Will Robotics Outclass Humans?

  • In recent years we have seen increasing waves of advanced computing technology which have revolutionized our daily lives
  • As our computing speed increases so does the speed of its growth
  • These faster and faster processors are starting to be able to learn and adapt to their environments
  • As the technology continues to grow we will see more and more computers capable of "learning" and "thought"

As the field of artificial intelligence (AI) continues to accelerate, the concept of intelligent robotics is far closer than ever before. As our computing power grows, we have begun to learn to “teach” devices to carry out tasks. We are teaching computers every day as we use predictive texting, Google, or Siri. These programs use the information we give them to make an estimation of what we need, or to react differently to a given input. As they receive and “learn” from more information, these programs will be able to intelligently respond to questions or predict your needs. While true artificial intelligence and robotics are something Isaac Asimov only dreamed about, scientists are making strides to turn science fiction into science fact. How far will technology go? Will robotics outclass humans one day?

Advancing Research

This technology isn’t just for helping Siri order a pizza or Google finding the fastest route to the best Waffle House. In the UK, a recent publication by a research group at the University of Manchester led by, computer scientist, Ross King, has developed and built a custom “robotic scientist” named “Eve” which has found compounds that may help fight a drug resistant strain of malaria.

Automated testing robotics

Photo of AI testing robot Eve, Credit University of Manchester

“Eve” is a group of computers and testing instruments which is working to test experiments. However, the innovation does not simply stop at an automated testing procedure. Eve has the capability to design its own experiments, generate and test its hypothesis, to then interpret the results to create a modified hypothesis. Given an ample supply of reagents, Eve would keep testing and continue to refine its knowledge. The ability to screen and constantly reevaluate the information is helpful to save on often wasted and costly reagents, as King says:

If you screen the whole library, you’ll find all the hits, but you’ve consumed some of all the compounds and a lot of time. Doing things Eve’s way could help solve what he calls pharma’s “fundamental problem”: It is too slow and expensive to develop new drugs.

While a machine like Eve is still in stages of infancy, it could potentially grow to be much more. A testing robot such as this may not match up with the strengths of a human computational chemist but can still provide a large value to pharmaceutical researchers to help identify useful compounds for drugs. However, if we use history as a guide, machines have been outperforming humans since the industrial revolution!

Programmable Learning Brains

One technology which could create a huge disruption would be a self learning and self expanding artificial intelligence. Currently ‘machine learning’ is a small subfield within the greater field of AI. Eve is one example of a program taking what it knows and expanding it, it is possible for computers to learn to perform complex tasks for which they were not originally programmed.

The field of machine learning is how programs like Google Now can adapt to your voice commands, Pandora/Spotify can recommend songs that you will enjoy or even a program like Watson  which can solve Jeopardy questions.

Robotics: self driving car

Image of Google’s autonomous car. Photo Credit: Google Self Driving Car Project

Disruptive Innovations

A noteworthy example of a technology which could disrupt tens of thousands of jobs is the computing power behind self driving cars. While all a stop sign or traffic light seemingly causes us to do is stop or slow down, these occurrences are actually used as learning opportunities for self driving vehicles. The software is able to observe human behavior and use that to pick the proper response given any situation.

However, there is a lot more to self-driving cars than just entering a set of common road rules into a computer. As Pedro Domingos, Professor of Computer Science at the University of Washington discusses:

People tried just imputing all the rules of the road, but that doesn’t work,” explains Pedro. “Most of what you need to know about driving are things that we take for granted, like looking at the curve in a road you’ve never seen before and turning the wheel accordingly. To us, this is just instinctive, but it’s difficult to teach a computer to do that. But [one] can learn by observing how people drive. A self-driving car is just a robotic controlled by a bunch of algorithms with the accumulated experience of all the cars it has observed driving before—and that’s what makes up for a lack of common sense.

These technologies are quite a ways from mass adoption, but they are showing promise of what the field of artificial intelligence can do in the future. While robotics, artificial intelligence and self learning machines are in their infancy, they are growing and changing faster than ever imaginable. These machines can allow more work to be done in less time, with fewer people. Although some jobs may be lost to automation and intelligent systems, these will open the way for even more complex problems to be evaluated with the benefit of technologies which can grow and adapt to their situation. While the future is uncertain, the continual growth and expansion of adaptive robotic technology will a field to keep an eye on.

About: Curtis Obert

Curtis Obert EIT, MEM is a graduate of Case Western Reserve University, with a passion for design and process improvements. He first studied as a Polymer Engineer, and followed his love of understanding how engineering and people management fits into the business as a whole. He spends his free time hiking, cooking, and restoring antique firearms.

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