Although the field of natural language processing keeps burgeoning, the reversal of the process, i.e., natural language generation remains a harder challenge while also promising greater outcomes for businesses.
Centered on the principles of ‘verifying before trusting anything and everything,’ zero trust security model promises to be an infallible cybersecurity approach meant for preventing the skyrocketing security attacks.
We are no longer in a period of rapid change. We have now entered an amazing inflection point where true transformation happens. For example, we changed how we listened to music and catalogued our media when we went from CDs and floppy discs to all our smartphones holding music, photos, files, email and access to information. Technological change transformed our processes in these industries.
Many of us have had the maddening experience of going to see a doctor and being told that your medical information is "not in our system". The reasons are many:
The Harvard Data Science Review has just published its first issue. Many of us in economics are cousins of burgeoning data science field, and will find it of interest. As one example, Harvard provost (and economist) Alan Garber offers a broad-based essay on "Data Science: What the Educated Citizen Needs to Know." Others may be more intrigued by the efforts of Mark Glickman, Jason Brown, and Ryan Song to use a machine learning approach to figure out whether Lennon or McCartney is more likely to have authored certain songs by the Beatles that are officially attributed to both, in "(A) Data in the Life: Authorship Attribution in Lennon-McCartney Songs."
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