Meta, the parent company of Facebook, has announced the launch of a long-term research project to develop a next-generation AI that can learn and process speech and text in the same way that the human brain does. Meta described an effort to create human-level artificial intelligence.
Meta is collaborating with NeuroSpin, a neuroimaging company that images the human brain, and Inria, a software company, to study how the human brain processes speech and text and then compare it to how AI language models do.
NeuroSpin is a research facility devoted solely to brain imaging. The researchers are physicists, mathematicians, neuroscientists, and doctors who collaborate to develop tools for learning about the human brain in various ways.
NeuroSpin explains what it does:
“Focused on neuroimaging, the research conducted ranges from technological and methodological developments (data acquisition and processing) to preclinical and clinical neuroscience, including cognitive neuroscience.”
Meta published:
“Today, we’re announcing a long-term AI research initiative to better understand how the human brain processes speech and text. In collaboration with neuroimaging center Neurospin (CEA) and Inria, we’re comparing how AI language models and the brain respond to the same spoken or written sentences.
We’ll use insights from this work to guide the development of AI that processes speech and text as efficiently as people.”
The issue with AI language models is that they require a large number of examples in order to learn. Human brains only require a few examples to learn.
The current study of brain-like AI language models discovered:
“Language models that most resemble brain activity are those that best predict the next word from context (e.g. once upon a …time).
While the brain anticipates words and ideas far ahead in time, most language models are trained to only predict the very next word. Unlocking this long-range forecasting capability could help improve modern AI language models.”
The announcement cited current research into modeling AI on human brain activity, which used MRIs and other imaging tools to observe human brain activity while humans performed various language-related tasks.
The cited research paper is titled Language processing in brains and deep neural networks: computational convergence and its limits, and it was published in 2021. (PDF).
In the first few paragraphs of the research paper, a summary of the findings is discussed:
“The results show that (1) the position of the layer in the network and (2) the ability of the network to accurately predict words from context are the main factors responsible for the emergence of brain-like representations in artificial neural networks.
Together, these results show how perceptual, lexical and compositional representations precisely unfold within each cortical region and contribute to uncovering the governing principles of language processing in brains and algorithms.”
The significance of the preceding research is that it demonstrates how studying how the brain processes data can yield insights into creating similar processes in an algorithm.
The Meta research teams are analyzing thousands of scans of human brain activity to determine which brain regions were activated during tasks.
This study was said to demonstrate the “computational organization of the human brain,” yielding insights useful toward Meta’s goal of developing “human-level AI.”
The research not only aids in the development of human-level AI but also aids neuroscientists in their understanding of the human brain.
Learn more from Facebook and read Meta Enhances Instagram Reels With Fundraising Features.
