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Web Scraping APIFeaturesInfrastructure FeaturesPlatformsTravel APIsReal Estate APIsPricing
Platforms
Google SearchGoogle TrendsBingBraveGoogle MapsDatasetsGeocodingJustWatchAirbnbTripAdvisorZillowCoinGeckoYahoo FinanceGoogle FinanceAmazon
Developers
DocsGetting StartedAuthenticationAPI ExamplesRecipesShowcasesBlogChangelogPlaygroundSDKsIntegrationsMCPGitHub
Use cases
SERP MonitoringGoogle Maps LeadsTravel & Hospitality ResearchProperty Market IntelligenceApp Review AnalysisReview & Reputation MonitoringTikTok Trend IntelligenceYouTube Creator IntelligenceAmazon Product MonitoringMusic Catalog / Playlist IntelligencePodcast & Audio IntelligenceCrypto Market ResearchFinance Market DataAI Agent Web Data
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YouTube video intelligence showcase

Yann LeCun on Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI

Yann LeCun discusses Meta AI’s open-source philosophy, the dangers of concentrated proprietary AI power, and why current autoregressive LLMs may fall short of human-like intelligence. The conversation explores grounding, world models, intuitive physics, and the future of AGI.

Lex FridmanOpen source AILimits of LLMsWorld models and grounding2 hrs 47 minMar 7, 20246 comment sample
Transcript API Comments API Source video

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Video intelligence API workflow

Video ID
5t1vTLU7s40
Available APIs
TranscriptCommentsMetadata
YouTube transcript API YouTube comments API YouTube video metadata API YouTube scraping API Creator intelligence workflow Pricing Source video
Open transcript in Playground Open comments in Playground Get API key

cURL

curl "https://api.crawlora.net/api/v1/youtube/transcript/5t1vTLU7s40" \
  -H "x-api-key: $CRAWLORA_API_KEY"

Video summary

SEO Summary

In this Lex Fridman conversation, Yann LeCun discusses Meta AI’s open-source approach, warns about concentrated power in proprietary AI systems, and explains why he считает current LLMs are not enough for human-level intelligence. The excerpt focuses on grounding, world models, memory, reasoning, planning, and the future of AGI.

Open source as a counterweight

Yann LeCun argues that concentrated proprietary AI power is a major risk and supports open source AI as a way to empower people.

Limits of current LLMs

He explains why autoregressive LLMs, while useful, may lack key ingredients of intelligence such as grounding, memory, reasoning, and planning.

Broader AI future debate

The conversation touches on AGI, intuitive physics, embodied intelligence, and the gap between text prediction and human-like understanding.

Topics

Open source AI

LeCun argues that AI should not be locked behind a few proprietary systems, and that open source can distribute power more broadly.

Limits of LLMs

He explains why text-prediction systems are useful but incomplete as a path to superhuman intelligence.

World models and grounding

The excerpt contrasts language-based learning with grounded interaction in the physical world.

Audience comments snapshot

What viewers are saying

Comments highlight strong interest in Yann LeCun’s perspective, appreciation for the long-form format, and praise for the clear discussion of the limits of current large language models. Several viewers say the conversation was eye-opening and educational.

Sampled comments
6
Visible likes
513
Public replies
27

Comment themes

Expert perspective

Many commenters value hearing an expert articulate a nuanced view on AI progress, open source, and intelligence.

Perspective-shifting conversation

The discussion appears to challenge existing assumptions and change some viewers’ minds about AI.

Audience signals

Strong interest in LeCun’s ideas

Viewers describe the episode as highly interesting and thought-provoking.

Appreciation for the format

The long, uninterrupted interview format is praised for letting ideas unfold in depth.

Insight into LLM limitations

Comments emphasize the clarity of LeCun’s critique of current LLMs.

Representative public comments

@lexfridman2024-05-30

Here are the timestamps. Please check out our sponsors to support this podcast. Transcript: https://lexfridman.com/yann-lecun-3-transcript 0:00 - Introduction & sponsor mentions: - HiddenLayer: https://hiddenlayer.com/lex - LMNT: https://drinkLMNT.com/lex to get free sample pack - Shopify: https://shopify.com/lex to...

149 likes19 replies
@Hacktheplanet_2024-05-30

Yann lecun is really interesting, thanks for having him on!

20 likes0 replies
@glock70612024-05-30

Always learning something interesting from Yann

29 likes0 replies
@araj19002024-05-30

These podcasts are better than most tv programs

154 likes5 replies
@ced14012024-05-30

Thanks a lot. Letting people speak with no interruption for 3 hours is the best format. It's the first time i hear Mr Lecun expose and develop his ideas. It opened and changed my mind on various subjects. Always a pleasure to hear such an expert. <3

13 likes1 replies
@burtharris63432024-05-30

Fantastic show! Until now, I hadn't been exposed to Yann's perspective. My background in symbolic NLP dates back nearly a quarter of a century, and Yann articulately highlights the limitations of current large language models in a manner I've found quite enlightening. I appreciate Lex for selecting such stimulating...

148 likes2 replies
Build with YouTube comments data

Use Crawlora's YouTube comments API with the video and transcript endpoints to collect viewer language, thread activity, and audience signals.

Comments API docs Playground
Build this workflow
1Fetch video metadata

Start with the video endpoint to capture ID, channel, publish date, duration, and source context.

2Fetch transcript

Pull timestamped transcript data for summarization, search, citation, and RAG preparation.

3Fetch public comments

Collect visible audience comments to identify themes, objections, questions, and engagement signals.

4Store, analyze, report

Persist structured JSON, run analysis, and publish dashboards, alerts, or research reports.

Public transcript excerpt

Transcript

Timestamped public transcript passages group captions into readable sections, making the video easier to scan, cite, and summarize.

Public excerpt
2:25

throughout your career actually but recently as well. You've said that autoregressive LLMs are not the way we're going to make progress towards superhuman intelligence. These are the large language models like GPT-4, like LLaMA 2 and 3 soon and so on. How do they work and why are they not going to take us all the way?

Build with YouTube transcript data

Use Crawlora's YouTube transcript API to fetch fresh timestamped transcript data for your own server-side workflows.

API docs Sign in

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Jensen Huang explains NVIDIA’s move from GPU acceleration to full-stack AI infrastructure, focusing on extreme co-design, distributed computing challenges, and the strategic evolution that led to CUDA and a broader computing platform.

Extreme co-designDistributed AI systems
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Jeff Kaplan on Warcraft, Overwatch, Blizzard, and the Making of Online Worlds

Jeff Kaplan reflects on the arcade, console, and PC games that shaped his love of gaming, including Pac-Man, Zork, Quake, and EverQuest. The excerpt follows his path from player to Blizzard designer, his emotional departure from the studio, and a preview of his new open-world multiplayer game set in 1800s California.

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Build this with Crawlora

Video intelligence API workflow

Video ID
5t1vTLU7s40
Available APIs
TranscriptCommentsMetadata
YouTube transcript API YouTube comments API YouTube video metadata API YouTube scraping API Creator intelligence workflow Pricing Source video
Open transcript in Playground Open comments in Playground Get API key

cURL

curl "https://api.crawlora.net/api/v1/youtube/transcript/5t1vTLU7s40" \
  -H "x-api-key: $CRAWLORA_API_KEY"