🔥 Trending AI Research: Let’s learn something new from the trending papers.

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🔥Trending AI Research

1️⃣ LightOn AI Releases Alfred-40B-0723: A New Open-Source Language Model (LLM) Based on Falcon-40B [Project]

Alfred-40B-0723 is powered by Falcon RLHF, the Generative AI Copilot, which represents a significant advancement in the field. Leveraging Reinforcement Learning from Human Feedback, Alfred has been extensively trained on a mix of public datasets and highly curated data annotated by LightOn’s dedicated team. This meticulous training process ensures that Alfred is not only robust but also capable of understanding and delivering high-quality outputs for complex tasks.

2️⃣ Sorbonne University Researchers Introduce UnIVAL: A Unified AI Model for Image, Video, Audio, and Language Tasks [Paper]

The researchers at Sorbonne University began by developing general-purpose models that can address any problem. They introduce UnIVAL, a method that avoids relying on any single modality. UnIVAL integrates two modalities and all four (text, pictures, video, and audio). UnIVAL is the first model to solve picture, video, and audio language challenges with a unified architecture, vocabulary, input/output format, and training aim without requiring massive amounts of data for training or massive model size. The 0.25 billion parameter model delivers performance on par with prior art tailored to a certain modality. The researchers obtained new SoTA on several jobs with similarly sized models.

3️⃣ Meet BeLFusion: A Behavioral Latent Space Approach for Realistic and Diverse Stochastic Human Motion Prediction Using Latent Diffusion  [Paper]

To address the limitations of existing Stochastic HMP methods, the University of Barcelona and Computer Vision Center researchers propose BeLFusion. This novel approach introduces a behavioral latent space to generate realistic and diverse human motion sequences. The main objective of BeLFusion is to disentangle behavior from motion, allowing smoother transitions between observed and predicted poses. This is achieved through a Behavioral VAE consisting of a Behavior Encoder, Behavior Coupler, Context Encoder, and Auxiliary Decoder.

4️⃣ Google DeepMind Researchers Introduce RT-2: A Novel Vision-Language-Action (VLA) Model that Learns from both Web and Robotics Data and Turns it into Action [Paper]

Google Deepmind’s research aims to improve generalization and enable emergent semantic reasoning by directly incorporating vision-language models trained on Internet-scale data into end-to-end robotic control. With the help of web-based language and vision-language data, we aim to make a single, comprehensively trained model to learn to link robot observations to actions. They propose fine-tuning state-of-the-art vision-language models together using data from robot trajectories and large-scale visual question-answering exercises conducted over the Internet. In contrast to other methods, they propose a straightforward, all-purpose recipe: express robotic actions as text tokens and incorporate them directly into the model’s training set as natural language tokens would. Researchers study vision-language-action models (VLA), and RT-2 instantiates one such model. Through rigorous testing (6k assessment trials), they could ascertain that RT-2 acquired various emergent skills through Internet-scale training and that the technique led to performant robotic policies.

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