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Can machines think? — Alan Turing, 1950

Practopoiesis: How cybernetics of biology can help AIIn the late 1950’s the Alan Turning asked the simple question in his paper “Computing Machinery and Intelligence”, while also establishing the fundamentals of an intelligent machine. While modern AI research has outgrown Turing’s definitions it still sticks to the same fundamentals. In this age of digital revolution and Internet of things Siri, Alexa and Google Assistant are household names, while they are digital assistants all the services provided by these systems are independent AI algorithm/ agents.

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The biological data science is illustrated by a massive amount of data from heterogeneous sources from different cross domains in the field of life sciences. To translate complex relationships among heterogeneous datasets related to do structure and functions of biological systems remains an urgent challenge. Although traditional model-driven approaches still play an important role in analyzing these kinds of data, it lacks capabilities to exploit the huge amount of available data or even big data to discover knowledge, predict data behaviors, and decipher complex relationships among data. Therefore, data-driven becomes the theme of biological data science for its capabilities in listening to data, interacting with data, and extracting knowledge from data.

Modern artificial intelligence will dominate biological data science for its unpreceded learning capabilities to process complex data. A deep learning machine has much more complicate learning topologies, which may change dynamically for the sake of learning, besides at least the same complicate-level learning mechanism as traditional machine learning models such as support vector machines

Keywords: Machine Learning, Bio Natural Language Processing, Deep Learning, Biological named entity recognition

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