My fascination with AI and automation in the workplace continues.

Aaron M. Bornstein offers up an article Is Artificial Intelligence Permanently Inscrutable? that points out some of the current issues with advancing AI.

As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM’s corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. “We couldn’t explain the model to them because they didn’t have the training in machine learning.”

“In fact, it may not have helped even if they were machine learning experts.”

“As exciting as their performance gains have been, though, there’s a troubling fact about modern neural networks: Nobody knows quite how they work. And that means no one can predict when they might fail.”

The article goes on to point out that “The hospital anecdote makes clear the practical value of interpretability.”

This leads me to my next point about the limitations of AI.

Also, today I read about 9 Artificial Intelligence Applications That Won’t Work and they point out that although there is an upside to AI in time, for now it is decidedly lacking in some areas.

For example:

“Let’s take one financial startup company that claims to be bringing the power of AI and machine learning to the retail investor. They do so by examining “millions of data points per second” and then giving each stock a score of 1 to 10. That’s it guys and gals. They give you a list of stocks and each one has a score of 1 to 10. Just what are you supposed to do with that? We suppose you could setup a trading algorithm that buys 9s and sells 7s which translates stock picking into the crude sort of ranking system that you used to label girls or guys in high school with. This is not a very useful application of AI for retail investors. Going forward, this startup is also looking at chart pattern recognition and also social media data which could be a useful feed for your trading algorithms. However, the usefulness of this site for retail investors that want to apply AI to investing just doesn’t seem to be there yet.”

They go on to point out 9 areas where AI is not up to snuff. If you are a quasi-luddite, there is hope yet for the need of humans in the following options:

· Retail Investing
· Love
· Product Recommendations
· Recruiting
· Online Marketing
· Travel Agents
· Fashion Design
· Car and Home Buying
· Art/Cinema/Music

We have recently been exposed to AI writing here on steemit and there are mixed feelings about that.

There also has been heated debates about the role and respect for bots.

Either way, I think it is here to stay.

However, just because computers can do something doesn’t mean they will do it well or the same as humans.

Writing or otherwise.

The list above might not work well for AI at the moment, but I think that will change. Especially with doing business online, you may not even know that your “advisor” is a robot in disguise.

This will continue to be the case as another article points out:

“Hiring full-time employees is increasingly a costly indulgence,” wrote Davis. “The corporate career...was replaced by the job, and now the job is being replaced by the task.”

And I would add that task are or will be completed by AI.