In the history of AI the famous xor problem is referenced as an early pessimistic challenge to show what computers can't do. In the 1970s, the Lighthill report also claimed, that robotics has limits. Since the advent of Deeplearning the former concers have been overcome and the modern description towards AI and robotics is, that any technical challenge can be overcome.
The xor problem and the lighthill report both mentioned a technical challenge. In the xor case, the problem was to train a perceptron neural network to detect a certain pattern, and the lighthill report was about computational complexity of so called np-hard problems. Both issues can be solved with modern technology. For current AI discipline it's very easy to tackle the xor and the np-hard problem as well. So called np-hard problems can be solved easily with heuristics and faster computers while the xor problem can be tackled with a simple backpropagation learning algorithm.
It's naive to assume that modern AI doesn't have any kind of limits. They are available but they are hidden deep in the existing literature. The problem has to do with man-machine interaction. A robot will work great in the laboratory but it fails in the reality. This kind of gap is the modern limit of AI. Let me give an example.
Today's engineers are able to build soccer playing robots, self-driving cars, grasping robots and drones. In synthetic AI challenges like micromouse challenge, Mario AI and the robocup soccer challenge, the software shows it full potential. On the first look, it's an example how to use modern technology in a sense making way. The problem is, that non of the algorithm will work in the reality. It's not the fault of enginees, but it's a general problem.
To reproduce the failure we can create a robot challenge from scratch. At first, we need a robot problem. For example, the robot arm should pick&place an object. The next step is to build the hardware and program the software. Then the system is demonstrated in a public robot challenge. Writing the software and building the robot is not very complicated. All what the developer has to do is to implement a modern trajectory planning algorithm, use some neural networks for image recognition and the system will work great.
And now comes the issue. If this newly created robot should be used not in the synthetic challenge but in a real kitchen, the overall system will fail. The exact reason why is unclear. But all experiments in the past have shown the same result. It seems, that programming a robot is easy, but let the robot do a meaningful task is an unsolvable problem. To focus on the concrete issue we have to define what current AI is able to deliver.
What is in the reach of state of the art robotics is to fulfill synthetic robotics challenges- This is done by building the hardware and program the software. The resulting robots will walk, fly and grasp without any problem. They are even able to master games like soccer, hockey, tennis or whatever. Lots of demonstration videos are available in the internet, but it's also possible to build such robots from scratch without using existing software.
The problem with these projects is, that they can be categorized as practical joke toys. The machine is doing something which is funny, but not more. A robot which is useful in reality has to become not a joke toy but a productive robot. The average customer likes to use a robot for reducing it's own workload. That means, the robot should do work which is normally done by humans. And this minimum requirement isn't fulfilled by current robotics.
It's not fulfilled by self-created robot projects from scratch, and it's not fulfilled by commercial grade robots. This kind of disappointed insight isn't discussed in the literature. Instead the mainsteam robotics community has the hope that the results from synthetic challenges can be transferred in the reality. The idea is, that if a robot is able to walk on two legs and can kick a ball into the goal, it is very easy to use such a software for practical application and build a business around the robot. This kind of optimism can't be fulfilled.
Under the term “limits of automation” the discussion was held on a theoretical level. The most obvious reason why robots have failed in reality is because the normal automation level was high before the advent of robots, and it's hard or even impossible to increase the automation level further. A simple example is a modern kitchen. Current technology consists of automatic refrigerators and automatic washing machines. This kind of automation level requires only a small amount of humans. It's not possible to replace these humans with robots and increase the automation level to 100%. Exactly this pessimistic point of view can be seen in every robotics project. The result is, that even kitchen robots are working great in the lab, they can't be introduced in real kitchens.
The danger is high, that Artificial Intelligence in general is a waste of time. A discipline which doesn't produce new kind of practical technology, is some kind of non-sense science. Sure, the AI discipline has evolved over the years and current algorithm have become much better than it's counterparts 30 years ago, but even today's AI software can't be used for practical applications. It's fair to summarize the issue so, that AI is a purely theoretical discipline.
Macro-economic experts have formulated the thesis under the term “productivity paradox”. It compares advanced technology for example biped robots which can do the backflip faster than a human with it's inability of doing something useful. That means, on the one hand, the enginees can build a walking robot who is able to climb stairs and can perceive the environment with advanced neural networks, but the engineers struggle to use this robot for simple tasks and increase the productivity by only 1%.
The perhaps most impressive example who Artificial Intelligence and practical applications doesn't fit together is the domain of game AI. Since the 1990s, lots of advanced strategies were developed to realize all sort of non player characters. An early example are the ghosts in Pacman, but it's possible to play other games like chess, Super Mario, Go and sokoban with Artificial Inteligence. A modern game AI beats a human player easily. This was perceived by the AI Community as an example how well the discipline has evolved.
The problem is, that all these game AI characters are useless. They are working great in the simulated environment but it's not possible to use the software for controllling real robots. A naive assumption is, that it's very easy because controlling a game character needs the same amount of artificial intelligence which is needed for a pick&place robot in the kitchen. The problem is, that a practical robot should provide a value for the customer. He likes to press the on button, and then the machine is doing something for the customer. Exactly this requirement is out of reach. Current AI is not working with this principle.
Perhaps this pessimistic outlook sounds a bit uncommon. The advice is to start a simple game. You as a reader, can try to browse through online robot stores and identify a practical robot. Then this robot should proove, that it is doing something useful. The hypothesis is, that such an experiment will fail. There is not a single robot available which will reduce human workload. As a result, most robotics companies from the past went into bankruptcy. They have designed certain robots, but they were not able to sell them to the customers. Instead of providing some examples, it's up to the reader to find the concrete models.
In contrast, it is very easy to identify non-robotic machine which will improve the daily live or can make a company more productive. Typical examples are normal cars, normal washing machines, all sorts of mechanical tools and so on. These products have in common, that the customer has to pay first the price for the product, and then he gets something in return which improves the activity. Let me give an example.
A device like a electric oven is not sold as a practical joke article which is able to prank people in the next party, but the machine has a practical application. It is bought by the customer, because he likes to heat water. The prediction is, that after buying an electric oven, the customer will use the product on a daily basis and because of this reason the paid price for the product make sense. That is some sort of normal product.
Robotics are different kind of category, they do not have a practical purpose but they are sold for different reasons. Somebody may think, that a robot can be used in meaningful way, similar to an electric oven, but this kind of outlook in naive. Making this unrealistic expection visible and find reasons why is an important step towards a modern Artificial Intelligence.