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Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from Joelle Pineau. This work is another important step towards our...

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150,222 views • 1 year ago •via X (Twitter)

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SaaS Junction || AI & SaaS Updates's profile picture
SaaS Junction || AI & SaaS Updates1 year ago

Sharing these advancements in AI, especially the Meta Segment Anything Model 2.1 and Meta Open Materials, is a huge step forward for collaborative innovation. It opens up avenues for addressing real-world challenges in fields like materials science and computer vision. Appreciate Meta's dedication to fostering a thriving open-source community.

Lukateake's profile picture
Lukateake1 year ago

@jpineau1 The REAL open AI.

$Q*🍓on Ethereum's profile picture
$Q*🍓on Ethereum1 year ago

@jpineau1 Ok just don’t shut us out if you reach AGI

Desmond's profile picture
Desmond1 year ago

This is awesome. So with the introduction of Meta Segment Anything Model 2.1, additional data augmentation techniques were applied to improve occlusion handling and segmentation of visually similar objects. Can you elaborate on how the positional encoding changes in spatial and object pointer memory were designed to accommodate these improvements, and what specific performance metrics were observed in these areas compared to SAM 2?

GPT.Biz's profile picture
GPT.Biz1 year ago

@jpineau1 These new AI tools look amazing, especially the Meta Spirit LM and SAM 2.1 update! Can't wait to see how developers use them for future innovations.

𝚐𝚎𝚛𝚛𝚢's profile picture
𝚐𝚎𝚛𝚛𝚢1 year ago

@jpineau1 Awesome work! On that final note, will we ever get an mLow release? It was announced in June, but still no code or even a binary?

Toha Khan's profile picture
Toha Khan1 year ago

@jpineau1 Meta FAIR just dropped some amazing new AI tools! From language models to security benchmarks, there’s a lot for developers and researchers to explore. Excited to see how the community runs with this!

Zero's profile picture
Zero1 year ago

@jpineau1 And i created a new form of AI:

Phil Chirchir 🇰🇪's profile picture
Phil Chirchir 🇰🇪1 year ago

@jpineau1 Amazing stuff Meta

BAIJ's profile picture
BAIJ1 year ago

@jpineau1 Amazing!!

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47,822 views • 2 months ago

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408,851 views • 2 years ago

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21,947 views • 1 year ago

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AI at Meta

899,643 views • 1 year ago

NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.
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NVIDIA just released a new open source transcription model, Nemotron Speech ASR, designed from the ground up for low-latency use cases like voice agents. Here's a voice agent built with this new model. 24ms transcription finalization and total voice-to-voice inference time under 500ms. This agent actually uses *three* NVIDIA open source models: - Nemotron Speech ASR - Nemotron 3 Nano 30GB in a 4-bit quant (released in December) - A preview checkpoint of the upcoming Magpie text-to-speech model These models are all truly open source: weights, training data, training code, and inference code. This is a big deal! Jensen said in the CES keynote yesterday that he expects open source models to catch up to proprietary models this year in a number of categories. NVIDIA is putting their weight behind making this happen. (As Alan Kay said, the best way to predict the future is to invent it.) The code for this agent is open source too, of course. You can deploy it to production with Modal and Pipecat AI cloud, or run locally on an NVIDIA DGX Spark or RTX 5090.

kwindla

274,590 views • 7 months ago