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
The genome offers a recipe, not a blueprint, for the brain
During brain development, genes set the rules and turn the knobs; self-organization does much of the rest.
The genome offers a recipe, not a blueprint, for the brain
During brain development, genes set the rules and turn the knobs; self-organization does much of the rest.
Autism research involving monkeys contends with uphill battle
Macaques and marmosets recapitulate the condition’s core traits better than mice can, but funding uncertainty and challenges at primate centers in the United States could make this work difficult to continue.
Autism research involving monkeys contends with uphill battle
Macaques and marmosets recapitulate the condition’s core traits better than mice can, but funding uncertainty and challenges at primate centers in the United States could make this work difficult to continue.
When is self-organization a mechanism in brain development?
Retinal waves offer a powerful example of a self-organizing system that can build structure without external guidance. The field would do well to embrace such phenomena more broadly as a type of mechanistic explanation.
When is self-organization a mechanism in brain development?
Retinal waves offer a powerful example of a self-organizing system that can build structure without external guidance. The field would do well to embrace such phenomena more broadly as a type of mechanistic explanation.