Those who work in creative fields may enjoy greater future job security.

Dave Conley, Founder and CEO, Innomation, kicked off Tuesday afternoon’s RECONVERGE sessions with his interesting observations on the influence of change concerning Automation and Artificial Intelligence Control Systems: Trends of Engineering System Evolution Outlook.”

Conley spoke about the future impact of AI on careers and provided a look about how some tool sets might help practitioners in their evolving marketplace—or–“The machines are taking over the world.”

AI is the development of computing systems to replicate activity that would normally require human intelligence. One interesting application is that once in use, it is no longer artificial intelligence, but standard computing; a natural advancement of computing platforms.

This is not a new “intelligent” method in computing.

As in all systems, all the computer knows is zero or one, we can replicate intelligence by these computations. Machine learning allows a change to perhaps prompt unexpected results.

Some examples:

  • Programmable thermostat
  • Cameras with auto features
  • Accounting and tax prep software
  • Traffic sensing traffic signals
  • Auto pilot systems

Trends in the technical system of evolution will continue; two trends are present. The trend of elimination of human involvement and the trend of increasing system completeness; as the number of engineering functions increases, the human interaction required becomes less.

When the human stops performing transmission, energy source, control and decision making, a progression is noted and this applies across a variety of functions.

The first tools were mechanical systems to aid human effort: shovels, bow and arrow. The next step was to replace human effort: a windmill to pump water, for instance. Third, computing systems were infused to aid in effort, and mechanical systems then move on to replace human effort—the current state.

We are pushing to have computational systems highly integrated in machines they control; machines will become more intelligent and computational systems may be infused throughout the machine itself.

The commonality in all systems is that they all have a sensor of some type, a computing engine, and some sort of output control.

In order for this to be effective, technology must gather data, do something with it, and then control a system.

The control could be information on a computer screen or actual physical control of another system.

The trend of S-Curve evolution is that as a system evolves the effectiveness of output describes an S-shape in time; there are many ways to apply these trends in engineering systems.

Systems that effectively utilize thermal, acoustics and electro-magnetic fields for sensing will be utilized first. Systems that utilize mechanical, chemical and biological fields for sensing will follow.

This trend will reveal capabilities to first appear.

Coding languages also progress. The current state of object oriented languages and machine coders appear to accelerate the ability of the coding to better integrate with the machine. To change the S-curve, self-organizing cold/hardware, meta material will be required.

The current state of code writing is fairly poor overall, this has to do with the low level of integration of the coding with systems and environment.

Concerning output control: what system are you analyzing?

Consider the trend of increasing coordination: one is to look at it with dimensionality. In sensing tools, note that as engineering systems evolve they become more coordinated with themselves and the environment.

Output control: cutting and drilling as an example—0D, 1D, 2D, 3D.

Another trend is that there is a fine line between business and technology.

Increasing customer expectations are a result of business evolution. A service transformation is that business delivers customized products to customers based on the business’s understanding of the client, and not the client’s request.

In sensing: systems that sense customer requirements appear first (3D sensing systems).

In the logic engine, computing power and the complexity is less

Output control: systems that can interact with customer through a 0, 1, and 2D interfaces, followed by 3D.

These predictions of sensing, logic engine and output control will reveal where AI will affect us the most, and show what will come later. As sensing and logic and output capabilities increase, lesser then more skilled systems will evolve. Less judgment will appear first, followed by a need for increased judgment. We can replicate judgment with computations; value in a market will lead to expenditures for development–consider self-driving cars as an example.

Thus, the forecast of automation of jobs may occur, not necessarily in this order:

First losses will be in simple tax prep, freight handling, fast food, janitorial service, ground transportation, mail delivery, furniture making, mining, accounting, loan processing photography, masonry, and interior design.

Next to go will be waiter, full service teller, pilot, mechanic, tax prep (CPA), architecture, equipment maintenance, primary medical care, preventive dental care, athletic trainer, pharmacist, general surgeon, lecturing professor, and those working in responsive security.

Jobs Conley believes are not at immediate risk for automation include acting, advertising, architecture (creative), artists, computer scientists, engineering, investigative journalism, legal, musicians, politicians, professors (interactive/planning) R&D scientists, ER surgeons, and veterinarians. These jobs all require unique decision making scenarios; creative careers are not at risk.