Is Machine Learning Research Moving in the Right Direction

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Student Name : Vanessa 黃妮莎
Student ID : D0726551

Is Machine Learning Research Moving in the Right Direction

            
            Machine learning is the study of computer algorithms that develop automatically through experience. It is seen as a division of artificial intelligence. Some example of machine learning are deep learning, reinforcement learning and natural language processing. The study of machine learning has seen some of the biggest and brightest minds times and numerous budget of funding channeled into the chase of better, safer and more generalizable algorithms.
            There are likely 3 general goals for the study of machine learning. First is to create a machine that can operate in the world in a way that’s identical from humans. Secondly, as a promising tool to model the human brain and further our understanding of human intellectual. Third is focusing only on building commercially possible products that can replicate and automate simple processes and even be better than humans on highly specific tasks. Each of  these goals requires a different weighting of the algorithms being used and therefore a differential investment in lines of research. 
            The fact is that much these hard work to pursuit highly developed machine learning will create libraries and artifacts that can be derived from those focusing on another goal. In the same streak, as researches push for more information, it’s necessary that they collaborate with researches and sources across multiple disciplines to ensure what’s being built isn’t only algorithmically excellent but also methodologically sound and most importantly avoid bias opinions.
            An absence of clear definition around the direction of machine learning research can stimulate larger paranoia and involve people that aren’t related to the research to clain a direction.  For example is when there was an argument that Al is a “military technology” and corrected Google for building Al research labs in China. This so biased opinion had caused some problem. So, in my opinion researchers should instead seek to include those with complementary subject matter expertise and continue the culture of open science in machine learning.

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