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
Is our intelligence rooted in how living organisms are organized?
Kathryn Nave explains how a concept called constraint closure may be fundamental to understanding brains, minds and cognition.
Is our intelligence rooted in how living organisms are organized?
Kathryn Nave explains how a concept called constraint closure may be fundamental to understanding brains, minds and cognition.
Making an impact through academic administration
As executive director of research at Harvard Medical School’s Department of Neurobiology, Soha Ashrafi supports more than 300 scientists, students and staff members.
Making an impact through academic administration
As executive director of research at Harvard Medical School’s Department of Neurobiology, Soha Ashrafi supports more than 300 scientists, students and staff members.
This paper changed my life: Embracing an early model for naturalistic neuroscience
A 1992 PNAS paper showed how birdsong upregulates the expression of an immediate early gene in bird forebrains. The work revealed to Ribeiro the importance of studying molecular responses in naturalistic contexts.
This paper changed my life: Embracing an early model for naturalistic neuroscience
A 1992 PNAS paper showed how birdsong upregulates the expression of an immediate early gene in bird forebrains. The work revealed to Ribeiro the importance of studying molecular responses in naturalistic contexts.