Brokoslaw Laschowski’s Post

[Today's Article] MIT CSAIL unveils a groundbreaking method, "Diffusion Forcing," that combines next-token prediction with video diffusion to enhance computer vision and robotics. This innovative technique sorts through noisy data, predicting the next steps and generating high-quality video, thereby boosting the capability of robots and AI agents in complex environments. By training neural networks to remove noise while predicting future actions, this method aids robots in performing precise tasks, such as rearranging objects with high distraction levels. It supports AI in generating stable video sequences from game environments. Perfect for both real-world applications and digital simulations, "Diffusion Forcing" could revolutionize how AI interprets and interacts with the world around it. More detail in this link : https://lnkd.in/eQVszFbH 🤖 #AI #Robotics #Innovation #MITCSAIL #DiffusionForcing #ComputerVision

Google DeepMind unveils virtual rat with artificial brain Breakthrough sees computerized creature accurately mimic movements of a real rodent A breakthrough in artificial intelligence (AI) research by Google DeepMind, a subsidiary of Alphabet Inc. Google DeepMind's researchers have created an artificial brain that can learn and adapt like a rat's brain. The AI system, called "RatBrain," is designed to mimic the neural connections and behavior of a rat's brain, allowing it to learn and adapt in a more human-like way. The researchers used a combination of machine learning algorithms and neural networks to create #RatBrain. The system is capable of learning and adapting to new situations, much like a rat would in a maze. The implications of this technology are significant, as it could potentially be used to improve AI systems in areas such as: 1. Robotics: #RatBrain could be used to create more intelligent and adaptable robots that can learn and adapt to new situations. 2. Healthcare: The technology could be used to develop more accurate and personalized treatments for neurological disorders, such as Alzheimer's disease. 3. Autonomous vehicles: #RatBrain could be used to improve the decision-making abilities of autonomous vehicles, allowing them to adapt to new situations and environments. #ai #artificialintelligence #deepmind #RatBrain https://lnkd.in/epsjBVsm

Researchers have made a groundbreaking advancement in imitating human motion by combining central pattern generators (CPGs) and deep reinforcement learning (DRL). This new method not only replicates walking and running movements but also generates motions for frequencies where data is lacking, facilitates smooth transitions between walking and running, and adapts to unstable surfaces. By merging imitation learning with CPGs and reflex neural networks, the adaptive imitated CPG (AI-CPG) method achieves unparalleled adaptability and stability in motion generation. This innovative approach sets a new standard for generating human-like movement in robotics, with vast potential applications across industries. #AI #Robotics #motiongeneration #drl #cpgs https://lnkd.in/gHusvJ4N

This article had me hooked from start to finish! It's a long but very fascinating read for anyone interested in tech and literature. This article not only discusses about the world of robotics but also the evolution of the autonomous learning methodologies powering today’s model progress. My day to day revolves around the exciting world of AI in document processing, content management and agentic workflow. While reading the article, I was particularly excited by the discussion around RL, RLHF and imitation learning. And in every paragraph my braincells were tinkling with so many parallel opportunities we could adapt to our world of document processing, information management and intelligence automation. Especially, there’s so much potential to use RL, RLHF, Imitation learning to help IDP models self-learn, improve LLM prompts, boost agentic systems' autonomy and refine HITL agents with auto-correction for better performance. [Enjoy reading or listening 👂 ](https://lnkd.in/gsYgKH-v).

Harvard Neuroscientists and Google DeepMind Create Artificial Brain in Virtual Rat "Scientists create realistic virtual rodent with digital neural network to study how brain controls complex, coordinated movement." https://lnkd.in/dNyJcsGH #MVYL #AI #Robotics #Harvard #VirtualBrain #DeepMind

Generative AI that imitates human motion: Walking and running is notoriously difficult to recreate in robots. Now, a group of researchers has overcome some of these challenges by creating an innovative method that employs central pattern generators -- neural circuits located in the spinal cord that generate rhythmic patterns of muscle activity -- with deep reinforcement learning. The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adapting to environments with unstable surfaces. #ArtificialIntelligence #MachineLearning #DataScience

Researchers have made a groundbreaking advancement in imitating human motion by combining central pattern generators (CPGs) and deep reinforcement learning (DRL). This new method not only replicates walking and running movements but also generates motions for frequencies where data is lacking, facilitates smooth transitions between walking and running, and adapts to unstable surfaces. By merging imitation learning with CPGs and reflex neural networks, the adaptive imitated CPG (AI-CPG) method achieves unparalleled adaptability and stability in motion generation. This innovative approach sets a new standard for generating human-like movement in robotics, with vast potential applications across industries. #AI #Robotics #motiongeneration #drl #cpgs

Can We Replace Turing Machines with RNN-Based Architectures? The current architecture of computers – built on Turing principles – is incredibly powerful. 💪 But what if we could do more? 🤯 Imagine a system where computers don’t just follow rules, they learn and adapt over time. 🧠 This is the promise of RNN-based architectures. 💡 Here’s the catch: while RNNs are theoretically capable of performing any computation a traditional computer can, they do so in a fundamentally different way. Instead of precise, step-by-step execution, RNNs process information over time, learning from sequences – just like how humans understand and react to the world. 🌍 Could we shift from instruction-based computing to neural network-driven architectures? Picture a future where instead of CPUs processing logic, RNNs are driving real-time learning on hardware optimized for it. 🤖💻 Of course, this vision comes with challenges. We’d need to rethink hardware 🛠️ and overcome limitations like resource efficiency and training overhead. But the potential is huge. 🚀 Imagine machines that evolve, adapt, and continuously improve their understanding of the data they encounter. 💥 In my final post, we’ll dive into the advantages and trade-offs of this new architecture. What will the future of computing look like? 🔮 #AI #RNNArchitecture #ComputingRevolution #NeuralComputers #Innovation #TechTransformation 🌐

Incorporating artificial intelligence and machine learning into robotics presents both opportunities and challenges. One of the main hurdles is the perception challenge, where technical errors related to perception, such as misidentifying objects due to poor lighting or reflective surfaces, can impact the accuracy of scanners. AI and ML, particularly convolutional neural networks, play a crucial role in addressing these challenges: - Perception Tasks: AI algorithms enhance the ability of robots to perceive and interpret their environment accurately. - Identifying Objects: Advanced neural networks improve the identification and classification of objects, even under challenging conditions. By leveraging AI and ML, we can improve the accuracy and reliability of robotic systems in various applications, ensuring they perform well in real-world scenarios. This progress in perception tasks will be essential for the broader adoption and success of robotics across multiple industries. #robotics #automation #logistics #AI #smartrobotics

Generative AI that imitates human motion | ScienceDaily: A group of international researchers has developed an innovative method for robots to imitate human walking and running motions. By combining central pattern generators (CPGs) and deep reinforcement learning (DRL), the team has enabled robots to generate movements for frequencies where motion data is absent, smoothly transition from walking to running, and adapt to unstable surfaces. The breakthrough, outlined in the journal IEEE Robotics and Automation Letters, overcomes limitations of existing approaches by utilizing imitation learning to train a CPG-like controller and applying deep learning to a reflex neural network that supports the CPGs. This method, known as the adaptive imitated CPG (AI-CPG) method, represents a significant advancement in generative AI technologies for robot control and offers potential applications across various industries.