How Meta’s Chief AI Scientist Believes We’ll Get To Autonomous AI Models

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Meta’s Chief AI Scientist Yann LeCun discusses why he supports open source large learning models and why models need to live in the world to achieve autonomy.
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Опубликовано:

1 май 2024

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Комментарии : 168

@causeneffect8563

The interviewers were so busy trying to show how smart and relevant they are that they stepped on the interview quite a bit. I’m sure they are brilliant but I would have appreciated if they let the interviewee speak for himself.

@GatherVerse

Welcome to Forbes; brilliant guest, terrible interviewer and guy that cut Yann off at the end. Super disrespectful.

@miguelmaig

stop interrupting him

@wolfsblade88

The corporate idiots kept interrupting the smartest guy in the room.

@lootster

00:06

@user-fh7tg3gf5p

The announcer has assumed on his own that 2000 of the smartest people "on the planet" are sitting in his audience in Cambridge.

@philtrubey7480

Perfect example why long form podcasts like Joe Rogan are so much better. Yann kept getting cut off. Would love to hear his complete views.

@ReflectionOcean

By YouSum Live

@danecjensen 28 дней назад

Chapters (Powered by ChapterMe) -

@JustAThought01

Take away: we are closer to the beginning than the end of our LLM journey.

@tommyhuffman7499

The person whispering into the mic😂

@user-el1hd3iz6m

I question the % of the population that are actually fortunate enough to be able to make use of such "open source" models.

@breaktherules6035 28 дней назад

Amazing insights from Yann LeCun! THANK YOU so much for sharing!

@leeme179

I believe the GPUs bought by Meta will be considered as assets and not expense...

@lighteningrod36

Hmm, I would have liked to hear Yann for another, hmmm, hour.

@pladselsker8340 21 день назад

I was about to watch the "interview", but reading the comments changed my mind.

@AaronEden

I think shareware is the proper label for this. Not open source. I appreciate them sharing anyway.

@k1uf 28 дней назад

Running a 400 billion parameter dense model, even in an open model environment, requires substantial computational resources. The sheer size of these models demands high memory, powerful processing capabilities, and considerable costs. Given these requirements, it seems that using such a large dense model could defeat the purpose of an open model environment, which is meant to be more accessible. Does anyone else think that the accessibility of open models is compromised by these large-scale requirements?

@eysmanobando9863

“How do we train the AI how the world works?”, You let the AI play Grand Theft Auto that’s how. 😂

@leslietetteh7292

This is interesting, i was listening to the creators of openai's sora the other day talking about training on 3d tokens which included time as an extra dimension. In using traditional diffusion models with 3d tokens that used video frame stacking/time as an extra dimension they kinda do learn to predict the future to an extent, and they were similarly seeing the real world modelling gleaned from this, as a step on the path to AGI. Great minds think alike it seems.