Emerson Segura @emerson

CTO,ML,Research Joined April 2007

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Emerson Segura @emerson

3 days ago

@micoolcho @SharpaRobotics Wow... how did it do that paper separation..didn't even have to lick its own finger :)

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@micoolcho Its impressive , yet likely differet rl policies for each config.. so rl does not need to know that robot can be reconfigured..as long as correct policy is active .. so should be almost regular approach to making these run -- very clever mechanical engineering tho

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Emerson Segura @emerson

3 days ago

@micoolcho @DirectDriveTech so clever

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Emerson Segura @emerson

5 days ago

@MatthieuWyart @mattmiesnieks wow clever work

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Emerson Segura @emerson

a week ago

@ErenChenAI nice work

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Emerson Segura @emerson

a week ago

@scaling01 @GaryMarcus Nvidia will not have a moat like it has now(pytorch,vllm etc. dont need cuda already) it's peak nvidia today. No technical reason why Google and others can't match Antrpc/OAI at coding&soon (look at Cursor already)- also coding is only killerapp atm,yet other uses will emerge.

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Emerson Segura @emerson

a week ago

@chamath also its over 60% "software assistant" -- a market that MSFT and Google will eat up once they get their poop togather (as Cursor recently did)

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Emerson Segura @emerson

a week ago

@GaryMarcus There is a positive: all the paid for and deoloyed GPUs (spent capex) will make AI more affordable --- like all then Sun servers and ISPs and fiber broadband investment in the original dot com era, didnt go to waste after the bubble burst they were well utilized and grew later

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Emerson Segura @emerson

a week ago

@chris_j_paxton Clever hack! Yet not ideal.. even paint job would help (zebra pattern etc). --- the new "Everything's Computer" is "Everything is Image" lol -- why add more dimentions to your VLA whe ya can just cram force as an image lol

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Emerson Segura @emerson

a week ago

@GaryMarcus we don't talk about this :) please don't pop the .. lets call it," ai dot com" bubble -- the last time this happened it took the Nasdaq about 15 years to recover back to 5000

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Emerson Segura @emerson

a week ago

@elonmusk When do all the 220k GB300 go online?

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Emerson Segura @emerson

a week ago

@HedgieMarkets so they lost $500 million selling pizza? Wow, could have started Anthropic with that money..

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Emerson Segura @emerson

a week ago

@PTrubey @SakanaAILabs extreme example(if this works): u buy a regular 96GB GPU, train DeepSeekV4 at home,if you just wait long enough -- Atm. this is not possible,the min hardware needed is a GPU like B200 to "fit" the entire model in VRAM, and atm that is at min one DGX (8xB200) server, about $500k

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Emerson Segura @emerson

a week ago

@DavidSHolz Power/Electricity is why... also likely why OpenAI paused Sora.. its something like 20x to 10x the # of conccurent users that can be supported for llm vs videogen per GPU -- llms are efficient on compute vs diffusion (albeit diffusion type models are current irreplaceable)

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Emerson Segura @emerson

a week ago

Impressive work from Japan! (new way to train llms with less VRAM required per compute cluster, while all-reduce is still needed)

hardmaru @hardmaru

a week ago

For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall. We found a new way to break the network into blocks and train them

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Emerson Segura @emerson

a week ago

@JayKapoorNYC @startupjag Hey: #1 robot backflips are not staged or teleop #2 VLAs are not used for most humanoid robot locomotion, like dancing , jumps, etc. There are people doing useful work in all those domains, and many years of work left, yet that's no reason to missinform and conflate facts ..

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Emerson Segura @emerson

a week ago

@thestreamingdev what is this.. a prompt for what model?

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Emerson Segura @emerson

a week ago

@CongrongX nice work

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Emerson Segura @emerson

a week ago

@DominiqueCAPaul sounds fun!

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Emerson Segura @emerson

2 weeks ago

@Scobleizer yep, 1) LLM are not good enough out of the box for most enterprise cases.2) post-training is hard, requires both ml skills & clean domain-specific data--enterprise and their consultants do not have the skills to implement this yet,for the next while Vertical Startups will do well