Possibilities for Low-Fidelity Mind Uploading (zaira.blog)

Researchers are exploring low-fidelity mind uploading by applying deep learning to EEG data, aiming to decode imagined speech and ultimately simulate human consciousness. Early studies have achieved around 60% accuracy interpreting imagined speech from EEG with limited data, but scaling these models could dramatically improve performance. The primary challenge is acquiring massive, high-quality EEG datasets—potentially tens of millions of hours across thousands of individuals—to train autoregressive models analogous to large language models like LLaMA. Unlike text, EEG data carries rich, high-dimensional information, suggesting models trained on it could capture a substantial portion of conscious experience. This work is significant because it frames mind uploading as a feasible deep learning problem if data and compute scale up, potentially enabling radical new forms of human-AI integration. Such models could not only simulate EEG signals capturing a person’s mental state but also enable direct brain-computer interfaces, memory transfer, telepathic communication, and collective hive minds. Though still early-stage, these developments hint at a future where humans might merge with AI systems to augment cognition, communicate instantaneously, and acquire new skills like “downloading” knowledge. This research shifts mind uploading from speculative sci-fi to a tangible frontier for AI and neuroscience, emphasizing data strategy and multimodal learning as key to unlocking these transformative possibilities.