A major concern in the economy literature is adressed by the term of “productivity paradox”. It means, that the productivity hasn't increased with the advent of robotics at the workplace and in the worst case it will become lower with the introduction of robotics. From an economic perspective the productivity is a very important measurements, it has to do with how much the company has to spend to produce a product.
A possible technology which will result into powerful robots is teleoperation. Teleoperation is opposite from classical Artificial Intelligence because the idea is that a human operator is needed. The only new thing about teleoperation is, that the human operator can be located everywhere. From a productivity standpoint it's possible to guess what will happen with the productivity. It will remain the same.
That means, if 10 human workers are replaced by 10 robots and for each robot one human operator is needed in the loop the overall costs are the same. Or a bit higher, because the robotics hardware produces additional costs. So it's a zero sum game, isn't it? It's true that the productivity itself remains unchanged. A human controlled robot will have the same or a slower speed than a normal human. And the promise of robots to replace the human wasn't fulfilled.
So why exactly should a company give the technology a chance? I don't know. Perhaps the idea is, that teleoperation has non-measurable effects or another reason is, that the companies likes to robotics on the workplace but didn't want to wait until human level AI is available, so they are doing a step in between and using human operators in the loop. In the case of autonomous cars, the advantages of teleoperation are easier to grasp. Most today's cars are operating less than 5% of the hours per day. The average car is parking all the time, and no sharing takes place. With teleoperated cars, the situation can change drastically. This would allow – in theory – to let a single car drive 24/7 and less cars are needed overall. The pairing between cars, human operators and customers can be managed more flexible than with existing cars.`
household robot
A different application for a teleoperated robot is a household robot for the elderly. The idea is, that the robot is controlled by family members. That means, the task isn't solved by an AI, but a human is needed.
The underlying assumption is the same like for self-driving cars. The idea is, that only human level AI can replace human workers. It's not possible to remove a human from the loop or increase the productivity. The only thing what technology can provide is to increase the distance between the request for work and the human operator who provides the work. That means, a robot for the elderly is different from a normal machine. It has more in commong with an advanced telephone which also needs somebody on the other side.
Showing posts with label Productivity paradox. Show all posts
Showing posts with label Productivity paradox. Show all posts
December 20, 2019
December 18, 2019
Switch off the robot and and have fun -- Analyzing the bottleneck in modern automation technology
From a technical perspective, Artificial Intelligence research has developed algorithm for controlling robots. The most advanced one are motion planning with model predictive control. The idea is to create a forward model of the system and use the model for trajectory planning. This allows to build biped robots and manipulation robot hands.
Somebody may argue, that the practical applications are obvious. Because it's possible to utilize the technique in self-driving cars and in pick&place robots which can be used for industrial applications. There is only a smaller problem. It seems, that solving a robotics task from a technical application is not enough. That means, on the one hand it's possible to build a pick&place robot and on the other side it's not possible.
What can be solved with modern AI techniques easily are so called robotics challenges. That are synthetic challenges like micromouse, robocup, Mario AI or robot pick&place tasks. The mentioned combination of a forward model, motion planning and model predictive control results into a working system. The problem is, that a synthetic robot challenge is very different from a practical application of a robot. A practical application is equal to convert the technology into a product, and sell it to customers. Exactly this is not possible and attempts from the past into that direction have failed.
What does that mean? Creating a robot with the help of model predictive control and planning is the best practice method for building a pick&place robot which works in a challenge. Using the same technique to build a commercial robot which is sold on the market will fail. That means, the same technique is a powerful one and a useless one at the same time. This paradox is hard to grasp. Mostly it's assumed, that artificial Intelligence is a technical challenge. That means, if the algorithm was identified to control a robot than the overall problem is solved.
To measure the bottleneck in reality it's not enough to describe robotics from a technical perspective. The more elaborated form is to investigate the history of robotics companies and their failed attempt in selling a product to customers. The good news is, that in the last decade many examples are visible. The interesting question is why a certain robotics company have failed to sell the product. There are some arguments available:
- price of the product is too high. This was the case for the PR2 robot which costs around 400k US$
- robot technology is not advanced enough. This was the case for the helpmate robot in the 1990s. In that time, the onboard computer was slow and the sensors were not accurate.
- price is low and robot technology is advanced but the customer doesn't buy the product too, this was the case for the Baxter robot from rethink robotics
The pessimistic prediction is, that even the robot is sold for little money and is working with the latest hardware and software the customer won't buy the product. This outlook is equal to a general failure of robotics. Which means, that it's not possible overall to sell a robot to customers. Let us construct a hypothetical example. Suppose a company builds a pick&place robot which is very cheap and is working with model predictive control. Will this product become successful on the market or not?
The prediction is, that the robot won't find it's customers. The reason is, that even a lowcost, MPC-based robot is not able to increase the productivity in a real life condition. The task which is solved by the robot, and the requirements in reality are not the same. Or let me give another example which is in the domain of most people.
Suppose, a car company develops a self-driving car for the same price like a normal car. It's equipped with the latest sensor technology and advanced AI software. Technically spoken the car is able to drive autonomously. Will this car get customers or not? The naive prediction is, that such a car will get sold many million times worldwide. Because it reduces the workload of all the human drivers. It is useful for private households and commercial applications as well.
The problem is, that such an optimistic assumption is maybe wrong. In reality, a self-driving car is not fulfilling the real requirements. It won't reduce the workload of the driver but it increases the workload. That means, the human driver will deactivate the autopilot if he likes to relax a bit. This kind of counter-intuitive strategy isn't showing a missing knowledge of the human driver but it shows, that something with robotics technology is wrong.
Autopilots in ships
An interesting example of automated controlled vehicles is a ship. Autopilots for ships are available for decades, or at least it is written in the literature that such autopilots are available. A more realistic investigation comes to the conclusion that 0% of all ships today are using an autopilot. In contrast, all the miles were driven manual without computerized decision support. How can this mismatch be explained?
It's important to separate between autopilots in ships and the literature about autopilots. What is explained in the literature is the technical working of an autopilot. In the average book it's mentioned that steering a ship is a mathematical challenge. In some newer publication it's described as a problem for control theory which can be solved with modern algorithms. It's possible to make this problem more obvious by developing an autopilot from scratch and compare different RC controlled ships in a challenge.
On the other hand, there is the problem of ship steering in reality. In reality, the autopilot is something which isn't available. A ship is working with a human operator in the loop. The operator has some buttons he is able to press and before he can do so, he has to ask the captain what he likes to do next. An autonomous controlled ship would be equal to replace human work with software. This futuristic vision is not available in the reality, and it won't happen in the next 50 years. In reality, the amount of human operators on the bridge is constant. That means today ships are controlled the same way, like in the 1950s.
Sure, the technology has evolved a little bit. Today's ships are using computers and modern sensors. But the productivity is the same. Productivity is the measurements how many human workers are needed to control a ship with a certain size. The productivity has never increased. That means, computer technology was not able to replace human work with algorithms.
The surprising fact is, that even remote controlled ships are not available right now. It's a vision for the future to increase the efficiency for freight transport. The idea is, that if the human operator can stay outside of the ship it's easier to control the device. In case of a remote controlled ship, the amount of needed humans in the loop remains the same. The only advantage is, that they doesn't need to by physical on the ship. This kind of low end automation was never realized because the disadvantage is, that many new sensors and costly equipment has to be installed on the ship. That means, even in the year 2019, the amount of remote control freight ships is 0%. The prediction is, that in the next 50 years the amount will be the same.
Conclusion: Autopilot for ships are not available. Remote controlled ships are not available. The productivity over the last 50 years hasn't increased and it's not possible to use Autopilots in the reality. What is written in the book about Artificial Intelligence is wishful thinking.
Somebody may argue, that the practical applications are obvious. Because it's possible to utilize the technique in self-driving cars and in pick&place robots which can be used for industrial applications. There is only a smaller problem. It seems, that solving a robotics task from a technical application is not enough. That means, on the one hand it's possible to build a pick&place robot and on the other side it's not possible.
What can be solved with modern AI techniques easily are so called robotics challenges. That are synthetic challenges like micromouse, robocup, Mario AI or robot pick&place tasks. The mentioned combination of a forward model, motion planning and model predictive control results into a working system. The problem is, that a synthetic robot challenge is very different from a practical application of a robot. A practical application is equal to convert the technology into a product, and sell it to customers. Exactly this is not possible and attempts from the past into that direction have failed.
What does that mean? Creating a robot with the help of model predictive control and planning is the best practice method for building a pick&place robot which works in a challenge. Using the same technique to build a commercial robot which is sold on the market will fail. That means, the same technique is a powerful one and a useless one at the same time. This paradox is hard to grasp. Mostly it's assumed, that artificial Intelligence is a technical challenge. That means, if the algorithm was identified to control a robot than the overall problem is solved.
To measure the bottleneck in reality it's not enough to describe robotics from a technical perspective. The more elaborated form is to investigate the history of robotics companies and their failed attempt in selling a product to customers. The good news is, that in the last decade many examples are visible. The interesting question is why a certain robotics company have failed to sell the product. There are some arguments available:
- price of the product is too high. This was the case for the PR2 robot which costs around 400k US$
- robot technology is not advanced enough. This was the case for the helpmate robot in the 1990s. In that time, the onboard computer was slow and the sensors were not accurate.
- price is low and robot technology is advanced but the customer doesn't buy the product too, this was the case for the Baxter robot from rethink robotics
The pessimistic prediction is, that even the robot is sold for little money and is working with the latest hardware and software the customer won't buy the product. This outlook is equal to a general failure of robotics. Which means, that it's not possible overall to sell a robot to customers. Let us construct a hypothetical example. Suppose a company builds a pick&place robot which is very cheap and is working with model predictive control. Will this product become successful on the market or not?
The prediction is, that the robot won't find it's customers. The reason is, that even a lowcost, MPC-based robot is not able to increase the productivity in a real life condition. The task which is solved by the robot, and the requirements in reality are not the same. Or let me give another example which is in the domain of most people.
Suppose, a car company develops a self-driving car for the same price like a normal car. It's equipped with the latest sensor technology and advanced AI software. Technically spoken the car is able to drive autonomously. Will this car get customers or not? The naive prediction is, that such a car will get sold many million times worldwide. Because it reduces the workload of all the human drivers. It is useful for private households and commercial applications as well.
The problem is, that such an optimistic assumption is maybe wrong. In reality, a self-driving car is not fulfilling the real requirements. It won't reduce the workload of the driver but it increases the workload. That means, the human driver will deactivate the autopilot if he likes to relax a bit. This kind of counter-intuitive strategy isn't showing a missing knowledge of the human driver but it shows, that something with robotics technology is wrong.
Autopilots in ships
An interesting example of automated controlled vehicles is a ship. Autopilots for ships are available for decades, or at least it is written in the literature that such autopilots are available. A more realistic investigation comes to the conclusion that 0% of all ships today are using an autopilot. In contrast, all the miles were driven manual without computerized decision support. How can this mismatch be explained?
It's important to separate between autopilots in ships and the literature about autopilots. What is explained in the literature is the technical working of an autopilot. In the average book it's mentioned that steering a ship is a mathematical challenge. In some newer publication it's described as a problem for control theory which can be solved with modern algorithms. It's possible to make this problem more obvious by developing an autopilot from scratch and compare different RC controlled ships in a challenge.
On the other hand, there is the problem of ship steering in reality. In reality, the autopilot is something which isn't available. A ship is working with a human operator in the loop. The operator has some buttons he is able to press and before he can do so, he has to ask the captain what he likes to do next. An autonomous controlled ship would be equal to replace human work with software. This futuristic vision is not available in the reality, and it won't happen in the next 50 years. In reality, the amount of human operators on the bridge is constant. That means today ships are controlled the same way, like in the 1950s.
Sure, the technology has evolved a little bit. Today's ships are using computers and modern sensors. But the productivity is the same. Productivity is the measurements how many human workers are needed to control a ship with a certain size. The productivity has never increased. That means, computer technology was not able to replace human work with algorithms.
The surprising fact is, that even remote controlled ships are not available right now. It's a vision for the future to increase the efficiency for freight transport. The idea is, that if the human operator can stay outside of the ship it's easier to control the device. In case of a remote controlled ship, the amount of needed humans in the loop remains the same. The only advantage is, that they doesn't need to by physical on the ship. This kind of low end automation was never realized because the disadvantage is, that many new sensors and costly equipment has to be installed on the ship. That means, even in the year 2019, the amount of remote control freight ships is 0%. The prediction is, that in the next 50 years the amount will be the same.
Conclusion: Autopilot for ships are not available. Remote controlled ships are not available. The productivity over the last 50 years hasn't increased and it's not possible to use Autopilots in the reality. What is written in the book about Artificial Intelligence is wishful thinking.
November 16, 2019
Identify the productivity paradox
In the economic literature there is a mystery available called the productivity paradox. It's about a mismatch between computer technology which is available everywhere and low productivity rates in the office and in the service industry. It make sense to describe the paradox in detail.
The first important fact is, that the productivity paradox has to do with the transition from the forth to the fifth computer generation. The forth generation was from 1970-1980, while the fifth computer generation started after the year 1980. During the forth generation no productivity paradox was visible. In that time lots of innovation were made which are used for practical applications, for example the barcode reader which revolutionized the supermarket, the CNC machine which allows to increase the productivity in the factory and the electronic pocket calculator which makes office number crunching more easily.
In the early 1980s the first books were published about a potential future, in which robots and artificial Intelligence is used to increase the productivity further. The idea was that robots can improve factory automation and help the service industry to reduce the costs. This vision was never realized. In contrast to the technology from the 1970s, the next technology step since the 1980s was never introduced in the reality. The mismatch has resulted into the productivity paradox. It's situation in which high speed computer, advanced robots and modern expert systems are available in theory, but the technology can be used for practical application.
To explain this situation better we have to go back to the golden 1970s. The situation was, that technology invented in that time was useful for practical tasks. The CNC machine is a good example. In the 1970s the technology was new and it helped to improve the factory. A CNC machine is superior to technology used before. Superior means, that the company can reduce the costs and the employees are motivated in using it. The surprising fact is, that the 1970s was the last decade in which innovation took place. Modern factories in the year 2019 are using the same technology available in the 1970s which is a combination of barcode reader, mainframe computer, CNC machines, and telephone communication.
On the first look, the companies have a need for introducing more modern technology, namely robotics and Artificial Intelligence. It's important to know, that the companies have tried so in the past, since the 1980s many projects were started with the attempt to introduce advanced robots in the factory automation. All of these projects have failed. In contrast to CNC machines a robot has no advantage.
The productivity paradoxon and the missing fifth computer generation is the same problem. Both can be dated back to the early 1980s. It has to do with the absense of innovation after the 1970s were over. Or to explain it from the other perspective, the automation technology has freezed since 40 years. State of art factory automation is the same like in the mid 1970s. To understand the issue in detail we have to describe what the term fifth computer generation is about.
In the beginning it was a vision about future computer technology. The idea was, to develop robots which can help to increase the productivity. This plan was never realized. Not because of the technology itself, but because the robot prototypes can't be used in real applications. What is possible with today's robots is to use them in synthetic benchmarks, for example the Robocup challenge. In such a task the robot is able to play soccer in a team. The problem is, that the robot in the challenge can't be used for a task in the real world. The robot technology is locked into the synthetic challenge. From an academic perspective such robot competitions have become very successful. The early micromouse challenge evolved into more modern challenges in which the teams have build robots which are walking like humans on two legs. Today's robots are more advanced then their counterparts 30 years ago and they are able to master more complicated challenge. Importunately, the gap between a synthetic challenge and a real project is larger than ever. All the robot shown in youtube videos are nothing but show robots. They are working as a prototype in a fictional challenge and the technology can't be used for increasing the productivity in a real application.
That is the major difference to technology within the forth computer generation. Innovations like the barcode reader and the CNC machine can be utilized for real tasks. It seems, that Fifth generation computers in general are struggling with the reality. What can be seen is, that fifth generation robot projects have a tendency to flip the social roles. The machine isn't a tool which helps the human but it's the other way around. The team of human programmers has to invest lots of hours until the robot is able to participate in the robocup challenge. That means, the robot won't provide work, but it wastes human energy. The question is, what is the purpose of robocup like challenges? The main idea is to tell a story about future society. According to the Robocup challenge, robots will become successful in under 10 years and will help human employees. THat is in short the plot which is told by the participents of robot challenges. They are programming the robots and creating the videos to support their story about fifth generation computer. It's an optimistic outlook into the future which should inspire more users to participate in the movement.
It's important to know that this kind of vision isn't realistic. Robots can't be build, and they won't help human workers in the factories. Projects in the past which are trying to realize such projects in factories have shown the opposite. it seems, that every failed robot project has increased the need to tell an optimistic vision about future robotics.
Let us go a step back and describe the situation from an economy perspective. Suppose a companies is interested in increasing the productivity in a factory. Which kind of technology supports this attempt? The only technology which works was invented in the 1970s. Everything what was invented later won't increase the productivity but it will reduce it. That means, if the company buys some of the machines invented in the 1970s the factory will run on the maximum productivity level. It's not possible to increase it further.
Perhaps it make sense to define the difference between CNC machines and robots briefly. On the first look, there is no difference, because on timeline the CNC technology was invented in the 1970s and the robots were build in the 1980s. But CNC machines can be used for practical applications, while robots not. CNC machines are part of the forth computer generation while robots are part of the fifth generation computer. Between both there is a large gap. It's important to become aware of the gap, because it helps to explain the productivity paradox.
The major problem of CNC machines is, that they can automate some tasks in a factory but not everything. A CNC machine will only work together with humans in the loop. Most factories are using CNC machines at the assembly line and for automated welding. And from the cost perspective, it's a here to stay. The problem is, that no technology is available which can automate the factory more. The human workers can't be replaced by robots but they are working together with CNC machines. This problem can't be solved by explaining to the factory what a robot is, they know it from failed projects from the past.
Understanding the needs of a factory
Instead of asking how robots can help to automate a factory, the more elaborate question is what kind of technology a factory needs. Modern companies have a demand for barcode readers, CNC machine and other technology invented in the 1970s. They are using these tools to reduce their costs and increase the output. In contrast, technology which was developed in the fifth generation computer revolution namely robots, Artificial Intelligence and neural networks doesn't fulfill the needs of a modern factory. It's not possible to use them to increase the productivity, but they are developed for it's own purpose. The main reason why robotics has become popular since the 1980s is because the AI programmers have a need for it. They are using robot problems as a vehicle to talk about artificial Intelligence. The fifth generation computer is mostly an academic discipline which isn't solving problems but is creating a new sandbox.
None of the newly developed robots will be introduced in the mainstream market as a product. A robot can't be sold to customers, because the customer won't profit from it. What a customer likes to buy in exchange for money is a modern CNC welding machine, because such machine provides added value. In contrast a robot from the latest generation doesn't provide something in return. It's a loose-loose situation.
From an economic perspective there are two sorts of robotics company on the market. The first one are selling CNC machines under the label robots. Notable example are the Fanuc, ABB and the Kuka company. These companies are successful in the business not because their robots are great, but because their CNC machine are working reliable and are the same like in the 1970s. The second sort of robot companies are real innovators. For example, Rethink robotics, Jibo and Willow Garage have produced robots which are fitting great into the fifth generation computer revolution. What the companies have in common is that they are bankrupt or will become so within 2 years. The reason is, that the product they are selling have no added value to the customer. The only place in which a Baxter robot from Rethink robotics make sense is a academic robot challenge. The funny thing is, that especially the Baxter model was a success and a failure at the same time. It was successful because many papers were written about the model with an academic background, and it was a failure, because the robot can't be used for real applications.
The first important fact is, that the productivity paradox has to do with the transition from the forth to the fifth computer generation. The forth generation was from 1970-1980, while the fifth computer generation started after the year 1980. During the forth generation no productivity paradox was visible. In that time lots of innovation were made which are used for practical applications, for example the barcode reader which revolutionized the supermarket, the CNC machine which allows to increase the productivity in the factory and the electronic pocket calculator which makes office number crunching more easily.
In the early 1980s the first books were published about a potential future, in which robots and artificial Intelligence is used to increase the productivity further. The idea was that robots can improve factory automation and help the service industry to reduce the costs. This vision was never realized. In contrast to the technology from the 1970s, the next technology step since the 1980s was never introduced in the reality. The mismatch has resulted into the productivity paradox. It's situation in which high speed computer, advanced robots and modern expert systems are available in theory, but the technology can be used for practical application.
To explain this situation better we have to go back to the golden 1970s. The situation was, that technology invented in that time was useful for practical tasks. The CNC machine is a good example. In the 1970s the technology was new and it helped to improve the factory. A CNC machine is superior to technology used before. Superior means, that the company can reduce the costs and the employees are motivated in using it. The surprising fact is, that the 1970s was the last decade in which innovation took place. Modern factories in the year 2019 are using the same technology available in the 1970s which is a combination of barcode reader, mainframe computer, CNC machines, and telephone communication.
On the first look, the companies have a need for introducing more modern technology, namely robotics and Artificial Intelligence. It's important to know, that the companies have tried so in the past, since the 1980s many projects were started with the attempt to introduce advanced robots in the factory automation. All of these projects have failed. In contrast to CNC machines a robot has no advantage.
The productivity paradoxon and the missing fifth computer generation is the same problem. Both can be dated back to the early 1980s. It has to do with the absense of innovation after the 1970s were over. Or to explain it from the other perspective, the automation technology has freezed since 40 years. State of art factory automation is the same like in the mid 1970s. To understand the issue in detail we have to describe what the term fifth computer generation is about.
In the beginning it was a vision about future computer technology. The idea was, to develop robots which can help to increase the productivity. This plan was never realized. Not because of the technology itself, but because the robot prototypes can't be used in real applications. What is possible with today's robots is to use them in synthetic benchmarks, for example the Robocup challenge. In such a task the robot is able to play soccer in a team. The problem is, that the robot in the challenge can't be used for a task in the real world. The robot technology is locked into the synthetic challenge. From an academic perspective such robot competitions have become very successful. The early micromouse challenge evolved into more modern challenges in which the teams have build robots which are walking like humans on two legs. Today's robots are more advanced then their counterparts 30 years ago and they are able to master more complicated challenge. Importunately, the gap between a synthetic challenge and a real project is larger than ever. All the robot shown in youtube videos are nothing but show robots. They are working as a prototype in a fictional challenge and the technology can't be used for increasing the productivity in a real application.
That is the major difference to technology within the forth computer generation. Innovations like the barcode reader and the CNC machine can be utilized for real tasks. It seems, that Fifth generation computers in general are struggling with the reality. What can be seen is, that fifth generation robot projects have a tendency to flip the social roles. The machine isn't a tool which helps the human but it's the other way around. The team of human programmers has to invest lots of hours until the robot is able to participate in the robocup challenge. That means, the robot won't provide work, but it wastes human energy. The question is, what is the purpose of robocup like challenges? The main idea is to tell a story about future society. According to the Robocup challenge, robots will become successful in under 10 years and will help human employees. THat is in short the plot which is told by the participents of robot challenges. They are programming the robots and creating the videos to support their story about fifth generation computer. It's an optimistic outlook into the future which should inspire more users to participate in the movement.
It's important to know that this kind of vision isn't realistic. Robots can't be build, and they won't help human workers in the factories. Projects in the past which are trying to realize such projects in factories have shown the opposite. it seems, that every failed robot project has increased the need to tell an optimistic vision about future robotics.
Let us go a step back and describe the situation from an economy perspective. Suppose a companies is interested in increasing the productivity in a factory. Which kind of technology supports this attempt? The only technology which works was invented in the 1970s. Everything what was invented later won't increase the productivity but it will reduce it. That means, if the company buys some of the machines invented in the 1970s the factory will run on the maximum productivity level. It's not possible to increase it further.
Perhaps it make sense to define the difference between CNC machines and robots briefly. On the first look, there is no difference, because on timeline the CNC technology was invented in the 1970s and the robots were build in the 1980s. But CNC machines can be used for practical applications, while robots not. CNC machines are part of the forth computer generation while robots are part of the fifth generation computer. Between both there is a large gap. It's important to become aware of the gap, because it helps to explain the productivity paradox.
The major problem of CNC machines is, that they can automate some tasks in a factory but not everything. A CNC machine will only work together with humans in the loop. Most factories are using CNC machines at the assembly line and for automated welding. And from the cost perspective, it's a here to stay. The problem is, that no technology is available which can automate the factory more. The human workers can't be replaced by robots but they are working together with CNC machines. This problem can't be solved by explaining to the factory what a robot is, they know it from failed projects from the past.
Understanding the needs of a factory
Instead of asking how robots can help to automate a factory, the more elaborate question is what kind of technology a factory needs. Modern companies have a demand for barcode readers, CNC machine and other technology invented in the 1970s. They are using these tools to reduce their costs and increase the output. In contrast, technology which was developed in the fifth generation computer revolution namely robots, Artificial Intelligence and neural networks doesn't fulfill the needs of a modern factory. It's not possible to use them to increase the productivity, but they are developed for it's own purpose. The main reason why robotics has become popular since the 1980s is because the AI programmers have a need for it. They are using robot problems as a vehicle to talk about artificial Intelligence. The fifth generation computer is mostly an academic discipline which isn't solving problems but is creating a new sandbox.
None of the newly developed robots will be introduced in the mainstream market as a product. A robot can't be sold to customers, because the customer won't profit from it. What a customer likes to buy in exchange for money is a modern CNC welding machine, because such machine provides added value. In contrast a robot from the latest generation doesn't provide something in return. It's a loose-loose situation.
From an economic perspective there are two sorts of robotics company on the market. The first one are selling CNC machines under the label robots. Notable example are the Fanuc, ABB and the Kuka company. These companies are successful in the business not because their robots are great, but because their CNC machine are working reliable and are the same like in the 1970s. The second sort of robot companies are real innovators. For example, Rethink robotics, Jibo and Willow Garage have produced robots which are fitting great into the fifth generation computer revolution. What the companies have in common is that they are bankrupt or will become so within 2 years. The reason is, that the product they are selling have no added value to the customer. The only place in which a Baxter robot from Rethink robotics make sense is a academic robot challenge. The funny thing is, that especially the Baxter model was a success and a failure at the same time. It was successful because many papers were written about the model with an academic background, and it was a failure, because the robot can't be used for real applications.
A possible explanation of the productivity paradox
From a descriptive perspective it can be shown, that most robotics projects in the reality fail. That means, the new pick&place robot arm isn't able to increase the productivity at the assembly line. Using not a robot but human workers is from an economic standpoint the better choice. What these description doesn't provide is the reason why.
Apart from anecdotes about failed robotics automation projects in the car industry, in hospitals or in restaurants there is need to give reason, why all these projects have failed. A possible explanation has to do with the social role of a robot in a project. There are two possible roles available:
1. robot as a superstar, which is provided in dedicated robotics challenges like micromouse and robocup
2. robot as a tool, which is requested in automation projects in factories and hospitals
The reason why car companies are starting robot projects in the factory is because they are interested in a robot as a tool for improving the workflow. The hope is, that a robot is able to increase the productivity and reduce the costs. Robots are seen as advanced sort of a hammer or a CNC machine which helps the human workers. This social requirement for a robot can't be realized. All attempt in utilizing a robot as a tool have failed.
Only the first role (robot as a superstar) results into a successful project. Building a 2wheeled robot which is able to travel through a micromouse maze, is an engineering problem which can be solved if enough skills are available in the team. Many succesful demonstrations of the task are recorded in the past, and the experiment can be repeated with new hardware and new engineering teams. Sure, it's possible that the robot gets lost in the maze, but this is only a detail problem, which can be fixed with better programming. In general most robotics challenges can be solved within the given time frame. It's important to know, that the social role in all of these competitions is, that the robot isn't seen as a tool, but as the most important subject. So it's a superstar and the engineers have to improve the machine.
The difference between the social roles isn't only an academic one. It has to do if a project becomes economic productive or not. Dedicated robotics challenges in which a robot is the superstar are costing lots of money but they are providing nothing in return. The micromouse who is traversing the maze doesn't fulfill the needs of an external customer, but the robot was created for it's own. In contrast, real automation projects in a company are focussed on customer needs. The car factories likes to sell a car to a customer, and the robot should do a subtask in the production facility. In such a social role the robot fails.
It make sense to describe the situation from an abstract point of view:
1. CNC machines = forth generation computer = social role as a tool = increase the productivity
2. robots = fifth generation computer = social role as a superstar = lowering the productivity
With such a template it's easy to predict the outcome of a certain project. Using a CNC machine in a robotics challenge won't work, because the CNC machine can't be programmed freely. The same mistake is obvious if a robot is used in an automation project in a company with the aim to increase the productivity. Each of the technology has a certain sweet spot in which the device can be used in a meaningful way. It's interesting to know that outdated CNC machines are able to increase the productive, while advanced robotics aren't able to do so. Sure, every factory can test out the hypthesis for themself. It's possible to start new robot project to proof that the thesis is wrong. But according to the known projects from the past, it can be estimated what will happen.
The productivity paradoxon has to do with using a certain technology for the wrong purpose. In most cases, the idea is to utilize an advanced robot for increasing the productivity in a factory. Such projects will fail, this is called productivity paradox. It's possible to avoid the bottleneck in defining the requirements first. Which means, it's possible to increase the productive or to play with robots, but not at the same time.
It make sense to observe successful robot projects in synthetic challenges closely. In competitions like Robocup, the robot is the superstar. The task he should do is given by the rulebook, for example one requirement is, that a team of robots should play a game of soccer. This includes object recognition, pathplanning and teamplay. The most interesting feature is, that most of the participants are successful in the competition. Which means, that the robots are working great, that they are driving by software and that each year the skills become a bit higher. Without any doubt it's possible to program even biped robot in a way that they are successful in the robocup challenge.
The only thing what is a bit surprising is, that these technology can't transfered into other domains. The origin of the robocup challenge was to create a testbed for experiment with new robotics technology with the longterm goal to use the newly acquired knowledge in practical applications, for example in factory automation. The robocup challenge itself runs great. Each year, the teams are become better and lots of new AI related knowledge was written in the papers around the competition. What is missing is the knowledge transfer into real applications.
The prediction is, that this knowledge transfer is not possible. That means, the advanced robot are succeed in the synthetic challenge, but they fail in real applications outside the competition. To understand the reason why it make sense to observe the robot projects in an academic context.
The typical university driven academic project is not motivated by increasing the productivity, but the main purpose is to explore new knowledge. In the standard case, a team of researchers is unsure how to build a biped robot and they are starting the project to develop new biped walking algorithm. If they are trained well, they get after a while the first results and write a paper about the walking robot machine. This paper motivates other researchers to experiment with more advanced robots. From an academic standpoint such projects are producing sense. Because at the end, many new papers were written, and new technology was developed which was not available before. It's important to know, that the needs of university robotic projects are different from automation projects in the reality. That means, the robot in the lab is capable of biped walking and has a built in vision system but the factory automation project can utilize this technology in a meaningful way.
Or let me explain it from a different point of view. Suppose a university team has build and programmed an advanced humanoid robot which was successful in a robocup challenge. Lots of money was invested in the project, and hundred of researchers have supported the project. The problem is, that from the perspective of factory automation all the written software and all the advanced hardware is useless. It won't increase the productivity at the assembly line.
To understand the situation better we have to go back in the 1980s. In that area there was lots of interaction available between universities and factory automation projects. The idea was, to utilize the latest knowledge from the academic domain to increase the productivity in the car industry and build advanced service robot which helps to reduce the costs for the customers. The problem was that most or even all of the university-factory projects have failed. The needs of the factory can't be fulfilled by Robotics-experts, and the latest robots developed in the university are useless for factory automation. As a consequence the collaboration has stopped.
The upraising of synthetic robotics challenge is a sign that the university driven robotics community has built it's own challenges. The aim is no longer to automate existing factories, instead new challenges are created which are needed by the robotics community. Basically spoken, robotics development is working for it's own need. There is no plan to transfer the knowledge from the university into practical applications. Both domains are separated.
Today, both parties are working with opposite technology. In practical automation projects, the well known CNC machines are used which were developed in the 1970s. These machines provides the maximum productivity and help the companies to reduce the costs. On the other hand, the robot projects in the universities are working with different goals. CNC machines are not researched in the university domain, instead the prefered technology is deeplearning, biped robots and modern robot control systems. The prediction is, that in the future the gap will increase. That means, university driven robotics projects and automation projects in the factory have nothing in common. And it's done with different ideology in mind. Simply spoken, both parties have unlearned how to communicate with each other. University researchers who are interested in robotics have no reason to start a project in which a CNC machine is utilized, because this technology is 40 years old and it's not interesting enough from an academic standpoint. On the other hand, automation experts in a car factory have no obligation to introduce modern robotics in the workplace, because these devices are costing too much and doesn't provide a value.
This unwillingness to communicate is something which was not there in the mid 1970s. In that time, university research and the need of the industrial automation was the same. The latest CNC machine technology was developed first by researchers in the lab and then the technology was transfered into the practical domain. With the advent of fifth generation computer the situation changes drastically. Basically spoken academic research and industrial needs have developed into opposite direction.
To understand the reason why we have to describe the situation in the reality. What car companies and the service sector is trying to do is to earn money by providing products. At first, the company is producing a car, and then the car is sold to a customer. The money is reinvested into the factory and more cars are produced. Research projects driven by companies have the obligation to make the process more efficient. The aim is to reduce the costs of producing a car, and if en engineers has an idea how to do so, the factory will use it as soon as possible. The disadvantage of this principle is, that a company is profit oriented. They are only interested in technology which helps to reduce the costs. The problem is, that the entire domain of Artificial Intelligence and robotics won't help to reduce the costs, but it's doing the opposite. From the perspective of a car factory it make no sense to research Artificial Intelligence in detail. Because everytime a detail problem was solved, new problems become obvious. As a consequence, Artificial Intelligence isn't researched by profit oriented companies, but the research is delegated to universities or outsourced into research teams which are not profit oriented.
This kind of hypothesis can be proofed by investigating robotics company in the past. Some examples are available in which companies are trying to earn money by developing robot hardware and software. The most advanced example is the Willow Garage company which has programmed the ROS operating system. What all these companies have in common is, that they are struggle from an economic perspective. The reason is, that created hardware and software finds no customer. Basically spoken nobody likes to pay 100000 US$ for a household robot which can do nothing.
It's not the fault of a certain company, but it has to do that Artificial Intelligence in general has problems to find customers. The market principle is, that a customer pays money and then he gets something in return. A robot works a bit different. If a customer pays 100000 US$ for the PR2 Robot from willow garage he gets nothing in return. What he gets instead is the need to invest more time and more money into the robot.
In a previous blogpost, I have compared robotics projects with a flame which has no purpose. It's possible to throw more fuel into the flame but the flame wouldn't provide something back. The problem is, that market oriented products have to provide an added value for the customer. He pays for example 100 US$ and he expects something in return for the money. And exactly this is missing for AI projects. In the 1980s the naive assumption was, that robotics projects have a long duration. That means, that before the robot can improve the productivity in the company, the engineers will need 10 years in which they can explore the new technology. In the meantime it's known, that the duration is not 10 years, but 100 years and longer. That means, the AI community will research a topic for decades and at the end they won't have something to offer which helps the customer. The problem is not, that the research is hidden behind closed doors. The problem is, that even all the papers are published they are useless for automation tasks.
Perhaps some numbers make the situation more obvious. Each year around 1 million papers about artificial Intelligence were created newly in the Google Scholar directory. Most of them can be downloaded in fulltext. The papers itself are great, the authors are experts on the field and each year they are describing more complicated robots which were build in the laboratory. But a closer look into the paper will show, that nothing new was discovered. They academic community has researched a topic in detail, but they haven't found anything what can be converted into a practical product. The result is, that car factories, hospitals and restaurants are working unchanged since 40 years. The technological development has stopped. No technology is available and all the work is done by human workers.
What we can observe is, that the world is on a low technological level which was froozen in the 1970s and at the same time, the AI revolution has started and the development speed has increased over the years. Latest robotics research is more advanced than ever and nearly each week a breakthrough is available and at the same time the companies in the real world are working with outdated CNC machines, barcode scanners and repetitive human work. The hypothesis is, that the gap can't be overcome and it's described in the literature as the productivity paradox.
Apart from anecdotes about failed robotics automation projects in the car industry, in hospitals or in restaurants there is need to give reason, why all these projects have failed. A possible explanation has to do with the social role of a robot in a project. There are two possible roles available:
1. robot as a superstar, which is provided in dedicated robotics challenges like micromouse and robocup
2. robot as a tool, which is requested in automation projects in factories and hospitals
The reason why car companies are starting robot projects in the factory is because they are interested in a robot as a tool for improving the workflow. The hope is, that a robot is able to increase the productivity and reduce the costs. Robots are seen as advanced sort of a hammer or a CNC machine which helps the human workers. This social requirement for a robot can't be realized. All attempt in utilizing a robot as a tool have failed.
Only the first role (robot as a superstar) results into a successful project. Building a 2wheeled robot which is able to travel through a micromouse maze, is an engineering problem which can be solved if enough skills are available in the team. Many succesful demonstrations of the task are recorded in the past, and the experiment can be repeated with new hardware and new engineering teams. Sure, it's possible that the robot gets lost in the maze, but this is only a detail problem, which can be fixed with better programming. In general most robotics challenges can be solved within the given time frame. It's important to know, that the social role in all of these competitions is, that the robot isn't seen as a tool, but as the most important subject. So it's a superstar and the engineers have to improve the machine.
The difference between the social roles isn't only an academic one. It has to do if a project becomes economic productive or not. Dedicated robotics challenges in which a robot is the superstar are costing lots of money but they are providing nothing in return. The micromouse who is traversing the maze doesn't fulfill the needs of an external customer, but the robot was created for it's own. In contrast, real automation projects in a company are focussed on customer needs. The car factories likes to sell a car to a customer, and the robot should do a subtask in the production facility. In such a social role the robot fails.
It make sense to describe the situation from an abstract point of view:
1. CNC machines = forth generation computer = social role as a tool = increase the productivity
2. robots = fifth generation computer = social role as a superstar = lowering the productivity
With such a template it's easy to predict the outcome of a certain project. Using a CNC machine in a robotics challenge won't work, because the CNC machine can't be programmed freely. The same mistake is obvious if a robot is used in an automation project in a company with the aim to increase the productivity. Each of the technology has a certain sweet spot in which the device can be used in a meaningful way. It's interesting to know that outdated CNC machines are able to increase the productive, while advanced robotics aren't able to do so. Sure, every factory can test out the hypthesis for themself. It's possible to start new robot project to proof that the thesis is wrong. But according to the known projects from the past, it can be estimated what will happen.
The productivity paradoxon has to do with using a certain technology for the wrong purpose. In most cases, the idea is to utilize an advanced robot for increasing the productivity in a factory. Such projects will fail, this is called productivity paradox. It's possible to avoid the bottleneck in defining the requirements first. Which means, it's possible to increase the productive or to play with robots, but not at the same time.
It make sense to observe successful robot projects in synthetic challenges closely. In competitions like Robocup, the robot is the superstar. The task he should do is given by the rulebook, for example one requirement is, that a team of robots should play a game of soccer. This includes object recognition, pathplanning and teamplay. The most interesting feature is, that most of the participants are successful in the competition. Which means, that the robots are working great, that they are driving by software and that each year the skills become a bit higher. Without any doubt it's possible to program even biped robot in a way that they are successful in the robocup challenge.
The only thing what is a bit surprising is, that these technology can't transfered into other domains. The origin of the robocup challenge was to create a testbed for experiment with new robotics technology with the longterm goal to use the newly acquired knowledge in practical applications, for example in factory automation. The robocup challenge itself runs great. Each year, the teams are become better and lots of new AI related knowledge was written in the papers around the competition. What is missing is the knowledge transfer into real applications.
The prediction is, that this knowledge transfer is not possible. That means, the advanced robot are succeed in the synthetic challenge, but they fail in real applications outside the competition. To understand the reason why it make sense to observe the robot projects in an academic context.
The typical university driven academic project is not motivated by increasing the productivity, but the main purpose is to explore new knowledge. In the standard case, a team of researchers is unsure how to build a biped robot and they are starting the project to develop new biped walking algorithm. If they are trained well, they get after a while the first results and write a paper about the walking robot machine. This paper motivates other researchers to experiment with more advanced robots. From an academic standpoint such projects are producing sense. Because at the end, many new papers were written, and new technology was developed which was not available before. It's important to know, that the needs of university robotic projects are different from automation projects in the reality. That means, the robot in the lab is capable of biped walking and has a built in vision system but the factory automation project can utilize this technology in a meaningful way.
Or let me explain it from a different point of view. Suppose a university team has build and programmed an advanced humanoid robot which was successful in a robocup challenge. Lots of money was invested in the project, and hundred of researchers have supported the project. The problem is, that from the perspective of factory automation all the written software and all the advanced hardware is useless. It won't increase the productivity at the assembly line.
To understand the situation better we have to go back in the 1980s. In that area there was lots of interaction available between universities and factory automation projects. The idea was, to utilize the latest knowledge from the academic domain to increase the productivity in the car industry and build advanced service robot which helps to reduce the costs for the customers. The problem was that most or even all of the university-factory projects have failed. The needs of the factory can't be fulfilled by Robotics-experts, and the latest robots developed in the university are useless for factory automation. As a consequence the collaboration has stopped.
The upraising of synthetic robotics challenge is a sign that the university driven robotics community has built it's own challenges. The aim is no longer to automate existing factories, instead new challenges are created which are needed by the robotics community. Basically spoken, robotics development is working for it's own need. There is no plan to transfer the knowledge from the university into practical applications. Both domains are separated.
Today, both parties are working with opposite technology. In practical automation projects, the well known CNC machines are used which were developed in the 1970s. These machines provides the maximum productivity and help the companies to reduce the costs. On the other hand, the robot projects in the universities are working with different goals. CNC machines are not researched in the university domain, instead the prefered technology is deeplearning, biped robots and modern robot control systems. The prediction is, that in the future the gap will increase. That means, university driven robotics projects and automation projects in the factory have nothing in common. And it's done with different ideology in mind. Simply spoken, both parties have unlearned how to communicate with each other. University researchers who are interested in robotics have no reason to start a project in which a CNC machine is utilized, because this technology is 40 years old and it's not interesting enough from an academic standpoint. On the other hand, automation experts in a car factory have no obligation to introduce modern robotics in the workplace, because these devices are costing too much and doesn't provide a value.
This unwillingness to communicate is something which was not there in the mid 1970s. In that time, university research and the need of the industrial automation was the same. The latest CNC machine technology was developed first by researchers in the lab and then the technology was transfered into the practical domain. With the advent of fifth generation computer the situation changes drastically. Basically spoken academic research and industrial needs have developed into opposite direction.
To understand the reason why we have to describe the situation in the reality. What car companies and the service sector is trying to do is to earn money by providing products. At first, the company is producing a car, and then the car is sold to a customer. The money is reinvested into the factory and more cars are produced. Research projects driven by companies have the obligation to make the process more efficient. The aim is to reduce the costs of producing a car, and if en engineers has an idea how to do so, the factory will use it as soon as possible. The disadvantage of this principle is, that a company is profit oriented. They are only interested in technology which helps to reduce the costs. The problem is, that the entire domain of Artificial Intelligence and robotics won't help to reduce the costs, but it's doing the opposite. From the perspective of a car factory it make no sense to research Artificial Intelligence in detail. Because everytime a detail problem was solved, new problems become obvious. As a consequence, Artificial Intelligence isn't researched by profit oriented companies, but the research is delegated to universities or outsourced into research teams which are not profit oriented.
This kind of hypothesis can be proofed by investigating robotics company in the past. Some examples are available in which companies are trying to earn money by developing robot hardware and software. The most advanced example is the Willow Garage company which has programmed the ROS operating system. What all these companies have in common is, that they are struggle from an economic perspective. The reason is, that created hardware and software finds no customer. Basically spoken nobody likes to pay 100000 US$ for a household robot which can do nothing.
It's not the fault of a certain company, but it has to do that Artificial Intelligence in general has problems to find customers. The market principle is, that a customer pays money and then he gets something in return. A robot works a bit different. If a customer pays 100000 US$ for the PR2 Robot from willow garage he gets nothing in return. What he gets instead is the need to invest more time and more money into the robot.
In a previous blogpost, I have compared robotics projects with a flame which has no purpose. It's possible to throw more fuel into the flame but the flame wouldn't provide something back. The problem is, that market oriented products have to provide an added value for the customer. He pays for example 100 US$ and he expects something in return for the money. And exactly this is missing for AI projects. In the 1980s the naive assumption was, that robotics projects have a long duration. That means, that before the robot can improve the productivity in the company, the engineers will need 10 years in which they can explore the new technology. In the meantime it's known, that the duration is not 10 years, but 100 years and longer. That means, the AI community will research a topic for decades and at the end they won't have something to offer which helps the customer. The problem is not, that the research is hidden behind closed doors. The problem is, that even all the papers are published they are useless for automation tasks.
Perhaps some numbers make the situation more obvious. Each year around 1 million papers about artificial Intelligence were created newly in the Google Scholar directory. Most of them can be downloaded in fulltext. The papers itself are great, the authors are experts on the field and each year they are describing more complicated robots which were build in the laboratory. But a closer look into the paper will show, that nothing new was discovered. They academic community has researched a topic in detail, but they haven't found anything what can be converted into a practical product. The result is, that car factories, hospitals and restaurants are working unchanged since 40 years. The technological development has stopped. No technology is available and all the work is done by human workers.
What we can observe is, that the world is on a low technological level which was froozen in the 1970s and at the same time, the AI revolution has started and the development speed has increased over the years. Latest robotics research is more advanced than ever and nearly each week a breakthrough is available and at the same time the companies in the real world are working with outdated CNC machines, barcode scanners and repetitive human work. The hypothesis is, that the gap can't be overcome and it's described in the literature as the productivity paradox.
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