If self-driving vehicles can navigate this complex road challenge safely, the learnings could help advance the safety of automated driving in urban scenarios.
2 minutes read
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The moonshot of many roboticists is cooking up the proper hardware and software combination so that a machine can learn “generalist” policies (the rules and strategies that guide robot behavior) that work everywhere, under all conditions.
BrainChip Audio Denoising solution uses Temporal Event Neural Networks (TENNs) to support better audio quality by removing unwanted voices.
Article #2 of Engineering the Quantum Future Series: Recent breakthroughs in quantum computing made substantial strides, driving closer to practical applications that could revolutionize industries by solving complex computational problems more efficiently.
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
Introducing the Engineering the Quantum Future Series: Quantum computing redefines our approach to complex challenges, expanding the scope of what's possible across multiple disciplines.
Even if you’re not very familiar with deep learning, you’ve probably heard about it and how it can, among other things, help automate the driving experience, increase manufacturing efficiency and change the consumer shopping experience.
Integrating AI and Digital Tools Beyond Digital Transformation.
6 minutes read
CUDA Cores and Tensor Cores are specialized units within NVIDIA GPUs; the former are designed for a wide range of general GPU tasks, while the latter are specifically optimized to accelerate AI and deep learning through efficient matrix operations.
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
Explore the future of connected communities in intelligent cities and underserved regions, highlighting the role of technologies like 5G, AI, and blockchain in enhancing connectivity and promoting inclusive development.
LLMs and GenAI lower barriers to accessing intelligence, shifting AI systems to be built and controlled by domain experts. In the future, organizations can use large scale LLM models instead of building custom machine learning models, impacting sectors like manufacturing and supply chain partners.
Researchers at EPFL have made a breakthrough in understanding how neural network-based generative models perform against traditional data sampling techniques in complex systems, unveiling both challenges and opportunities for AI's future in data generation.
3 minutes read
In a broad sense, "machine vision" refers to the utilization of visual information and optical information using digital technologies.
6 minutes read
In this episode, we discuss how 2 Carnegie Mellon University graduate students developed an AI system capable of giving you feedback on your interview performance in real time!
14 minutes read