Abstract
Mind uploading is the futurist idea of emulating all brain processes of an individual on a computer. Progress towards achieving this technology is currently limited by society's capability to study the human brain and the development of complex artificial neural networks capable of emulating the brain's architecture. The goal of this chapter is to provide a brief history of both categories, discuss the progress made, and note the roadblocks hindering future research. Then, by examining the roadblocks of neuroscience and artificial intelligence together, this chapter will outline a way to overcome their respective limitations by using the other field's strengths.
TopBackground
To develop technology that emulates brain function, we must first have a way to measure and record brain function. The ability to analyze brain waves and electrical impulses was, and still is, fundamental for all experiments to follow. In this section, this paper will discuss three of the most important developments towards understanding the human brain and how it relates to mind uploading: Brain Computer Interfacing, the Blue Brain Project, and nanobots. The main issues in mind uploading, from a neurological standpoint, are addressed at the end of the section.
Key Terms in this Chapter
Transfer Learning: A high-performance learning algorithm that is trained by data already collected or used by other machine learning structures. This method is very effective when the source of the data necessary is too scarce or expensive to gather.
Nanobots: A hypothetical, microscopic machine, capable of being injected into the bloodstream and used for data acquisition and administration of drugs.
Electroencephalography (EEG): The measurement and recording of electrical impulses in the brain, usually using a headset.
Machine Learning: A branch of artificial intelligence dealing with computer systems that learn and adjust the system using algorithms and statistical analysis.
Brain Computer Interfacing (BCI) Technology: A device that establishes a direct link to the brain and reads brain signals to gather data or manipulate another external device.
Artificial Neural Network (ANN): A machine learning method inspired by the biological neural networks that constitute animal brains. It consists of a series of algorithms that discover underlying patterns in a dataset through a process that mimics the way the human brain operates.
Meta-Learning: A subset of machine learning that applies a learning algorithm to look at the meta-features of existing machine learning experiments so that it can be further improved.
Artificial Intelligent (AI): A branch of computer science dealing with tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Blue Brain Project: A Swiss brain research initiative whose main goal is to digitally reconstruct the brain of a mouse by gathering data through scans, organizing the data, building a model, and performing a simulation to see if it behaved correctly.
Meta-Features: Descriptions of an experiment that are beyond the experiment itself. Examples of this are measurement tools, uncertainties, and error measurements.