There are still many challenges to overcome in the area of communication between robots and humans. For example, will a robot ever be capable of identifying our intentions?

Can a robot detect if we say something in a fearful or more self-assured way? In which case, this can be important in certain situations. Or when we carry out an action, what does this say about our actual intentions? For instance, it is no easy task to get a driverless car to recognize whether a pedestrian intends to cross the road, or is simply standing at the side of the road. Typically, as a pedestrian, we will try to make eye contact with the driver to indicate that we would like to cross. But this type of ‘unwritten rules’ in human-to-human communication is not easy to transfer to AI systems.

The way humans and cobots work together on the workfloor can take the form of the human demonstrating how something is done and the cobot learning from it so that it can then perform a particular action perfectly. In a single, repetitive process it may be that the human worker will only have to show the robot how to do something a few times and the robot will then take it from there.

But in more complex situations, the cobot may always need a human workmate on hand to give it instructions and to instruct it how to do things. One example of this would be collecting waste in a city. It can be a complex business distinguishing what is waste and what isn’t. It’s also hard to know how to react if someone waves to the waste truck driver and then runs up behind with a bag of waste to be picked up. A robot would not know how to respond, whereas a human knows that the friendliest thing to do is wait.

This means that in some situations, humans and robots will always have to work together, with the robot taking on the heavier work and its human workmate having more time for interaction with other people and knowing how to respond to unexpected situations.

AI systems need to be tested regularly

As we have already said, the human always needs to understand how a robot arrives at a certain conclusion or action – and must always be able to make adjustments where necessary. Recent examples of problems with artificial systems have demonstrated exactly that.

For example, there is the instance of the chatbot Tay, which began posting racist messages on Twitter after certain other Twitter users left politically incorrect posts. The chatbot had not been given any instructions to recognize these types of statements as being inappropriate.

‘Norman’ also made the news in 2018. Norman is an AI system that displayed psychopathic characteristics when doing a well-known test with Rorschach inkblots. It happened because Norman had previously been shown mainly sensational and violent images from Reddit and he had built up a picture of the world based on those images. MIT researchers wanted to use the experiment to demonstrate the danger of ‘false data’ being used as input for AI systems.

And finally, there is also the example of the COMPAS algorithm that was used by the judicial system in America to make predictions about the recidivism of convicts. What happened? Based on the historical data used as input for the algorithm, it reached the conclusion that blacks were more likely to re-offend than whites.