Meta AI

Last updated
Meta AI
Company type Division
Industry Artificial intelligence
FoundedDecember 11, 2015;8 years ago (2015-12-11)
Founders
Headquarters Astor Place, New York City, New York, U.S.
Products LLaMA
Owner Meta Platforms
Website ai.meta.com

Meta AI is an American company owned by Meta (formerly Facebook). The company develops artificial intelligence and augmented and artificial reality technologies. Meta AI deems itself an academic research laboratory, focused on generating knowledge for the AI community, and should not be confused with Meta's Applied Machine Learning (AML) team, which focuses on the practical applications of its products.

Contents

History

The laboratory was founded as Facebook Artificial Intelligence Research (FAIR) with locations at the headquarters in Menlo Park, California, London, United Kingdom, and a new laboratory in Manhattan. FAIR was officially announced in September 2013. [1] FAIR was first 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. Vapnik 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 potential proteins in two weeks. [18]

Current research

In 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

In February 2023, Meta AI launched LLaMA (Large Language Model Meta AI), a large language model ranging from 7B to 65B parameters. [30] Subsequently, 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

FeatureMTIA v1MTIA v2
Process nodeTSMC 7nmTSMC 5nm
Frequency800MHz1.35GHz
Instances1.12B gates, 65M flops2.35B gates, 103M flops
Die size19.34mm x 19.1mm, 373mm²25.6mm x 16.4mm, 421mm²
Package43mm x 43mm50mm x 40mm
Voltage0.67V logic, 0.75V memory0.85V
TDP25W90W
Host Connection8x PCIe Gen4 (16 GB/s)8x PCIe Gen5 (32 GB/s)
GEMM TOPS102.4 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 TOPSVector 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 CapacityLocal memory: 128 KB per PELocal memory: 384 KB per PE
On-chip memory: 128 MBOn-chip memory: 256 MB
Off-chip LPDDR5: 64 GBOff-chip LPDDR5: 128 GB
Memory BandwidthLocal memory: 400 GB/s per PELocal memory: 1 TB/s per PE
On-chip memory: 800 GB/sOn-chip memory: 2.7 TB/s
Off-chip LPDDR5: 176 GB/sOff-chip LPDDR5: 204.8 GB/s

User controls

Meta AI offers options for users to customize their interaction with its features. Users are able to mute the AI chatbot on platforms like Facebook, Instagram, and WhatsApp [41] , temporarily halting notifications from the chatbot. Some platforms also offer the ability to hide certain AI elements from their interface. To locate the relevant settings, users can consult the platform's help documentation or settings menu.

Concerns

Since May 2024, the Meta AI 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 has raised ethical and legal concerns, especially as Meta continues to reduce news visibility on its platforms. [42]

See also

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References

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  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].
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  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.
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  36. ↑ "Meta AI Research Topic - Theory". ai.facebook.com. Retrieved 2022-05-08.
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