The Autonomic Computing Journey: Self-Driving Revolution
Our acceptance of machines' ability to think is already happening. What started with an oddity in Watson has become part of some people's commute.
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In 2004, we watched iRobot when it came out and probably winced at the idea that AI could become something to fear. There is a distinct difference between true AI (autonomous) and systems that can operate as autonomic.
Self-Driving Miss Daisy
Tesla is positioned to do what Apple did for us on personal computing and personal devices. They are creating a market where one does not exist, and they are already telling us that it is essential. Elon Musk could be the Steve Jobs of this generation. I’m an admirer of the story, and the vision across all of his companies, but not because of the sleekness of any of the designs. The really important bits are what is happening in the center of it all.
Just like the mainframe started us out in the industry, you can see what it is that Musk is doing: increasing inputs, and harnessing an autonomic engine to create spectacular output. Self-driving Tesla automobiles have made the news in a number of ways lately. We did see the terribly unfortunate accident which took the life of a Tesla owner recently. This raised the question of whether we can trust
As discussed here recently, the reality is that much of what we do is driven by algorithms, and automation is built around it. The heightened awareness of risk with something like self-driving cars makes the use case a little scarier because it has a real, physical effect if it goes wrong.
Tesla themselves refer to their system as semi-autonomous and outwardly state that it is “assisted” rather than completely self-driving.
Google Cars Proving to Be Positive
Out of the millions of miles logged by the Google self-driving car project, only a handful of accidents have occurred, and none have incurred any physical risk so far. The message there is that the majority of self-driven mileage has been without incident. If you pair that with Tesla’s results, you can statistically point to the success of self-driving automobiles in this stage of their evolution.
Google and Tesla are only two of many companies investing in this area of the market. Semi-autonomous trucks are in development by a few different manufacturers. The real issue during the initial adoption won’t be the autonomic systems, it will be the people who think they know better.
With greater adoption of self-driving automobiles and self-organizing traffic patterns, we will see a leap in productivity because of how much more efficient the systems that operate on those self-driving environments can run. In other words, the better the underlying infrastructure gets, the more we can concentrate on moving back to clearing up bottlenecks in other more meaningful areas. It’s the very basis if the Theory of Constraints which we have discussed in a few blogs in the past, such as this one.
The Story Starts Here
This is the next generation. We are at the inflection point of something very cool. We have a rather interesting next step to take which will need the leap of faith that brought us from the mainframe to the distributed data center. The real leap of faith has been made by the innovators, and as consumers of these products and platforms, we just have to dabble until we are comfortable. Then we can truly embrace the next generation of systems.
That is where we get to the biggest part of the story yet. The fun part is that you won’t even believe that you’ve already accepted more of this than you realize. Before we close out the story and bring all of the back together, let’s take one more trip into the traditional data center journey and look at the evolution of a rather interesting part of the IT stack in our next post.
Published at DZone with permission of Eric Wright, DZone MVB. See the original article here.
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