Four years ago, UC Santa Cruz’s Jason Eshraghian developed a Python library that combines neuroscience with artificial intelligence to create spiking neural networks, a machine learning method that takes inspiration from the brain’s ability to efficiently process data. Now, his open source code library, called “snnTorch,” has surpassed 100,000 downloads and is used in a wide variety of projects, from NASA satellite tracking efforts to semiconductor companies optimizing chips for AI.Four years ago, UC Santa Cruz’s Jason Eshraghian developed a Python library that combines neuroscience with artificial intelligence to create spiking neural networks, a machine learning method that takes inspiration from the brain’s ability to efficiently process data. Now, his open source code library, called “snnTorch,” has surpassed 100,000 downloads and is used in a wide variety of projects, from NASA satellite tracking efforts to semiconductor companies optimizing chips for AI.[#item_full_content]

A team of AI researchers from EquiLibre Technologies, Sony AI, Amii and Midjourney, working with Google’s DeepMind project, has developed an AI system called Student of Games (SoG) that is capable of both beating humans at a variety of games and learning to play new ones. In their paper published in the journal Science Advances, the group describes the new system and its capabilities.A team of AI researchers from EquiLibre Technologies, Sony AI, Amii and Midjourney, working with Google’s DeepMind project, has developed an AI system called Student of Games (SoG) that is capable of both beating humans at a variety of games and learning to play new ones. In their paper published in the journal Science Advances, the group describes the new system and its capabilities.[#item_full_content]

A study aimed at improving the accuracy and reliability of grid electricity meters, particularly under challenging on-site conditions is published in the International Journal of Information and Communication Technology. The research offers practical suggestions for assessing and optimizing measurement performance.A study aimed at improving the accuracy and reliability of grid electricity meters, particularly under challenging on-site conditions is published in the International Journal of Information and Communication Technology. The research offers practical suggestions for assessing and optimizing measurement performance.[#item_full_content]

A team of researchers led by Assoc Prof Lu Shijian from the NTU School of Computer Science and Engineering has developed a computer program that creates realistic videos that reflect the facial expressions and head movements of the person speaking, only requiring an audio clip and a face photo.A team of researchers led by Assoc Prof Lu Shijian from the NTU School of Computer Science and Engineering has developed a computer program that creates realistic videos that reflect the facial expressions and head movements of the person speaking, only requiring an audio clip and a face photo.[#item_full_content]

In the background of image recognition software that can ID our friends on social media and wildflowers in our yard are neural networks, a type of artificial intelligence inspired by how own our brains process data.In the background of image recognition software that can ID our friends on social media and wildflowers in our yard are neural networks, a type of artificial intelligence inspired by how own our brains process data.[#item_full_content]

New research suggests reframing how we talk about and interact with large language models may help us adapt to their intelligence.New research suggests reframing how we talk about and interact with large language models may help us adapt to their intelligence.[#item_full_content]

Personalized deep-learning models can enable artificial intelligence chatbots that adapt to understand a user’s accent or smart keyboards that continuously update to better predict the next word based on someone’s typing history. This customization requires constant fine-tuning of a machine-learning model with new data.Personalized deep-learning models can enable artificial intelligence chatbots that adapt to understand a user’s accent or smart keyboards that continuously update to better predict the next word based on someone’s typing history. This customization requires constant fine-tuning of a machine-learning model with new data.[#item_full_content]

Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”Google DeepMind researchers this week unveiled a highly accurate AI-based weather prediction model they say marks “a turning point in weather forecasting.”[#item_full_content]

Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.Several years ago, MIT anthropologist Héctor Beltrán ’07 attended an event in Mexico billed as the first all-women’s hackathon in Latin America. But the programmers were not the only women there. When the time came for the hackathon pitches, a large number of family members arrived to watch.[#item_full_content]

Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.Unsupervised domain adaptation has garnered a great amount of attention and research in past decades. Among all the deep-based methods, the autoencoder-based approach has achieved sound performance for its fast convergence speed and a no-label requirement. The existing methods of autoencoders just serially connect the features generated by different autoencoders, which poses challenges for discriminative representation learning and which fails to find the real cross-domain features.[#item_full_content]