Jitendra MALIK ✓
@JitendraMalikCV · joined 04 Dec 2021
Prof, EECS, UC Berkeley. VP & Distinguished Scientist, Amazon. https://t.co/sFAc0Y7opv
11 602Followers
124Following
119Posts total
725.4KViews on collected posts
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@JitendraMalikCV from teaching this stuff: also please don't add to the confusion by putting CLIP-style models with Llava-style ones in one bucket.
mostly seeing the former being called VLMs and the latter MLLMs or more rarely VLLMs.
507 views · 7 likes
· 1 reposts · 2 replies
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@JitendraMalikCV @ylecun That VQA captures only the statics and not the dynamics, you're just making that up lol
VQA captures whatever you put into the data and yes, that can and does absolutely include dynamics.
4.8K views · 34 likes
· 2 reposts · 2 replies
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@JitendraMalikCV Exactly.
We also should not confuse world models, as per your definition, with video prediction or video generation models.
Understanding the dynamics of a system in order to control it is not the same as producing cute videos.
35K views · 236 likes
· 13 reposts · 19 replies
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Scientific terms should have precision. If we use the terms VLM, VLA, WAM in an indiscriminate fashion, as is becoming common in robotics, we are not helping clarity in communication. Let's keep the historical origins of these terms in mind. VLMs arose as multimodal extensions of
72.3K views · 768 likes
· 94 reposts · 23 replies
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Nice idea to train to predict future sensory inputs along with action sequences. @ir413 Ilija Radosavovic et al (NeurIPS 2024) showed this for humanoid locomotion but the idea is perfectly general https://t.co/PH4gtpUF7E PS: Calling these models VLAs is a stretch.
24.8K views · 162 likes
· 13 reposts · 2 replies
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I concur, and the point is broader than just for Biology. Science does not advance by "pure thinking" alone. We need to do experiments, and interpret the results of those experiments, which in turn raises new questions which are resolved by new experiments.
42.6K views · 182 likes
· 19 reposts · 10 replies
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Highly performant open weights frontier models such as Kimi are a competitive threat to OpenAI & Anthropic, but probably for everyone else these are a win. Hope more US entities will release top quality open weights models as well. Government regulation of AI models to prevent
37.4K views · 322 likes
· 24 reposts · 15 replies
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Novel approach to training tactile policies..
33.6K views · 190 likes
· 14 reposts · 3 replies
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This was my final Ph.D. work co-led by Rosy Chen and I.
Prior work fixates on zero-shot sim-to-real resulting in compromises such as the choice of simple sensors like proprioception.
With PTLD we show that if you're allowed to collect a little real data, you can actually deploy
37K views · 82 likes
· 6 reposts · 2 replies
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@JitendraMalikCV Sam 3D was extremely cool. Best of luck!
745 views · 0 likes
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2/4 I am proud of the various accomplishments from our computer vision and embodied AI teams at FAIR over the years: video action recognition models, Ego4D, Ego-Exo4D, the Habitat Simulation environment, tactile sensors (DIGIT 360) and policies for dexterous manipulation.
18.2K views · 130 likes
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3/4 Most recently, we released a major advance in 3D vision: SAM 3D, which has the capability to reconstruct any object in 3D starting from just a single image. It has been a great run of research accomplishments in vision and robotics.
89.8K views · 153 likes
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4/4 But it is time to move on, and I look forward to collaborating with colleagues such as Pieter Abbeel and Peter Chen to advance intelligent robotics at Amazon.
19.7K views · 177 likes
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1/4 For the last several years I worked part-time at the FAIR lab at Meta, in addition to being a professor at UC Berkeley. That phase is now over, and starting Jan. 5, I will be leading a robotics research effort at Amazon FAR in San Francisco, while continuing at Berkeley.
308.9K views · 1.5K likes
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Em comparação com contas do mesmo porte
9 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. aparece para muita gente, mas poucos desses espectadores reagem.
Mediana de visualizações35 014esta conta995mediana para 10K–100K
Alcance, %301.79%esta conta3.59%mediana para 10K–100K
Engajamento, %0.78%esta conta1.92%mediana para 10K–100K
| Métrica | Esta conta | Mediana para 10K–100K | Proporção |
|---|---|---|---|
| Mediana de visualizações por post | 35 014 | 995 | 35.2× |
| Alcance (visualizações ÷ seguidores) | 3.0× audience | 3.59% | 84.1× |
| Taxa de engajamento | 0.78% | 1.92% | 0.41× |
Outras contas desta faixa → Comparar com outra conta → Como estas referências são construídas →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
308.9K4 Jan
19.7K
89.8K
18.2K
745
37K11 Jul
33.6K13 Jul
37.4K21 Jul
42.6K7 Aug
24.8K23 Aug
72.3K
35K
4.8K
50724 Aug
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.52%4 Jan
0.92%
0.18%
0.72%
0.00%
0.25%11 Jul
0.62%13 Jul
0.97%21 Jul
0.50%7 Aug
0.72%23 Aug
1.23%
0.78%
0.78%
1.97%24 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.
What the audience does
Likes74.9%3 938 in total
Reposts4.5%236 in total
Replies2.6%135 in total
Quotes0.6%34 in total
Bookmarks17.4%914 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
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5 Sep
Daily snapshots since 05 Sep 2026; the dashed line is the starting count.
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