Large language models
Recent articles
How artificial agents can help us understand social recognition
Neuroscience is chasing the complexity of social behavior, yet we have not answered the simplest question in the chain: How does a brain know “who is who”? Emerging multi-agent artificial intelligence may help accelerate our understanding of this fundamental computation.
How artificial agents can help us understand social recognition
Neuroscience is chasing the complexity of social behavior, yet we have not answered the simplest question in the chain: How does a brain know “who is who”? Emerging multi-agent artificial intelligence may help accelerate our understanding of this fundamental computation.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.
‘Digital humans’ in a virtual world
By combining large language models with modular cognitive control architecture, Robert Yang and his collaborators have built agents that are capable of grounded reasoning at a linguistic level. Striking collective behaviors have emerged.
‘Digital humans’ in a virtual world
By combining large language models with modular cognitive control architecture, Robert Yang and his collaborators have built agents that are capable of grounded reasoning at a linguistic level. Striking collective behaviors have emerged.
Are brains and AI converging?—an excerpt from ‘ChatGPT and the Future of AI: The Deep Language Revolution’
In his new book, to be published next week, computational neuroscience pioneer Terrence Sejnowski tackles debates about AI’s capacity to mirror cognitive processes.
Are brains and AI converging?—an excerpt from ‘ChatGPT and the Future of AI: The Deep Language Revolution’
In his new book, to be published next week, computational neuroscience pioneer Terrence Sejnowski tackles debates about AI’s capacity to mirror cognitive processes.
Explore more from The Transmitter
When we sleep, our brain is filled with spontaneous neural activity. What is it doing?
Daniel Levenstein explains how internally generated brain activity during sleep shapes our cognition, and why NeuroAI is such a powerful approach to understanding the brain.
When we sleep, our brain is filled with spontaneous neural activity. What is it doing?
Daniel Levenstein explains how internally generated brain activity during sleep shapes our cognition, and why NeuroAI is such a powerful approach to understanding the brain.
Unconfounding genetic and environmental factors, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 31 August.
Unconfounding genetic and environmental factors, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 31 August.
Amid funding uncertainty, neuroscientists report a mix of cynicism, persistence
Two years into one of the most tumultuous tenures in U.S. federal funding, researchers outline how they are coping with ongoing changes and reflect on the impact these changes are having on the system more broadly.
Amid funding uncertainty, neuroscientists report a mix of cynicism, persistence
Two years into one of the most tumultuous tenures in U.S. federal funding, researchers outline how they are coping with ongoing changes and reflect on the impact these changes are having on the system more broadly.