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
Fruit flies use memory to track odors
When flies encounter a tasty smell, they stroll in and out of the odor plume—a process that depends on encoding a memory of the angle needed to travel back to the plume, according to a new study.
Fruit flies use memory to track odors
When flies encounter a tasty smell, they stroll in and out of the odor plume—a process that depends on encoding a memory of the angle needed to travel back to the plume, according to a new study.
New methods to contextualize autism-linked genes, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 7 September.
New methods to contextualize autism-linked genes, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 7 September.
Dimensionality—neuroscience’s red herring?
Placing too much emphasis on a specific interpretation of dimensionality, or treating it as an end-all quantification of some aspect of neural computation, may hinder progress in understanding the brain.
Dimensionality—neuroscience’s red herring?
Placing too much emphasis on a specific interpretation of dimensionality, or treating it as an end-all quantification of some aspect of neural computation, may hinder progress in understanding the brain.