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
Microglia implicated in infantile amnesia
The glial cells could explain the link between maternal immune activation and autism-like behaviors in mice, but methodological challenges prompt questions about the new evidence.
Microglia implicated in infantile amnesia
The glial cells could explain the link between maternal immune activation and autism-like behaviors in mice, but methodological challenges prompt questions about the new evidence.
Oligodendrocytes need mechanical cues to myelinate axons correctly
Without the mechanosensor TMEM63A, the cells cannot deposit the appropriate amount of insulation, according to a new study.
Oligodendrocytes need mechanical cues to myelinate axons correctly
Without the mechanosensor TMEM63A, the cells cannot deposit the appropriate amount of insulation, according to a new study.
Modern AI is simply no match for the complexity likely required for harboring consciousness, says Jaan Aru
He argues that our brain’s computations are of a completely different nature than any artificial intelligence because they take place across many spatial and temporal scales and are inextricably entwined with biological materials.
Modern AI is simply no match for the complexity likely required for harboring consciousness, says Jaan Aru
He argues that our brain’s computations are of a completely different nature than any artificial intelligence because they take place across many spatial and temporal scales and are inextricably entwined with biological materials.