR). FAIR was directed by Yann LeCun until 2018, when Jérôme Pesenti succeeded him. FAIR's research has included self-supervised learning, generative adversarial networks, document classification and translation, and computer vision. In 2016, FAIR released fastText, an open-source library for efficient text classification and learning word representations, which introduced methods based on character n-grams. In 2017, FAIR released Torch deep-learning modules and PyTorch, an open-source machine learning framework that became widely adopted in deep learning research and industry. Following the 2021 rebranding of Facebook, Inc. as Meta Platforms, FAIR continued as Meta's Fundamental AI Research (FAIR) team. In 2023, Meta introduced Meta AI assistant based on the company's Llama models and integrated into products. In 2025, Meta reorganized its artificial intelligence efforts under Meta Superintelligence Labs, which brought together FAIR, large language model development teams, and other AI research and product groups.

Virtual assistant Meta AI is also the name of the virtual assistant developed by the team, now integrated as a chatbot into Meta's social networking products. It is also available as a subscription-based stand-alone app. The virtual assistant was pre-installed on the second generation of Ray-Ban Meta smartglasses, and can incorporate inputs from the glasses' cameras after an update. It is also available on Quest 2 and newer HMDs. Since May 2024, the chatbot has summarized news from various outlets without linking directly to original articles, including in Canada, where news links are banned on its platforms. This use of news content without compensation and attribution has raised ethical and legal concerns, especially as Meta continues to reduce news visibility on its platforms.

Research

Natural language processing One of Meta AI's research areas is natural language processing, including machine translation, natural language generation, and question answering. In 2023, the Meta AI team released a set of natural language processing models named Seamless, designed for enabling real-time voice translation. In 2024, a team including researchers from Meta AI published a paper about developing machine translation for languages not typically supported by machine translation. An editorial article in the journal Nature commented on the paper, emphasizing the importance of involving people who specialize in languages in the development and application of machine learning technology.

Galactica Galactica is a large language model (LLM) designed for generating scientific text. It was available for three days from 15 November 2022, before being withdrawn for generating racist and inaccurate content.

Llama

Llama is an LLM released in February 2023. As of January 2026, the most recent release is the Llama 4.

Hardware Meta used CPUs and in-house custom chips before 2022; they switched to Nvidia GPUs since then. MTIA v1, one of their early chips, is designed for the company's content recommendation algorithms. It was fabricated on TSMC's 7 nm process technology and consumed 25W, capable of 51.2 TFlops FP16.

Controversy The French media outlet Mediapart reported that in 2022, Facebook's parent company illegally used works accumulated by the pirate site LibGen to train its artificial intelligence.

References

External links Official website

Illustrations

Worked examples

Example 1 — a first encounter with Meta AI

Start with the simplest possible case. Write down what Meta AI claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Meta AI before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Meta AI ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Meta AI

In research: Meta AI appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Meta AI in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Meta AI is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2013 establishments in California, Artificial intelligence industry in the United States, Artificial intelligence laboratories, so understanding it makes those chapters shorter.
In everyday life: Look for Meta AI outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Meta AI in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Meta AI means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Meta AI out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Meta AI in simple terms?

Why does Meta AI matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Meta AI?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Meta AI.