You probably have gotten a sense of the tremendous and overwhelming flood of publishing that is happening in the machine learning space. So much content is being created right now that nobody could possibly consume it all. The flood of content is real and overwhelming. That is one of the reasons that I work really hard to distill the complex topics I select into a readable format for people to consume. I really try to provide a pathway to people who want to be a part of this journey. Maybe those two observations are inherently in conflict, but I think helping people navigate the deluge of information to focus on key things helps break down the conflict.
As I sit down to rethink the future of machine learning, my thoughts are circling back to some of the original things I was writing about use cases and how people select what they want to work on based on ROI. Right now, machine learning is being built into everything, and we are seeing a creative explosion of people using machine learning to generate things on the fly that otherwise would have never been possible. Some of the models within the image- and video-creation spaces are really changing how we interact with the world. My honest guess about the future of machine learning is that it will become seamless within the background of our daily lives. It will be ever present and constantly just on the edge of how we perceive and interact with the world around us.
I grabbed five papers where the future of machine learning was discussed from 2001 to 2018.
Zhou, Z. H. (2016). Learnware: On the future of machine learning. Frontiers in Computer Science, 10(4), 589–590. https://www.lamda.nju.edu.cn/publication/fcs16learnware.pdf
Mjolsness, E., & DeCoste, D. (2001). Machine learning for science: State of the art and future prospects. Science, 293(5537), 2051–2055. https://www.researchgate.net/profile/Eric-Mjolsness/publication/11789794_Machine_Learning_for_Science_State_of_the_Art_and_Future_Prospects/links/09e415147695a42e12000000/Machine-Learning-for-Science-State-of-the-Art-and-Future-Prospects.pdf
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P., Brink, J. A., & Dreyer, K. J. (2018). Current applications and future impact of machine learning in radiology. Radiology, 288(2), 318–328. https://doi.org/10.1148/radiol.2018171820
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018). eDoctor: Machine learning and the future of medicine. Journal of Internal Medicine, 284(6), 603–619. https://doi.org/10.1111/joim.12822
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Week 104: That 2nd year of posting recap
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Rethinking the future of ML