Machine Learning - Intern-S1 A Scientific Multimodal Foundation Model

Hey PaperLedge learning crew, Ernis here, ready to dive into some seriously cool research! Today, we're talking about a new AI model that's shaking things up, particularly in the world of science. It's called Intern-S1, and it's not your average AI.

Think of it this way: you've got these super-smart, closed-source AI models – the ones developed by big companies behind closed doors. They're often amazing, but access can be limited. On the other hand, we have open-source models, which are like community projects – everyone can use and improve them. Now, in areas like understanding general language or images, these open-source models are getting pretty close to the performance of their closed-source rivals. But when it comes to really complex scientific stuff, there's still a huge gap.

That's where Intern-S1 comes in. It's designed to bridge that gap and push the boundaries of what AI can do in scientific research. Imagine you're building a team of experts, each with specialized knowledge. Intern-S1 is kind of like that team, but it's all in one AI! It's what they call a Mixture-of-Experts (MoE) model.

Let's break that down: Intern-S1 has a massive brain (241 billion parameters!), but it only activates a smaller portion (28 billion parameters) for each specific task. It's like having a huge toolbox but only grabbing the right tools for the job. This makes it efficient and powerful.

So, how did they train this super-scientist AI? Well, they fed it a ton of data – 5 trillion "tokens" worth! Over half of that (2.5 trillion tokens) came from scientific domains. Think research papers, scientific databases, and all sorts of technical information. It's like sending Intern-S1 to the world's biggest science library.

But it's not just about memorizing information. Intern-S1 also went through something called Reinforcement Learning (RL) in something they called InternBootCamp. Imagine training a dog with treats, but instead of treats, it gets rewarded for making correct scientific predictions. They used a clever technique called Mixture-of-Rewards (MoR) to train it on over 1000 tasks at once, making it a true scientific generalist.

The result? Intern-S1 is seriously impressive. It holds its own against other open-source models on general reasoning tasks. But where it really shines is in scientific domains. It's not just keeping up; it's surpassing the best closed-source models in areas like:

• Planning how to synthesize molecules


• Predicting the conditions needed for chemical reactions


• Predicting the stability of crystal structures

Basically, tasks that are incredibly important for chemists, materials scientists, and other researchers.

So, why should you care? Well, if you're a scientist, Intern-S1 could be a game-changer for your research. It could help you design new drugs, discover new materials, and accelerate scientific breakthroughs. If you're interested in AI, this shows how far we're coming in creating AI that can truly understand and contribute to complex fields. And even if you're just a curious learner, it's exciting to see AI tackle some of the world's biggest challenges.

This is a big leap forward and the team is releasing this model on Hugging Face so anyone can get their hands on it.

Here's a quote that really stuck with me:

"Through integrated innovations in algorithms, data, and training systems, Intern-S1 achieved top-tier performance in online RL training."

That really sums up the innovative approach the researchers took!

Now, a few questions that popped into my head while reading this:

• How will access to models like Intern-S1 change the way scientific research is done, especially for smaller labs or researchers in developing countries?


• What are the ethical considerations of using AI to accelerate scientific discovery? Could it lead to unintended consequences or biases?


• What happens when models like this become even more powerful? Will AI eventually be able to design experiments and interpret results entirely on its own?

I'm excited to see where this research goes and how it will shape the future of science. What do you guys think? Let me know your thoughts in the comments. Until next time, keep learning!

Credit to Paper authors: Lei Bai, Zhongrui Cai, Maosong Cao, Weihan Cao, Chiyu Chen, Haojiong Chen, Kai Chen, Pengcheng Chen, Ying Chen, Yongkang Chen, Yu Cheng, Yu Cheng, Pei Chu, Tao Chu, Erfei Cui, Ganqu Cui, Long Cui, Ziyun Cui, Nianchen Deng, Ning Ding, Nanqin Dong, Peijie Dong, Shihan Dou, Sinan Du, Haodong Duan, Caihua Fan, Ben Gao, Changjiang Gao, Jianfei Gao, Songyang Gao, Yang Gao, Zhangwei Gao, Jiaye Ge, Qiming Ge, Lixin Gu, Yuzhe Gu, Aijia Guo, Qipeng Guo, Xu Guo, Conghui He, Junjun He, Yili Hong, Siyuan Hou, Caiyu Hu, Hanglei Hu, Jucheng Hu, Ming Hu, Zhouqi Hua, Haian Huang, Junhao Huang, Xu Huang, Zixian Huang, Zhe Jiang, Lingkai Kong, Linyang Li, Peiji Li, Pengze Li, Shuaibin Li, Tianbin Li, Wei Li, Yuqiang Li, Dahua Lin, Junyao Lin, Tianyi Lin, Zhishan Lin, Hongwei Liu, Jiangning Liu, Jiyao Liu, Junnan Liu, Kai Liu, Kaiwen Liu, Kuikun Liu, Shichun Liu, Shudong Liu, Wei Liu, Xinyao Liu, Yuhong Liu, Zhan Liu, Yinquan Lu, Haijun Lv, Hongxia Lv, Huijie Lv, Qidang Lv, Ying Lv, Chengqi Lyu, Chenglong Ma, Jianpeng Ma, Ren Ma, Runmin Ma, Runyuan Ma, Xinzhu Ma, Yichuan Ma, Zihan Ma, Sixuan Mi, Junzhi Ning, Wenchang Ning, Xinle Pang, Jiahui Peng, Runyu Peng, Yu Qiao, Jiantao Qiu, Xiaoye Qu, Yuan Qu, Yuchen Ren, Fukai Shang, Wenqi Shao, Junhao Shen, Shuaike Shen, Chunfeng Song, Demin Song, Diping Song, Chenlin Su, Weijie Su, Weigao Sun, Yu Sun, Qian Tan, Cheng Tang, Huanze Tang, Kexian Tang, Shixiang Tang, Jian Tong, Aoran Wang, Bin Wang, Dong Wang, Lintao Wang, Rui Wang, Weiyun Wang, Wenhai Wang, Yi Wang, Ziyi Wang, Ling-I Wu, Wen Wu, Yue Wu, Zijian Wu, Linchen Xiao, Shuhao Xing, Chao Xu, Huihui Xu, Jun Xu, Ruiliang Xu, Wanghan Xu, GanLin Yang, Yuming Yang, Haochen Ye, Jin Ye, Shenglong Ye, Jia Yu, Jiashuo Yu, Jing Yu, Fei Yuan, Bo Zhang, Chao Zhang, Chen Zhang, Hongjie Zhang, Jin Zhang, Qiaosheng Zhang, Qiuyinzhe Zhang, Songyang Zhang, Taolin Zhang, Wenlong Zhang, Wenwei Zhang, Yechen Zhang, Ziyang Zhang, Haiteng Zhao, Qian Zhao, Xiangyu Zhao, Xiangyu Zhao, Bowen Zhou, Dongzhan Zhou, Peiheng Zhou, Yuhao Zhou, Yunhua Zhou, Dongsheng Zhu, Lin Zhu, Yicheng Zou