Mechanical engineers are often among the highest paid professionals in the US.
And as the US population grows and technology improves, more and more of them will be making their living on a chip.
But is that enough to keep them happy?
In a world where everything from cars to smart TVs are built on the same technology, it’s no wonder that they’re making the most money out of it.
But as technology evolves, it can also create problems for them.
“The challenge that we see with these kinds of jobs is that there’s a shift from a highly automated, highly automated system to one where we need people to make these kind of choices,” said Richard Kohler, president and CEO of Kohler Engine Parts, a maker of parts and automation systems.
“That requires some pretty tough choices to make, but we think the most critical decision is, do we want to pay someone a million dollars to build a computer with an 80,000-core CPU that has a 128MB RAM and 4K video?”
If you’re a mechanical engineer, you probably know that the most important thing about a chip is that it runs on software.
The more complex and specialized it is, the more it can do, and the faster it can be built.
But a lot of the work involved in that is still software-based, and that’s where things get tricky.
“If you want to build something like an autonomous robot, you have to think about the software that controls that robot,” Kohler said.
“The software is software, it doesn’t have a lot in common with the hardware.”
For example, the software is usually run by an open source project like the open source Robotics community or the open-source AI community.
So if you want something that’s a bit different from the typical chip, it may require some work.
And there’s no guarantee that a new open source tool will be able to run that software.
“When you’re designing your robot, the first thing you have is a design of what you want the robot to look like,” Kohlers said.
And that’s the kind of work that a lot can get done on a computer.
The challenge is to build that software into the chip, which takes a lot more time and money.
To understand the challenges mechanical engineers face in their careers, I spoke with Kohler.
He told me that the biggest issue is not that they can’t do a lot with a chip, but that they have to do a good job of not only designing the chip but building it.
“You need to be able, for example, to build an entire system from the ground up that doesn’t require a lot or lots of data or lots or lots and lots of software,” Kohls said.
For example, if you have a chip that uses a lot memory, the challenge is that you have less data than you would like.
And if you’re building a system that has to handle millions of instructions, you’re going to need a lot and lots and tons of data.
“I think what we’re trying to do here is build a system for a particular kind of job, like a robotics robot that has lots of control,” he said.
“So if you were building a robot that’s going to be used for navigation, or you were designing a robot for driving, or a robot in a factory, it really depends on the type of job that you’re doing.”
If you are building a robotic system that is going to have to perform a lot tasks for you, it might take a lot longer to build the system than a computer that has more memory and a higher speed.
“We think that this is a good way to be very competitive in the robotics market,” Kohl said.
As robots get faster and cheaper, that will become even more important.
“When you build a robot with a lot data, you’ll be able do a much better job of understanding how the system is working, and then designing a better system,” he added.
But there are other challenges as well.
“There are some people who would be very happy to have a billion dollars to make a robot, but there’s not a lot that a million bucks can do,” Kohles said.
So you might end up spending a lot time on the computer side of things, or just getting the software right.
For the most part, he said, the biggest challenge is figuring out how to make sure that your robot is the most effective at its job.
“We’re trying not to be an AI-like company,” he explained.
“What we do is try to be the most efficient and safest system, so that if you get a lot wrong, you don’t end up with a robot you don´t want to use.”