đ Team BrainGraphers, Deep Learningđ„ Team members: Mohamamd Mohammadi, Mohammadhosein Shakiba, Rana RokniđĄMentor: Nima Dehghani đ https://impact-scholarsâŠ
Category: robotics/AI – Page 284
Germany and Europe lead digital innovation and AI with collaborative health data use at continental level
Collaborative use of population-level health data and artificial intelligence is essential for achieving precision health through a learning health system. Two groundbreaking initiativesâthe European Health Data Space (EHDS), covering 449 million EU citizens, and Germanyâs forthcoming Health Data Lab, providing access to data from 75 million insured individuals (90% of the countryâs population)âoffer unprecedented opportunities to advance digital health innovation and research with global impact.
Triple equivalence for the emergence of biological intelligence
Characterizing the intelligence of biological organisms is challenging yet crucial. This paper demonstrates the capacity of canonical neural networks to autonomously generate diverse intelligent algorithms by leveraging an equivalence between concepts from three areas of cognitive computation: neural network-based dynamical systems, statistical inference, and Turing machines.
Integrating physical units into high-performance AI-driven scientific computing
Existing numerical computing libraries lack native support for physical units, limiting their application in rigorous scientific computing. Here, the authors developed SAIUnit, which integrates physical units, and unit-aware mathematical functions and transformations into numerical computing libraries for artificial intelligence-driven scientific computing.
When Machines Dream: AI Designs Strange New Tools to Listen to the Cosmos
Einstein imagined gravitational waves over a hundred years ago, but it wasnât until 2016 that technology finally caught up. Now, researchers are pushing the boundaries again â this time with the help of an AI named Urania. Developed by Dr. Mario Krenn and his team, Urania has designed a series of
Famed AI Researcher, Tamay Besiroglu, Launches Controversial Startup To Replace All Human Workers
âCompletely automating labor could generate vast abundance, much higher standards of living, and new goods and services that we canât even imagine today.â
Photonic computing needs more nonlinearity: Acoustics can help
Neural networks are one typical structure on which artificial intelligence can be based. The term âneuralâ describes their learning ability, which to some extent mimics the functioning of neurons in our brains. To be able to work, several key ingredients are required: one of them is an activation function which introduces nonlinearity into the structure.
A photonic activation function has important advantages for the implementation of optical neural networks based on light propagation. Researchers in the Stiller Research Group at the MPL and LUH in collaboration with MIT have now experimentally shown an all-optically controlled activation function based on traveling sound waves.
It is suitable for a wide range of optical neural network approaches and allows operation in the so-called synthetic frequency dimension. The work is published in the journal Nanophotonics.