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An Overview of Three Prominent Systems for Graph Neural Network-based Motion Planning

Marktechpost

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. Experiments: 2D Maze to 14D Dual KUKA Robotic Arm: GraphMP significantly improved path quality and planning speed over existing planners.

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Revolutionizing Robotic Surgery with Neural Networks: Overcoming Catastrophic Forgetting through Privacy-Preserving Continual Learning in Semantic Segmentation

Marktechpost

Deep Neural Networks (DNNs) excel in enhancing surgical precision through semantic segmentation and accurately identifying robotic instruments and tissues. This limitation underscores the need for innovative solutions to ensure continual learning and data management in robot-assisted surgery.

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This Paper Explores Efficient Predictive Control with Sparsified Deep Neural Networks

Marktechpost

Robotics is currently exploring how to enhance complex control tasks, such as manipulating objects or handling deformable materials. This research niche is crucial as it promises to bridge the gap between current robotic capabilities and the nuanced dexterity found in human actions. If you like our work, you will love our newsletter.

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NVIDIA and Alphabet’s Intrinsic Put Next-Gen Robotics Within Grasp

NVIDIA

Intrinsic, a software and AI robotics company at Alphabet, has integrated NVIDIA AI and Isaac platform technologies to advance the complex field of autonomous robotic manipulation. Foundation models are based on a transformer deep learning architecture that allows a neural network to learn by tracking relationships in data.

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New Neural Model Enables AI-to-AI Linguistic Communication

Unite.AI

This development opens up unprecedented possibilities in AI, particularly in the realm of human-AI interaction and robotics, where effective communication is crucial. Central to this advancement in NLP is the development of artificial neural networks, which draw inspiration from the biological neurons in the human brain.

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How MIT’s Liquid Neural Networks can solve AI problems from robotics to self-driving cars

Flipboard

In the current artificial intelligence (AI) landscape, the buzz around large language models (LLMs) has led to a race toward creating increasingly larger neural networks. However, not every application can support the computational and memory demands of very large deep learning models.

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This 200-Page AI Report Covers Vector Retrieval: Unveiling the Secrets of Deep Learning and Neural Networks in Multimodal Data Management

Marktechpost

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. The reach of these transformations extends beyond the confines of computer science, influencing diverse fields such as robotics, biology, and chemistry, showcasing the pervasive impact of AI across various disciplines.