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
Trading places: What happens when neuroscience turns into machine learning, and machine learning turns into neuroscience?
Neuroscience has become increasingly concerned with prediction, and machine learning with causal explanation, with each field adopting methods from the other. I asked eight experts to weigh in on what we stand to learn from this exchange.
Trading places: What happens when neuroscience turns into machine learning, and machine learning turns into neuroscience?
Neuroscience has become increasingly concerned with prediction, and machine learning with causal explanation, with each field adopting methods from the other. I asked eight experts to weigh in on what we stand to learn from this exchange.
Exon-skipping approach boosts levels of key Rett syndrome protein
Deleting a small region of the MECP2 gene partially restored function in neurons derived from people with Rett-associated variants.
Exon-skipping approach boosts levels of key Rett syndrome protein
Deleting a small region of the MECP2 gene partially restored function in neurons derived from people with Rett-associated variants.
Frameshift: How Caitlin Vander Weele made science communication her business
Her favorite part of research was talking about it. So she left academia and turned that passion into a successful company.
Frameshift: How Caitlin Vander Weele made science communication her business
Her favorite part of research was talking about it. So she left academia and turned that passion into a successful company.