Frey and Osborne: The Future of Employment: How Susceptible are Jobs to Computerisation?, Figure III

How Likely is it that your job can be automated?





Rank Prob.% Occupation Type
6 0.4% Occupational Therapists HEALTH
11 0.4% Dietitians and Nutritionists HEALTH
14 0.4% Sales Engineers HEALTH
15 0.4% Physicians and Surgeons HEALTH
17 0.4% Psychologists, All Other HEALTH
19 0.4% Dentists, General HEALTH
25 0.5% Mental Health Counsellors HEALTH
28 0.6% Human Resources Managers MGMNT
40 0.8% Special Education Teachers, Secondary School EDU
41 0.8% Secondary School Teachers, Except Special and Career/Technical Education EDU
46 0.9% Registered Nurses HEALTH
53 1.1% Mechanical Engineers TECH
54 1.2% Pharmacists HEALTH
63 1.4% Engineers, All Other TECH
70 1.5% Chief Executives MGMNT
77 1.7% Chemical Engineers TECH
79 1.7% Aerospace Engineers TECH
84 1.9% Civil Engineers TECH
82 1.8% Architects, Except Landscape and Naval TECH
98 2.5% Electronics Engineers, Except Computer TECH
104 2.9% Industrial Engineers TECH
112 3.2% Postsecondary Teachers EDU
115 3.5% Lawyers MONEY
120 3.7% Biomedical Engineers TECH
152 6.9% Financial Managers MONEY
153 7% Nuclear Engineers TECH
163 8.4% Childcare Workers EDU
188 14% Optometrists HEALTH
191 15% Kindergarten Teachers, Except Special Education EDU
192 15% Electricians TECH
226 25% Managers, All Other MGMNT
249 35% Plumbers, Pipefitters, and Steamfitters TECH
253 36% Computer Numerically Controlled Machine Tool Programmers, Metal and Plastic TECH
261 38% Electrical and Electronics Repairers, Powerhouse, Substation, and Relay TECH
263 38% Mechanical Engineering Technicians TECH
290 48% Aerospace Engineering and Operations Technicians TECH
317 56% Teacher Assistants EDU
386 70% Avionics Technicians TECH
398 72% Carpenters TECH
422 77% Bartenders FOOD
435 79% Motorcycle Mechanics TECH
441 81% Cooks, Fast Food FOOD
442 81% Word Processors and Typists MONEY
443 81% Electrical and Electronics Drafters TECH
453 82% Sheet Metal Workers TECH
460 83% Cooks, Institution and Cafeteria FOOD
477 84% Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic TECH
489 85% Nuclear Technicians TECH
514 88% Semiconductor Processors TECH
522 89% Bakers FOOD
583 93% Butchers and Meat Cutters FOOD
596 94% Bicycle Repairers TECH
625 95% Postal Service Clerks MONEY
629 96% Office Clerks, General MONEY
641 96% Cooks, Restaurant FOOD
657 97% Cashiers MONEY
671 98% Bookkeeping, Accounting, and Auditing Clerks MONEY
688 98% Brokerage Clerks MONEY
698 99% Insurance Underwriters MONEY


Joshua Greene: ‘From neural ‘is’ to moral ‘ought’: what are the moral implications of neuroscientific moral psychology?’ – Nature reviews Neuroscience 4(10) pp.846-9 (2003)

Should you send the aid agency $200?


Joshua Greene: ‘From neural ‘is’ to moral ‘ought’: what are the moral implications of neuroscientific moral psychology?’ – Nature reviews Neuroscience 4(10) pp.846-9 (2003)

Should you take the injured hitchhiker to hospital?


Psychology Fun!

Trajectory of the tamping iron through Phineas Gage’s head


Hierarchical ‘wiring’ of the brain


Charitable giving in USA


Larry Young

Prairie vole brain


Sally Anne test


Georgia State University


Google Art Project, Licensed under Public Domain via Wikimedia Commons

Gassed, by John Singer Sargent RA

Start of the writing up of the CADOES talk…

Modelling neurons


Hollywood-type zombies

Noone Gets Wet in a Simulation of a Tropical Storm

Briefly back to the question of whether a whole brain simulation could really feel pain, you might ask: why would we expect a simulation to have consciousness? After all, it’s just a simulation! It’s not the real thing. Noone gets wet in a simulation of a tropical storm!

simulating the weather in a region, such as over Japan, below. Imagine. Divide the volume into 1km3 cubes and model wind speed, direction, pressure, water vapour etc. Can see predictions of rain but obviously no wetness. Divide further into 1m^3 cubes (now have 20×10^15 instead of 20 million cubes). Now model the terrain underneath, including height. Now model surface water and run-off into rivers, out to sea (and evaporation back into the air). Seeing the water movement in the simulation, could point and say – there is liquid flow. Divide further to 1mm^3 (there would now be 20×10^24 cubes – astronomically large!). Now, individual raindrops falling out of the sky would be modelled. As the raindrops hit the ground, or any other modelled object, it would run off it just like in the fluid simulation, below. Now I think you can say there would be wetness in the simulation. So whether you get a particular phenomenon in a physics simulation depends upon the level of the modelling in the simulation.

What Is the Right Level?


3. Could an Android phone be conscious?

  • My initial reaction: outrageous idea!
  • Show of hands… few can accept.
  • See red, feel pain
  • Design of electronic hardware and the software that sits on top of that – understand sufficiently and with enough time completely. No mystery.
  • Specific intelligence: Beat me at chess – no mystery.
  • Generalized intelligence: ‘search’ / ‘optimization’ / problem solving – no problem.
  • But to see and feel?!

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