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
Neuroscientists weigh in on White House proposal for ‘moonshot infrastructure’ for connectomics
A report from the Trump administration calls for a “new golden age” of science, in which the federal government supports mission-driven collaborations to deliver high returns on research investments.
Neuroscientists weigh in on White House proposal for ‘moonshot infrastructure’ for connectomics
A report from the Trump administration calls for a “new golden age” of science, in which the federal government supports mission-driven collaborations to deliver high returns on research investments.
In memoriam: Kevin Dunbar, who traced the neural basis of human reasoning
Using imaging technologies, he transformed our understanding of how creativity and scientific discovery arise in the brain.
In memoriam: Kevin Dunbar, who traced the neural basis of human reasoning
Using imaging technologies, he transformed our understanding of how creativity and scientific discovery arise in the brain.
Neurons engage in risky, DNA-breaking business during migration
The cells later repair the damage, which occurs mostly in transcriptionally silent parts of the genome.
Neurons engage in risky, DNA-breaking business during migration
The cells later repair the damage, which occurs mostly in transcriptionally silent parts of the genome.