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Saturday, December 28, 2019

Five types of job/work left for humans in the era three of Automation (21st century) - machines take away decisions, in addition to dirty, dangerous, and dull jobs

Five types of job/work left for humans in the era three of Automation (21st century) - machines take away decisions, in addition to dirty, dangerous, and dull jobs

(1) Step up.
See machines based on higher intellectual ground, design, evaluate, apply, and expand machines

(2) Step aside.
Jobs that need human intelligence and/or hands. Understand humans subtle feeling and care. Do fine-tuning by hands.

(3) Step in.
Jobs that bridge new technologies and business including entrepreneurs.

(4) Step narrowly.
Non-cost-effective jobs for machines. Very special jobs that can be done by a very few people.

(5) Step forward.
Jobs that produce new systems including IT specialists, data scientists, machine learning engineers, IT consultants, programmers, white hackers, etc.


Reference:
Beyond Automation
by Thomas H. Davenport and Julia Kirby
https://hbr.org/2015/06/beyond-automation

Monday, December 9, 2019

Checklist: hiring an administrative / operational member

This is my own checklist when hiring an administrative / operational member (neither specialist/creative nor management).

Skills:
1. Careful and passive listening
2. Process streamlining (What needs to be done, how, when, by who?)
3. Operations (Documentation, IT)

Mindset:
1. Not selfish. “For members and/or executive/management of an organization”
2. Mentally stable - be able to manage his/her own feeling by him/herself
3. Can-do (proactive) attitude with caution
4. Be based on objective facts, not subjective opinions (personal preferences) - knowing that acting from a sense of “personal justice” is selfish and cheap entertainment
5. A doer, not a critic

Deep Learning (Regression, Multiple Features/Explanatory Variables, Supervised Learning): Impelementation and Showing Biases and Weights

Deep Learning (Regression, Multiple Features/Explanatory Variables, Supervised Learning): Impelementation and Showing Biases and Weights ...