2019/12/21 by Jordan Ott, Ott, Jordan · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Machine Learning in Bioinformatics #Domain Adaptation and Few-Shot Learning #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.1912.10305
The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observation heavy, providing few guiding theories. It is unlikely that artificial intelligence will emerge through only one of these disciplines. Instead, it is likely to be some amalgamation of their algorithmic and observational findings. As a result, there are a number of problems that should be addressed in order to select the beneficial aspects of both fields. In this article, we propose leading questions to guide the future of artificial intelligence research. There are clear computational principles on which the brain operates. The problem is finding these computational needles in a haystack of biological complexity. Biology has clear constraints but by not using it as a guide we are constraining ourselves.