science

Meta Superintelligence Labs

Meta Superintelligence Labs 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 Superintelligence Labs rather than just read about it. In short: Meta Superintelligence Labs (MSL) is an American artificial intelligence division of Meta Platforms, headquartered in Menlo Park, California, founded in June 2025. The division focuses on research and development in the field of artificial superintelligence.

Key takeaways

Meta Superintelligence Labs 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 Superintelligence Labs to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Meta Superintelligence Labs from memory before moving on to harder problems.

Reference excerpt

Meta Superintelligence Labs (MSL) is an American artificial intelligence division of Meta Platforms, headquartered in Menlo Park, California, founded in June 2025. The division focuses on research and development in the field of artificial superintelligence. MSL produces the Muse family of generative AI models, including the Muse Spark large language model (LLM), Muse Image and Muse Video. MSL was preceded by Facebook Artificial Intelligence Research (FAIR), directed by Yann LeCun. In 2017 FAIR released PyTorch, an open source deep learning framework later used by ChatGPT, Tesla Autopilot, Uber's Pyro, and Hugging Face's Transformers. When Meta rebranded in 2021, FAIR was renamed Fundamental Artificial Intelligence Research, and Meta AI was formed. From 2023 to 2025, Meta released four iterations of the Llama LLM. In June 2025, Meta began developing a larger model codenamed Behemoth, reportedly motivated by CEO Mark Zuckerberg's displeasure with Llama 4. Meta spent more than $14 billion to acquire a 49% non-voting stake in Scale AI, a data annotation and language model benchmarking company. Scale AI's CEO Alexandr Wang moved to Chief AI Officer at Meta, leading MSL. Former GitHub CEO Nat Friedman joined as head of product. Meta had also unsuccessfully sought the acquisition of Safe Superintelligence Inc., Thinking Machines Lab, and Perplexity AI. In November 2025, LeCun left Meta to found Advanced Machine Intelligence Labs. Muse Spark was released in July 2026, achieving a top five result in benchmarks by Artificial Analysis.

History

Background The then-named Facebook founded a AI division in 2013 as Facebook Artificial Intelligence Research (FAIR). It has workspaces in Menlo Park, London, New York City, Paris, Seattle, Pittsburgh, Tel Aviv, and Montreal as of 2025. In 2016, FAIR partnered with Google, Amazon, IBM, and Microsoft in creating the Partnership on Artificial Intelligence to Benefit People and Society. FAIR was directed by Yann LeCun until 2018, when Jérôme Pesenti succeeded the role. Pesenti is formerly the CTO of IBM's big data group. FAIR's research includes self-supervised learning, generative adversarial networks, document classification and translation, and computer vision. FAIR released Torch deep-learning modules as well as PyTorch in 2017, an open-source machine learning framework, which was subsequently used in several deep learning technologies, such as ChatGPT, Tesla Autopilot, Uber's Pyro. and Hugging Face's Transformers. That same year, a pair of chatbots were falsely rumored to be discontinued for developing a language that was unintelligible to humans. FAIR clarified that the research had been shut down because they had accomplished their initial goal to understand how languages are generated by their models, rather than out of fear. FAIR's full name was renamed Fundamental Artificial Intelligence Research following Facebook's rebranding to Meta Platforms Inc.

Founding

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Meta Superintelligence Labs

Start with the simplest possible case. Write down what Meta Superintelligence Labs 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 Superintelligence Labs 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 Superintelligence Labs 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 Superintelligence Labs

In research: Meta Superintelligence Labs 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 Superintelligence Labs 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 Superintelligence Labs is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2025 establishments in California, American companies established in 2025, American corporate subsidiaries, so understanding it makes those chapters shorter.
In everyday life: Look for Meta Superintelligence Labs 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.

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How to study Meta Superintelligence Labs in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Meta Superintelligence Labs 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 Superintelligence Labs out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Meta Superintelligence Labs in simple terms?

Why does Meta Superintelligence Labs 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 Superintelligence Labs?

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 Superintelligence Labs.