Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool research! Today, we're tackling a paper that's all about keeping our AI systems safe and reliable. Think of it like this: imagine you're teaching a self-driving car to recognize stop signs. It gets really good at spotting the typical stop signs, but what happens when it encounters a stop sign that's faded, covered in snow, or just a weird, artistic rendition? That's where out-of-distribution detection, or OOD, comes in. It's the AI's ability to say, "Whoa, this is something I've never seen before, and I'm not sure what to do!"
Now, the most straightforward way to do this with generative AI models is to use something called likelihood. Imagine likelihood like a probability score. If the AI thinks the input data is very probable or likely to come from the same place as its training data, it gives it a high score. If the input is very different and improbable, it gets a low score. Under ideal conditions, likelihood should be the perfect OOD detector.
But here’s the catch: previous research has shown that likelihood often fails in practice. It’s like the self-driving car confidently identifies that weird, snowy stop sign as a perfectly normal one, leading to potential problems. So, the big question is: why does likelihood let us down? Is it something fundamentally wrong with how we're using it, or is there a specific part of the AI system that's causing the issue?
This paper dives deep into that question. The researchers wondered if the problem lies in the "pixel space," which is basically the raw image data the AI sees. Think of it like trying to describe a person using only their height, weight, and hair color – you're missing a lot of important details! They hypothesized that maybe the representation space – a more abstract and meaningful way of representing the data – might be better for OOD detection.
To test this, they did something really clever. They didn't train their AI, a Variational Diffusion Model (think of it as a fancy AI art generator), directly on images. Instead, they trained it on the representation of those images, created by another AI called ResNet-18. It's like training the art generator not on pictures of faces, but on descriptions of facial features like "high cheekbones," "wide eyes," and "strong jawline."
The goal was to see if likelihood-based detection worked better in this representation space compared to the usual pixel space. And guess what? They then compared their results to other state-of-the-art OOD detection methods to see how they stacked up!
"We explore whether, in practice, the representation space also suffers from the inability to learn good density estimation for OOD detection, or if it is merely a problem of the pixel space typically used in generative models."
So, why does this matter? Well, for those of you in the AI field, this research could lead to more robust and reliable AI systems. For the rest of us, it means safer self-driving cars, more accurate medical diagnoses, and fewer AI-related mishaps in general!
Here are some things I was thinking about while reading:
If the representation space is better for OOD detection, how can we design AI systems to automatically learn and utilize the best representations?

Are there certain types of OOD data that are inherently more difficult to detect, regardless of the space used? And if so, how can we specifically target those weaknesses?

Let me know what you think, PaperLedge crew! What are your thoughts about AI safety and out-of-distribution detection? I'm looking forward to hearing your insights!Credit to Paper authors: Joonas Järve, Karl Kaspar Haavel, Meelis Kull

Hey PaperLedge learning crew, Ernis here, ready to dive into some cutting-edge research that’s got me really excited! Today, we’re talking about something that could seriously change how we interact with maps and the world around us.
Think about Google Maps. It's amazing, right? You can zoom in on almost any street in the world, get directions, and find nearby restaurants. But what if you wanted to know, say, “Are there more oak trees than maple trees on this street?” or "Does this building look like it needs repairs?" Google Maps as we know it can't really answer that because it relies on pre-existing information – things like road names, business locations, and pre-defined points of interest.
But what if maps could actually "see" the world, analyze what they see, and answer questions based on that visual information? That's the vision behind what researchers are calling Geo-Visual Agents.
Imagine a super-smart AI that can look at street-level photos like Google Street View, photos from TripAdvisor and Yelp, and even satellite images, and then combine that visual data with traditional map information. This AI could then answer all sorts of questions that are impossible to answer right now. It's like giving maps eyes… and a brain!
This research paper lays out the plan for how we could build these Geo-Visual Agents. They're not just talking about it; they're thinking about the sensors you'd need, how you'd interact with them, and even giving us some cool examples of what they could do.
Let's break down some examples of what Geo-Visual Agents could achieve:

Assessing neighborhood character: Imagine asking: "Show me streets in this city with a vibrant, pedestrian-friendly feel." The Agent could analyze photos, looking for things like outdoor cafes, trees, benches, and pedestrian crossings, and then create a map highlighting those areas.


Disaster response: After a hurricane, you could ask: "Identify buildings with visible roof damage in this area." The Agent could analyze aerial imagery and quickly pinpoint structures that need immediate attention, helping rescue teams prioritize their efforts.


Urban planning: Let's say you're thinking of opening a new business and want to know what kind of signage is common in the area. Instead of physically walking or driving around, a Geo-Visual Agent could answer that question for you.

Of course, building these Geo-Visual Agents is no easy task. The researchers point out some major challenges, like:

How do we teach the AI to "see" and understand complex visual information? It's one thing to identify a building; it's another to assess its condition or understand its architectural style.


How do we deal with all the different types of images? Street-level photos are different from satellite images, and they all have different levels of quality and detail.


How do we ensure privacy and ethical use of this technology? We need to make sure that these Agents aren't used to discriminate against certain neighborhoods or individuals.

So, why does all of this matter?

For travelers: Imagine planning a trip and being able to find the most scenic routes or the most authentic local restaurants just by asking the map.


For city planners: This technology could help them make better decisions about urban development, transportation, and resource allocation.


For emergency responders: Geo-Visual Agents could be invaluable in disaster relief efforts, helping them quickly assess damage and coordinate rescue operations.


For anyone who's just curious about the world: This could be a powerful tool for exploring and understanding our planet in new and exciting ways.

"Geo-Visual Agents: a future where maps aren't just directories, but active observers and interpreters of the world around us."
This research is a really exciting step toward that future. It opens up so many possibilities, and I can’t wait to see how it develops!
Now, a couple of things that really got me thinking while reading this paper:

Given the potential for bias in the images that these agents are trained on (e.g., certain areas being over-represented in datasets), how can we ensure that Geo-Visual Agents provide fair and accurate information for all communities?


How will the widespread adoption of Geo-Visual Agents change the way we interact with our physical environment? Will it lead to a deeper appreciation of our surroundings, or will it create a sense of detachment as we increasingly rely on AI to interpret the world for us?

What do you think, learning crew? Are you excited about the potential of Geo-Visual Agents, or are you concerned about the challenges and ethical considerations? Let's discuss!Credit to Paper authors: Jon E. Froehlich, Jared Hwang, Zeyu Wang, John S. O'Meara, Xia Su, William Huang, Yang Zhang, Alex Fiannaca, Philip Nelson, Shaun Kane

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're talking about something super important for the future of AI: making AI agents better at using tools to solve real-world problems.
Think of it like this: you need to plan a surprise birthday party for your best friend. You wouldn't just magically know everything, right? You'd use different tools – your phone to text friends, Google to find party supply stores, a calendar to check availability, and maybe even a budgeting app to keep track of expenses. AI agents need to do the same thing, but digitally!
Now, there's a protocol called the Model Context Protocol (MCP), kind of like a universal language for AI agents to talk to these tools. It's meant to make it easier for them to use different tools together. But... how do we actually test if they're any good at it? That's where this paper comes in.
These researchers created something called LiveMCP-101. Imagine it as a super challenging obstacle course for AI agents. It's a benchmark, a way to measure how well they can handle 101 real-world queries that require using multiple MCP tools in a coordinated way. These queries are carefully designed and tested to be realistic.

Think of questions like: "Find the current stock price of Tesla, then calculate how much profit I would have made if I bought 10 shares last week."


Or, "Search for the highest-rated Italian restaurant in my city, then make a reservation for two people at 7 PM."

These aren't simple tasks! They require the AI to use web search, file operations, math, and data analysis – all working together.
What's really cool is how they're evaluating the AI agents. Instead of just checking if the final answer is correct, they look at the plan the AI creates to solve the problem. It's like judging a chef not just on the taste of the dish, but also on their recipe and cooking process. This is important because in the real world, things change! The restaurant might be fully booked, or the stock price might fluctuate. The AI needs to adapt its plan.
"LiveMCP-101 sets a rigorous standard for evaluating real-world agent capabilities, advancing toward autonomous AI systems that reliably execute complex tasks through tool use."

Here's the kicker: even the best AI models only succeeded in less than 60% of these tasks! That means there's still a lot of room for improvement. The researchers dug into why the AI agents were failing, looking at things like:

Were they choosing the right tools for the job?


Were they using those tools efficiently?


Were they getting confused when things didn't go exactly as planned?

By understanding these failure points, the researchers can give us concrete ideas on how to make these AI agents smarter and more reliable.
So, why does this research matter? Well, imagine a future where AI assistants can truly help us with complex tasks, from managing our finances to planning our vacations. This requires them to be able to use tools effectively and adapt to changing circumstances. This benchmark, LiveMCP-101, is a crucial step towards making that future a reality.
This is relevant to:

Developers: It gives them a clear target to aim for and helps them identify areas for improvement in their AI models.


Researchers: It provides a standardized way to compare different AI approaches and track progress over time.


Everyone else: It gives us a glimpse into the potential of AI and the challenges we need to overcome to unlock its full potential.

Now, a couple of things that jumped out at me while reading this:

How do we ensure that these AI agents are using tools ethically and responsibly? What safeguards need to be in place?


As these AI agents become more sophisticated, how do we prevent them from becoming overly reliant on tools, potentially hindering their own problem-solving abilities?

Food for thought, PaperLedge crew! Until next time, keep learning!Credit to Paper authors: Ming Yin, Dinghan Shen, Silei Xu, Jianbing Han, Sixun Dong, Mian Zhang, Yebowen Hu, Shujian Liu, Simin Ma, Song Wang, Sathish Reddy Indurthi, Xun Wang, Yiran Chen, Kaiqiang Song

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool research that's tackling a major hurdle in training those massive Large Language Models – think of the AI brains behind chatbots and advanced text generators. We're talking about making the training process way more efficient.
Now, imagine you're trying to teach a friend a complex concept. You could tell them everything all at once, right? That's like the traditional way of training these LLMs. But what if you only focused on the most important parts and then let them fill in the gaps? That's the basic idea behind this paper. It's all about communicating the essential information needed to train these models without overwhelming the system.
The big problem is bandwidth, which is like the size of the pipe that data flows through. Training these massive models requires a lot of data flowing back and forth, especially when different parts of the model are being worked on in different places, like separate data centers. Sending everything across these connections is slow and expensive. It's like trying to squeeze an elephant through a garden hose! Current solutions, while reducing how often data is sent, still send huge chunks of data each time.
This research introduces SparseLoCo, a new training algorithm that's designed to be super communication-efficient. Think of it as a smart way to compress the training information, so it takes up much less space.
So, how does SparseLoCo work its magic?

First, it uses sparsification. Imagine you have a huge list of numbers, but only a few of them are really important. Sparsification means focusing only on those key numbers (the top k most important ones) and ignoring the rest. In this case, they're getting down to as little as 1-3% of the original data! It's like highlighting only the most important sentences in a textbook.


Second, it uses quantization. This is like rounding off numbers to make them simpler. Instead of using super-precise numbers, they use fewer bits to represent them. Think of it like trading accuracy for efficiency. They're going down to just 2 bits – a huge reduction!


The researchers found that by cleverly combining something called "outer momentum" with this aggressive sparsification, they could actually improve the model's performance. It's kind of counterintuitive, but sometimes, less really is more! It's like pruning a plant – by cutting away some branches, you can encourage it to grow stronger.

The researchers observed that local approximation of outer momentum by error feedback combined with aggressive sparsity, and sparse aggregation can actually improve model performance. This suggests that carefully designed communication strategies can not only reduce bandwidth usage but also potentially enhance training dynamics.
"...SparseLoCo provides significant benefits in both performance and communication cost."

Why does this matter?

For researchers and AI developers: This could be a game-changer for training larger, more powerful LLMs without breaking the bank on infrastructure and bandwidth costs.


For businesses: Faster and cheaper training means faster innovation and deployment of AI-powered products and services.


For everyone: More efficient AI training could lead to more accessible and affordable AI tools, benefiting society as a whole.

Essentially, this research unlocks the potential to train massive AI models faster, cheaper, and with less strain on network resources. That's a win-win-win!
So, here's a couple of things to chew on. First, what are the potential drawbacks of being too aggressive with sparsification and quantization? Could we lose some critical nuances in the data? And second, how might these techniques be adapted to other types of machine learning models beyond LLMs?
That's all for this week's PaperLedge deep dive. Until next time, keep learning and keep questioning!Credit to Paper authors: Amir Sarfi, Benjamin Thérien, Joel Lidin, Eugene Belilovsky

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research that's super relevant to anyone who's ever shopped online – which, let's be honest, is probably all of us!
Today, we're unpacking a paper that tackles a question I've personally pondered while scrolling through endless product pages: do all those product images actually help me make a better decision, or are some just...noise? You know, that feeling when you've seen 10 different angles of a coffee mug, and you're still not sure if it's the right shade of blue?
So, these researchers created something called EcomMMMU. Think of it as a massive online shopping mall simulation, but instead of physical stores, it's a giant collection of product listings – over 400,000 of them, with almost 9 million images! That's a lot of virtual browsing.
"EcomMMMU is comprised of multi-image visual-language data designed with 8 essential tasks and a specialized VSS subset to benchmark the capability of multimodal large language models (MLLMs) to effectively utilize visual content."
The clever thing is, they designed this dataset to test how well AI models – specifically, what they call "multimodal large language models" or MLLMs – can understand products when given both text descriptions and images. Imagine training a robot to be the ultimate online shopping assistant.
Now, here's the kicker. They found something really interesting. Adding more product images doesn't always improve the AI's understanding. In fact, sometimes it made things worse! It's like overloading your brain with too much information – the AI gets confused and makes poorer decisions. It's like trying to explain something to a toddler, sometimes less is more!
This raises a big question: if AI struggles with this, what about us humans? Are we also being tricked into thinking more images equal more clarity?
To address this problem, the researchers developed a system called SUMEI. The analogy I like to use is that SUMEI acts like a savvy shopper who knows how to curate their visual attention before making a purchase. It predicts the "visual utility" of each image – basically, how helpful it is – and then only uses the most useful ones for the task at hand. So, instead of showing the AI every image, SUMEI picks the best ones and focuses its attention.
Their experiments showed that SUMEI actually worked really well, improving the AI's ability to understand the products and make better decisions.
So, why does this research matter? Well, for:
Online Retailers: It suggests that simply throwing up tons of product images isn't necessarily the best strategy. Maybe focusing on high-quality, informative images and good image selection is key.
AI Researchers: It highlights the challenges of multimodal understanding and points to new directions for improving AI models.
Everyday Shoppers (like us!): It reminds us to be critical consumers of information and not to assume that more visuals always equal better understanding.

This research really gets you thinking about how we consume information online. Here are some questions that popped into my head:
Could this concept of "visual utility" be applied to other areas, like news consumption or social media, to help us filter out irrelevant or misleading information?
How much of our online shopping behavior is driven by visual overload, and are we actually making worse decisions because of it?
What kind of image features are the most important for product understanding, and how can retailers highlight those features more effectively?
That's all for this episode, PaperLedge crew! Let me know what you think about this research in the comments. Until next time, keep learning!Credit to Paper authors: Xinyi Ling, Hanwen Du, Zhihui Zhu, Xia Ning

Hey PaperLedge crew, Ernis here! Get ready to dive into some seriously cool tech that could revolutionize how doctors diagnose diseases. We're talking about AI, medical knowledge, and a dash of good ol' reinforcement learning – all mixed together to create something pretty special.
So, the problem is this: medical large language models, or LLMs – think souped-up versions of the AI that powers chatbots – are getting really good, but they still stumble when it comes to accurate diagnosis. They sometimes have knowledge gaps, and even worse, they hallucinate! That means they make stuff up, which is definitely not what you want from your doctor's assistant.
Researchers have tried to fix this by giving these AI systems tools and access to tons of information. It's like giving them a huge library and a search engine. But even with that, they weren't using the information as effectively as they could, and it was hard to follow their thought process – you couldn't really see why they arrived at a certain diagnosis.

That's where Deep-DxSearch comes in. Think of it as a super-smart medical detective, trained from the ground up to find the right answers. The key idea is to turn the LLM into an agent, kind of like a player in a game, and the medical knowledge into its environment.
Here's how it works:

First, they built this massive library of medical information, including patient records and reliable medical textbooks.
Then, they let the AI loose in this library! But they didn't just leave it to wander around aimlessly.
They used reinforcement learning. Remember how they trained that AI to play Go? It's the same principle! They gave the AI rewards for doing things right, like using the right information, reasoning logically, and ultimately, making the correct diagnosis. And they penalized it for making mistakes.

It's like training a dog: you give it treats for good behavior and gently correct it when it messes up. Over time, the AI learns how to be a top-notch diagnostician.

The results were pretty impressive! Deep-DxSearch consistently outperformed other AI systems, including some really advanced ones like GPT-4o and specialized medical AIs. It was better at diagnosing both common and rare diseases, even when faced with unfamiliar situations. The researchers even did experiments to prove that each part of their system – the rewards, the library, everything – was crucial to its success.
They also looked at specific cases and analyzed how Deep-DxSearch arrived at its conclusions. This helps us understand why it's so good and gives doctors more confidence in its recommendations. It's not just a black box spitting out answers; you can see the reasoning behind it.
"After training, Deep-DxSearch achieves substantial gains in diagnostic accuracy...surpassing strong diagnostic baselines...for both common and rare disease diagnosis."

So, why does this matter? Well, for doctors, Deep-DxSearch could be a powerful tool to help them make more accurate and faster diagnoses, especially in complex cases. For patients, this could mean getting the right treatment sooner, leading to better outcomes. And for the AI community, it shows the power of combining large language models with reinforcement learning and carefully curated knowledge.
This research really highlights the importance of having AI systems that are not only accurate but also transparent and trustworthy.
Here are a few things that pop into my head:

How do we ensure that the medical knowledge used to train these AI systems is always up-to-date and unbiased?
What are the ethical considerations of using AI in medical diagnosis, especially when it comes to patient privacy and data security?
Could systems like Deep-DxSearch eventually be used to provide medical advice directly to patients, and if so, how do we ensure that this advice is safe and reliable?

You can even check out the code and data on GitHub (link in the show notes!). This is a fascinating area, and I'm excited to see where it goes. Until next time, keep learning!Credit to Paper authors: Qiaoyu Zheng, Yuze Sun, Chaoyi Wu, Weike Zhao, Pengcheng Qiu, Yongguo Yu, Kun Sun, Yanfeng Wang, Ya Zhang, Weidi Xie

Hey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about Large Language Models, or LLMs. You know, those AI powerhouses like GPT-4 that can write poems, answer questions, and even generate code. But sometimes, even these super-smart models struggle, especially when it comes to tasks that need precise calculations or specific knowledge.
Think of it like this: your brain is amazing at creative problem-solving, but you probably still use a calculator for complex math, right? That's where the idea of Tool-Integrated Reasoning (TIR) comes in. It's like giving LLMs access to external tools, like calculators, search engines, or specialized databases, to help them reason more effectively.
Now, the big question is: does this tool integration really make a difference? Does it just give the LLM a crutch, or does it actually improve its ability to think better? That's what the researchers behind this paper wanted to find out.
To tackle this, they created something called ReasonZoo. Imagine it as a diverse testing ground for LLMs, with nine different categories of reasoning challenges, from math problems to logical puzzles to tasks requiring common-sense knowledge. It's designed to really push LLMs to their limits and see how well they can handle different types of reasoning.
"ReasonZoo is designed to evaluate the effectiveness of TIR across various domains."
But it's not just about whether the LLM gets the right answer. The researchers also wanted to know how efficiently the LLM reasons. Did it take a long, convoluted path to the solution, or did it get there quickly and directly? To measure this, they came up with two new metrics: Performance-Aware Cost (PAC) and Area Under the Performance-Cost Curve (AUC-PCC). Think of PAC like measuring how much effort (or "cost") the LLM expends to achieve a certain level of accuracy. AUC-PCC then summarizes the overall efficiency across different performance levels.
So, what did they find? Well, the results were pretty clear: LLMs equipped with TIR consistently outperformed their non-TIR counterparts. Whether it was solving math equations or tackling real-world scenarios, having access to the right tools made a significant difference.
Math Tasks: TIR helped LLMs crunch numbers more accurately and efficiently.
Non-Math Tasks: TIR improved reasoning and decision-making in diverse scenarios.
But even more interesting, the researchers found that TIR also improved reasoning efficiency, as demonstrated by better PAC and AUC-PCC scores. This suggests that TIR doesn't just help LLMs get the right answer; it helps them get there faster and with less "overthinking." It's like giving them a sharper, more focused mind.
The key takeaway here is that TIR seems to offer domain-general benefits. It's not just a one-trick pony that works for a specific type of problem. It has the potential to significantly advance the capabilities of LLMs in all sorts of complex reasoning tasks.
This research has implications for a lot of people:
AI Developers: TIR offers a promising path to building more powerful and reliable LLMs.
Businesses: TIR-enhanced LLMs could automate complex decision-making processes and improve efficiency.
Everyone: As LLMs become more integrated into our lives, understanding how to make them reason more effectively is crucial for ensuring their responsible and beneficial use.
So, here are a couple of questions that popped into my head while reading this paper:
If we give LLMs access to tools, how do we ensure they are using those tools appropriately and not just blindly following their output?
What are the ethical considerations of using TIR? Could it lead to LLMs becoming too reliant on external tools and losing their ability to reason independently?
That's all for today's deep dive! I hope you found this paper as interesting as I did. Until next time, keep those neurons firing!Credit to Paper authors: Yufeng Zhao, Junnan Liu, Hongwei Liu, Dongsheng Zhu, Yuan Shen, Songyang Zhang, Kai Chen

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