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
How Nobel Prize-winning optogenetics has revolutionized neuroscience
The technique has enabled neuroscientists to target brain circuits and cells with unprecedented specificity, transforming the study of memory, interoception, brain disorders and reward, among other domains.
How Nobel Prize-winning optogenetics has revolutionized neuroscience
The technique has enabled neuroscientists to target brain circuits and cells with unprecedented specificity, transforming the study of memory, interoception, brain disorders and reward, among other domains.
Is confidence a second thought—or part of the first one?
In some settings, neural circuits may calculate confidence in a decision simultaneously with the choice itself. That changes the types of questions researchers should ask.
Is confidence a second thought—or part of the first one?
In some settings, neural circuits may calculate confidence in a decision simultaneously with the choice itself. That changes the types of questions researchers should ask.
What AI can learn from hunger
To enhance AI safety, take a lesson from biological brains; a small set of wants maintains durable control over a much larger cognitive system.
What AI can learn from hunger
To enhance AI safety, take a lesson from biological brains; a small set of wants maintains durable control over a much larger cognitive system.