The most valuable takeaway for me was learning the difference between a computer, a computer system, AI, machine learning, and deep learning (AI Robertson ML Robertson DL). In Peter J. a computer originally represented as a job title which refers to people who calculates an artifact that can automated processing info, and eventually developed into a machine that can understand the info. Denning and Craig H. Martell’s Great Principles of Computing, the author broke the statement about “Computer is just coding or programming” by stating the long progress of computing development and its controversial evolution.
Early 1970, “Computer science equals programming.”
In the 1970s, “Computing is the automation of information processes.”
Late 1970 s, “Computing as the study of ‘what can be automated.”
1980 s, “Understanding their information processes and what algorithms might govern them.”
Looking back to history makes me even more surprised about how rapidly the computing technology has been developed and how fast people can keep up with all these updates and react to such changes. But still, “with the bounty come anxieties.” In Kashmir Hill’s article The Secretive Company That Might End Privacy as We Know it clearly spilled out our concerns. Using ML as a tool to help law enforcement should be a way to decrease the criminal rates and processing the case even faster by replaying human labor into tireless machines. However, because these machines can have “unintended operations”, the results aren’t always right, especially towards specific groups of people, and the idea of extracting a face behind every phone or even videos it occurs freaks people out. This face recognition technology hasn’t been generally authorized.
It reminded me of a case that happened several years ago about how the public concerns about their privacy while “enjoying” the convenience the private intruding technology brings them, like location sharing and tagging. IPhone by that time took advantage of it and started advertising how important they value their customers’ privacy. The ironic thing is, they still cooperate with Google and many other information collecting companies to spy on their customers and predict their preferences to make more profit, but refuse to provide a password to the law enforcement for providing evidence and solving a crucial murder case to prove to the public about how much they “value their privacy”.
Another takeaway I contracted was from John D. Kelleher’s Deep Learning about how machine learning was designed to learn the patterns from the massive data by providing a calibration so they “understand” what right or wrong. It reminds me of how humans learn from the beginning. Our past experience (knowledges, relationships with others, rewards…) are the massive data base, we learn from the past to find the pattern so we know what to do what’s not to do, and what works what doesn’t.