Unless you’ve been on one of those new planets discovered by an amateur astronomer as reported by Brian Cox and and Julia Zemiro last week, you’d know that this week marks the opening of selection for the 2018 AGPT intake. And for the first time since the AGPT’s inception, the colleges will take on selection following AGPT determining eligibility.
Information abounds but if you’ve somehow missed it, find it here:
All’s quiet on the policy front as we move into pre-budget mode. So in the meantime, here’s an interesting piece for you.
Tim Dunlop. Why the Future is Workless. published 2016.
I found it to be an excellent book on innovation, employment and how we as a society are dealing with it (or not), and floats some really innovative solutions like basic income. These are issues that are rapidly making their way into the current political narrative (eg Richard de Natale spoke about a basic income for Australia at the National Press Club recently). It also asks questions like: “will a robot take my job?” (almost certainly for some) and “will an app take my job” (already happening – think Uber and AirbnB)
There is a reference in the book that lists the probability of 702 occupations in the USA becoming computerised. This article is fascinating reading, written in 2013. They estimate that around 47% of total US employment is at high risk of being automated in the next decade or two.
The list of occupations with probabilities is in the appendix on page 57. Here’s a link to the article:
Here’s a snippet to whet your appetite:
In health care, diagnostics tasks are already being computerised. Oncologists at Memorial Sloan-Kettering Cancer Center are, for example, using IBM’s Watson computer to provide chronic care and cancer treatment diagnostics. Knowledge from 600,000 medical evidence reports, 1.5 million patient records and clinical trials, and two million pages of text from medical journals, are used for benchmarking and pattern recognition purposes. This allows the computer to compare each patient’s individual symptoms, genetics, family and medication history, etc., to diagnose and develop a treatment plan with the highest probability of success (Cohn, 2013).