Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating research! Today, we're tackling a paper that asks a really intriguing question: What's the smartest way for AI to get things done online?
Think of it like this: Imagine you need to book a flight. You could spend ages clicking around on a travel website, comparing prices, and filling out forms. That's kind of like how AI "browsing agents" traditionally work – they navigate the web just like we do, trying to achieve a goal.
But what if there was a secret back door? A direct line to the airline's computer system where you could just tell it what you want? That's essentially what an API is – an Application Programming Interface. It's a structured way for computers to talk to each other, bypassing all the visual clutter of a website.
This paper explores two types of AI agents:
API-Calling Agents: These are like super-efficient coders. They _only_ use APIs to get the job done. They're like the person who knows exactly which buttons to push to get the desired result.
Hybrid Agents: These are the best of both worlds. They can browse the web and use APIs. Think of them as having both a map and a GPS. They can navigate the website like a human, but also use the API back channels when possible.
So, the researchers put these agents to the test using something called WebArena, which is a realistic simulation of online tasks. And guess what? The API-based agents did better than the browsing agents! And the Hybrid Agents absolutely crushed it! They were successful about 35.8% of the time, a 20% improvement over browsing alone! That's SOTA, or State Of The Art, performance!
"These results strongly suggest that when APIs are available, they present an attractive alternative to relying on web browsing alone."
Now, why should you care? Well, if you're a:
Business Owner: This research shows how to make your AI more efficient, potentially saving you time and money. Think about automating tasks like customer service or data analysis.
Web Developer: It highlights the importance of well-designed APIs. The easier it is for AI to interact with your website through an API, the more valuable your site becomes.
AI Enthusiast: This is a glimpse into the future of AI. It's about finding the most effective ways for machines to interact with the world around them.
The researchers found that using APIs, when available, is a much more efficient way for AI to accomplish tasks online. It's like giving them a direct line instead of making them wade through a crowded store. And the hybrid approach? That's like having a seasoned shopper who knows all the shortcuts and best deals.
This makes me wonder, with the rise of no-code/low-code platforms, will we see even more accessible APIs that allow anyone to build these super-efficient AI agents? And what kind of new tasks will AI be able to tackle when it has access to these "back doors"?
Finally, what ethical considerations do we need to be aware of as AI becomes more and more efficient at using APIs? Could this lead to unfair advantages or even manipulation of online systems?
That's all for this episode of PaperLedge! Keep learning, keep exploring, and I'll catch you next time!Credit to Paper authors: Yueqi Song, Frank Xu, Shuyan Zhou, Graham Neubig

Hey learning crew, Ernis here, ready to dive into another fascinating paper! Today, we're talking about something super relevant as AI gets smarter and more integrated into our lives: how we manage AI agents when they're out there doing things in the real world.
Think of it this way: imagine you've got a super-efficient personal assistant AI. It can book flights, order groceries, even negotiate prices online. That's awesome, right? But what happens if it accidentally breaks the law while trying to get you the best deal, or unintentionally violates someone's privacy?
This paper basically says that we need more than just making sure the AI wants to do good things (that's what "alignment" is all about). We need systems and rules around the AI to make sure things run smoothly and fairly. The researchers call this agent infrastructure.
So, what is agent infrastructure? Well, it's like the roads, traffic lights, and laws that govern how cars operate. Without them, driving would be chaos! Agent infrastructure includes:
Tools to figure out who's responsible. Imagine your AI orders something it shouldn't. We need ways to trace that action back to the AI, its user, or even the company that built it. This could build upon existing systems, like how you log in to websites.
Ways to shape how AIs interact with the world. This means setting rules of the road for AI behavior. It ensures AI agents play nice with existing systems, like legal and economic ones.
Mechanisms to detect and fix problems. Think of this as the AI equivalent of a quality control system. We need ways to catch harmful actions and correct them quickly.
The paper highlights three key functions of agent infrastructure:
Attribution: Figuring out who's responsible for an AI's actions. Like putting a license plate on a car.
Shaping: Guiding how AIs interact with the world. Like creating traffic laws so cars drive safely.
Remediation: Fixing problems caused by AIs. Like having emergency services respond to a car accident.
The authors argue that agent infrastructure is just as important to AI ecosystems as fundamental infrastructure like HTTPS is to the Internet. Without it, we risk creating a Wild West scenario where AI agents can run rampant.
“Just as the Internet relies on infrastructure like HTTPS, we argue that agent infrastructure will be similarly indispensable to ecosystems of agents.”
Why does this matter? Well, if you're a:
Developer: This gives you a framework for building responsible AI systems.
Business owner: This helps you understand how to safely deploy AI in your company.
Policymaker: This offers ideas for regulating AI in a way that protects the public.
Everyday user: This makes you aware of the importance of responsible AI development.
Ultimately, getting agent infrastructure right will help us unlock the amazing potential of AI while minimizing the risks.
So, here are a couple of things that are bouncing around in my head after reading this paper:
How do we balance innovation with regulation when it comes to AI? Do we risk stifling creativity if we're too heavy-handed with the rules?
Who should be responsible for creating and maintaining this agent infrastructure? Is it the government, the tech companies, or some combination of both?
Alright learning crew, that's the gist of the paper. Let me know your thoughts. Until next time, keep learning!Credit to Paper authors: Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, Markus Anderljung

Alright learning crew, Ernis here, ready to dive into some seriously cool tech that's changing how machines talk! We're unpacking a new paper about something called Spark-TTS, and trust me, it's not just another robot voice upgrade.
Think of it like this: imagine you're a voice actor, but instead of reading a script, you're giving a computer instructions on how to become a voice actor. That's kind of what Spark-TTS is doing.
See, normally, getting a computer to speak realistically involves a whole bunch of complicated steps. Like, first it has to understand the words, then figure out the pronunciation, then add emotion, and finally, try to sound like a real person. It's like building a car on an assembly line with a million different parts.
But the brilliant minds behind Spark-TTS have found a way to streamline the process. They've created a system that uses something called BiCodec – think of it as a super-efficient translator that breaks down speech into two key ingredients:
Semantic tokens: These are the core meaning of what's being said – the actual words and the way they're strung together. It’s the ‘what’ is being said.
Global tokens: These are the flavor – the speaker's unique characteristics, like their gender, accent, and even their emotional state. It’s the ‘who’ is saying it and ‘how.’
So, instead of a million different parts, we're down to two crucial ones. And that makes things much faster and easier.
Now, here's where it gets really interesting. Spark-TTS uses a powerful language model called Qwen2.5 (imagine a super-smart AI brain) to take these two token types and generate speech. But not just any speech – controllable speech. Meaning, we can tweak things like:
Coarse-grained control: Broad strokes like "make the speaker sound male" or "make them sound excited."
Fine-grained control: Super precise adjustments, like "raise the pitch by exactly this much" or "speak at this specific speed."
It's like having a vocal equalizer with a million knobs, giving you ultimate control over the final sound.
"Spark-TTS not only achieves state-of-the-art zero-shot voice cloning but also generates highly customizable voices that surpass the limitations of reference-based synthesis."

But wait, there's more! To make this all possible, the researchers created something called VoxBox – a massive library of 100,000 hours of speech data with detailed labels for all sorts of speaker attributes. Think of it as a gigantic training ground for the AI, teaching it everything it needs to know about how humans speak.
So, why does all this matter? Well, imagine the possibilities:
For content creators: Imagine creating custom voiceovers for your videos without needing to hire a voice actor.
For accessibility: Imagine creating personalized voices for people with speech impairments.
For entertainment: Imagine your favorite book being read to you by a voice that sounds exactly like the main character.
The potential is huge! And the best part? The researchers have made their code, models, and audio samples available online. So, anyone can start experimenting with this technology.
But this raises some interesting questions, doesn't it?
Could this technology be used to create convincing deepfakes of people's voices? What are the ethical implications?
If AI can perfectly mimic human voices, what does that mean for voice actors in the future? How will they adapt?
Could this lead to more personalized and engaging interactions with AI assistants and other technologies?
Food for thought, learning crew! This is definitely a space to watch. Until next time, keep exploring!Credit to Paper authors: Xinsheng Wang, Mingqi Jiang, Ziyang Ma, Ziyu Zhang, Songxiang Liu, Linqin Li, Zheng Liang, Qixi Zheng, Rui Wang, Xiaoqin Feng, Weizhen Bian, Zhen Ye, Sitong Cheng, Ruibin Yuan, Zhixian Zhao, Xinfa Zhu, Jiahao Pan, Liumeng Xue, Pengcheng Zhu, Yunlin Chen, Zhifei Li, Xie Chen, Lei Xie, Yike Guo, Wei Xue

Alright learning crew, Ernis here, ready to dive into some seriously cool computer vision research! Today, we're talking about teaching computers to see and understand the world around them, like recognizing objects in a picture or video.
Now, you've probably heard of things like self-driving cars or security cameras that can identify people. All of this relies on something called object detection and segmentation. Think of it like this: object detection is like pointing at a picture and saying "That's a car!" while segmentation is like carefully tracing the outline of that car to separate it from the background.
For a long time, the models used for this, like the YOLO series (You Only Look Once), were really good at recognizing things they were specifically trained to recognize. But what if you wanted them to identify something completely new, something they'd never seen before? That's where things got tricky.
Imagine you've taught a dog to fetch tennis balls. What happens when you throw a frisbee? It's not a tennis ball, so the dog might get confused! That's the challenge these researchers are tackling: making computer vision systems more adaptable and able to recognize anything.
This paper introduces a new model called YOLOE (catchy, right?). What makes YOLOE special is that it's designed to be super efficient and can handle different ways of telling it what to look for. It's like giving our dog different kinds of instructions for what to fetch.
Text Prompts: You can tell YOLOE "Find all the cats in this picture!" and it will use those words to guide its search. The researchers came up with a clever trick called Re-parameterizable Region-Text Alignment (RepRTA). It’s like giving the model a quick refresher course on the meaning of "cat" without slowing it down.
Visual Prompts: Instead of words, you can show YOLOE a picture of what you're looking for. For example, you could show it a picture of a specific type of bird and ask it to find others like it. The secret sauce here is Semantic-Activated Visual Prompt Encoder (SAVPE). This helps the model focus on the important visual features without getting bogged down in the details.
Prompt-Free: And here's the coolest part: YOLOE can even identify objects without any specific prompts! It's like giving our dog a huge vocabulary list of all the things it might encounter. They achieve this with something called Lazy Region-Prompt Contrast (LRPC). This allows YOLOE to recognize a wide range of objects without relying on super expensive language models.

So, why does this matter? Well, think about it. A more adaptable and efficient object detection system could revolutionize:
Robotics: Imagine robots that can understand their environment and interact with objects they've never seen before.
Healthcare: Doctors could use these systems to quickly identify diseases in medical images.
Accessibility: Object detection can help visually impaired people navigate the world more easily by describing objects around them.
The researchers showed that YOLOE is not only more adaptable but also faster and cheaper to train than previous models. For example, it outperformed a similar model (YOLO-Worldv2-S) by a significant margin while using less training data and processing power!
"Notably, on LVIS, with 3$\times$ less training cost and 1.4$\times$ inference speedup, YOLOE-v8-S surpasses YOLO-Worldv2-S by 3.5 AP."
This research really pushes the boundaries of what's possible with computer vision. It's exciting to think about the potential applications of YOLOE and similar models in the future. You can check out the code and models yourself over at their GitHub repo: https://github.com/THU-MIG/yoloe
But here's where I'm curious, what do you all think?
Could YOLOE-like systems eventually replace human security guards or quality control inspectors?
What ethical considerations arise when we give computers the ability to "see" and interpret the world around us?
Let me know your thoughts in the comments! Until next time, keep learning!Credit to Paper authors: Ao Wang, Lihao Liu, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding

Hey PaperLedge learning crew, Ernis here, ready to dive into some seriously cool AI research! Today, we're tackling a paper about making AI language models even smarter and more versatile. Think of language models as the brains behind things like ChatGPT or Google Translate – they're trained to understand and generate human-like text.
Now, there are different ways to build these "brains." Two main approaches are autoregressive models and diffusion models. Autoregressive models are like writing a story one word at a time, predicting the next word based on what came before. They're great at generating coherent text, but it can be slow because you have to wait for each word to be generated before moving on to the next. It's like building a Lego tower brick by brick.
Diffusion models, on the other hand, are a bit more abstract. Imagine taking a perfectly clear image and slowly adding noise until it's just static. A diffusion model learns how to reverse this process – starting from the noise and gradually removing it to reveal the original image. In the context of language, it's like starting with random gibberish and gradually refining it into meaningful text. One of the big advantages of diffusion models is they can potentially generate different parts of the text all at the same time – parallelized generation – making them faster than autoregressive models. Plus, they offer more controllability, which means you can steer the generation process to get the kind of output you want.
So, diffusion models sound amazing, right? Well, they have their downsides. Historically, they haven't been as good as autoregressive models at accurately predicting the probability of a sentence – what we call likelihood modeling. And they've been mostly limited to generating text of a fixed length. It's like having a fancy Lego factory that can only build towers of a specific height.
This is where the paper we're discussing comes in. The researchers introduce something called Block Diffusion Language Models. Think of it as a hybrid approach, combining the best features of both autoregressive and diffusion models. They're essentially building a bridge between these two worlds.
The key idea is to break the text down into "blocks." Instead of generating one word at a time (like autoregressive models) or the entire sequence at once (like some diffusion models), they generate these blocks in parallel. This allows for flexible-length generation, meaning the model can create text of any length. It's like having a Lego factory that can build towers of any height using pre-fabricated Lego blocks.
Furthermore, they improved the efficiency of the model using a technique called KV caching, which helps the model remember information from previous blocks, and parallel token sampling, which allows them to generate multiple words within a block simultaneously. These improvements speed up the generation process significantly.
The researchers also came up with a clever "recipe" for building effective block diffusion models. This includes:
An efficient training algorithm (a better way to teach the model).
Estimators of gradient variance (techniques to make the training process more stable).
Data-driven noise schedules (smart ways to add and remove noise during the diffusion process).
All of this boils down to a model that's not only fast and flexible but also performs really well! The paper claims that their block diffusion model achieves state-of-the-art performance among diffusion models on language modeling benchmarks.
So, why does this research matter? Well, for AI researchers, it provides a new and promising approach to language modeling. For developers, it opens up possibilities for building more efficient and controllable AI applications. And for the average person, it means potentially better and more creative AI tools in the future. Imagine AI that can write personalized stories, generate realistic dialogue for video games, or even help you brainstorm ideas – all faster and with more control than ever before.
"Block diffusion overcomes key limitations of both approaches by supporting flexible-length generation and improving inference efficiency."
You can even find the code, model weights, and a blog post about the project on their website: https://m-arriola.com/bd3lms/
Here are some questions that popped into my head while reading this paper:
How easily can this block diffusion approach be adapted to different languages, especially those with very different sentence structures than English?
What are the ethical considerations of having such a controllable and powerful language model? Could it be used to generate highly realistic fake news or propaganda?
How do the computational resources required to train and run these block diffusion models compare to traditional autoregressive models? Is it more accessible to researchers and developers with limited resources?
That's all for this episode of PaperLedge. Keep learning, keep questioning, and I'll catch you next time!Credit to Paper authors: Marianne Arriola, Aaron Gokaslan, Justin T Chiu, Zhihan Yang, Zhixuan Qi, Jiaqi Han, Subham Sekhar Sahoo, Volodymyr Kuleshov

Hey PaperLedge learning crew, Ernis here! Get ready to dive into a fascinating piece about how computers are getting really good at understanding and using language. Think of it like this: remember when your phone's voice assistant could barely understand you? Well, things are changing fast, and this paper is about one of the key tools making it happen.
This paper introduces something called Transformers – and no, we're not talking about robots in disguise (although that would be cool!). In the world of Artificial Intelligence, Transformers are a special type of computer program architecture that's revolutionizing how machines process language. Think of it like building a super-efficient engine for understanding words.
Now, you might be thinking, "Why is this important?" Well, imagine a world where computers can:
Understand your questions with incredible accuracy.
Translate languages flawlessly.
Write stories, poems, or even code!
That’s the kind of potential Transformers unlock. They allow us to build much bigger and more powerful language models than ever before.
But here's the thing: just having a powerful engine isn't enough. You need to fuel it! That's where "pretraining" comes in. Think of it like giving the engine a massive library of books, articles, and websites to learn from before it even starts tackling specific tasks. This pretraining process allows the Transformer to learn general language patterns, making it much better at understanding and generating text.
The paper describes a library called "Transformers" (yes, the same name!), which is like a toolbox filled with all the best parts and blueprints for building these language-understanding engines. It's an open-source project, meaning anyone can use it, contribute to it, and improve it. The goal is to make these powerful tools accessible to everyone – from researchers pushing the boundaries of AI to everyday developers building language-based applications.
"Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments."
So, what makes this library so special?
It's carefully engineered: The "parts" inside are top-of-the-line, designed for optimal performance.
It's unified: All the different components work together seamlessly.
It's open and accessible: Anyone can use it and build upon it.
Basically, it's like giving everyone access to the cutting-edge technology behind things like advanced chatbots, sophisticated search engines, and even AI-powered writing assistants. This library also contains a collection of these pretrained models that were created by community members. This is important because each model is like a person who was raised in a certain culture, and so each one has its own unique and interesting way of interpreting information.
This research matters because it democratizes access to incredibly powerful AI tools. It empowers researchers to experiment and innovate, and it allows developers to build new and exciting applications that can benefit all of us. It essentially opens the door to a future where computers can truly understand and communicate with us on a deeper level.
Now, a couple of things that popped into my head while reading this:
How do we ensure these powerful language models are used responsibly and ethically?
Could these Transformers eventually replace human writers or translators, or will they primarily serve as tools to augment our abilities?
Food for thought, right? Let me know your thoughts in the comments, and until next time, keep learning!Credit to Paper authors: Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander M. Rush

Hey PaperLedge learning crew! Ernis here, ready to dive into some fascinating research. Today, we're tackling a problem that's like a secret saboteur hiding inside our AI systems, specifically in the realm of language processing. We're talking about backdoor attacks on those clever Deep Neural Networks (DNNs) that power things like sentiment analysis and text translation.
Think of DNNs as incredibly complex recipes. They learn from data, like ingredients, to perform tasks. Now, imagine someone secretly swaps out one of your ingredients with something poisonous. That's essentially what a backdoor attack does. It injects a hidden trigger into the DNN's training data, so that when that trigger appears later, the AI misbehaves, even if the rest of the input seems perfectly normal.
This is especially concerning with Pre-trained Language Models (PLMs). These are massive, powerful language models, like BERT or GPT, that have been trained on gigantic datasets. They're then fine-tuned for specific tasks. The problem? If someone poisons the fine-tuning process with those backdoored samples, we've got a compromised AI.
Now, here's the interesting part. These PLMs start with clean, untainted weights – essentially, the original, uncorrupted recipe. The researchers behind this paper asked a crucial question: can we use that "clean recipe" to help us detect and neutralize these backdoor attacks after the fine-tuning process has been compromised? They found a clever way to do just that!
They came up with two main techniques:
Fine-mixing: Imagine you have a cake that's been slightly poisoned. Fine-mixing is like taking that poisoned cake, mixing it with a fresh, unpoisoned cake (the pre-trained weights), and then baking it again with just a little bit of the good ingredients (clean data). This helps dilute the poison and restore the cake's original flavor. The paper describes this as a "two-step" technique. First, they mix the potentially backdoored weights (from the fine-tuned model) with the clean, pre-trained weights. Then, they fine-tune this mixed model on a small amount of untainted data.
Embedding Purification (E-PUR): This is like carefully examining each ingredient (each word embedding) to see if it's been tampered with. Word embeddings are numerical representations of words, and they can be manipulated to trigger the backdoor. E-PUR identifies and corrects these potentially compromised embeddings.
The researchers tested their methods on various NLP tasks, including sentiment classification (determining if a sentence is positive or negative) and sentence-pair classification (determining the relationship between two sentences). And guess what? Their techniques, especially Fine-mixing, significantly outperformed existing backdoor mitigation methods!
"Our work establishes a simple but strong baseline defense for secure fine-tuned NLP models against backdoor attacks."
They also found that E-PUR could be used alongside other mitigation techniques to make them even more effective.
Why does this matter?
For AI developers: This provides a practical way to defend against backdoor attacks, making your models more secure.
For businesses using AI: This helps ensure that your AI-powered applications are reliable and trustworthy. Imagine your customer service bot suddenly starts promoting a competitor – that's the kind of risk these defenses can mitigate.
For everyone: As AI becomes more pervasive, it's crucial to ensure its safety and integrity. This research is a step in that direction.
This study is really insightful because it reminds us that the knowledge embedded in pre-trained models can be a strong asset in defense. It's not just about having a model; it's about understanding its history and leveraging that understanding to enhance its security. It opens up the possibility of building more resilient AI systems that are harder to manipulate.
So, here are a couple of thoughts to ponder:
Could these techniques be adapted to defend against other types of attacks on AI models, not just backdoor attacks?
What are the ethical implications of using potentially compromised models, even after applying these mitigation techniques? Are we ever truly sure the backdoor is gone?
That's all for today's PaperLedge deep dive. Keep learning, stay curious, and I'll catch you next time!Credit to Paper authors: Zhiyuan Zhang, Lingjuan Lyu, Xingjun Ma, Chenguang Wang, Xu Sun

Hey PaperLedge crew, Ernis here! Get ready for a mind-blowing episode because we're diving into a paper that's shaking up the world of Artificial Intelligence. We're talking about GPT-3, a language model so massive, it's like comparing a tiny rowboat to a colossal ocean liner!
Now, for a while, the best way to get AI to understand language was to train it on tons and tons of specific examples. Think of it like teaching a dog a trick – you need to repeat the command and reward the right action over and over. But what if we could build an AI that learns more like a human, able to understand new tasks with just a few examples, or even just simple instructions? That's the holy grail, right?
Well, this paper explores exactly that. The researchers built GPT-3, and get this, it has 175 billion parameters! That's ten times bigger than any language model before it. Imagine it like this: if other language models are like small towns with a few hundred people, GPT-3 is like the entire planet earth, with billions of people, all with their own unique knowledge and skills.
What makes GPT-3 truly special is that it can perform a wide range of language tasks – from translating languages to answering questions – with very few examples. They call this "few-shot learning." Think of it as showing someone a picture of a cat just a couple of times, and then they can identify cats anywhere. That's the kind of learning leap we're talking about.
Here's a quote that really highlights the ambition:
"GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation..."
So, what are some things GPT-3 can do? Imagine it unscrambling jumbled words, figuring out how to use a brand new word in a sentence, or even doing simple math problems. It's like having a super-smart language assistant that can handle a bunch of different tasks without needing constant retraining.
But it's not all sunshine and rainbows. The paper also points out some limitations. GPT-3 still struggles with certain tasks, and because it’s trained on so much data from the web, it can sometimes pick up biases or inaccuracies. Think of it like learning from the internet – you're bound to encounter some misinformation along the way.
Perhaps the most mind-blowing part is that GPT-3 can even generate news articles that are difficult for humans to distinguish from articles written by actual journalists! That raises some serious questions about the future of content creation and the potential for misuse. This is where things get a little sci-fi.
Why does this matter?
For AI researchers: GPT-3 shows that scaling up language models can lead to significant improvements in few-shot learning, paving the way for more adaptable and human-like AI systems.
For businesses: Imagine being able to automate customer service, generate marketing content, or translate documents instantly, all with minimal training data.
For everyone: We need to be aware of the potential societal impacts of these powerful language models, including the spread of misinformation and the potential for job displacement.
So, here are a couple of questions I'm pondering:
If AI can generate convincing news articles, how do we combat the spread of fake news and ensure people can distinguish between real and AI-generated content?
As language models become more powerful, how do we ensure they are used ethically and responsibly, and that they don't perpetuate existing biases or create new ones?
This paper is a fascinating glimpse into the future of AI, and it's something we all need to be thinking about. Until next time, keep learning, PaperLedge crew!Credit to Paper authors: Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei