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
Remembering Annette Dolphin, who helped explain gabapentin’s effects
The "intuitive" neuropharmacologist pushed against the status quo.
Remembering Annette Dolphin, who helped explain gabapentin’s effects
The "intuitive" neuropharmacologist pushed against the status quo.
Revised statistical bar extracts less-common variants from autism genetics studies
Adjusting genetic analyses could help plug autism’s heritability gap, according to a new preprint.
Revised statistical bar extracts less-common variants from autism genetics studies
Adjusting genetic analyses could help plug autism’s heritability gap, according to a new preprint.
Tom Griffiths describes how neural networks, logic and probability theory together explain cognition
In his new book, “The Laws of Thought,” Griffiths shows how these three pillars of study complement one another and together form a solid foundation to eventually explain all of our cognition, from brain to mind.
Tom Griffiths describes how neural networks, logic and probability theory together explain cognition
In his new book, “The Laws of Thought,” Griffiths shows how these three pillars of study complement one another and together form a solid foundation to eventually explain all of our cognition, from brain to mind.