Tesla’s problem: overestimating automation, underestimating humans

Tesla hoped to provide 5,000 latest Model 3 electric cars each week in 2018. So far, it has failed to fabricate even half that number. Questioned on the matter, the corporate’s CEO, Elon Musk, claimed that “excessive automation was a mistake” and that “humans are underrated”.

He’s not mistaken – the recent drive for full automation has missed the importance of adaptability. Humans are still much more capable of adapt to vary than artificial intelligence (AI). In the long-term, AI has the potential to interchange human employees, but for now leaders need to find out the correct speed of change.

The Tesla factory in Silicon Valley is very automated. Early on, Musk understood that any process following a sequence of predefined steps and going down on a reasonably controlled environment, comparable to a factory floor, could possibly be automated by artificial intelligence and robots. And that is something he must be credited for.

But while autonomous systems are developing rapidly, humans remain much better at adapting to unexpected changes. When it involves complex factory work, that is something that shouldn’t be underestimated. Looking back on Tesla’s productivity issues, Musk undoubtedly missed the importance of adaptability in manufacturing. The probability of small errors and unexpected situations is proportional to the complexity of the method, especially when the method takes place within the physical world.

Adaptive intelligence

Humans and other types of intelligent life evolved to survive in a consistently changing world. For this reason, they’ll cope remarkably well with unexpected situations and discrepancies between expected and actual events. As cognitive scientist Gary Marcus emphasises, there are numerous things “that go into human intelligence, like our ability to take care of the correct things at the identical time, to reason about them to construct models of what’s occurring with a view to anticipate what might occur next and so forth.”

Humans and animals can even adapt their bodies to radically different situations with a view to achieve their goals. For example, we will move forward by walking, swimming, jumping, climbing and crawling – and we will accomplish that even when we lose using a limb. These dynamic features of biological systems help them address radical change under highly complex situations.

Machine learning, then again, isn’t yet at the extent of human intelligence and adaptableness. Sure, we now have made great progress. Today, advanced AI algorithms, inspired by nervous systems, can learn to recognise similar situations like a traffic light turning red or a ball falling on the road even higher than humans. Developments in robotics also mean that latest robots product of soft materials can physically adapt to unexpected objects within the physical environment. But in each cases, adaptability is restricted to variations inside a restricted category of objects or events.

The truth is that we now have not yet mastered the design of robots and AI which might be resilient enough to reply to unpredictable environments. Take the instance of robots utilized in the packaging industry. Automated guided vehicles with limited on-board intelligence can only follow easy programming instructions taking them along fixed routes in an outlined environment. These robots might find a way to select up a product and place it right into a carton, without the power to do anything more complex. When the job changes, the robot can have to get replaced.

More complex mobile robots are also in use. They have built-in sensors and scanners, in addition to software that permits them to detect their surroundings and select probably the most efficient route in order that a product isn’t necessarily placed in the identical location each time. These more complex robots are more flexible and adaptable, but they’re still quite distant from what biological systems can do.

This could possibly be an issue for overly automated factories where small physical discrepancies (a broken wheel, wear and tear on the bottom, imprecisely positioned parts) can rapidly accumulate and lead to unpredictable situations (a component isn’t where it must be, a robot is missing). When a process changes or the factory starts making a brand new product, then there’s a must reconfigure the equipment and find a unique solution. This isn’t yet entirely close by of AI and robotics.

Full automation

Musk has publicly noted his desire to create a totally autonomous factory. His underlying goal is to beat the boundaries of human speed. With greater speed, higher outputs could be achieved. But in complex environments, comparable to a highly automated factory, there’s a necessity for highly adaptable robots that may reply to unexpected situations and to one another like biological systems do. Introducing that form of biological resilience in robotics and AI requires further research.

The first involves testing robotic automation inside an outlined set of processes, comparable to picking raw material and placing it on the assembly line. The second involves expanding that test to multiple functions and processes, comparable to combining the raw material and packaging the product. The third stage is to deploy robotic co-workers and adaptive AI as human assistants. Today, that is one of the best we will aim for.

It isn’t yet clear after we can have the technology for full automation without human intervention (stage 4) and what form it should take but Musk must be praised for trying. He could have underestimated humans but what he’s learning is precious and can help him to drive ahead of others in the long run.