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Is Meta still chasing a frontier model? Well, yes, Muse Spark was but an appetizer, according to Alexandr Wang. Full pod here -

Ashlee Vance

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18,297 次观看 • 4 个月前 •via X (Twitter)

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Tried to prompt this Seedance 2 creature transform with other models. Only Meta Muse Spark 1.1 actually understood the assignment and gave me something usable. Ill post the chatgpt version in the comments. AI at Meta Alexandr Wang

Tried to prompt this Seedance 2 creature transform with other models. Only Meta Muse Spark 1.1 actually understood the assignment and gave me something usable. Ill post the chatgpt version in the comments. AI at Meta Alexandr Wang

JSFILMZ

10,192 次观看 • 2 个月前

META COOOOOOKED!!! Wow. Meta muse spark 1.3 was just released and it seems to be amazing?! used my 2 custom skills and got this in a oneshot. The whole Website was a simple prompt and oneshot. Muse spark 1.3 is creative and good at frontend. approved.

META COOOOOOKED!!! Wow. Meta muse spark 1.3 was just released and it seems to be amazing?! used my 2 custom skills and got this in a oneshot. The whole Website was a simple prompt and oneshot. Muse spark 1.3 is creative and good at frontend. approved.

Justin

42,508 次观看 • 23 天前

Meta CTO confirmed that over the last year Zuck had to go founder mode. He also really appreciated Alexandr Wang joining the team last year. To build their latest model, Meta executed an emergency pivot, pulling thousands of employees off their normal duties to generate expert traces, which are hand-guided coding examples required to post-train the model. This confirms that the bottleneck for frontier models is not just raw compute, but high-fidelity, human-verified reinforcement data. Good listen. $META

Meta CTO confirmed that over the last year Zuck had to go founder mode. He also really appreciated Alexandr Wang joining the team last year. To build their latest model, Meta executed an emergency pivot, pulling thousands of employees off their normal duties to generate expert traces, which are hand-guided coding examples required to post-train the model. This confirms that the bottleneck for frontier models is not just raw compute, but high-fidelity, human-verified reinforcement data. Good listen. $META

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Mark Zuckerberg introducing Meta's Muse — "Muse spark model is trained specifically to excel as a personal agent."

Mark Zuckerberg introducing Meta's Muse — "Muse spark model is trained specifically to excel as a personal agent."

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Excited to share Muse Spark! It's a strong natively multimodal model with many surprising properties that emerged. Here, the model is able to use Python tools to make a playable Sudoku game on the web from an image input of the board. ✨

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This beautiful presentation is by Muse 1.2. It used gpt-image-v2 to create the images by itself. I told it to make a history page from Llama to Muse 1.2, and it did a great job. Alexandr Wang , you guys created magic with agentic tasks. I'm now waiting for the big model from you guys.

This beautiful presentation is by Muse 1.2. It used gpt-image-v2 to create the images by itself. I told it to make a history page from Llama to Muse 1.2, and it did a great job. Alexandr Wang , you guys created magic with agentic tasks. I'm now waiting for the big model from you guys.

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Meta’s Alexandr Wang shared how he pitches top AI talent on joining MSL. He says Meta has: – the business model to back hundreds of billions in compute – a small, cracked team of 100 scientists

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Meta paid $14 billion to pry Alex Wang away from Scale AI, the company he co-founded and ran. The hope - Zuck's hope - was that Wang could fix what ailed Meta's AI program. Over the past year, Meta has spent billions more pulling researchers away from other labs and has raced to create it first new model called Muse Spark under a regime led by Wang, Nat Friedman, Daniel Gross and Shengjia Zhao. This interview is the first time Wang has talked about all the drama of the past year and where Meta is heading with AI. The Core Memory podcast is on Apple, Spotify, YouTube and all major platforms. Enjoy! Timestamps 00:00 Intro 02:02 Inside Meta Superintelligence Labs 06:23 The Tahoe Conversation 12:50 How Do You Rebuild a Frontier Lab 24:35 The Soup and Being "Too Young" 32:48 MuseSpark: Appetizer, Not Entrée 48:47 Why Does Everyone Hate AI? 53:50 Open Source and the Manus Question 1:04:59 Robots and Mango 1:11:08 Sci-Fi, BCI, and Do AI Models Deserve Moral Weight?

Meta paid $14 billion to pry Alex Wang away from Scale AI, the company he co-founded and ran. The hope - Zuck's hope - was that Wang could fix what ailed Meta's AI program. Over the past year, Meta has spent billions more pulling researchers away from other labs and has raced to create it first new model called Muse Spark under a regime led by Wang, Nat Friedman, Daniel Gross and Shengjia Zhao. This interview is the first time Wang has talked about all the drama of the past year and where Meta is heading with AI. The Core Memory podcast is on Apple, Spotify, YouTube and all major platforms. Enjoy! Timestamps 00:00 Intro 02:02 Inside Meta Superintelligence Labs 06:23 The Tahoe Conversation 12:50 How Do You Rebuild a Frontier Lab 24:35 The Soup and Being "Too Young" 32:48 MuseSpark: Appetizer, Not Entrée 48:47 Why Does Everyone Hate AI? 53:50 Open Source and the Manus Question 1:04:59 Robots and Mango 1:11:08 Sci-Fi, BCI, and Do AI Models Deserve Moral Weight?

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Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

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16,374 次观看 • 1 个月前