Using only a few seconds of brain activity data, the AI guesses what a person has heard. It lists the correct answer in its top 10 possibilities up to 73 percent of the time, researchers found in a preliminary study. The AI's "performance was above what many people thought was possible at this stage," says Giovanni Di Liberto, a computer scientist at Trinity College Dublin who was not involved in the research. Thank you for signing up! There was a problem signing you up.

The AI's "performance was above what many people thought was possible at this stage," says Giovanni Di Liberto, a computer scientist at Trinity College Dublin who was not involved in the research. Developed at the parent company of Facebook, Meta, the AI could eventually be used to help thousands of people around the world unable to communicate through speech, typing or gestures, researchers report August 25 at arXiv.org. This new approach "could provide a viable path to help patients with communication deficits … without the use of invasive methods," says neuroscientist Jean-Rémi King, a Meta AI researcher currently at the École Normale Supérieure in Paris. In these databases, participants listened to various stories and sentences from, for example, Ernest Hemingway's The Old Man and the Sea and Lewis Carroll's Alice's Adventures in Wonderland while the people's brains were scanned using either magnetoencephalography or electroencephalography. For it to become a meaningful communication tool, scientists will need to learn how to decrypt from brain activity what these patients intend on saying, including expressions of hunger, discomfort or a simple "yes" or "no."

In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing. Insilico Medicine is working on over a dozen different applications of deep learning methods to regenerative medicine, embryonic development, cross-species comparison and drug discovery and repurposing providing contract research services and developing a range of molecules for cancer, metabolic and CNS pathologies. We want to minimize animal testing and simulate many biological processes in silico", said Putin, deep learning lead at Insilico Medicine, Inc. To develop a data set of blood biochemistry and cell count samples Insilico Medicine collaborated with the largest independent laboratory test service provider in Eastern Europe, Invitro Laboratories. Using this data set Insilco Medicine scientists then trained 40 different deep neural networks (DNNs) of different depth with a single neuron output predicting chronological age and optimized using different optimizers and started organizing these DNNs into an ensemble.

"It is exciting to see the power of deep learning applied to potential aging biomarkers. The availability of such markers is an essential prerequisite for any future clinical trials to try to ameliorate the effects of human aging", said Charles Cantor, PhD, CSO of Agena, Inc, former director of the Human Genome Project (DOE). The availability of big data coupled with advances in highly-parallel high-performance computing led to a renaissance in artificial neural networks resulting in trained algorithms surpassing human performance in image and voice recognition, autonomous driving and many other tasks. However, the adoption of deep learning in biomedicine and especially in the pharmaceutical industry has been reasonably slow. In order to outperform more traditional machine learning methods, deep neural nets require large amounts of data and expertise with highly-parallel and high-performance graphics processing unit (GPU) computing.