Meta has introduced Brain2Qwerty, an AI model designed to decode brain activity into text using non-invasive methods such as magnetoencephalography (MEG) and electroencephalography (EEG). Tested on 35 participants, the system achieved up to 80% character decoding accuracy and a character-error rate of 32% with MEG, significantly outperforming EEG. The technology bridges the gap between invasive and non-invasive brain-computer interfaces (BCIs), though challenges like MEG's non-portability and real-time processing remain. The Brain2Qwerty model was evaluated by having participants type memorized sentences while their brain activity was recorded. The AI employs deep learning to translate neural signals into keystrokes, demonstrating its potential for aiding individuals with communication impairments. However, practical limitations, including the need for specialized scanning environments, must be addressed for broader adoption. In parallel, researchers at Johns Hopkins University have developed a bionic hand capable of mimicking human touch. The device combines rigid and flexible components, allowing it to grasp 15 different objects, including a plastic cup filled with water, with 99.69% precision. Machine learning algorithms process tactile signals, providing users with realistic feedback on textures, temperatures, and grip strength. These innovations underscore advancements in AI-driven neuroprosthetics and robotics, offering new possibilities for individuals with disabilities and enhancing human-machine interaction.