Hey PaperLedge crew, Ernis here, ready to dive into some seriously cool audio tech! Today, we're tuning into a paper that's trying to teach computers to create sound, not just play it back. Think of it like this: instead of a musician playing an instrument, we're building a digital instrument that can learn to "play" itself.
Now, the traditional way computers generate audio is, well, complicated. But this paper uses something called a "Transformer" – and no, we're not talking about robots in disguise! In the world of AI, a Transformer is a specific type of neural network architecture that excels at understanding relationships in sequences of data. Think of it as the AI equivalent of a super-attentive listener.
The researchers built a system that, like a super-attentive listener, predicts the next tiny piece of a sound – what we call a waveform – based on all the pieces that came before. It's like predicting the next note in a melody, but at a microscopic level. They call their system "fully probabilistic, auto-regressive, and causal." Let's break that down:
Fully Probabilistic: It's not just guessing one outcome; it's figuring out the probabilities of different possible sounds.
Auto-Regressive: It uses its own previous predictions to make the next one. Imagine a painter who uses the colors they've already put on the canvas to decide what to paint next.
Causal: Crucially, it only looks at what came before. It can't cheat and look into the future of the sound. This keeps things realistic.
The really exciting part? They claim their Transformer-based system is about 9% better than a popular existing method called WaveNet. That's a pretty big jump! The key seems to be the "attention mechanism." Think of it as the AI focusing on the important parts of the sound to make a better prediction. It's like a musician focusing on the rhythm and melody instead of getting distracted by background noise.
So, what does this all mean? Well, the potential applications are vast. Imagine:
Realistic Video Game Soundscapes: Creating dynamic, evolving sounds that react to the player's actions.
Personalized Audio Therapy: Generating calming sounds tailored to an individual's specific needs.
New Musical Instruments: Exploring completely new sonic textures and possibilities.
The researchers even found they could improve the system's performance by another 2% by giving it more context – a longer "memory" of the sound. This shows that understanding the bigger picture is key to creating realistic audio.
"The flexibility of the current model to synthesize audio from latent representations suggests a large number of potential applications."
Now, before we get too carried away, the paper also points out that this technology isn't quite ready to compose symphonies on its own. It still needs some help – like "latent codes" or metadata – to guide the creative process. It's like giving the AI a starting point or a set of rules to follow.
This research is significant because it pushes the boundaries of what's possible with AI-generated audio. It demonstrates that Transformers, with their powerful attention mechanisms, can be a game-changer in waveform synthesis. It's still early days, but the potential is huge!
But here are some things I'm wondering about:
If this system is so good at predicting sounds, could it be used to remove unwanted noise from audio recordings?
The paper mentions needing "latent codes" to generate meaningful music. What are some creative ways to generate those codes automatically, so the AI can be more independent?
How far away are we from AI that can understand and generate complex musical forms, like sonatas or concertos?
What do you think, PaperLedge crew? Let me know your thoughts in the comments!Credit to Paper authors: Prateek Verma, Chris Chafe

Hey PaperLedge crew, Ernis here, ready to dive into another fascinating piece of research! Today, we're heading into the cosmos to explore something called cosmic-ray feedback and its role in shaping entire galaxies. Buckle up, because this is going to be an exciting ride!
So, what are cosmic rays? Think of them as super-fast, high-energy particles zooming through space. Now, these aren't your everyday sunbeams; they're more like intergalactic bullets accelerated by powerful events like supernova explosions – the death throes of massive stars. These cosmic rays, as they travel, interact with the gas and dust that fill the space between stars, what we call the interstellar medium (ISM).
The big question is: how do these cosmic rays influence the formation and evolution of galaxies? Well, imagine a galaxy as a giant construction site. Cosmic rays act like tiny but mighty construction workers, pushing and pulling on the materials – the gas and dust – that make up the galaxy. This pushing and pulling is what we call cosmic-ray feedback. It’s like they're regulating the whole building process!
Now, here's where things get a bit tricky. The effectiveness of this cosmic-ray feedback depends on how quickly these particles can travel through the ISM. It's like trying to navigate a crowded city: sometimes you can zip through, and other times you're stuck in traffic. In the ISM, this "traffic" is determined by things called plasma instabilities and wave damping processes. Think of these as speed bumps and detours in the cosmic-ray's journey.
This particular study used a sophisticated computer simulation called Arepo, along with a framework called Crisp, to model how cosmic rays move through different phases of the ISM. Imagine the ISM as a layered cake, with cold, dense layers and warm, diffuse layers. The researchers found that the speed at which cosmic rays travel, their effective diffusion coefficient, is influenced by the type of "speed bumps" they encounter in each layer.
In cold, dense regions, a process called ion-neutral damping acts like a super sticky molasses, slowing the cosmic rays down. But in warm, diffuse regions, a different process called non-linear Landau damping is weaker, allowing cosmic rays to zoom through more easily.
The researchers discovered something really interesting: even though the intrinsic diffusion coefficient, which is the baseline measure of cosmic ray movement, can vary wildly, the effective diffusion coefficient, the actual speed at which the cosmic rays propagate, tends to hover around a specific range: 1028 to 1029 cm2/s. That’s like saying, even though the speed limit on different roads varies, the average speed of cars traveling across a city stays roughly the same!
“Overall, CR transport speeds increase systematically with gas density.”
They also found that when they only accounted for Landau damping in their simulations, the transport rates were significantly slower than when they included both Landau and ion-neutral damping. This highlights the importance of considering all these factors when modeling cosmic-ray feedback.
So, why does all this matter? Well, understanding cosmic-ray feedback is crucial for understanding how galaxies form and evolve. For astrophysicists, this research provides valuable insights into the complex interplay between cosmic rays and the ISM, helping them refine their models of galaxy formation. For anyone interested in the origins of the universe, this study sheds light on the fundamental processes that shape the cosmos.
Ultimately, this research suggests that cosmic rays, despite facing various obstacles, manage to traverse through different ISM phases at speeds only a few times faster than something called the Alfv\'en speed. This gives us a clearer picture of how these energetic particles contribute to the delicate balance within galaxies.
Here are a couple of thoughts that popped into my head while reading this paper:
If cosmic-ray transport speed is dependent on gas density, how does this feedback loop affect the distribution of star formation within a galaxy?
How might the different metallicities (the abundance of elements heavier than hydrogen and helium) of galaxies impact these damping processes and ultimately alter CR transport speed?
That's all for this episode, folks! I hope you enjoyed this cosmic journey. Until next time, keep exploring!Credit to Paper authors: Timon Thomas, Christoph Pfrommer, Rüdiger Pakmor, Rouven Lemmerz, Mohamad Shalaby

Hey PaperLedge learning crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about AI, specifically how it "sees" and "understands" the world. But here's the thing: a lot of AI is trained on data that's heavily skewed towards Western cultures. What happens when we ask it to understand, say, Egyptian culture?
Well, a group of researchers tackled this head-on. They noticed a big gap: there just aren't enough good, multimodal datasets – meaning datasets with both images and text – that accurately represent diverse cultures, especially from the Middle East and Africa. Think of it like this: imagine trying to learn about a country only by looking at tourist brochures. You'd miss so much of the real, lived experience!
So, what did they do? They created EgMM-Corpus, a brand new dataset specifically focused on Egyptian culture. It's like building a digital museum, filled with over 3,000 images covering everything from famous landmarks like the pyramids, to delicious Egyptian food like Koshari, and even traditional folklore and stories.
Landmarks: Think stunning photos of the Sphinx or the Karnak Temple.
Food: Mouth-watering images of Molokhia and other culinary delights.
Folklore: Visual representations of traditional stories and cultural practices.
The cool part is that each image and accompanying description was carefully checked by humans to make sure it was culturally authentic and that the image and text matched up perfectly. They wanted to make sure the AI was learning the right things!
Why is this important? Well, imagine an AI trying to identify a picture of Ful Medames (a popular Egyptian breakfast dish). If it's only been trained on Western food images, it might completely misidentify it! This highlights a real problem: cultural bias in AI.
"These results underscore the existing cultural bias in large-scale vision-language models and demonstrate the importance of EgMM-Corpus as a benchmark for developing culturally aware models."
To really drive this point home, the researchers tested a popular AI model called CLIP on their new Egyptian dataset. CLIP is designed to connect images and text. The results? It only got things right about 21% of the time for the top guess, and about 36% of the time within its top 5 guesses. That's not great! It shows that these models, trained on mostly Western data, struggle to understand Egyptian culture.
EgMM-Corpus is like a much-needed cultural infusion for AI. It gives researchers a way to test and improve AI models, making them more globally aware and less biased. It’s a crucial step towards building AI that truly reflects the diversity of our world.
So, as we wrap up, here are a few things to ponder:
How can we encourage the creation of more culturally diverse datasets for AI in other underrepresented regions?
What are the potential consequences of using culturally biased AI in real-world applications, like education or tourism?
Beyond image and text, what other types of data (like audio or video) could be included to further enhance cultural understanding in AI models?
Thanks for tuning in, learning crew! Until next time, keep exploring!Credit to Paper authors: Mohamed Gamil, Abdelrahman Elsayed, Abdelrahman Lila, Ahmed Gad, Hesham Abdelgawad, Mohamed Aref, Ahmed Fares

Hey PaperLedge learning crew, Ernis here, ready to dive into some seriously cool research coming out of Meta's Codec Avatars Lab. Get ready to have your mind bent a little because we're talking about Embody 3D, a massive dataset that's all about how we move and interact in the real world.
Imagine trying to teach a computer how to understand human behavior. It's a tough task, right? You can't just show it a few pictures and expect it to get the nuances of a conversation or the subtle differences between a friendly wave and an urgent signal. That's where Embody 3D comes in.
Think of it like this: If you were learning to bake, you wouldn't just look at a picture of a cake. You'd want a detailed recipe with step-by-step instructions, right? Well, Embody 3D is like a super-detailed recipe book for human motion. It's a collection of 500 hours of 3D motion data from almost 440 people.
So, what exactly does this data include? Well, it's not just people walking around. We're talking:
Specific movements: People following instructions, like "raise your left arm" or "jump three times."
Hand gestures: All sorts of hand movements that are crucial for communication.
Locomotion: Different ways of moving around, like walking, running, and even dancing!
Multi-person interactions: This is where it gets really interesting. The dataset captures people having discussions, conversations filled with different emotions, working together on tasks, and even just hanging out in a simulated apartment. It's like a virtual "Big Brother" house, but for science!
And the level of detail is insane! They've tracked not just body movements, but also hand movements and even the shape of people's bodies. Plus, they've included text descriptions of what's happening and separate audio recordings for each person. It's a treasure trove of information for researchers.
Now, you might be thinking, "Okay, Ernis, that sounds impressive, but why should I care?" Well, this research has the potential to impact a lot of areas. For the tech enthusiasts, this could lead to more realistic and responsive avatars in the metaverse. Imagine having a virtual version of yourself that moves and reacts just like you do! For the gamers, think about more immersive and believable characters in your favorite games. And for anyone interested in AI, this dataset could help create smarter and more human-like artificial intelligence.
As the authors themselves put it, this dataset allows for "unprecedented insights into complex human behavior and social interaction."
But it also raises some interesting questions. For example:
How will researchers use this data to ensure that AI systems aren't biased or discriminatory in how they interpret human behavior?
Could this level of detailed motion capture be used to create even more realistic deepfakes, and what are the ethical implications of that?
These are just some of the things that come to mind when I think about the potential of Embody 3D. It's a fascinating dataset with the power to shape the future of AI and virtual reality. What do you think learning crew? What applications are you most excited about?Credit to Paper authors: Claire McLean, Makenzie Meendering, Tristan Swartz, Orri Gabbay, Alexandra Olsen, Rachel Jacobs, Nicholas Rosen, Philippe de Bree, Tony Garcia, Gadsden Merrill, Jake Sandakly, Julia Buffalini, Neham Jain, Steven Krenn, Moneish Kumar, Dejan Markovic, Evonne Ng, Fabian Prada, Andrew Saba, Siwei Zhang, Vasu Agrawal, Tim Godisart, Alexander Richard, Michael Zollhoefer

Alright Learning Crew, Ernis here, ready to dive into some seriously fascinating research! Today, we're talking about AI, but not just any AI – the kind that's starting to help us make big decisions, even moral ones.
Think about it: self-driving cars making split-second choices in accidents, or AI doctors suggesting treatment plans. We're relying on these systems more and more, so it's crucial to make sure their values line up with ours. That's where this paper comes in.
These researchers have created something called MoReBench – think of it as a massive test for AI's moral compass. It's packed with 1,000 tricky moral scenarios, each with a detailed checklist of things a good decision-maker should consider. Imagine a friend asking for advice about a difficult situation – you'd want them to think about all the angles, right? This benchmark does the same for AI.
Now, why moral scenarios? Well, unlike math problems with one right answer, moral dilemmas often have multiple defensible conclusions. It's not about what decision the AI makes, but how it gets there. The researchers are focusing on the AI's reasoning process – the steps it takes to reach a conclusion.
"Unlike math and code problems which often have objectively correct answers, moral dilemmas are an excellent testbed for process-focused evaluation because they allow for multiple defensible conclusions."
So, what does MoReBench actually test? It checks if the AI considers things like:
Moral considerations: Does the AI identify the important ethical factors at play?
Trade-offs: Does it weigh the pros and cons of different options?
Actionable recommendations: Does it offer practical advice that can actually be followed?
And it covers scenarios where AI is both advising humans (like suggesting a course of action) and making decisions autonomously (like a self-driving car reacting to an emergency).
On top of this, they created MoReBench-Theory, a smaller set of 150 examples specifically designed to test if AI can reason using established ethical frameworks. Think of it like checking if the AI is familiar with the big names in moral philosophy, like Kant or Mill.
"MoReBench contains over 23 thousand criteria including identifying moral considerations, weighing trade-offs, and giving actionable recommendations to cover cases on AI advising humans moral decisions as well as making moral decisions autonomously."
Here's the really interesting part: the researchers found that just because an AI is good at math, code, or science, doesn't mean it's good at moral reasoning. In fact, the things that predict performance in those areas don't seem to apply to moral reasoning!
Even more surprisingly, the AI showed biases towards certain moral frameworks. Some models favored utilitarianism (the greatest good for the greatest number), while others leaned towards deontology (following moral rules and duties). This might be a side effect of how these AIs are trained. Kind of like how some people grow up with certain ingrained beliefs, these AIs are developing preferences based on their training data.
This research is super important because it shows us that we can't just assume AI will make ethical decisions on its own. We need to actively test and train them to consider all the relevant factors and avoid biases.
"Our results show that scaling laws and existing benchmarks on math, code, and scientific reasoning tasks fail to predict models' abilities to perform moral reasoning."
So, that's the gist of the paper. It's a deep dive into how we can evaluate and improve AI's moral reasoning abilities. Now, a few questions that popped into my head:
If AI models are showing biases towards specific moral frameworks, how can we ensure they're making decisions that are fair and impartial to everyone?
How can we best teach AI to understand and apply complex moral concepts like empathy, compassion, and justice?
Ultimately, what role should AI play in making moral decisions? Should it be an advisor, a decision-maker, or something else entirely?
Let me know what you think Learning Crew! This is definitely a conversation we need to keep having.Credit to Paper authors: Yu Ying Chiu, Michael S. Lee, Rachel Calcott, Brandon Handoko, Paul de Font-Reaulx, Paula Rodriguez, Chen Bo Calvin Zhang, Ziwen Han, Udari Madhushani Sehwag, Yash Maurya, Christina Q Knight, Harry R. Lloyd, Florence Bacus, Mantas Mazeika, Bing Liu, Yejin Choi, Mitchell L Gordon, Sydney Levine

Alright learning crew, Ernis here, and welcome back to PaperLedge! Today we're diving into a fascinating paper about making our computer code run faster and smarter – automatically!
Now, we all know that writing code can be tricky. Sometimes, even though our code works, it's not the most efficient way to do things. It's like driving to the grocery store – you might get there, but maybe you took a longer route than you needed to. That's where code optimization comes in!
Traditionally, optimizing code has been a manual process, with programmers carefully tweaking things to squeeze out every last bit of performance. But what if we could get computers to do this for us? Well, that's exactly what researchers are exploring, using the power of Large Language Models, or LLMs, those AI brains that can understand and generate text.
Previous attempts at automated code optimization have tried to learn from existing code. Imagine having a giant cookbook of code changes – programmers find similar code snippets to the one they are working on and modify it according to the cookbook. But here's the catch: many ways to optimize code can look completely different on the surface, even if they achieve the same result. Because of that, these cookbook approaches often fail to find the best examples for optimization.
But hold on, here's where the paper we're discussing today comes in with something truly new! These researchers have developed a system called SemOpt, and it tackles this problem head-on. SemOpt is like having a super-smart code detective that uses static program analysis to precisely identify optimizable code segments, retrieve the corresponding optimization strategies, and generate the optimized results.
Think of it like this: imagine you're trying to improve the fuel efficiency of a car. Instead of just looking at similar cars and copying their designs, SemOpt is like having a mechanic who understands exactly how each part of the engine works and can identify precisely which components can be improved and how.
SemOpt has three main parts:

Strategy Library Builder: This part extracts and groups together different ways people have optimized code in the real world. It's like building that code optimization cookbook.


Rule Generator: This part uses LLMs to create rules that tell the system when a particular optimization strategy can be applied. It's like writing instructions for using the cookbook.


Optimizer: This part uses the library and the rules to automatically generate optimized code. It's like having the cookbook read and modify the code all on its own!

So, what did they find? Well, the results are pretty impressive! SemOpt significantly outperformed the existing approaches, in some cases increasing the number of successful optimizations by a factor of 28! And when tested on real-world C/C++ projects, SemOpt improved performance by up to 218%. That's a huge improvement!
Why does this matter? Well, for programmers, this could mean less time spent manually optimizing code and more time focusing on creating new features. For businesses, it could mean faster, more efficient software, which translates to cost savings and improved user experience. And for all of us, it could mean faster, more responsive devices and applications.
"SemOpt demonstrates its effectiveness under different LLMs by increasing the number of successful optimizations by 1.38 to 28 times compared to the baseline."
This research opens up some fascinating questions:

Could SemOpt be adapted to optimize code for different programming languages or different types of applications?


How can we ensure that automated code optimization tools like SemOpt don't introduce unintended bugs or security vulnerabilities?


As LLMs become even more powerful, will automated code optimization eventually replace human programmers altogether?

That's all for today's episode of PaperLedge! I hope you found this discussion of automated code optimization as interesting as I did. Until next time, keep learning and keep exploring!Credit to Paper authors: Yuwei Zhao, Yuan-An Xiao, Qianyu Xiao, Zhao Zhang, Yingfei Xiong

Alright learning crew, Ernis here, ready to dive into another fascinating paper that's got me buzzing. This time, we're tackling the world of Vision-Language Models, or VLMs. Think of them as AI systems that can see and understand the world around them, kinda like a super-smart toddler exploring a new room. They can look at a picture of a cat wearing a hat and not only identify the cat and the hat but also understand the humorous situation.
Now, these VLMs are pretty impressive, thanks to the combination of large language models, which are great at understanding and generating text, and visual inputs, which allow them to "see." But here's the snag: sometimes, they don't really look at the picture! They might rely too much on what they already know about cats and hats (their "linguistic priors") or take textual shortcuts instead of actually processing the visual information. It's like guessing the ending of a movie without watching it – you might be right, but you missed the whole experience.
So, how do we teach these AI systems to truly see and understand what they're looking at? That's where reinforcement learning, or RL, comes in. Think of RL like training a dog: you give it rewards when it does something right. But with VLMs, finding a good "reward system" has been tough. We don't want to rely on human feedback all the time (that's not scalable), and we definitely don't want to trust another AI to judge its performance (that can be unreliable!).
This is where the researchers behind this paper stepped in with a brilliant idea: SSL4RL. That stands for Self-Supervised Learning for Reinforcement Learning. Basically, they're using self-supervised learning (SSL) tasks to create automatic and verifiable rewards for RL-based fine-tuning. I know, it's a mouthful, but stick with me!
Imagine you're teaching a child about shapes. You could give them a bunch of scrambled puzzles. The act of completing the puzzle (predicting the correct shape) is its own reward! That's similar to what SSL does. The researchers reformulate SSL objectives – things like predicting the rotation of an image or reconstructing a masked part of an image – into reward signals. If the VLM correctly predicts the rotation, it gets a "reward." If it reconstructs the masked part accurately, another "reward!"
This is a clever way to provide dense, automatic feedback to guide the VLM towards better visual understanding, without relying on humans or other potentially biased AI systems.
Think of it like this: instead of someone telling the VLM "good job" when it recognizes a cat, the VLM gets a reward for correctly solving a visual puzzle related to the cat image, proving it actually processed the visual information.
The results? The researchers found that SSL4RL significantly improved the performance of VLMs on both vision-centric and vision-language reasoning tasks. They also identified key factors that influence the effectiveness of SSL4RL, like the difficulty of the SSL task and how well it aligns with the target domain. The cool part is that they were able to generalize this approach to graph learning, which means it could be applied to many other domains!
Why does this matter? Well, for one, it means we can build more reliable and trustworthy AI systems that truly understand the world around them. This has implications for everything from self-driving cars to medical diagnosis. It also provides a way to improve the model without human interaction. This allows for continued learning and improvement of these systems.
Here are a couple of things that popped into my head while reading this:
How might we design SSL tasks that are specifically tailored to address the biases we see in VLMs, ensuring they don't rely on shortcuts?
Could this approach be used to help VLMs understand abstract concepts or nuanced emotions in images, going beyond simple object recognition?
Pretty cool stuff, right? It's exciting to see researchers finding innovative ways to teach AI to see and understand the world more like we do.Credit to Paper authors: Xiaojun Guo, Runyu Zhou, Yifei Wang, Qi Zhang, Chenheng Zhang, Stefanie Jegelka, Xiaohan Wang, Jiajun Chai, Guojun Yin, Wei Lin, Yisen Wang

Alright learning crew, Ernis here, ready to dive into some seriously cool research! Today, we're unpacking a paper that's trying to solve a HUGE problem in the world of AI: How do we get computers to understand and create things using all sorts of information – not just text, but also images, audio, and video?
Think about it. You can describe a picture in words, or you can draw a picture instead of writing words. A computer needs to be able to do both, and understand how they relate. That's where this paper comes in.
The researchers have come up with something called Latent Language Modeling (LatentLM). The core idea is to create a universal translator of sorts, a single system that can handle both discrete data, like words and code, and continuous data, like images, audio, and video. It's like teaching a computer to speak all the languages of the world, not just one!
So how does it work? Well, imagine you want to describe a photo to someone who doesn't speak your language. You might draw a quick sketch instead. LatentLM does something similar. It uses a clever technique called a Variational Autoencoder (VAE) to turn complex data like images into a simpler, more manageable form – a "latent vector." Think of it like creating a simplified blueprint of the image. This blueprint captures the essence of the image without all the messy details.
But here's the tricky part: How do you generate these blueprints in the first place? That's where something called next-token diffusion comes in. Imagine you're painting a picture one brushstroke at a time, each stroke building on the previous one. Next-token diffusion is kind of like that, but for creating these latent vectors. It starts with nothing and gradually adds information, step by step, until you have a complete blueprint.
Now, VAEs can sometimes run into a problem called variance collapse. It's like the blueprint becomes too simple and loses important details. The researchers came up with a clever fix called $\sigma$-VAE to prevent this from happening, ensuring that the blueprint captures all the important information.
Okay, so what does all this mean in the real world? The researchers tested LatentLM on a bunch of different tasks, and the results were pretty impressive:
Image Generation: LatentLM was able to create images that were just as good, if not better, than other cutting-edge AI models, and it could handle much larger images.
Multimodal Language Models: When they added LatentLM to existing language models, it made them much better at understanding and generating all sorts of data, not just text.
Text-to-Speech Synthesis: LatentLM was able to create realistic-sounding speech from text, and it did it much faster than other models. It even did a better job of capturing the speaker's unique voice.
"The results establish LatentLM as a highly effective and scalable approach to advance large multimodal models."
In essence, LatentLM is a big step towards creating AI that can truly understand and interact with the world around us in a more natural and intuitive way.
So, why should you care about all this? Well, if you're a:
Developer: This could unlock new possibilities for creating AI-powered applications that can understand and generate all sorts of data.
Artist: Imagine using AI to create new and innovative art forms that blend images, audio, and text in unexpected ways.
Educator: This could lead to new and engaging ways to teach complex concepts using multimodal learning experiences.
Anyone interested in the future of AI: This research is pushing the boundaries of what's possible and bringing us closer to a world where AI can truly understand and interact with us in a more meaningful way.
This research opens up some exciting possibilities. Here are a couple of questions that popped into my head:
Could LatentLM be used to create AI assistants that can understand our emotions and respond in a more empathetic way?
What are the ethical implications of creating AI that can generate realistic-sounding speech and images? How do we prevent it from being used for malicious purposes?
That's all for today, learning crew! I hope this gave you a good overview of LatentLM and why it matters. Until next time, keep learning and keep questioning!Credit to Paper authors: Yutao Sun, Hangbo Bao, Wenhui Wang, Zhiliang Peng, Li Dong, Shaohan Huang, Jianyong Wang, Furu Wei