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Rohin Shah

@rohinmshah · London, UK · joined 03 Oct 2017

AGI Safety & Alignment @ Google DeepMind

12 423Followers
96Following
401Posts total
101.3KViews on collected posts

Neueste Beiträge

My team at GDM is hiring once again! Join us in the foothills of the singularity, developing the techniques we need during the ascent to the peak. https://t.co/YDlJ8lFB8g 12.3K views · 161 likes · 10 reposts · 3 replies Open on X →
Though the project itself is about the transparency of DiffusionGemma, I'm most excited about this as an example of what it could look like to assess the transparency of latent reasoning models, in a manner that lets us compare to autoregressive CoT https://t.co/7INq3NTRip 8.3K views · 60 likes · 6 reposts · 5 replies Open on X →
Excited to share our control roadmap! There is a *lot* to learn from prior art in security against rogue human insiders. At the same time, AI does have several systematic differences, so we thought about how to adapt these approaches for AI. https://t.co/Z6ejSx15Ca 8.3K views · 76 likes · 7 reposts · 3 replies Open on X →
We're releasing the GDM AI Control Roadmap -- our plan for building internal security against potentially adversarial AI agents, as they grow harder to oversee and contain. Paper: https://t.co/6iVzpUx2Um Blog: https://t.co/3zAHITrkZp 🧵👇 51.2K views · 215 likes · 42 reposts · 5 replies Open on X →
With @schmidtsciences, @coop_ai, @ARIA_research and @Googleorg, we're launching a funding call for multi-agent AI safety. As AI scales, we'll move from models in isolation to complex ecosystems of interacting agents. This creates new risks that we need to solve. 10.8K views · 197 likes · 18 reposts · 6 replies Open on X →
Hello to my new followers! I'm assuming you all came from the 80K podcast -- if so you'll probably also like our work on alignment evaluations for Gemini https://t.co/DMFl0uMQSZ 4.7K views · 68 likes · 1 reposts · 1 replies Open on X →
Will coding agents take opportunities to undermine safeguards designed to oversee them? We tackle this with automated auditing using simulated agentic environments, and scheming honeypot evaluations based on real internal alignment research codebases. Read more in our blog post 5.7K views · 23 likes · 1 reposts · 1 replies Open on X →

Im Vergleich zu Konten gleicher Größe

4 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.

Medianaufrufe10 324dieses Konto1 054Median für 10K–100K
Reichweite, %83.10%dieses Konto3.62%Median für 10K–100K
Interaktion, %0.94%dieses Konto1.71%Median für 10K–100K
KennzahlDieses KontoMedian für 10K–100KVerhältnis
Medianaufrufe pro Beitrag10 3241 0549.80×
Reichweite (Aufrufe ÷ Follower)83.10%3.62%23.0×
Interaktionsrate0.94%1.71%0.55×

Weitere Konten dieser Größe →   Mit einem anderen Konto vergleichen →   Wie diese Vergleichswerte entstehen →

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

5.7K29 May
4.7K8 Jun
10.8K11 Jun
51.2K18 Jun
8.3K
8.3K22 Jun
12.3K1 Aug

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.48%29 May
1.50%8 Jun
2.05%11 Jun
0.52%18 Jun
1.03%
0.85%22 Jun
1.42%1 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.71%.

What the audience does

Likes71.6%800 in total
Reposts7.6%85 in total
Replies2.1%24 in total
Quotes0.8%9 in total
Bookmarks17.9%200 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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