The human brain, with its intricate networks of neurons and synapses, holds immense potential for unlocking the mysteries of cognition, perception and behavior. In recent years, advancements in deep learning and machine learning have revolutionized neuroscience research by enabling scientists to analyze and predict brain data movement with unprecedented accuracy and granularity. This interesting subject came to us from Neuroscience News in their article, “AI Predicts Movement from Brain Data.”

Deep learning and machine learning techniques play a pivotal role in analyzing and predicting brain data movement from various sources, including electroencephalography (EEG), functional magnetic resonance imaging (fMRI) and neural recordings. These technologies leverage large datasets and sophisticated algorithms to uncover hidden patterns, make predictions and extract meaningful insights from complex neural signals.

As we continue to harness the power of artificial intelligence (AI) in neuroscience, we embark on a journey of discovery that promises to unravel the mysteries of the human brain and transform the landscape of healthcare and human enhancement.

The biggest challenge, no matter the field, is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.