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Pedro Domingos ✓

@pmddomingos · Seattle, WA · joined 07 Jul 2015

Professor of computer science at UW and author of '2040' and 'The Master Algorithm'. Into machine learning, AI, and anything that makes me curious.

134 580Followers
178Following
26 794Posts total
1.9MViews on collected posts

Posts recentes

@pmddomingos How did you estimate this ? 27.9K views · 66 likes · 0 reposts · 10 replies Open on X →
@pmddomingos That’s the really crazy number, but AI is improving rapidly 337.6K views · 5.6K likes · 240 reposts · 418 replies Open on X →
@pmddomingos Training time, humans 1B years LLM 10 years 4.6K views · 169 likes · 0 reposts · 12 replies Open on X →
IQ points per watt: Humans: 5 AI: 0.000001 512K views · 5.4K likes · 326 reposts · 260 replies Open on X →
@pmddomingos Thanks for letting know, I’ve been trying to create an infinite vortex in my teacup for an hour now 1.1K views · 16 likes · 0 reposts · 0 replies Open on X →
@pmddomingos And here I was worried about my water faucet exploding 2.1K views · 43 likes · 0 reposts · 1 replies Open on X →
@pmddomingos And because of the sound speed. Fluids with higher velocity stop being incompressible 576 views · 9 likes · 0 reposts · 0 replies Open on X →
BTW, in the real world fluids can't accelerate to infinite speeds because of special relativity. 46K views · 594 likes · 28 reposts · 56 replies Open on X →
@pmddomingos I think it's simpler than that: acceleration is linear in force, but it doesn't mean that the resulting motion can't be nonlinear. 4K views · 11 likes · 0 reposts · 0 replies Open on X →
A: Because in Navier-Stokes the velocity is not the velocity of a particle, but the velocity at a point. The particle at a point keeps changing, and the faster the motion, the faster it changes, making the acceleration quadratic. Without this there'd be no turbulence. (2/2) 12.9K views · 179 likes · 4 reposts · 7 replies Open on X →
Q: Navier-Stokes is just Newton's laws for fluids, and Newton's laws are linear, so why is Navier-Stokes nonlinear? (1/2) 74K views · 328 likes · 26 reposts · 19 replies Open on X →
AlphaGo did not beat Lee Sedol. The humans who built AlphaGo beat Lee Sedol. 4.2K views · 48 likes · 5 reposts · 11 replies Open on X →
Resist the delusional/dishonest attempt by OpenAI and Anthropic to redefine AGI as what they've already done. 6.7K views · 196 likes · 26 reposts · 20 replies Open on X →
And meanwhile LLMs still can't add two numbers of arbitrary length. 10.2K views · 132 likes · 8 reposts · 27 replies Open on X →
Slides from the @ECMLPKDD 2025 opening keynote: https://t.co/hQaSaxJeZP 9.1K views · 20 likes · 3 reposts · 4 replies Open on X →
@pmddomingos @pmddomingos where is the compiler/interpreter, system libs, an examples repo? 10.1K views · 26 likes · 0 reposts · 1 replies Open on X →
I've found the path to AGI: https://t.co/NRvZViBVUc 802.9K views · 2.1K likes · 258 reposts · 205 replies Open on X →

Em comparação com contas do mesmo porte

14 posts dos últimos 90 dias, ao lado da faixa de 100K–1M seguidores. aparece muito além do próprio público, e esse público reage.

Mediana de visualizações8 413esta conta5 983mediana para 100K–1M
Alcance, %6.25%esta conta1.49%mediana para 100K–1M
Engajamento, %1.53%esta conta0.93%mediana para 100K–1M
MétricaEsta contaMediana para 100K–1MProporção
Mediana de visualizações por post8 4135 9831.41×
Alcance (visualizações ÷ seguidores)6.25%1.49%4.20×
Taxa de engajamento1.53%0.93%1.64×

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

10.2K9 Sep
6.7K
4.2K
74K
12.9K
4K
46K10 Sep
576
2.1K
1.1K
512K12 Sep
4.6K13 Sep
337.6K
27.9K14 Sep

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

1.68%9 Sep
3.70%
1.62%
0.51%
1.48%
0.28%
1.47%10 Sep
1.56%
2.08%
1.49%
1.20%12 Sep
3.97%13 Sep
1.87%
0.28%14 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes72.7%14 985 in total
Reposts4.5%924 in total
Replies5.1%1 051 in total
Quotes1.0%207 in total
Bookmarks16.7%3 436 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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