Suzanne Jin

PhD Bioinformatics @ CRG | Bio x AI is the next move | Current status: processing random thoughts 🤖🧬🍲🎨🐈‍⬛✨

[Anthropic @AnthropicAI]

New Science Blog: Why has AI advanced faster in coding than in biology?

To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic.

How do we build infrastructure agents can use?
https://t.co/PQaNQ4GRJZ

[Noelia Ferruz @ferruz_noelia]

Generative AI models have transformed protein design, but have they helped us understand proteins better?

In our perspective, we review XAI for protein language models and ask how we can open these black boxes and extract scientific knowledge from them.

Read our manuscript now out at @NatMachIntell!:
https://t.co/d6YFJaY9nm

(with Twitter-less Andrea Hunklinger :))

[Goodfire @GoodfireAI]

Neural networks do math by rotating shapes.

We found a shape-rotating calculator hidden inside an LLM – and it’s used for more than just math! (1/6)

[Suzanne Jin @suzannejin]

Very insightful article about what we CAN and we CANNOT YET with AI in biology 🧬

[Ming "Tommy" Tang @tangming2005]

chatomics! new blog post: Has AI Changed the Course of Drug Development? Three Years Later https://t.co/8bps6da66Q

[Bo Wang @BoWang87]

New @Nature paper today : Sony's Ace robot beats 3 of 5 elite table tennis players. Loses to professionals.

Human players win points with faster-than-average shots (p<0.001 between won vs returned). Ace wins with ordinary shots. Same speed and spin profile whether it wins or loses the rally (p=0.88).

It's playing a completely different sport than the humans are.

Trained entirely in simulation. Zero sim-to-real tricks beyond good physics modeling and asymmetric actor-critic (critic sees ground truth, actor sees noisy sensors).

Best part — after watching a point, 1992 Olympian Kinjiro Nakamura said:

"I didn't think it was possible. But the fact that it was possible... means that there is a possibility that a human could do it too."

Code: https://t.co/8ZZEh3BeVm

Paper: https://t.co/NhFPzYVOyi

[Hedgie @HedgieMarkets]

🦔A researcher invented a fake eye condition called bixonimania, uploaded two obviously fraudulent papers about it to an academic server, and watched major AI systems present it as real medicine within weeks.
The fake papers thanked Starfleet Academy, cited funding from the Professor Sideshow Bob Foundation and the University of Fellowship of the Ring, and stated mid-paper that the entire thing was made up. Google's Gemini told users it was caused by blue light. Perplexity cited its prevalence at one in 90,000 people.

ChatGPT advised users whether their symptoms matched. The fake research was then cited in a peer-reviewed journal that only retracted it after Nature contacted the publisher.

My Take
The researcher made the papers as obviously fake as possible on purpose. The AI systems didn't catch it. Neither did the human researchers who cited it in real journals, which means people are feeding AI-generated references into their work without reading what they're actually citing.

I've covered the FDA using AI for drug review, the NYC hospital CEO ready to replace radiologists, and ChatGPT Health launching this year. All of that is happening in the same environment where a condition funded by a Simpsons character and endorsed by the crew of the Enterprise was being presented as emerging medical consensus. The people making these deployment decisions seem to believe the pipeline from research to AI to patient is more supervised than it actually is. This experiment suggests it isn't supervised much at all.

Hedgie🤗
https://t.co/8Kg8FOrgHW

[Suzanne Jin @suzannejin]

🚀🌔

[NASA Science @NASAScience_]

Right now, four astronauts are flying in space with their own personalized organ chips.

The AVATAR investigation is using bone marrow cells from each Artemis II crew member to study how deep space affects individual human health. https://t.co/Zm5c9bXolp

[Max Fu @letian_fu]

Robotics: coding agents’ next frontier.

So how good are they?

We introduce CaP-X: an open-source framework and benchmark for coding agents, where they write code for robot perception and control, execute it on sim and real robots, observe the outcomes, and iteratively improve code reliability.

From @NVIDIA @Berkeley_AI @CMU_Robotics @StanfordAILab

https://t.co/MVcc6XWQhY

🧵

[Suzanne Jin @suzannejin]

@letian_fu I’d love to train a robot, but I have none.
Is it possible to provide a platform where people around the world can train and monitor a robot remotely?

[Generalist @GeneralistAI]

Introducing GEN-1.

Our latest milestone in scaling robot learning.

We believe it to be the first general-purpose AI model to master simple physical tasks.

99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data.

More🧵👇

[Suzanne Jin @suzannejin]

@AnthropicAI At the end models are trained on human written texts, with human emotions driven writing style / action.

[Phylo @phylo_bio]

Excited to partner with @adaptyvbio! You can now design proteins, and then send them to the wet-lab - all within Biomni Lab. This pushes towards a fully close-loop integrated biology environment.

Try it out today by specifying Adaptyv key in the settings page, and happy testing!

[Adam Lyttle @adamlyttleapps]

Screw it, I made it open source..

This is Notchy -_-

He stops you getting distracted when using Claude code by replacing your Macbooks notch with a terminal

He lets you know when claude needs your attention
And plays a sound when tasks are complete

Best of all: he stops your macbook going to sleep while claude is working

I built this for me, maybe you will find it useful too?

As a swift developer Notchy has some custom functionality I built:

- When a new XCode project is open he launches a new tab
- If claude.md is detected he launches straight into claude code
- Command + S saves a quick snapshot of code and I can restore from that checkpoint any time

Enjoy :)

https://t.co/bImzV9tWJx

[euan ashley @euanashley]

New AI paper from us this week. When my student first showed me his initial findings, I really didn’t know what to make of them. I felt that this was an interesting but curious loophole phenomenon that would shortly be closed. I was very wrong.
https://t.co/H3YIyl01FR

[Suzanne Jin @suzannejin]

@nullbytes00 @garrytan And now with a voice model by @mistralvibe

[Alex @AlexanderTw33ts]

holy shit

[Debora Marks @deboramarks]

Meet evedesign: a new open-source ML framework that makes protein design accessible and interoperable.

📢 See our post: https://t.co/lK3Szb3ff8

Protein design models are powerful, but combining them shouldn’t require custom glue code.

✅Combine models for multi-objective optimization
✅Integrate lab-in-the-loop experimental of data
✅100% secure: run on your own infra, no data sharing

Get started building therapeutics & industrial enzymes today 👇
📄Paper: https://t.co/eTOTY6kfZ0
💻Code: https://t.co/Hn3zatn5nc
🌐Webserver: https://t.co/cAM8NVMbL4

Reach out to collaborate: [email protected]

[Suzanne Jin @suzannejin]

@akshay_pachaar @anxiousyaroslav I guess it is an era where testing/reviewing gain main roles

[Anthropic @AnthropicAI]

Introducing the Anthropic Science Blog.

Increasing the pace of scientific progress is a core part of Anthropic’s mission. The Science Blog will feature new research and stories of how scientists are using AI to accelerate their work.

Read the intro: https://t.co/1P9BDyX3xG

[Simon Kohl @saakohl]

Today we're launching Latent-Y: the world's first autonomous agent for drug design, lab-validated end to end.

Give it a research goal. Latent-Y reasons, designs, iterates, and delivers lab-ready antibodies, autonomously or collaboratively, with the biological reasoning of a PhD protein design expert.

Technical report: https://t.co/E7IHfkvvD3
Blog post: https://t.co/GfJAfzj0Qx
Apply for access: https://t.co/E0SR9znZiP