Toggle light / dark theme

But MIT scientists now claim to have cracked this problem by creating “a treasure trove” of natural language “abstractions” that could lead to more powerful AI models. Abstractions turn complex subjects into high-level characterizations and omit non-important information — which could help chatbots reason, learn, perceive, and represent knowledge just like humans.

Currently, scientists argue that LLMs have difficulty abstracting information in a human-like way. However, they have organized natural language abstractions into three libraries in the hope that they will gain greater contextual awareness and give more human-like responses.

The scientists detailed their findings in three papers published on the arXiv pre-print server Oct. 30 2023, Dec. 13 2023 and Feb. 28. The first library, called the “Library Induction from Language Observations” (LILO) synthesizes, compresses, and documents computer code. The second, named “Action Domain Acquisition” (Ada) covers AI sequential decision making. The final framework, dubbed “Language-Guided Abstraction” (LGA), helps robots better understand environments and plan their movements.

As people age, their brains do, too. But if a brain ages prematurely, there is potential for age-related diseases such as mild cognitive impairment, dementia, or Parkinson’s disease. If “brain age” could be easily calculated, then premature brain aging could be addressed before serious health problems occur.

Researchers from Drexel University’s Creativity Research Lab have developed an artificial intelligence technique that can effectively estimate an individual’s brain age based on electroencephalogram (EEG) brain scans. The technology could help to make early, regular screening for degenerative brain diseases more accessible. The work is published in the journal Frontiers in Neuroergonomics.

Led by John Kounios, Ph.D., professor in Drexel’s College of Arts and Sciences and Creativity Research Lab director, the research team used a type of artificial intelligence called machine learning to estimate an individual’s brain age similar to the way one might guess another person’s age based on their physical appearance.