Artificial Intelligence and the Future of Power: 5 Battlegrounds by Rajiv Malhotra

Author:Rajiv Malhotra [Malhotra, Rajiv]
Language: eng
Format: epub
ISBN: 9789390356430
Amazon: B08Q4G6MYD
Goodreads: 56312171
Publisher: Rupa Publications India
Published: 2021-01-09T18:30:00+00:00

Figure 19: Collaboration Between Life Sciences and Computer Sciences My point is incredibly significant in the philosophy of science: machine learning and biological learning seem to share conceptual similarities in the way they learn from experience. This is shown in Figure 20.

Figure 20: Machine Learning of Individual Psychology For instance, the epigenetic system in biological cells is a mechanism analogous to a learning machine; it uses interactions with the environment and the responses it gets to learn about the environment and improve its own performance. The experiences of a cell are stored in the epigenetic material surrounding the DNA in the cell.

The epigenetic mechanism learns and generates multiple genetic algorithms; these are tested and the algorithms that perform well in a given environment (as measured by some criteria such as the rate of survival) carry more weight than the ones that do not perform as well. Over time, the successful algorithms become the new normal mechanism for a given type of cells.

The same theory can be applied to an entire species: a learning system that interacts with its environment. Natural evolution is essentially a trial-and-error learning system, with the responses from environmental encounters being equivalent to big data.

While the body’s cells are evolving through training in the environment, so also the pathogens are learning and mutating. Pathogens are also biological mechanisms that learn by attempting to outsmart the body of a host and overcome the antibiotics and other medicines meant to kill them. Like any other learning system, pathogens can be modeled as algorithms whose parameters adapt to optimize survival in a given environment. Because a virus or bacteria functions like an algorithm, scientists can develop designer viruses and bacteria for purposes both good and bad.

Genetic mapping research is rapidly advancing to help customize treatments in specific populations. Machine learning could be useful to model a patient’s biological system and develop a customized response. The successful conjunction of biologists, neuroscientists, psychologists and computer scientists that has delivered such practical benefits has commercially strengthened the reductionist perspective.

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