Monday, June 26, 2017

Jobs of the Future

The Impact of AI and Machine Learning on our Jobs

Over the past year there have been an  increasing number of articles written about jobs that can be done by a machine versus a
person.  I tend to be pretty optimistic about the future, but I don't believe anyone can know how the nature of jobs will be transformed as automation is introduced into various aspects of life.

Here's an article from Fast Company that appeared 3 1/2 years ago about the changes coming from machine learning and artificial intelligence technologies.  I don't think it's aged well. Here are three things it listed:

1) Unstructured problem-solving: solving for problems in which the rules do not currently exist. Examples: a doctor diagnosing a disease, a lawyer writing a persuasive argument, a designer creating a new web application
When looking at the examples given, I think diagnosing a disease is probably the first one to be taken over by a machine.  But, depending on how you frame the problem, an AI could be written to determine which styles of argument may be most likely to succeed in certain scenarios.  Neither eliminates the need for a doctor or lawyer, but they are definitely tools that could reduce the time spent by the primary doctor or lawyer.  This could have significant downstream effects on the number of doctors, lawyers, research aids, paralegals, etc.  AI and machine learning are tools.  By themselves they don't do what a person can do, but they change the nature of the work, and the downstream effects of that are unknown.
2) Acquiring and processing new information, deciding what is relevant in a flood of undefined phenomena. Examples: a scientist discovering the properties of a medicine, an underwater explorer, or a journalist reporting on a story.
Here is an example of where some forms of journalism aren't safe from automation.    
3) Nonroutine physical work. Performing complex tasks in 3-D space, from cleaning to driving to cooking to giving manicures, which is thought of as relatively low-skilled work for humans, but actually requires a combination of skill #1 and skill #2 that is still very difficult for computers to master.
This is true, because we constantly break tasks down to their simplest essentials and teach machines to do that one task.  Then we start layering automated processes on top of each other.  Machine learning is all about determining what data is relevant.  A person sets the parameters of what kind of data is relevant, but machines are best at determining the specific data that will be treated relevant.  I think the rise of self-driving cars is a great illustration of this.  It's been true for a while that machines are better at routine driving than people would are (machines don't daydream or get distracted), but people are better at the unexpected (icy patch on a road).

Machines and the Future of Work

AI will change the face of the workplace.  The problems that you'll find with articles that try to predict what jobs will be safe, is that they think of the jobs as they currently exist for humans.  But, AI won't do the exact same jobs as people do, they'll do different jobs that will in turn change the nature of the jobs that people will be needed for.

Josh Wood (a tech recruiter), makes similar points that I was trying to make here and here.   Namely, that we can't really predict what jobs are safe nor that all the jobs will be automated.  It is the downstream effects of machine learning and AI on the economy that are utterly unknown.

He makes a statement that automation may be overblown:
After all, had the Blacksmiths Unions of the early 20th century been given the power to do so they would have outlawed the motorcar. My point here is that for every piece of ‘job-destroying technology’ there is a new job-creating industry being born which can never be properly foreseen. Winner of the Nobel Prize in Economics Milton Friedman accurately pointed out that most economic fallacies derive from the ‘zero-sum fallacy’ – the idea that if one party gains another party must always lose, when in fact provided all parties consent to a change all must be gaining (Otherwise, said party would not consent). The entire IT industry as we know it today would have been unthinkable to even the most prophetic sci-fi writers fifty years ago (We don’t all seem to wear identical silver clothing, either…), just as the number of Application Developers working today would have been unfathomable to leading technology economists writing in 2006.
The tech industry is huge part of the economy, and it is an industry where jobs are constantly being created that didn't exist before.

Things are going to change as AI and other ML techniques make their way through various forms of business and the truly beneficial uses of these technologies are found.  Change is constant, and the creation and destruction of the types of jobs that need to be done will continue.  At Contextant, a lot of our current work is focused on improving physical processes at warehouses.  The goal is to simplify the management of the work and try to make the work being done more efficient.  But what are the long-term down stream effects of these changes?  That is what is unknown. 

If something is completely unexpected or has never been considered before, then a machine won't make the same kind of value judgements that a person will.  Technology has always allowed us to build machines and devices that replace tasks or jobs that used to be done by hand.  AI and Machine Learning are no different.  They are tools that will transform what needs to be done.  But they are still just tools.  Machines will never be human.

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