Jump to content

Meta AI

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by WikiEwout (talk | contribs) at 09:35, 29 May 2024 (→Hardware: Add MTIA v2 section and comparison table). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Meta AI
IndustryArtificial intelligence
FoundedDecember 11, 2015; 8 years ago (2015-12-11)
Founders
HeadquartersAstor Place, New York City, New York, U.S.
ProductsLLaMA
OwnerMeta Platforms
Websiteai.meta.com
This article is part of a series about
Meta Platforms
Products and services
Facebook
Other products
People
Executives and board members
Notable employees
Related organizations
Business
Criticism
Litigation
Related

Meta AI is an artificial intelligence laboratory owned by Meta Platforms Inc. (formerly known as Facebook, Inc.). Meta AI develops various forms of artificial intelligence, including augmented and artificial reality technologies. Meta AI is also an academic research laboratory focused on generating knowledge for the AI community. This is in contrast to Facebook's Applied Machine Learning (AML) team, which focuses on practical applications of its products.

History

The laboratory started as Facebook Artificial Intelligence Research (FAIR) with locations in the Menlo Park, California, headquarters, London, United Kingdom, and a new laboratory in Manhattan. FAIR was officially announced in September 2013.[1] FAIR was directed by New York University's Yann LeCun, a deep learning Professor and Turing Award winner.[2] Working with NYU's Center for Data Science, FAIR's initial goal was to research data science, machine learning, and artificial intelligence and to "understand intelligence, to discover its fundamental principles, and to make machines significantly more intelligent".[3] Research at FAIR pioneered the technology that led to face recognition, tagging in photographs, and personalized feed recommendation.[4] Vladimir Vapnik, a pioneer in statistical learning, joined FAIR[5] in 2014, he is the co-inventor of the support-vector machine, and one of the developers of the Vapnik–Chervonenkis theory.

FAIR opened a research center in Paris, France in 2015,[6] and subsequently launched smaller satellite research labs in Seattle, Pittsburgh, Tel Aviv, Montreal and London.[7] In 2016, FAIR partnered with Google, Amazon, IBM, and Microsoft in creating the Partnership on Artificial Intelligence to Benefit People and Society, an organization with a focus on open licensed research, supporting ethical and efficient research practices, and discussing fairness, inclusivity, and transparency.

In 2018, Jérôme Pesenti, former CTO of IBM's big data group, assumed the role of president of FAIR, while LeCun stepped down to serve as chief AI scientist.[8] In 2018, FAIR was placed 25th in the AI Research Rankings 2019, which ranked the top global organizations leading AI research.[9] FAIR quickly rose to eighth position in 2019,[10] and maintained eighth position in the 2020 rank.[11] FAIR had approximately 200 staff in 2018, and had the goal to double that number by 2020.[12]

FAIR's initial work included research in learning-model enabled memory networks, self-supervised learning and generative adversarial networks, text classification and translation, as well as computer vision.[3] FAIR released Torch deep-learning modules as well as PyTorch in 2017, an open-source machine learning framework,[3] which was subsequently used in several deep learning technologies, such as Tesla's autopilot [13] and Uber's Pyro.[14] Also in 2017, FAIR discontinued a research project once AI bots developed a language that was unintelligible to humans,[15] inciting conversations about dystopian fear of artificial intelligence going out of control.[16] However, FAIR clarified that the research had been shut down because they had accomplished their initial goal to understand how languages are generated, rather than out of fear.[15]

FAIR was renamed Meta AI following the rebranding that changed Facebook, Inc. to Meta Platforms Inc.[17]

In 2022, Meta AI predicted the 3D shape of 600 million of potential proteins in two weeks.[18]

Current research

In the February 23, 2022, live event Inside the Lab: Building for the Metaverse with AI, the Meta AI team discussed the major advancements in research and development in artificial intelligence.[19] One such tool is the BuilderBot, which allows users to generate virtual worlds by using voice commands. Other tools include the No Language Left Behind, a system capable of automatic translation between written languages, and a Universal Speech Translator, a system capable of instantaneous speech-to-speech translation.

Computer vision

Meta AI's computer vision research aims to extract information about the environment from digital images and videos.[20] One example of computer vision technology developed by AI is panoptic segmentation, which recognizes objects in the foreground but also classifies the scenes in the background.[21] Meta AI seeks to improve Visual Question Answering technology, in which a machine answers human user questions about images using cycle-consistency, having the machine generate a question in addition to the answer to address linguistic variations in the questions.[22]

Natural language processing and conversational AI

Artificial intelligence communication requires a machine to understand natural language and to generate language that is natural. Meta AI seeks to improve these technologies to improve safe communication regardless of what language the user might speak.[23] Thus, a central task involves the generalization of natural language processing (NLP) technology to other languages. As such, Meta AI actively works on unsupervised machine translation.[24][25] Meta AI seeks to improve natural-language interfaces by developing aspects of chitchat dialogue such as repetition, specificity, response-relatedness and question-asking,[26] incorporating personality into image captioning,[27] and generating creativity-based language.[28]

In 2018, Meta AI launched the open-source PyText, a modeling framework focused on NLP systems.[29]

Llama

Main article: Llama (language model)

In February 2023, Meta AI announced LLaMA (Large Language Model Meta AI), a large language model ranging from 7B, to 65B parameters.[30] Meta AI released Llama 2 in July 2023,[31] and Llama 3 in April 2024.[32]

Ranking and recommendations

Facebook and Instagram use Meta AI research in ranking & recommendations in their newsfeeds, ads, and search results.[33] Meta AI has also introduced ReAgent, a toolset that generates decisions and evaluates user feedback.[34]

Systems research

Machine learning and AI depend on the development of novel algorithms, software, and hardware technologies. As such, Meta AI's systems research teams study computer languages, compilers, and hardware applications.[35]

Theory

Meta AI studies the mathematical and theoretical foundations of artificial intelligence. Meta AI has publications in learning theory, optimization, and signal processing.[36]

Hardware

MTIA v1

The MTIA v1 is Meta's first-generation AI training and inference accelerator, developed specifically for Meta's recommendation workloads. It was fabricated using TSMC's 7 nm process technology and operates at a frequency of 800 MHz. In terms of processing power, the accelerator provides 102.4 TOPS at INT8 precision and 51.2 TFLOPS at FP16 precision, while maintaining a thermal design power (TDP) of 25 W.[37][38][39]

The accelerator is structured around a grid of 64 processing elements (PEs), arranged in an 8x8 configuration, and it is furnished with on-chip and off-chip memory resources along with the necessary interconnects. Each PE houses two processor cores (one with a vector extension) and several fixed-function units optimized for tasks such as matrix multiplication, accumulation, data movement, and nonlinear function calculation. The processor cores utilize the RISC-V open instruction set architecture (ISA), with extensive customization to perform the required compute and control tasks.

The accelerator's memory subsystem uses LPDDR5 for off-chip DRAM resources and can be scaled up to 128 GB. Additionally, it possesses 128 MB of on-chip SRAM that is shared amongst all the PEs for faster access to frequently used data and instructions. The design encourages parallelism and data reuse, offering thread and data-level parallelism (TLP and DLP), instruction-level parallelism (ILP), and memory-level parallelism (MLP).

MTIA accelerators are mounted on compact dual M.2 boards, enabling easier integration into a server. The boards connect to the host CPU via PCIe Gen4 x8 links and have a power consumption as low as 35 W. The servers hosting these accelerators utilize the Yosemite V3 server specification from the Open Compute Project. Each server houses 12 accelerators, interconnected through a hierarchy of PCIe switches, allowing workloads to be distributed across multiple accelerators and executed concurrently.

MTIA v2

MTIA v2 is Meta's second-generation AI training and inference accelerator, significantly enhancing performance and efficiency for AI workloads, particularly in recommendation and ranking models. Fabricated with TSMC's 5 nm technology, it operates at 1.35 GHz and provides 708 TOPS at INT8 precision (with sparsity) and 354 TFLOPS at FP16 precision, representing substantial improvements over MTIA v1.[40]

Key architectural enhancements include an 8x8 grid of processing elements (PEs), increased local PE storage (384 KB per PE), on-chip SRAM (256 MB), and off-chip LPDDR5 memory (128 GB). Memory bandwidth improvements are also significant, with local memory at 1 TB/s per PE, on-chip memory at 2.7 TB/s, and off-chip LPDDR5 at 204.8 GB/s.

MTIA v2 features an improved network on chip (NoC) architecture for low-latency coordination between PEs. The system supports up to 72 accelerators in a rack-based setup, using PCIe Gen5 links for enhanced bandwidth and scalability.

The software stack, fully integrated with PyTorch 2.0, includes the Triton-MTIA compiler backend for high-performance kernel optimization, improving developer productivity. Early results show a 3x performance improvement over MTIA v1, with a 6x increase in model serving throughput and a 1.5x improvement in performance per watt.

Comparison of MTIA chips

Feature MTIA v1 MTIA v2
Process node TSMC 7nm TSMC 5nm
Frequency 800MHz 1.35GHz
Instances 1.12B gates, 65M flops 2.35B gates, 103M flops
Die size 19.34mm x 19.1mm, 373mm² 25.6mm x 16.4mm, 421mm²
Package 43mm x 43mm 50mm x 40mm
Voltage 0.67V logic, 0.75V memory 0.85V
TDP 25W 90W
Host Connection 8x PCIe Gen4 (16 GB/s) 8x PCIe Gen5 (32 GB/s)
GEMM TOPS 102.4 TFLOPS/s (INT8) 708 TFLOPS/s (INT8) (sparsity)
51.2 TFLOPS/s (FP16/BF16) 354 TFLOPS/s (INT8)
354 TFLOPS/s (FP16/BF16) (sparsity)
177 TFLOPS/s (FP16/BF16)
SIMD TOPS Vector core: Vector core:
3.2 TFLOPS/s (INT8) 11.06 TFLOPS/s (INT8)
1.6 TFLOPS/s (FP16/BF16) 5.53 TFLOPS/s (FP16/BF16)
0.8 TFLOPS/s (FP32) 2.76 TFLOPS/s (FP32)
SIMD: SIMD:
3.2 TFLOPS/s (INT8/FP16/BF16) 5.53 TFLOPS/s (INT8/FP16/BF16)
1.6 TFLOPS/s (FP32) 2.76 TFLOPS/s (FP32)
Memory Capacity Local memory: 128 KB per PE Local memory: 384 KB per PE
On-chip memory: 128 MB On-chip memory: 256 MB
Off-chip LPDDR5: 64 GB Off-chip LPDDR5: 128 GB
Memory Bandwidth Local memory: 400 GB/s per PE Local memory: 1 TB/s per PE
On-chip memory: 800 GB/s On-chip memory: 2.7 TB/s
Off-chip LPDDR5: 176 GB/s Off-chip LPDDR5: 204.8 GB/s

User Controls

Meta AI offers limited options for users to customize their interaction with its features. Users may be able to mute the AI chatbot on platforms like Facebook, Instagram, and WhatsApp. People aggressively searching turn off meta AI[41] on varies app. This will temporarily stop notifications from the AI. Some platforms may also offer the ability to hide certain AI elements from their interface. To locate the relevant settings, users may search within the platform's help documentation or settings menu.

In May 2024, Meta's new Chabot, Meta AI, summarizes news from various outlets without linking directly to original articles, even in Canada where news links are banned on its platforms. This use of news content without compensation has raised ethical and legal concerns, as Meta continues to reduce news visibility on its platforms.[42]

References

  1. ^ "NYU "Deep Learning" Professor LeCun Will Head Facebook's New Artificial Intelligence Lab". TechCrunch. 9 December 2013. Retrieved 2022-05-08.
  2. ^ "Yann LeCun - A.M. Turing Award Laureate". amturing.acm.org. Retrieved 2022-05-08.
  3. ^ a b c "FAIR turns five: What we've accomplished and where we're headed". Engineering at Meta. 2018-12-05. Retrieved 2022-05-08.
  4. ^ Metz, Cade (December 12, 2013). "Facebook's 'Deep Learning' Guru Reveals the Future of AI". Wired Business. Retrieved May 7, 2022.
  5. ^ "Facebook's AI team hires Vladimir Vapnik, father of the popular support vector machine algorithm". VentureBeat. 2014-11-25. Retrieved 2022-05-08.
  6. ^ Dillet, Romain (June 2, 2015). "Facebook Opens New AI Research Center in Paris". TechCrunch. Retrieved May 7, 2022.
  7. ^ "Facebook Opens New AI Research Center In Paris". TechCrunch. 2 June 2015. Retrieved 2022-05-08.
  8. ^ Dave, Greshgorn (January 23, 2018). "The head of Facebook's AI research is stepping into a new role as it shakes up management". Quartz. Retrieved May 7, 2022.
  9. ^ Chuvpilo, Gleb (2021-05-19). "Who's Ahead in AI Research? Insights from NIPS, Most Prestigious AI Conference". Medium. Retrieved 2022-05-08.
  10. ^ Chuvpilo, Gleb (2021-05-19). "AI Research Rankings 2019: Insights from NeurIPS and ICML, Leading AI Conferences". Medium. Retrieved 2022-05-08.
  11. ^ Chuvpilo, Gleb (2021-05-19). "AI Research Rankings 2020: Can the United States Stay Ahead of China?". Medium. Retrieved 2022-05-08.
  12. ^ Shead, Sam. "Facebook Plans To Double Size Of AI Research Unit By 2020". Forbes. Retrieved 2022-05-08.
  13. ^ Karpathy, Andrej. "PyTorch at Tesla - Andrej Karpathy, Tesla". YouTube.
  14. ^ "Pyro". pyro.ai. Retrieved 2022-05-08.
  15. ^ a b "Facebook researchers shut down AI bots that started speaking in a language unintelligible to humans". Tech2. 2017-07-31. Retrieved 2022-05-08.
  16. ^ Magid, Larry. "Dystopian Fear Of Facebook's AI Experiment Is Highly Exaggerated". Forbes. Retrieved 2022-05-08.
  17. ^ Murphy Kelly, Samantha (October 29, 2021). "Facebook changes its company name to Meta". CNN Business. Retrieved May 7, 2022.
  18. ^ "Meta's new AI just predicted the shape of 600 million proteins in 2 weeks". Live Science. November 4, 2022.
  19. ^ "Inside the Lab: Building for the Metaverse With AI". Meta. 2022-02-23. Retrieved 2022-05-08.
  20. ^ "Meta AI Research Topic - Computer Vision". ai.facebook.com. Retrieved 2022-05-08.
  21. ^ "Improving scene understanding through panoptic segmentation". ai.facebook.com. Retrieved 2022-05-08.
  22. ^ Shah, Meet; Chen, Xinlei; Rohrbach, Marcus; Parikh, Devi (2019-02-14). "Cycle-Consistency for Robust Visual Question Answering". arXiv:1902.05660 [cs.CV].
  23. ^ "Meta AI Research Topic - Natural Language Processing". ai.facebook.com. Retrieved 2022-05-08.
  24. ^ Lample, Guillaume; Ott, Myle; Conneau, Alexis; Denoyer, Ludovic; Ranzato, Marc'Aurelio (2018-08-13). "Phrase-Based & Neural Unsupervised Machine Translation". arXiv:1804.07755 [cs.CL].
  25. ^ Conneau, Alexis; Lample, Guillaume; Rinott, Ruty; Williams, Adina; Bowman, Samuel R.; Schwenk, Holger; Stoyanov, Veselin (2018-09-13). "XNLI: Evaluating Cross-lingual Sentence Representations". arXiv:1809.05053 [cs.CL].
  26. ^ See, Abigail; Roller, Stephen; Kiela, Douwe; Weston, Jason (2019-04-10). "What makes a good conversation? How controllable attributes affect human judgments". arXiv:1902.08654 [cs.CL].
  27. ^ Shuster, Kurt; Humeau, Samuel; Hu, Hexiang; Bordes, Antoine; Weston, Jason (2019-03-20). "Engaging Image Captioning Via Personality". arXiv:1810.10665 [cs.CV].
  28. ^ Fan, Angela; Lewis, Mike; Dauphin, Yann (2018-05-13). "Hierarchical Neural Story Generation". arXiv:1805.04833 [cs.CL].
  29. ^ "Open-sourcing PyText for faster NLP development". Engineering at Meta. 2018-12-14. Retrieved 2022-05-08.
  30. ^ "Introducing LLaMA: A foundational, 65-billion-parameter language model". ai.facebook.com. Retrieved 2023-02-26.
  31. ^ "Meta and Microsoft Introduce the Next Generation of Llama". ai.meta.com.
  32. ^ "Introducing Meta Llama 3: The most capable openly available LLM to date". ai.meta.com.
  33. ^ "Meta AI Research Topic - Ranking & Recommendations". ai.facebook.com. Retrieved 2022-05-08.
  34. ^ "Open-sourcing ReAgent, a modular, end-to-end platform for building reasoning systems". ai.facebook.com. Retrieved 2022-05-08.
  35. ^ "Meta AI Research Topic - Systems Research". ai.facebook.com. Retrieved 2022-05-08.
  36. ^ "Meta AI Research Topic - Theory". ai.facebook.com. Retrieved 2022-05-08.
  37. ^ "MTIA v1: Meta's first-generation AI inference accelerator". ai.facebook.com. Retrieved 2023-06-07.
  38. ^ "Meta Training Inference Accelerator (MTIA) Explained". encord.com. Retrieved 2023-06-07.
  39. ^ Peters, Jay (2023-05-19). "Meta is working on a new chip for AI". The Verge. Retrieved 2023-06-07.
  40. ^ "Our next generation Meta Training and Inference Accelerator". ai.meta.com. Retrieved 2024-05-29.
  41. ^ UBB, Ajit (May 2, 2024). "How to Turn OFF Meta AI Facebook". UBB.
  42. ^ "Meta walked away from news. Now the company's using it for AI content". The Washington Post. 21 May 2024. Archived from the original on 22 May 2024. Retrieved 22 May 2024. {{cite news}}: |archive-date= / |archive-url= timestamp mismatch; 21 May 2024 suggested (help)
Retrieved from "https://en.wikipedia.org/w/index.php?title=Meta_AI&oldid=1226227345"