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Tuesday, June 1, 2021

Industrial robot

From Wikipedia, the free encyclopedia
Articulated industrial robot operating in a foundry.

An industrial robot is a robot system used for manufacturing. Industrial robots are automated, programmable and capable of movement on three or more axes.

Typical applications of robots include welding, painting, assembly, disassembly, pick and place for printed circuit boards, packaging and labeling, palletizing, product inspection, and testing; all accomplished with high endurance, speed, and precision. They can assist in material handling.

In the year 2020, an estimated 1.64 million industrial robots were in operation worldwide according to International Federation of Robotics (IFR).

Types and features

A set of six-axis robots used for welding.
 
Factory Automation with industrial robots for palletizing food products like bread and toast at a bakery in Germany

There are six types of industrial robots.

Articulated robots

Articulated robots are the most common industrial robots. They look like a human arm, which is why they are also called robotic arm or manipulator arm. Their articulations with several degrees of freedom allow the articulated arms a wide range of movements.

Cartesian coordinate robots

Cartesian robots, also called rectilinear, gantry robots, and x-y-z robots have three prismatic joints for the movement of the tool and three rotary joints for its orientation in space.

To be able to move and orient the effector organ in all directions, such a robot needs 6 axes (or degrees of freedom). In a 2-dimensional environment, three axes are sufficient, two for displacement and one for orientation.

Cylindrical coordinate robots

The cylindrical coordinate robots are characterized by their rotary joint at the base and at least one prismatic joint connecting its links. They can move vertically and horizontally by sliding. The compact effector design allows the robot to reach tight workspaces without any loss of speed.

Spherical coordinate robots

Spherical coordinate robots only have rotary joints. They are one of the first robots to have been used in industrial applications. They are commonly used for machine tending in die-casting, plastic injection and extrusion, and for welding.

SCARA robots

SCARA is an acronym for Selective Compliance Assembly Robot Arm. SCARA robots are recognized by their two parallel joints which provide movement in the X-Y plane. Rotating shafts are positioned vertically at the effector..

SCARA robots are used for jobs that require precise lateral movements. They are ideal for assembly applications.

Delta robots

Delta robots are also referred to as parallel link robots. They consist of parallel links connected to a common base. Delta robots are particularly useful for direct control tasks and high maneuvering operations (such as quick pick-and-place tasks). Delta robots take advantage of four bar or parallelogram linkage systems.

Furthermore, industrial robots can have a serial or parallel architecture.

Serial manipulators

Serial architectures a.k.a Serial manipulators are the most common industrial robots and they are designed as a series of links connected by motor-actuated joints that extend from a base to an end-effector. SCARA , Stanford manipulators are typical examples of this category.

Parallel Architecture

A parallel manipulator is designed so that each chain is usually short, simple and can thus be rigid against unwanted movement, compared to a serial manipulator. Errors in one chain's positioning are averaged in conjunction with the others, rather than being cumulative. Each actuator must still move within its own degree of freedom, as for a serial robot; however in the parallel robot the off-axis flexibility of a joint is also constrained by the effect of the other chains. It is this closed-loop stiffness that makes the overall parallel manipulator stiff relative to its components, unlike the serial chain that becomes progressively less rigid with more components.

Lower mobility parallel manipulators and concomitant motion

A full parallel manipulator can move an object with up to 6 degrees of freedom (DoF), determined by 3 translation 3T and 3 rotation 3R coordinates for full 3T3R mobility. However, when a manipulation task requires less than 6 DoF, the use of lower mobility manipulators, with fewer than 6 DoF, may bring advantages in terms of simpler architecture, easier control, faster motion and lower cost. For example, the 3 DoF Delta robot has lower 3T mobility and has proven to be very successful for rapid pick-and-place translational positioning applications. The workspace of lower mobility manipulators may be decomposed into `motion’ and `constraint’ subspaces. For example, 3 position coordinates constitute the motion subspace of the 3 DoF Delta robot and the 3 orientation coordinates are in the constraint subspace. The motion subspace of lower mobility manipulators may be further decomposed into independent (desired) and dependent (concomitant) subspaces: consisting of `concomitant’ or `parasitic’ motion which is undesired motion of the manipulator. The debilitating effects of concomitant motion should be mitigated or eliminated in the successful design of lower mobility manipulators. For example, the Delta robot does not have parasitic motion since its end effector does not rotate.

Autonomy

Robots exhibit varying degrees of autonomy. Some robots are programmed to faithfully carry out specific actions over and over again (repetitive actions) without variation and with a high degree of accuracy. These actions are determined by programmed routines that specify the direction, acceleration, velocity, deceleration, and distance of a series of coordinated motions

Other robots are much more flexible as to the orientation of the object on which they are operating or even the task that has to be performed on the object itself, which the robot may even need to identify. For example, for more precise guidance, robots often contain machine vision sub-systems acting as their visual sensors, linked to powerful computers or controllers. Artificial intelligence, or what passes for it, is becoming an increasingly important factor in the modern industrial robot.

History of industrial robotics

The earliest known industrial robot, conforming to the ISO definition was completed by "Bill" Griffith P. Taylor in 1937 and published in Meccano Magazine, March 1938. The crane-like device was built almost entirely using Meccano parts, and powered by a single electric motor. Five axes of movement were possible, including grab and grab rotation. Automation was achieved using punched paper tape to energise solenoids, which would facilitate the movement of the crane's control levers. The robot could stack wooden blocks in pre-programmed patterns. The number of motor revolutions required for each desired movement was first plotted on graph paper. This information was then transferred to the paper tape, which was also driven by the robot's single motor. Chris Shute built a complete replica of the robot in 1997.

George Devol, c. 1982

George Devol applied for the first robotics patents in 1954 (granted in 1961). The first company to produce a robot was Unimation, founded by Devol and Joseph F. Engelberger in 1956. Unimation robots were also called programmable transfer machines since their main use at first was to transfer objects from one point to another, less than a dozen feet or so apart. They used hydraulic actuators and were programmed in joint coordinates, i.e. the angles of the various joints were stored during a teaching phase and replayed in operation. They were accurate to within 1/10,000 of an inch (note: although accuracy is not an appropriate measure for robots, usually evaluated in terms of repeatability - see later). Unimation later licensed their technology to Kawasaki Heavy Industries and GKN, manufacturing Unimates in Japan and England respectively. For some time Unimation's only competitor was Cincinnati Milacron Inc. of Ohio. This changed radically in the late 1970s when several big Japanese conglomerates began producing similar industrial robots.

In 1969 Victor Scheinman at Stanford University invented the Stanford arm, an all-electric, 6-axis articulated robot designed to permit an arm solution. This allowed it accurately to follow arbitrary paths in space and widened the potential use of the robot to more sophisticated applications such as assembly and welding. Scheinman then designed a second arm for the MIT AI Lab, called the "MIT arm." Scheinman, after receiving a fellowship from Unimation to develop his designs, sold those designs to Unimation who further developed them with support from General Motors and later marketed it as the Programmable Universal Machine for Assembly (PUMA).

Industrial robotics took off quite quickly in Europe, with both ABB Robotics and KUKA Robotics bringing robots to the market in 1973. ABB Robotics (formerly ASEA) introduced IRB 6, among the world's first commercially available all electric micro-processor controlled robot. The first two IRB 6 robots were sold to Magnusson in Sweden for grinding and polishing pipe bends and were installed in production in January 1974. Also in 1973 KUKA Robotics built its first robot, known as FAMULUS, also one of the first articulated robots to have six electromechanically driven axes.

Interest in robotics increased in the late 1970s and many US companies entered the field, including large firms like General Electric, and General Motors (which formed joint venture FANUC Robotics with FANUC LTD of Japan). U.S. startup companies included Automatix and Adept Technology, Inc. At the height of the robot boom in 1984, Unimation was acquired by Westinghouse Electric Corporation for 107 million U.S. dollars. Westinghouse sold Unimation to Stäubli Faverges SCA of France in 1988, which is still making articulated robots for general industrial and cleanroom applications and even bought the robotic division of Bosch in late 2004.

Only a few non-Japanese companies ultimately managed to survive in this market, the major ones being: Adept Technology, Stäubli, the Swedish-Swiss company ABB Asea Brown Boveri, the German company KUKA Robotics and the Italian company Comau.

Technical description

Defining parameters

  • Number of axes – two axes are required to reach any point in a plane; three axes are required to reach any point in space. To fully control the orientation of the end of the arm(i.e. the wrist) three more axes (yaw, pitch, and roll) are required. Some designs (e.g. the SCARA robot) trade limitations in motion possibilities for cost, speed, and accuracy.
  • Degrees of freedom – this is usually the same as the number of axes.
  • Working envelope – the region of space a robot can reach.
  • Kinematics – the actual arrangement of rigid members and joints in the robot, which determines the robot's possible motions. Classes of robot kinematics include articulated, cartesian, parallel and SCARA.
  • Carrying capacity or payload – how much weight a robot can lift.
  • Speed – how fast the robot can position the end of its arm. This may be defined in terms of the angular or linear speed of each axis or as a compound speed i.e. the speed of the end of the arm when all axes are moving.
  • Acceleration – how quickly an axis can accelerate. Since this is a limiting factor a robot may not be able to reach its specified maximum speed for movements over a short distance or a complex path requiring frequent changes of direction.
  • Accuracy – how closely a robot can reach a commanded position. When the absolute position of the robot is measured and compared to the commanded position the error is a measure of accuracy. Accuracy can be improved with external sensing for example a vision system or Infra-Red. See robot calibration. Accuracy can vary with speed and position within the working envelope and with payload (see compliance).
  • Repeatability – how well the robot will return to a programmed position. This is not the same as accuracy. It may be that when told to go to a certain X-Y-Z position that it gets only to within 1 mm of that position. This would be its accuracy which may be improved by calibration. But if that position is taught into controller memory and each time it is sent there it returns to within 0.1mm of the taught position then the repeatability will be within 0.1mm.

Accuracy and repeatability are different measures. Repeatability is usually the most important criterion for a robot and is similar to the concept of 'precision' in measurement—see accuracy and precision. ISO 9283 sets out a method whereby both accuracy and repeatability can be measured. Typically a robot is sent to a taught position a number of times and the error is measured at each return to the position after visiting 4 other positions. Repeatability is then quantified using the standard deviation of those samples in all three dimensions. A typical robot can, of course make a positional error exceeding that and that could be a problem for the process. Moreover, the repeatability is different in different parts of the working envelope and also changes with speed and payload. ISO 9283 specifies that accuracy and repeatability should be measured at maximum speed and at maximum payload. But this results in pessimistic values whereas the robot could be much more accurate and repeatable at light loads and speeds. Repeatability in an industrial process is also subject to the accuracy of the end effector, for example a gripper, and even to the design of the 'fingers' that match the gripper to the object being grasped. For example, if a robot picks a screw by its head, the screw could be at a random angle. A subsequent attempt to insert the screw into a hole could easily fail. These and similar scenarios can be improved with 'lead-ins' e.g. by making the entrance to the hole tapered.

  • Motion control – for some applications, such as simple pick-and-place assembly, the robot need merely return repeatably to a limited number of pre-taught positions. For more sophisticated applications, such as welding and finishing (spray painting), motion must be continuously controlled to follow a path in space, with controlled orientation and velocity.
  • Power source – some robots use electric motors, others use hydraulic actuators. The former are faster, the latter are stronger and advantageous in applications such as spray painting, where a spark could set off an explosion; however, low internal air-pressurisation of the arm can prevent ingress of flammable vapours as well as other contaminants. Nowadays, it is highly unlikely to see any hydraulic robots in the market. Additional sealings, brushless electric motors and spark-proof protection eased the construction of units that are able to work in the environment with an explosive atmosphere.
  • Drive – some robots connect electric motors to the joints via gears; others connect the motor to the joint directly (direct drive). Using gears results in measurable 'backlash' which is free movement in an axis. Smaller robot arms frequently employ high speed, low torque DC motors, which generally require high gearing ratios; this has the disadvantage of backlash. In such cases the harmonic drive is often used.
  • Compliance - this is a measure of the amount in angle or distance that a robot axis will move when a force is applied to it. Because of compliance when a robot goes to a position carrying its maximum payload it will be at a position slightly lower than when it is carrying no payload. Compliance can also be responsible for overshoot when carrying high payloads in which case acceleration would need to be reduced.

Robot programming and interfaces

Offline programming
 
A typical well-used teach pendant with optional mouse

The setup or programming of motions and sequences for an industrial robot is typically taught by linking the robot controller to a laptop, desktop computer or (internal or Internet) network.

A robot and a collection of machines or peripherals is referred to as a workcell, or cell. A typical cell might contain a parts feeder, a molding machine and a robot. The various machines are 'integrated' and controlled by a single computer or PLC. How the robot interacts with other machines in the cell must be programmed, both with regard to their positions in the cell and synchronizing with them.

Software: The computer is installed with corresponding interface software. The use of a computer greatly simplifies the programming process. Specialized robot software is run either in the robot controller or in the computer or both depending on the system design.

There are two basic entities that need to be taught (or programmed): positional data and procedure. For example, in a task to move a screw from a feeder to a hole the positions of the feeder and the hole must first be taught or programmed. Secondly the procedure to get the screw from the feeder to the hole must be programmed along with any I/O involved, for example a signal to indicate when the screw is in the feeder ready to be picked up. The purpose of the robot software is to facilitate both these programming tasks.

Teaching the robot positions may be achieved a number of ways:

Positional commands The robot can be directed to the required position using a GUI or text based commands in which the required X-Y-Z position may be specified and edited.

Teach pendant: Robot positions can be taught via a teach pendant. This is a handheld control and programming unit. The common features of such units are the ability to manually send the robot to a desired position, or "inch" or "jog" to adjust a position. They also have a means to change the speed since a low speed is usually required for careful positioning, or while test-running through a new or modified routine. A large emergency stop button is usually included as well. Typically once the robot has been programmed there is no more use for the teach pendant. All teach pendants are equipped with a 3-position deadman switch. In the manual mode, it allows the robot to move only when it is in the middle position (partially pressed). If it is fully pressed in or completely released, the robot stops. This principle of operation allows natural reflexes to be used to increase safety.

Lead-by-the-nose: this is a technique offered by many robot manufacturers. In this method, one user holds the robot's manipulator, while another person enters a command which de-energizes the robot causing it to go into limp. The user then moves the robot by hand to the required positions and/or along a required path while the software logs these positions into memory. The program can later run the robot to these positions or along the taught path. This technique is popular for tasks such as paint spraying.

Offline programming is where the entire cell, the robot and all the machines or instruments in the workspace are mapped graphically. The robot can then be moved on screen and the process simulated. A robotics simulator is used to create embedded applications for a robot, without depending on the physical operation of the robot arm and end effector. The advantages of robotics simulation is that it saves time in the design of robotics applications. It can also increase the level of safety associated with robotic equipment since various "what if" scenarios can be tried and tested before the system is activated.[8] Robot simulation software provides a platform to teach, test, run, and debug programs that have been written in a variety of programming languages.

Robotics Simulator

Robot simulation tools allow for robotics programs to be conveniently written and debugged off-line with the final version of the program tested on an actual robot. The ability to preview the behavior of a robotic system in a virtual world allows for a variety of mechanisms, devices, configurations and controllers to be tried and tested before being applied to a "real world" system. Robotics simulators have the ability to provide real-time computing of the simulated motion of an industrial robot using both geometric modeling and kinematics modeling.

Manufacturing independent robot programming tools are a relatively new but flexible way to program robot applications. Using a graphical user interface the programming is done via drag and drop of predefined template/building blocks. They often feature the execution of simulations to evaluate the feasibility and offline programming in combination. If the system is able to compile and upload native robot code to the robot controller, the user no longer has to learn each manufacturer's proprietary language. Therefore, this approach can be an important step to standardize programming methods.

Others in addition, machine operators often use user interface devices, typically touchscreen units, which serve as the operator control panel. The operator can switch from program to program, make adjustments within a program and also operate a host of peripheral devices that may be integrated within the same robotic system. These include end effectors, feeders that supply components to the robot, conveyor belts, emergency stop controls, machine vision systems, safety interlock systems, barcode printers and an almost infinite array of other industrial devices which are accessed and controlled via the operator control panel.

The teach pendant or PC is usually disconnected after programming and the robot then runs on the program that has been installed in its controller. However a computer is often used to 'supervise' the robot and any peripherals, or to provide additional storage for access to numerous complex paths and routines.

End-of-arm tooling

The most essential robot peripheral is the end effector, or end-of-arm-tooling (EOT). Common examples of end effectors include welding devices (such as MIG-welding guns, spot-welders, etc.), spray guns and also grinding and deburring devices (such as pneumatic disk or belt grinders, burrs, etc.), and grippers (devices that can grasp an object, usually electromechanical or pneumatic). Other common means of picking up objects is by vacuum or magnets. End effectors are frequently highly complex, made to match the handled product and often capable of picking up an array of products at one time. They may utilize various sensors to aid the robot system in locating, handling, and positioning products.

Controlling movement

For a given robot the only parameters necessary to completely locate the end effector (gripper, welding torch, etc.) of the robot are the angles of each of the joints or displacements of the linear axes (or combinations of the two for robot formats such as SCARA). However, there are many different ways to define the points. The most common and most convenient way of defining a point is to specify a Cartesian coordinate for it, i.e. the position of the 'end effector' in mm in the X, Y and Z directions relative to the robot's origin. In addition, depending on the types of joints a particular robot may have, the orientation of the end effector in yaw, pitch, and roll and the location of the tool point relative to the robot's faceplate must also be specified. For a jointed arm these coordinates must be converted to joint angles by the robot controller and such conversions are known as Cartesian Transformations which may need to be performed iteratively or recursively for a multiple axis robot. The mathematics of the relationship between joint angles and actual spatial coordinates is called kinematics.

Positioning by Cartesian coordinates may be done by entering the coordinates into the system or by using a teach pendant which moves the robot in X-Y-Z directions. It is much easier for a human operator to visualize motions up/down, left/right, etc. than to move each joint one at a time. When the desired position is reached it is then defined in some way particular to the robot software in use, e.g. P1 - P5 below.

Typical programming

Most articulated robots perform by storing a series of positions in memory, and moving to them at various times in their programming sequence. For example, a robot which is moving items from one place (bin A) to another (bin B) might have a simple 'pick and place' program similar to the following:

Define points P1–P5:

  1. Safely above workpiece (defined as P1)
  2. 10 cm Above bin A (defined as P2)
  3. At position to take part from bin A (defined as P3)
  4. 10 cm Above bin B (defined as P4)
  5. At position to take part from bin B. (defined as P5)

Define program:

  1. Move to P1
  2. Move to P2
  3. Move to P3
  4. Close gripper
  5. Move to P2
  6. Move to P4
  7. Move to P5
  8. Open gripper
  9. Move to P4
  10. Move to P1 and finish

For examples of how this would look in popular robot languages see industrial robot programming.

Singularities

The American National Standard for Industrial Robots and Robot Systems — Safety Requirements (ANSI/RIA R15.06-1999) defines a singularity as “a condition caused by the collinear alignment of two or more robot axes resulting in unpredictable robot motion and velocities.” It is most common in robot arms that utilize a “triple-roll wrist”. This is a wrist about which the three axes of the wrist, controlling yaw, pitch, and roll, all pass through a common point. An example of a wrist singularity is when the path through which the robot is traveling causes the first and third axes of the robot's wrist (i.e. robot's axes 4 and 6) to line up. The second wrist axis then attempts to spin 180° in zero time to maintain the orientation of the end effector. Another common term for this singularity is a “wrist flip”. The result of a singularity can be quite dramatic and can have adverse effects on the robot arm, the end effector, and the process. Some industrial robot manufacturers have attempted to side-step the situation by slightly altering the robot's path to prevent this condition. Another method is to slow the robot's travel speed, thus reducing the speed required for the wrist to make the transition. The ANSI/RIA has mandated that robot manufacturers shall make the user aware of singularities if they occur while the system is being manually manipulated.

A second type of singularity in wrist-partitioned vertically articulated six-axis robots occurs when the wrist center lies on a cylinder that is centered about axis 1 and with radius equal to the distance between axes 1 and 4. This is called a shoulder singularity. Some robot manufacturers also mention alignment singularities, where axes 1 and 6 become coincident. This is simply a sub-case of shoulder singularities. When the robot passes close to a shoulder singularity, joint 1 spins very fast.

The third and last type of singularity in wrist-partitioned vertically articulated six-axis robots occurs when the wrist's center lies in the same plane as axes 2 and 3.

Singularities are closely related to the phenomena of gimbal lock, which has a similar root cause of axes becoming lined up.

Market structure

According to the International Federation of Robotics (IFR) study World Robotics 2019, there were about 2,439,543 operational industrial robots by the end of 2017. This number is estimated to reach 3,788,000 by the end of 2021. For the year 2018 the IFR estimates the worldwide sales of industrial robots with US$16.5 billion. Including the cost of software, peripherals and systems engineering, the annual turnover for robot systems is estimated to be US$48.0 billion in 2018.

China is the largest industrial robot market, with 154,032 units sold in 2018. China had the largest operational stock of industrial robots, with 649,447 at the end of 2018. The United States industrial robot-makers shipped 35,880 robot to factories in the US in 2018 and this was 7% more than in 2017.

The biggest customer of industrial robots is automotive industry with 30% market share, then electrical/electronics industry with 25%, metal and machinery industry with 10%, rubber and plastics industry with 5%, food industry with 5%. In textiles, apparel and leather industry, 1,580 units are operational.

Estimated worldwide annual supply of industrial robots (in units):

Year supply
1998 69,000
1999 79,000
2000 99,000
2001 78,000
2002 69,000
2003 81,000
2004 97,000
2005 120,000
2006 112,000
2007 114,000
2008 113,000
2009 60,000
2010 118,000
2012 159,346
2013 178,132
2014 229,261
2015 253,748
2016 294,312
2017 381,335
2018 422,271

Health and safety

The International Federation of Robotics has predicted a worldwide increase in adoption of industrial robots and they estimated 1.7 million new robot installations in factories worldwide by 2020 [IFR 2017]. Rapid advances in automation technologies (e.g. fixed robots, collaborative and mobile robots, and exoskeletons) have the potential to improve work conditions but also to introduce workplace hazards in manufacturing workplaces.  Despite the lack of occupational surveillance data on injuries associated specifically with robots, researchers from the US National Institute for Occupational Safety and Health (NIOSH) identified 61 robot-related deaths between 1992 and 2015 using keyword searches of the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries research database (see info from Center for Occupational Robotics Research). Using data from the Bureau of Labor Statistics, NIOSH and its state partners have investigated 4 robot-related fatalities under the Fatality Assessment and Control Evaluation Program. In addition the Occupational Safety and Health Administration (OSHA) has investigated dozens of robot-related deaths and injuries, which can be reviewed at OSHA Accident Search page. Injuries and fatalities could increase over time because of the increasing number of collaborative and co-existing robots, powered exoskeletons, and autonomous vehicles into the work environment.

Safety standards are being developed by the Robotic Industries Association (RIA) in conjunction with the American National Standards Institute (ANSI). On October 5, 2017, OSHA, NIOSH and RIA signed an alliance to work together to enhance technical expertise, identify and help address potential workplace hazards associated with traditional industrial robots and the emerging technology of human-robot collaboration installations and systems, and help identify needed research to reduce workplace hazards. On October 16 NIOSH launched the Center for Occupational Robotics Research to "provide scientific leadership to guide the development and use of occupational robots that enhance worker safety, health, and wellbeing." So far, the research needs identified by NIOSH and its partners include: tracking and preventing injuries and fatalities, intervention and dissemination strategies to promote safe machine control and maintenance procedures, and on translating effective evidence-based interventions into workplace practice.

Articulated industrial robot operating in a foundry.

An industrial robot is a robot system used for manufacturing. Industrial robots are automated, programmable and capable of movement on three or more axes.

Typical applications of robots include welding, painting, assembly, disassembly, pick and place for printed circuit boards, packaging and labeling, palletizing, product inspection, and testing; all accomplished with high endurance, speed, and precision. They can assist in material handling.

In the year 2020, an estimated 1.64 million industrial robots were in operation worldwide according to International Federation of Robotics (IFR).

Types and features

A set of six-axis robots used for welding.
 
Factory Automation with industrial robots for palletizing food products like bread and toast at a bakery in Germany

There are six types of industrial robots.

Articulated robots

Articulated robots are the most common industrial robots. They look like a human arm, which is why they are also called robotic arm or manipulator arm. Their articulations with several degrees of freedom allow the articulated arms a wide range of movements.

Cartesian coordinate robots

Cartesian robots, also called rectilinear, gantry robots, and x-y-z robots have three prismatic joints for the movement of the tool and three rotary joints for its orientation in space.

To be able to move and orient the effector organ in all directions, such a robot needs 6 axes (or degrees of freedom). In a 2-dimensional environment, three axes are sufficient, two for displacement and one for orientation.

Cylindrical coordinate robots

The cylindrical coordinate robots are characterized by their rotary joint at the base and at least one prismatic joint connecting its links. They can move vertically and horizontally by sliding. The compact effector design allows the robot to reach tight workspaces without any loss of speed.

Spherical coordinate robots

Spherical coordinate robots only have rotary joints. They are one of the first robots to have been used in industrial applications. They are commonly used for machine tending in die-casting, plastic injection and extrusion, and for welding.

SCARA robots

SCARA is an acronym for Selective Compliance Assembly Robot Arm. SCARA robots are recognized by their two parallel joints which provide movement in the X-Y plane. Rotating shafts are positioned vertically at the effector..

SCARA robots are used for jobs that require precise lateral movements. They are ideal for assembly applications.

Delta robots

Delta robots are also referred to as parallel link robots. They consist of parallel links connected to a common base. Delta robots are particularly useful for direct control tasks and high maneuvering operations (such as quick pick-and-place tasks). Delta robots take advantage of four bar or parallelogram linkage systems.

Furthermore, industrial robots can have a serial or parallel architecture.

Serial manipulators

Serial architectures a.k.a Serial manipulators are the most common industrial robots and they are designed as a series of links connected by motor-actuated joints that extend from a base to an end-effector. SCARA , Stanford manipulators are typical examples of this category.

Parallel Architecture

A parallel manipulator is designed so that each chain is usually short, simple and can thus be rigid against unwanted movement, compared to a serial manipulator. Errors in one chain's positioning are averaged in conjunction with the others, rather than being cumulative. Each actuator must still move within its own degree of freedom, as for a serial robot; however in the parallel robot the off-axis flexibility of a joint is also constrained by the effect of the other chains. It is this closed-loop stiffness that makes the overall parallel manipulator stiff relative to its components, unlike the serial chain that becomes progressively less rigid with more components.

Lower mobility parallel manipulators and concomitant motion

A full parallel manipulator can move an object with up to 6 degrees of freedom (DoF), determined by 3 translation 3T and 3 rotation 3R coordinates for full 3T3R mobility. However, when a manipulation task requires less than 6 DoF, the use of lower mobility manipulators, with fewer than 6 DoF, may bring advantages in terms of simpler architecture, easier control, faster motion and lower cost. For example, the 3 DoF Delta robot has lower 3T mobility and has proven to be very successful for rapid pick-and-place translational positioning applications. The workspace of lower mobility manipulators may be decomposed into `motion’ and `constraint’ subspaces. For example, 3 position coordinates constitute the motion subspace of the 3 DoF Delta robot and the 3 orientation coordinates are in the constraint subspace. The motion subspace of lower mobility manipulators may be further decomposed into independent (desired) and dependent (concomitant) subspaces: consisting of `concomitant’ or `parasitic’ motion which is undesired motion of the manipulator. The debilitating effects of concomitant motion should be mitigated or eliminated in the successful design of lower mobility manipulators. For example, the Delta robot does not have parasitic motion since its end effector does not rotate.

Autonomy

Robots exhibit varying degrees of autonomy. Some robots are programmed to faithfully carry out specific actions over and over again (repetitive actions) without variation and with a high degree of accuracy. These actions are determined by programmed routines that specify the direction, acceleration, velocity, deceleration, and distance of a series of coordinated motions

Other robots are much more flexible as to the orientation of the object on which they are operating or even the task that has to be performed on the object itself, which the robot may even need to identify. For example, for more precise guidance, robots often contain machine vision sub-systems acting as their visual sensors, linked to powerful computers or controllers. Artificial intelligence, or what passes for it, is becoming an increasingly important factor in the modern industrial robot.

History of industrial robotics

The earliest known industrial robot, conforming to the ISO definition was completed by "Bill" Griffith P. Taylor in 1937 and published in Meccano Magazine, March 1938. The crane-like device was built almost entirely using Meccano parts, and powered by a single electric motor. Five axes of movement were possible, including grab and grab rotation. Automation was achieved using punched paper tape to energise solenoids, which would facilitate the movement of the crane's control levers. The robot could stack wooden blocks in pre-programmed patterns. The number of motor revolutions required for each desired movement was first plotted on graph paper. This information was then transferred to the paper tape, which was also driven by the robot's single motor. Chris Shute built a complete replica of the robot in 1997.

George Devol, c. 1982

George Devol applied for the first robotics patents in 1954 (granted in 1961). The first company to produce a robot was Unimation, founded by Devol and Joseph F. Engelberger in 1956. Unimation robots were also called programmable transfer machines since their main use at first was to transfer objects from one point to another, less than a dozen feet or so apart. They used hydraulic actuators and were programmed in joint coordinates, i.e. the angles of the various joints were stored during a teaching phase and replayed in operation. They were accurate to within 1/10,000 of an inch (note: although accuracy is not an appropriate measure for robots, usually evaluated in terms of repeatability - see later). Unimation later licensed their technology to Kawasaki Heavy Industries and GKN, manufacturing Unimates in Japan and England respectively. For some time Unimation's only competitor was Cincinnati Milacron Inc. of Ohio. This changed radically in the late 1970s when several big Japanese conglomerates began producing similar industrial robots.

In 1969 Victor Scheinman at Stanford University invented the Stanford arm, an all-electric, 6-axis articulated robot designed to permit an arm solution. This allowed it accurately to follow arbitrary paths in space and widened the potential use of the robot to more sophisticated applications such as assembly and welding. Scheinman then designed a second arm for the MIT AI Lab, called the "MIT arm." Scheinman, after receiving a fellowship from Unimation to develop his designs, sold those designs to Unimation who further developed them with support from General Motors and later marketed it as the Programmable Universal Machine for Assembly (PUMA).

Industrial robotics took off quite quickly in Europe, with both ABB Robotics and KUKA Robotics bringing robots to the market in 1973. ABB Robotics (formerly ASEA) introduced IRB 6, among the world's first commercially available all electric micro-processor controlled robot. The first two IRB 6 robots were sold to Magnusson in Sweden for grinding and polishing pipe bends and were installed in production in January 1974. Also in 1973 KUKA Robotics built its first robot, known as FAMULUS, also one of the first articulated robots to have six electromechanically driven axes.

Interest in robotics increased in the late 1970s and many US companies entered the field, including large firms like General Electric, and General Motors (which formed joint venture FANUC Robotics with FANUC LTD of Japan). U.S. startup companies included Automatix and Adept Technology, Inc. At the height of the robot boom in 1984, Unimation was acquired by Westinghouse Electric Corporation for 107 million U.S. dollars. Westinghouse sold Unimation to Stäubli Faverges SCA of France in 1988, which is still making articulated robots for general industrial and cleanroom applications and even bought the robotic division of Bosch in late 2004.

Only a few non-Japanese companies ultimately managed to survive in this market, the major ones being: Adept Technology, Stäubli, the Swedish-Swiss company ABB Asea Brown Boveri, the German company KUKA Robotics and the Italian company Comau.

Technical description

Defining parameters

  • Number of axes – two axes are required to reach any point in a plane; three axes are required to reach any point in space. To fully control the orientation of the end of the arm(i.e. the wrist) three more axes (yaw, pitch, and roll) are required. Some designs (e.g. the SCARA robot) trade limitations in motion possibilities for cost, speed, and accuracy.
  • Degrees of freedom – this is usually the same as the number of axes.
  • Working envelope – the region of space a robot can reach.
  • Kinematics – the actual arrangement of rigid members and joints in the robot, which determines the robot's possible motions. Classes of robot kinematics include articulated, cartesian, parallel and SCARA.
  • Carrying capacity or payload – how much weight a robot can lift.
  • Speed – how fast the robot can position the end of its arm. This may be defined in terms of the angular or linear speed of each axis or as a compound speed i.e. the speed of the end of the arm when all axes are moving.
  • Acceleration – how quickly an axis can accelerate. Since this is a limiting factor a robot may not be able to reach its specified maximum speed for movements over a short distance or a complex path requiring frequent changes of direction.
  • Accuracy – how closely a robot can reach a commanded position. When the absolute position of the robot is measured and compared to the commanded position the error is a measure of accuracy. Accuracy can be improved with external sensing for example a vision system or Infra-Red. See robot calibration. Accuracy can vary with speed and position within the working envelope and with payload (see compliance).
  • Repeatability – how well the robot will return to a programmed position. This is not the same as accuracy. It may be that when told to go to a certain X-Y-Z position that it gets only to within 1 mm of that position. This would be its accuracy which may be improved by calibration. But if that position is taught into controller memory and each time it is sent there it returns to within 0.1mm of the taught position then the repeatability will be within 0.1mm.

Accuracy and repeatability are different measures. Repeatability is usually the most important criterion for a robot and is similar to the concept of 'precision' in measurement—see accuracy and precision. ISO 9283 sets out a method whereby both accuracy and repeatability can be measured. Typically a robot is sent to a taught position a number of times and the error is measured at each return to the position after visiting 4 other positions. Repeatability is then quantified using the standard deviation of those samples in all three dimensions. A typical robot can, of course make a positional error exceeding that and that could be a problem for the process. Moreover, the repeatability is different in different parts of the working envelope and also changes with speed and payload. ISO 9283 specifies that accuracy and repeatability should be measured at maximum speed and at maximum payload. But this results in pessimistic values whereas the robot could be much more accurate and repeatable at light loads and speeds. Repeatability in an industrial process is also subject to the accuracy of the end effector, for example a gripper, and even to the design of the 'fingers' that match the gripper to the object being grasped. For example, if a robot picks a screw by its head, the screw could be at a random angle. A subsequent attempt to insert the screw into a hole could easily fail. These and similar scenarios can be improved with 'lead-ins' e.g. by making the entrance to the hole tapered.

  • Motion control – for some applications, such as simple pick-and-place assembly, the robot need merely return repeatably to a limited number of pre-taught positions. For more sophisticated applications, such as welding and finishing (spray painting), motion must be continuously controlled to follow a path in space, with controlled orientation and velocity.
  • Power source – some robots use electric motors, others use hydraulic actuators. The former are faster, the latter are stronger and advantageous in applications such as spray painting, where a spark could set off an explosion; however, low internal air-pressurisation of the arm can prevent ingress of flammable vapours as well as other contaminants. Nowadays, it is highly unlikely to see any hydraulic robots in the market. Additional sealings, brushless electric motors and spark-proof protection eased the construction of units that are able to work in the environment with an explosive atmosphere.
  • Drive – some robots connect electric motors to the joints via gears; others connect the motor to the joint directly (direct drive). Using gears results in measurable 'backlash' which is free movement in an axis. Smaller robot arms frequently employ high speed, low torque DC motors, which generally require high gearing ratios; this has the disadvantage of backlash. In such cases the harmonic drive is often used.
  • Compliance - this is a measure of the amount in angle or distance that a robot axis will move when a force is applied to it. Because of compliance when a robot goes to a position carrying its maximum payload it will be at a position slightly lower than when it is carrying no payload. Compliance can also be responsible for overshoot when carrying high payloads in which case acceleration would need to be reduced.

Robot programming and interfaces

Offline programming
 
A typical well-used teach pendant with optional mouse

The setup or programming of motions and sequences for an industrial robot is typically taught by linking the robot controller to a laptop, desktop computer or (internal or Internet) network.

A robot and a collection of machines or peripherals is referred to as a workcell, or cell. A typical cell might contain a parts feeder, a molding machine and a robot. The various machines are 'integrated' and controlled by a single computer or PLC. How the robot interacts with other machines in the cell must be programmed, both with regard to their positions in the cell and synchronizing with them.

Software: The computer is installed with corresponding interface software. The use of a computer greatly simplifies the programming process. Specialized robot software is run either in the robot controller or in the computer or both depending on the system design.

There are two basic entities that need to be taught (or programmed): positional data and procedure. For example, in a task to move a screw from a feeder to a hole the positions of the feeder and the hole must first be taught or programmed. Secondly the procedure to get the screw from the feeder to the hole must be programmed along with any I/O involved, for example a signal to indicate when the screw is in the feeder ready to be picked up. The purpose of the robot software is to facilitate both these programming tasks.

Teaching the robot positions may be achieved a number of ways:

Positional commands The robot can be directed to the required position using a GUI or text based commands in which the required X-Y-Z position may be specified and edited.

Teach pendant: Robot positions can be taught via a teach pendant. This is a handheld control and programming unit. The common features of such units are the ability to manually send the robot to a desired position, or "inch" or "jog" to adjust a position. They also have a means to change the speed since a low speed is usually required for careful positioning, or while test-running through a new or modified routine. A large emergency stop button is usually included as well. Typically once the robot has been programmed there is no more use for the teach pendant. All teach pendants are equipped with a 3-position deadman switch. In the manual mode, it allows the robot to move only when it is in the middle position (partially pressed). If it is fully pressed in or completely released, the robot stops. This principle of operation allows natural reflexes to be used to increase safety.

Lead-by-the-nose: this is a technique offered by many robot manufacturers. In this method, one user holds the robot's manipulator, while another person enters a command which de-energizes the robot causing it to go into limp. The user then moves the robot by hand to the required positions and/or along a required path while the software logs these positions into memory. The program can later run the robot to these positions or along the taught path. This technique is popular for tasks such as paint spraying.

Offline programming is where the entire cell, the robot and all the machines or instruments in the workspace are mapped graphically. The robot can then be moved on screen and the process simulated. A robotics simulator is used to create embedded applications for a robot, without depending on the physical operation of the robot arm and end effector. The advantages of robotics simulation is that it saves time in the design of robotics applications. It can also increase the level of safety associated with robotic equipment since various "what if" scenarios can be tried and tested before the system is activated.[8] Robot simulation software provides a platform to teach, test, run, and debug programs that have been written in a variety of programming languages.

Robotics Simulator

Robot simulation tools allow for robotics programs to be conveniently written and debugged off-line with the final version of the program tested on an actual robot. The ability to preview the behavior of a robotic system in a virtual world allows for a variety of mechanisms, devices, configurations and controllers to be tried and tested before being applied to a "real world" system. Robotics simulators have the ability to provide real-time computing of the simulated motion of an industrial robot using both geometric modeling and kinematics modeling.

Manufacturing independent robot programming tools are a relatively new but flexible way to program robot applications. Using a graphical user interface the programming is done via drag and drop of predefined template/building blocks. They often feature the execution of simulations to evaluate the feasibility and offline programming in combination. If the system is able to compile and upload native robot code to the robot controller, the user no longer has to learn each manufacturer's proprietary language. Therefore, this approach can be an important step to standardize programming methods.

Others in addition, machine operators often use user interface devices, typically touchscreen units, which serve as the operator control panel. The operator can switch from program to program, make adjustments within a program and also operate a host of peripheral devices that may be integrated within the same robotic system. These include end effectors, feeders that supply components to the robot, conveyor belts, emergency stop controls, machine vision systems, safety interlock systems, barcode printers and an almost infinite array of other industrial devices which are accessed and controlled via the operator control panel.

The teach pendant or PC is usually disconnected after programming and the robot then runs on the program that has been installed in its controller. However a computer is often used to 'supervise' the robot and any peripherals, or to provide additional storage for access to numerous complex paths and routines.

End-of-arm tooling

The most essential robot peripheral is the end effector, or end-of-arm-tooling (EOT). Common examples of end effectors include welding devices (such as MIG-welding guns, spot-welders, etc.), spray guns and also grinding and deburring devices (such as pneumatic disk or belt grinders, burrs, etc.), and grippers (devices that can grasp an object, usually electromechanical or pneumatic). Other common means of picking up objects is by vacuum or magnets. End effectors are frequently highly complex, made to match the handled product and often capable of picking up an array of products at one time. They may utilize various sensors to aid the robot system in locating, handling, and positioning products.

Controlling movement

For a given robot the only parameters necessary to completely locate the end effector (gripper, welding torch, etc.) of the robot are the angles of each of the joints or displacements of the linear axes (or combinations of the two for robot formats such as SCARA). However, there are many different ways to define the points. The most common and most convenient way of defining a point is to specify a Cartesian coordinate for it, i.e. the position of the 'end effector' in mm in the X, Y and Z directions relative to the robot's origin. In addition, depending on the types of joints a particular robot may have, the orientation of the end effector in yaw, pitch, and roll and the location of the tool point relative to the robot's faceplate must also be specified. For a jointed arm these coordinates must be converted to joint angles by the robot controller and such conversions are known as Cartesian Transformations which may need to be performed iteratively or recursively for a multiple axis robot. The mathematics of the relationship between joint angles and actual spatial coordinates is called kinematics. 

Positioning by Cartesian coordinates may be done by entering the coordinates into the system or by using a teach pendant which moves the robot in X-Y-Z directions. It is much easier for a human operator to visualize motions up/down, left/right, etc. than to move each joint one at a time. When the desired position is reached it is then defined in some way particular to the robot software in use, e.g. P1 - P5 below.

Typical programming

Most articulated robots perform by storing a series of positions in memory, and moving to them at various times in their programming sequence. For example, a robot which is moving items from one place (bin A) to another (bin B) might have a simple 'pick and place' program similar to the following:

Define points P1–P5:

  1. Safely above workpiece (defined as P1)
  2. 10 cm Above bin A (defined as P2)
  3. At position to take part from bin A (defined as P3)
  4. 10 cm Above bin B (defined as P4)
  5. At position to take part from bin B. (defined as P5)

Define program:

  1. Move to P1
  2. Move to P2
  3. Move to P3
  4. Close gripper
  5. Move to P2
  6. Move to P4
  7. Move to P5
  8. Open gripper
  9. Move to P4
  10. Move to P1 and finish

For examples of how this would look in popular robot languages see industrial robot programming.

Singularities

The American National Standard for Industrial Robots and Robot Systems — Safety Requirements (ANSI/RIA R15.06-1999) defines a singularity as “a condition caused by the collinear alignment of two or more robot axes resulting in unpredictable robot motion and velocities.” It is most common in robot arms that utilize a “triple-roll wrist”. This is a wrist about which the three axes of the wrist, controlling yaw, pitch, and roll, all pass through a common point. An example of a wrist singularity is when the path through which the robot is traveling causes the first and third axes of the robot's wrist (i.e. robot's axes 4 and 6) to line up. The second wrist axis then attempts to spin 180° in zero time to maintain the orientation of the end effector. Another common term for this singularity is a “wrist flip”. The result of a singularity can be quite dramatic and can have adverse effects on the robot arm, the end effector, and the process. Some industrial robot manufacturers have attempted to side-step the situation by slightly altering the robot's path to prevent this condition. Another method is to slow the robot's travel speed, thus reducing the speed required for the wrist to make the transition. The ANSI/RIA has mandated that robot manufacturers shall make the user aware of singularities if they occur while the system is being manually manipulated.

A second type of singularity in wrist-partitioned vertically articulated six-axis robots occurs when the wrist center lies on a cylinder that is centered about axis 1 and with radius equal to the distance between axes 1 and 4. This is called a shoulder singularity. Some robot manufacturers also mention alignment singularities, where axes 1 and 6 become coincident. This is simply a sub-case of shoulder singularities. When the robot passes close to a shoulder singularity, joint 1 spins very fast.

The third and last type of singularity in wrist-partitioned vertically articulated six-axis robots occurs when the wrist's center lies in the same plane as axes 2 and 3.

Singularities are closely related to the phenomena of gimbal lock, which has a similar root cause of axes becoming lined up.

Market structure

According to the International Federation of Robotics (IFR) study World Robotics 2019, there were about 2,439,543 operational industrial robots by the end of 2017. This number is estimated to reach 3,788,000 by the end of 2021. For the year 2018 the IFR estimates the worldwide sales of industrial robots with US$16.5 billion. Including the cost of software, peripherals and systems engineering, the annual turnover for robot systems is estimated to be US$48.0 billion in 2018.

China is the largest industrial robot market, with 154,032 units sold in 2018. China had the largest operational stock of industrial robots, with 649,447 at the end of 2018. The United States industrial robot-makers shipped 35,880 robot to factories in the US in 2018 and this was 7% more than in 2017.

The biggest customer of industrial robots is automotive industry with 30% market share, then electrical/electronics industry with 25%, metal and machinery industry with 10%, rubber and plastics industry with 5%, food industry with 5%. In textiles, apparel and leather industry, 1,580 units are operational.

Estimated worldwide annual supply of industrial robots (in units):

Year supply
1998 69,000
1999 79,000
2000 99,000
2001 78,000
2002 69,000
2003 81,000
2004 97,000
2005 120,000
2006 112,000
2007 114,000
2008 113,000
2009 60,000
2010 118,000
2012 159,346
2013 178,132
2014 229,261
2015 253,748
2016 294,312
2017 381,335
2018 422,271

Health and safety

The International Federation of Robotics has predicted a worldwide increase in adoption of industrial robots and they estimated 1.7 million new robot installations in factories worldwide by 2020. Rapid advances in automation technologies (e.g. fixed robots, collaborative and mobile robots, and exoskeletons) have the potential to improve work conditions but also to introduce workplace hazards in manufacturing workplaces. Despite the lack of occupational surveillance data on injuries associated specifically with robots, researchers from the US National Institute for Occupational Safety and Health (NIOSH) identified 61 robot-related deaths between 1992 and 2015 using keyword searches of the Bureau of Labor Statistics (BLS) Census of Fatal Occupational Injuries research database (see info from Center for Occupational Robotics Research). Using data from the Bureau of Labor Statistics, NIOSH and its state partners have investigated 4 robot-related fatalities under the Fatality Assessment and Control Evaluation Program. In addition the Occupational Safety and Health Administration (OSHA) has investigated dozens of robot-related deaths and injuries, which can be reviewed at OSHA Accident Search page. Injuries and fatalities could increase over time because of the increasing number of collaborative and co-existing robots, powered exoskeletons, and autonomous vehicles into the work environment.

Safety standards are being developed by the Robotic Industries Association (RIA) in conjunction with the American National Standards Institute (ANSI). On October 5, 2017, OSHA, NIOSH and RIA signed an alliance to work together to enhance technical expertise, identify and help address potential workplace hazards associated with traditional industrial robots and the emerging technology of human-robot collaboration installations and systems, and help identify needed research to reduce workplace hazards. On October 16 NIOSH launched the Center for Occupational Robotics Research to "provide scientific leadership to guide the development and use of occupational robots that enhance worker safety, health, and wellbeing." So far, the research needs identified by NIOSH and its partners include: tracking and preventing injuries and fatalities, intervention and dissemination strategies to promote safe machine control and maintenance procedures, and on translating effective evidence-based interventions into workplace practice.

Computational theory of mind

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Computational_theory_of_mind

In philosophy of mind, the computational theory of mind (CTM), also known as computationalism, is a family of views that hold that the human mind is an information processing system and that cognition and consciousness together are a form of computation. Warren McCulloch and Walter Pitts (1943) were the first to suggest that neural activity is computational. They argued that neural computations explain cognition. The theory was proposed in its modern form by Hilary Putnam in 1967, and developed by his PhD student, philosopher and cognitive scientist Jerry Fodor in the 1960s, 1970s and 1980s. Despite being vigorously disputed in analytic philosophy in the 1990s due to work by Putnam himself, John Searle, and others, the view is common in modern cognitive psychology and is presumed by many theorists of evolutionary psychology. In the 2000s and 2010s the view has resurfaced in analytic philosophy (Scheutz 2003, Edelman 2008).

The computational theory of mind holds that the mind is a computational system that is realized (i.e. physically implemented) by neural activity in the brain. The theory can be elaborated in many ways and varies largely based on how the term computation is understood. Computation is commonly understood in terms of Turing machines which manipulate symbols according to a rule, in combination with the internal state of the machine. The critical aspect of such a computational model is that we can abstract away from particular physical details of the machine that is implementing the computation. For example, the appropriate computation could be implemented either by silicon chips or biological neural networks, so long as there is a series of outputs based on manipulations of inputs and internal states, performed according to a rule. CTM, therefore holds that the mind is not simply analogous to a computer program, but that it is literally a computational system.

Computational theories of mind are often said to require mental representation because 'input' into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object, but must interpret and represent the object in some form and then compute the representation. The computational theory of mind is related to the representational theory of mind in that they both require that mental states are representations. However, the representational theory of mind shifts the focus to the symbols being manipulated. This approach better accounts for systematicity and productivity. In Fodor's original views, the computational theory of mind is also related to the language of thought. The language of thought theory allows the mind to process more complex representations with the help of semantics. (See below in semantics of mental states).

Recent work has suggested that we make a distinction between the mind and cognition. Building from the tradition of McCulloch and Pitts, the computational theory of cognition (CTC) states that neural computations explain cognition. The computational theory of mind asserts that not only cognition, but also phenomenal consciousness or qualia, are computational. That is to say, CTM entails CTC. While phenomenal consciousness could fulfill some other functional role, computational theory of cognition leaves open the possibility that some aspects of the mind could be non-computational. CTC therefore provides an important explanatory framework for understanding neural networks, while avoiding counter-arguments that center around phenomenal consciousness.

"Computer metaphor"

Computational theory of mind is not the same as the computer metaphor, comparing the mind to a modern-day digital computer. Computational theory just uses some of the same principles as those found in digital computing. While the computer metaphor draws an analogy between the mind as software and the brain as hardware, CTM is the claim that the mind is a computational system. More specifically, it states that a computational simulation of a mind is sufficient for the actual presence of a mind, and that a mind truly can be simulated computationally.

'Computational system' is not meant to mean a modern-day electronic computer. Rather, a computational system is a symbol manipulator that follows step by step functions to compute input and form output. Alan Turing describes this type of computer in his concept of a Turing machine.

Early proponents

One of the earliest proponents of the computational theory of mind was Thomas Hobbes, who said, "by reasoning, I understand computation. And to compute is to collect the sum of many things added together at the same time, or to know the remainder when one thing has been taken from another. To reason therefore is the same as to add or to subtract." Since Hobbes lived before the contemporary identification of computing with instantiating effective procedures, he cannot be interpreted as explicitly endorsing the computational theory of mind, in the contemporary sense.

Causal picture of thoughts

At the heart of the computational theory of mind is the idea that thoughts are a form of computation, and a computation is by definition a systematic set of laws for the relations among representations. This means that a mental state represents something if and only if there is some causal correlation between the mental state and that particular thing. An example would be seeing dark clouds and thinking "clouds mean rain", where there is a correlation between the thought of the clouds and rain, as the clouds causing rain. This is sometimes known as natural meaning. Conversely, there is another side to the causality of thoughts and that is the non-natural representation of thoughts. An example would be seeing a red traffic light and thinking "red means stop", there is nothing about the color red that indicates it represents stopping, and thus is just a convention that has been invented, similar to languages and their abilities to form representations.

Semantics of mental states

The computational theory of mind states that the mind functions as a symbolic operator, and that mental representations are symbolic representations; just as the semantics of language are the features of words and sentences that relate to their meaning, the semantics of mental states are those meanings of representations, the definitions of the 'words' of the language of thought. If these basic mental states can have a particular meaning just as words in a language do, then this means that more complex mental states (thoughts) can be created, even if they have never been encountered before. Just as new sentences that are read can be understood even if they have never been encountered before, as long as the basic components are understood, and it is syntactically correct. For example: "I have eaten plum pudding every day of this fortnight." While it's doubtful many have seen this particular configuration of words, nonetheless most readers should be able to glean an understanding of this sentence because it is syntactically correct and the constituent parts are understood.

Criticism

A range of arguments have been proposed against physicalist conceptions used in computational theories of mind.

An early, though indirect, criticism of the computational theory of mind comes from philosopher John Searle. In his thought experiment known as the Chinese room, Searle attempts to refute the claims that artificially intelligent agents can be said to have intentionality and understanding and that these systems, because they can be said to be minds themselves, are sufficient for the study of the human mind. Searle asks us to imagine that there is a man in a room with no way of communicating with anyone or anything outside of the room except for a piece of paper with symbols written on it that is passed under the door. With the paper, the man is to use a series of provided rule books to return paper containing different symbols. Unknown to the man in the room, these symbols are of a Chinese language, and this process generates a conversation that a Chinese speaker outside of the room can actually understand. Searle contends that the man in the room does not understand the Chinese conversation. This is essentially what the computational theory of mind presents us—a model in which the mind simply decodes symbols and outputs more symbols. Searle argues that this is not real understanding or intentionality. This was originally written as a repudiation of the idea that computers work like minds.

Searle has further raised questions about what exactly constitutes a computation:

the wall behind my back is right now implementing the WordStar program, because there is some pattern of molecule movements that is isomorphic with the formal structure of WordStar. But if the wall is implementing WordStar, if it is a big enough wall it is implementing any program, including any program implemented in the brain.

Objections like Searle's might be called insufficiency objections. They claim that computational theories of mind fail because computation is insufficient to account for some capacity of the mind. Arguments from qualia, such as Frank Jackson's knowledge argument, can be understood as objections to computational theories of mind in this way—though they take aim at physicalist conceptions of the mind in general, and not computational theories specifically.

There are also objections which are directly tailored for computational theories of mind.

Putnam himself (see in particular Representation and Reality and the first part of Renewing Philosophy) became a prominent critic of computationalism for a variety of reasons, including ones related to Searle's Chinese room arguments, questions of world-word reference relations, and thoughts about the mind-body relationship. Regarding functionalism in particular, Putnam has claimed along lines similar to, but more general than Searle's arguments, that the question of whether the human mind can implement computational states is not relevant to the question of the nature of mind, because "every ordinary open system realizes every abstract finite automaton." Computationalists have responded by aiming to develop criteria describing what exactly counts as an implementation.

Roger Penrose has proposed the idea that the human mind does not use a knowably sound calculation procedure to understand and discover mathematical intricacies. This would mean that a normal Turing complete computer would not be able to ascertain certain mathematical truths that human minds can.

Pancomputationalism

Supporters of CTM are faced with a simple yet important question whose answer has proved elusive and controversial: what does it take for a physical system (such as a mind, or an artificial computer) to perform computations? In other words, under what conditions does a physical system implement a computation? A very straightforward account is based on a simple mapping between abstract mathematical computations and physical systems: a system performs computation C if and only if there is a mapping between a sequence of states individuated by C and a sequence of states individuated by a physical description of the system.

Putnam (1988) and Searle (1992) argue that this simple mapping account (SMA) trivializes the empirical import of computational descriptions. As Putnam put it, “everything is a Probabilistic Automaton under some Description”.  Even rocks, walls, and buckets of water—contrary to appearances—are computing systems. Gualtiero Piccinini identifies different versions of Pancomputationalism, depending on how many computations—all, some, or just one—they attribute to each system. Among these various versions, unlimited Pancomputationalism—the view that every physical system performs every computation—is most worrisome. Because if it is true, then the claim that a system S performs a certain computation becomes trivially true and vacuous or nearly so; it fails to distinguish S from anything else.

In response to the trivialization criticism, and to restrict SMA, philosophers of mind have offered different accounts of computational systems. These typically include causal account, semantic account, syntactic account, and mechanistic account.  

Causal account: a physical system S performs computation C just in case (i) there is a mapping from the states ascribed to S by a physical description to the states defined by computational description C, such that (ii) the state transitions between the physical states mirror the state transitions between the computational states.

Semantic account: In addition to the causal restriction imposed by the causal account, the semantic account imposes a semantic restriction. Only physical states that qualify as representations may be mapped onto computational descriptions, thereby qualifying as computational states. If a state is not representational, it is not computational either.

Syntactic account: Instead of a semantic restriction, the syntactic account imposes a syntactic restriction: only physical states that qualify as syntactic may be mapped onto computational descriptions, thereby qualifying as computational states. If a state lacks syntactic structure, it is not computational.

Mechanistic account: First introduced by Gualtiero Piccinini in 2007, the mechanistic account of computational systems accounts for concrete computation in terms of the mechanistic properties of a system. According to the mechanistic account, concrete computing systems are functional mechanisms of a special kind—mechanisms that perform concrete computations.  

Prominent scholars

  • Daniel Dennett proposed the multiple drafts model, in which consciousness seems linear but is actually blurry and gappy, distributed over space and time in the brain. Consciousness is the computation, there is no extra step or "Cartesian theater" in which you become conscious of the computation.
  • Jerry Fodor argues that mental states, such as beliefs and desires, are relations between individuals and mental representations. He maintains that these representations can only be correctly explained in terms of a language of thought (LOT) in the mind. Further, this language of thought itself is codified in the brain, not just a useful explanatory tool. Fodor adheres to a species of functionalism, maintaining that thinking and other mental processes consist primarily of computations operating on the syntax of the representations that make up the language of thought. In later work (Concepts and The Elm and the Expert), Fodor has refined and even questioned some of his original computationalist views, and adopted a highly modified version of LOT (see LOT2).
  • David Marr proposed that cognitive processes have three levels of description: the computational level (which describes that computational problem (i.e., input/output mapping) computed by the cognitive process); the algorithmic level (which presents the algorithm used for computing the problem postulated at the computational level); and the implementational level (which describes the physical implementation of the algorithm postulated at the algorithmic level in biological matter, e.g. the brain). (Marr 1981)
  • Ulric Neisser coined the term 'cognitive psychology' in his book published in 1967 (Cognitive Psychology), wherein Neisser characterizes people as dynamic information-processing systems whose mental operations might be described in computational terms.
  • Steven Pinker described a "language instinct," an evolved, built-in capacity to learn language (if not writing).
  • Hilary Putnam proposed functionalism to describe consciousness, asserting that it is the computation that equates to consciousness, regardless of whether the computation is operating in a brain, in a computer, or in a "brain in a vat."
  • Georges Rey, professor at the University of Maryland, builds on Jerry Fodor's representational theory of mind to produce his own version of a Computational/Representational Theory of Thought.

 

Functionalism (philosophy of mind)

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In philosophy of mind, functionalism is the thesis that mental states (beliefs, desires, being in pain, etc.) are constituted solely by their functional role, which means, their causal relations with other mental states, sensory inputs and behavioral outputs. Functionalism developed largely as an alternative to the identity theory of mind and behaviorism.

Functionalism is a theoretical level between the physical implementation and behavioral output. Therefore, it is different from its predecessors of Cartesian dualism (advocating independent mental and physical substances) and Skinnerian behaviorism and physicalism (declaring only physical substances) because it is only concerned with the effective functions of the brain, through its organization or its "software programs".

Since mental states are identified by a functional role, they are said to be realized on multiple levels; in other words, they are able to be manifested in various systems, even perhaps computers, so long as the system performs the appropriate functions. While computers are physical devices with electronic substrate that perform computations on inputs to give outputs, so brains are physical devices with neural substrate that perform computations on inputs which produce behaviors.

Multiple realizability

An important part of some arguments for functionalism is the idea of multiple realizability. According to standard functionalist theories, mental states correspond to functional roles. They are like valves; a valve can be made of plastic or metal or other materials, as long as it performs the proper function (controlling the flow of a liquid or gas). Similarly, functionalists argue, mental states can be explained without considering the states of the underlying physical medium (such as the brain) that realizes them; one need only consider higher-level functions. Because mental states are not limited to a particular medium, they can be realized in multiple ways, including, theoretically, within non-biological systems, such as computers. A silicon-based machine could have the same sort of mental life that a human being has, provided that its structure realized the proper functional roles.

However, there have been some functionalist theories that combine with the identity theory of mind, which deny multiple realizability. Such Functional Specification Theories (FSTs) (Levin, § 3.4), as they are called, were most notably developed by David Lewis and David Malet Armstrong. According to FSTs, mental states are the particular "realizers" of the functional role, not the functional role itself. The mental state of belief, for example, just is whatever brain or neurological process that realizes the appropriate belief function. Thus, unlike standard versions of functionalism (often called Functional State Identity Theories), FSTs do not allow for the multiple realizability of mental states, because the fact that mental states are realized by brain states is essential. What often drives this view is the belief that if we were to encounter an alien race with a cognitive system composed of significantly different material from humans' (e.g., silicon-based) but performed the same functions as human mental states (for example, they tend to yell "Ouch!" when poked with sharp objects), we would say that their type of mental state might be similar to ours but it is not the same. For some, this may be a disadvantage to FSTs. Indeed, one of Hilary Putnam's arguments for his version of functionalism relied on the intuition that such alien creatures would have the same mental states as humans do, and that the multiple realizability of standard functionalism makes it a better theory of mind.

Types

Machine-state functionalism

Artistic representation of a Turing machine.

The broad position of "functionalism" can be articulated in many different varieties. The first formulation of a functionalist theory of mind was put forth by Hilary Putnam in the 1960s. This formulation, which is now called machine-state functionalism, or just machine functionalism, was inspired by the analogies which Putnam and others noted between the mind and the theoretical "machines" or computers capable of computing any given algorithm which were developed by Alan Turing (called Turing machines). Putnam himself, by the mid-1970s, had begun questioning this position. The beginning of his opposition to machine-state functionalism can be read about in his Twin Earth thought experiment.

In non-technical terms, a Turing machine is not a physical object, but rather an abstract machine built upon a mathematical model. Typically, a Turing Machine has a horizontal tape divided into rectangular cells arranged from left to right. The tape itself is infinite in length, and each cell may contain a symbol. The symbols used for any given "machine" can vary. The machine has a read-write head that scans cells and moves in left and right directions. The action of the machine is determined by the symbol in the cell being scanned and a table of transition rules that serve as the machine's programming. Because of the infinite tape, a traditional Turing Machine has an infinite amount of time to compute any particular function or any number of functions. In the below example, each cell is either blank (B) or has a 1 written on it. These are the inputs to the machine. The possible outputs are:

  • Halt: Do nothing.
  • R: move one square to the right.
  • L: move one square to the left.
  • B: erase whatever is on the square.
  • 1: erase whatever is on the square and print a '1.

An extremely simple example of a Turing machine which writes out the sequence '111' after scanning three blank squares and then stops as specified by the following machine table:


State One State Two State Three
B write 1; stay in state 1 write 1; stay in state 2 write 1; stay in state 3
1 go right; go to state 2 go right; go to state 3 [halt]

This table states that if the machine is in state one and scans a blank square (B), it will print a 1 and remain in state one. If it is in state one and reads a 1, it will move one square to the right and also go into state two. If it is in state two and reads a B, it will print a 1 and stay in state two. If it is in state two and reads a 1, it will move one square to the right and go into state three. If it is in state three and reads a B, it prints a 1 and remains in state three. Finally, if it is in state three and reads a 1, then it will stay in state three.

The essential point to consider here is the nature of the states of the Turing machine. Each state can be defined exclusively in terms of its relations to the other states as well as inputs and outputs. State one, for example, is simply the state in which the machine, if it reads a B, writes a 1 and stays in that state, and in which, if it reads a 1, it moves one square to the right and goes into a different state. This is the functional definition of state one; it is its causal role in the overall system. The details of how it accomplishes what it accomplishes and of its material constitution are completely irrelevant.

The above point is critical to an understanding of machine-state functionalism. Since Turing machines are not required to be physical systems, "anything capable of going through a succession of states in time can be a Turing machine". Because biological organisms “go through a succession of states in time”, any such organisms could also be equivalent to Turing machines.

According to machine-state functionalism, the nature of a mental state is just like the nature of the Turing machine states described above. If one can show the rational functioning and computing skills of these machines to be comparable to the rational functioning and computing skills of human beings, it follows that Turing machine behavior closely resembles that of human beings. Therefore, it is not a particular physical-chemical composition responsible for the particular machine or mental state, it is the programming rules which produce the effects that are responsible. To put it another way, any rational preference is due to the rules being followed, not to the specific material composition of the agent.

Psycho-functionalism

A second form of functionalism is based on the rejection of behaviorist theories in psychology and their replacement with empirical cognitive models of the mind. This view is most closely associated with Jerry Fodor and Zenon Pylyshyn and has been labeled psycho-functionalism.

The fundamental idea of psycho-functionalism is that psychology is an irreducibly complex science and that the terms that we use to describe the entities and properties of the mind in our best psychological theories cannot be redefined in terms of simple behavioral dispositions, and further, that such a redefinition would not be desirable or salient were it achievable. Psychofunctionalists view psychology as employing the same sorts of irreducibly teleological or purposive explanations as the biological sciences. Thus, for example, the function or role of the heart is to pump blood, that of the kidney is to filter it and to maintain certain chemical balances and so on—this is what accounts for the purposes of scientific explanation and taxonomy. There may be an infinite variety of physical realizations for all of the mechanisms, but what is important is only their role in the overall biological theory. In an analogous manner, the role of mental states, such as belief and desire, is determined by the functional or causal role that is designated for them within our best scientific psychological theory. If some mental state which is postulated by folk psychology (e.g. hysteria) is determined not to have any fundamental role in cognitive psychological explanation, then that particular state may be considered not to exist . On the other hand, if it turns out that there are states which theoretical cognitive psychology posits as necessary for explanation of human behavior but which are not foreseen by ordinary folk psychological language, then these entities or states exist.

Analytic functionalism

A third form of functionalism is concerned with the meanings of theoretical terms in general. This view is most closely associated with David Lewis and is often referred to as analytic functionalism or conceptual functionalism. The basic idea of analytic functionalism is that theoretical terms are implicitly defined by the theories in whose formulation they occur and not by intrinsic properties of the phonemes they comprise. In the case of ordinary language terms, such as "belief", "desire", or "hunger", the idea is that such terms get their meanings from our common-sense "folk psychological" theories about them, but that such conceptualizations are not sufficient to withstand the rigor imposed by materialistic theories of reality and causality. Such terms are subject to conceptual analyses which take something like the following form:

Mental state M is the state that is preconceived by P and causes Q.

For example, the state of pain is caused by sitting on a tack and causes loud cries, and higher order mental states of anger and resentment directed at the careless person who left a tack lying around. These sorts of functional definitions in terms of causal roles are claimed to be analytic and a priori truths about the submental states and the (largely fictitious) propositional attitudes they describe. Hence, its proponents are known as analytic or conceptual functionalists. The essential difference between analytic and psychofunctionalism is that the latter emphasizes the importance of laboratory observation and experimentation in the determination of which mental state terms and concepts are genuine and which functional identifications may be considered to be genuinely contingent and a posteriori identities. The former, on the other hand, claims that such identities are necessary and not subject to empirical scientific investigation.

Homuncular functionalism

Homuncular functionalism was developed largely by Daniel Dennett and has been advocated by William Lycan. It arose in response to the challenges that Ned Block's China Brain (a.k.a. Chinese nation) and John Searle's Chinese room thought experiments presented for the more traditional forms of functionalism (see below under "Criticism"). In attempting to overcome the conceptual difficulties that arose from the idea of a nation full of Chinese people wired together, each person working as a single neuron to produce in the wired-together whole the functional mental states of an individual mind, many functionalists simply bit the bullet, so to speak, and argued that such a Chinese nation would indeed possess all of the qualitative and intentional properties of a mind; i.e. it would become a sort of systemic or collective mind with propositional attitudes and other mental characteristics. Whatever the worth of this latter hypothesis, it was immediately objected that it entailed an unacceptable sort of mind-mind supervenience: the systemic mind which somehow emerged at the higher-level must necessarily supervene on the individual minds of each individual member of the Chinese nation, to stick to Block's formulation. But this would seem to put into serious doubt, if not directly contradict, the fundamental idea of the supervenience thesis: there can be no change in the mental realm without some change in the underlying physical substratum. This can be easily seen if we label the set of mental facts that occur at the higher-level M1 and the set of mental facts that occur at the lower-level M2. Given the transitivity of supervenience, if M1 supervenes on M2, and M2 supervenes on P (physical base), then M1 and M2 both supervene on P, even though they are (allegedly) totally different sets of mental facts.

Since mind-mind supervenience seemed to have become acceptable in functionalist circles, it seemed to some that the only way to resolve the puzzle was to postulate the existence of an entire hierarchical series of mind levels (analogous to homunculi) which became less and less sophisticated in terms of functional organization and physical composition all the way down to the level of the physico-mechanical neuron or group of neurons. The homunculi at each level, on this view, have authentic mental properties but become simpler and less intelligent as one works one's way down the hierarchy.

Mechanistic functionalism

Mechanistic functionalism, originally formulated and defended by Gualtiero Piccinini and Carl Gillett independently, augments previous functionalist accounts of mental states by maintaining that any psychological explanation must be rendered in mechanistic terms. That is, instead of mental states receiving a purely functional explanation in terms of their relations to other mental states, like those listed above, functions are seen as playing only a part—the other part being played by structures— of the explanation of a given mental state.

A mechanistic explanation involves decomposing a given system, in this case a mental system, into its component physical parts, their activities or functions, and their combined organizational relations. On this account the mind remains a functional system, but one that is understood in mechanistic terms. This account remains a sort of functionalism because functional relations are still essential to mental states, but it is mechanistic because the functional relations are always manifestations of concrete structures—albeit structures understood at a certain level of abstraction. Functions are individuated and explained either in terms of the contributions they make to the given system or in teleological terms. If the functions are understood in teleological terms, then they may be characterized either etiologically or non-etiologically.

Mechanistic functionalism leads functionalism away from the traditional functionalist autonomy of psychology from neuroscience and towards integrating psychology and neuroscience. By providing an applicable framework for merging traditional psychological models with neurological data, mechanistic functionalism may be understood as reconciling the functionalist theory of mind with neurological accounts of how the brain actually works. This is due to the fact that mechanistic explanations of function attempt to provide an account of how functional states (mental states) are physically realized through neurological mechanisms.

Physicalism

There is much confusion about the sort of relationship that is claimed to exist (or not exist) between the general thesis of functionalism and physicalism. It has often been claimed that functionalism somehow "disproves" or falsifies physicalism tout court (i.e. without further explanation or description). On the other hand, most philosophers of mind who are functionalists claim to be physicalists—indeed, some of them, such as David Lewis, have claimed to be strict reductionist-type physicalists.

Functionalism is fundamentally what Ned Block has called a broadly metaphysical thesis as opposed to a narrowly ontological one. That is, functionalism is not so much concerned with what there is than with what it is that characterizes a certain type of mental state, e.g. pain, as the type of state that it is. 

Previous attempts to answer the mind-body problem have all tried to resolve it by answering both questions: dualism says there are two substances and that mental states are characterized by their immateriality; behaviorism claimed that there was one substance and that mental states were behavioral disposition; physicalism asserted the existence of just one substance and characterized the mental states as physical states (as in "pain = C-fiber firings").

On this understanding, type physicalism can be seen as incompatible with functionalism, since it claims that what characterizes mental states (e.g. pain) is that they are physical in nature, while functionalism says that what characterizes pain is its functional/causal role and its relationship with yelling "ouch", etc. However, any weaker sort of physicalism which makes the simple ontological claim that everything that exists is made up of physical matter is perfectly compatible with functionalism. Moreover, most functionalists who are physicalists require that the properties that are quantified over in functional definitions be physical properties. Hence, they are physicalists, even though the general thesis of functionalism itself does not commit them to being so.

In the case of David Lewis, there is a distinction in the concepts of "having pain" (a rigid designator true of the same things in all possible worlds) and just "pain" (a non-rigid designator). Pain, for Lewis, stands for something like the definite description "the state with the causal role x". The referent of the description in humans is a type of brain state to be determined by science. The referent among silicon-based life forms is something else. The referent of the description among angels is some immaterial, non-physical state. For Lewis, therefore, local type-physical reductions are possible and compatible with conceptual functionalism. (See also Lewis's mad pain and Martian pain.) There seems to be some confusion between types and tokens that needs to be cleared up in the functionalist analysis.

Criticism

China brain

Ned Block argues against the functionalist proposal of multiple realizability, where hardware implementation is irrelevant because only the functional level is important. The "China brain" or "Chinese nation" thought experiment involves supposing that the entire nation of China systematically organizes itself to operate just like a brain, with each individual acting as a neuron. (The tremendous difference in speed of operation of each unit is not addressed.). According to functionalism, so long as the people are performing the proper functional roles, with the proper causal relations between inputs and outputs, the system will be a real mind, with mental states, consciousness, and so on. However, Block argues, this is patently absurd, so there must be something wrong with the thesis of functionalism since it would allow this to be a legitimate description of a mind.

Some functionalists believe China would have qualia but that due to the size it is impossible to imagine China being conscious. Indeed, it may be the case that we are constrained by our theory of mind and will never be able to understand what Chinese-nation consciousness is like. Therefore, if functionalism is true either qualia will exist across all hardware or will not exist at all but are illusory.

The Chinese room

The Chinese room argument by John Searle is a direct attack on the claim that thought can be represented as a set of functions. The thought experiment asserts that it is possible to mimic intelligent action without any interpretation or understanding through the use of a purely functional system. In short, Searle describes a person who only speaks English who is in a room with only Chinese symbols in baskets and a rule book in English for moving the symbols around. The person is then ordered by people outside of the room to follow the rule book for sending certain symbols out of the room when given certain symbols. Further suppose that the people outside of the room are Chinese speakers and are communicating with the person inside via the Chinese symbols. According to Searle, it would be absurd to claim that the English speaker inside knows Chinese simply based on these syntactic processes. This thought experiment attempts to show that systems which operate merely on syntactic processes (inputs and outputs, based on algorithms) cannot realize any semantics (meaning) or intentionality (aboutness). Thus, Searle attacks the idea that thought can be equated with following a set of syntactic rules; that is, functionalism is an insufficient theory of the mind.

In connection with Block's Chinese nation, many functionalists responded to Searle's thought experiment by suggesting that there was a form of mental activity going on at a higher level than the man in the Chinese room could comprehend (the so-called "system reply"); that is, the system does know Chinese. Of course, Searle responds that there is nothing more than syntax going on at the higher-level as well, so this reply is subject to the same initial problems. Furthermore, Searle suggests the man in the room could simply memorize the rules and symbol relations. Again, though he would convincingly mimic communication, he would be aware only of the symbols and rules, not of the meaning behind them.

Inverted spectrum

Another main criticism of functionalism is the inverted spectrum or inverted qualia scenario, most specifically proposed as an objection to functionalism by Ned Block. This thought experiment involves supposing that there is a person, call her Jane, that is born with a condition which makes her see the opposite spectrum of light that is normally perceived. Unlike normal people, Jane sees the color violet as yellow, orange as blue, and so forth. So, suppose, for example, that you and Jane are looking at the same orange. While you perceive the fruit as colored orange, Jane sees it as colored blue. However, when asked what color the piece of fruit is, both you and Jane will report "orange". In fact, one can see that all of your behavioral as well as functional relations to colors will be the same. Jane will, for example, properly obey traffic signs just as any other person would, even though this involves the color perception. Therefore, the argument goes, since there can be two people who are functionally identical, yet have different mental states (differing in their qualitative or phenomenological aspects), functionalism is not robust enough to explain individual differences in qualia.

David Chalmers tries to show that even though mental content cannot be fully accounted for in functional terms, there is nevertheless a nomological correlation between mental states and functional states in this world. A silicon-based robot, for example, whose functional profile matched our own, would have to be fully conscious. His argument for this claim takes the form of a reductio ad absurdum. The general idea is that since it would be very unlikely for a conscious human being to experience a change in its qualia which it utterly fails to notice, mental content and functional profile appear to be inextricably bound together, at least in the human case. If the subject's qualia were to change, we would expect the subject to notice, and therefore his functional profile to follow suit. A similar argument is applied to the notion of absent qualia. In this case, Chalmers argues that it would be very unlikely for a subject to experience a fading of his qualia which he fails to notice and respond to. This, coupled with the independent assertion that a conscious being's functional profile just could be maintained, irrespective of its experiential state, leads to the conclusion that the subject of these experiments would remain fully conscious. The problem with this argument, however, as Brian G. Crabb (2005) has observed, is that, while changing or fading qualia in a conscious subject might force changes in its functional profile, this tells us nothing about the case of a permanently inverted or unconscious robot. A subject with inverted qualia from birth would have nothing to notice or adjust to. Similarly, an unconscious functional simulacrum of ourselves (a zombie) would have no experiential changes to notice or adjust to. Consequently, Crabb argues, Chalmers' "fading qualia" and "dancing qualia" arguments fail to establish that cases of permanently inverted or absent qualia are nomologically impossible.

A related critique of the inverted spectrum argument is that it assumes that mental states (differing in their qualitative or phenomenological aspects) can be independent of the functional relations in the brain. Thus, it begs the question of functional mental states: its assumption denies the possibility of functionalism itself, without offering any independent justification for doing so. (Functionalism says that mental states are produced by the functional relations in the brain.) This same type of problem—that there is no argument, just an antithetical assumption at their base—can also be said of both the Chinese room and the Chinese nation arguments. Notice, however, that Crabb's response to Chalmers does not commit this fallacy: His point is the more restricted observation that even if inverted or absent qualia turn out to be nomologically impossible, and it is perfectly possible that we might subsequently discover this fact by other means, Chalmers' argument fails to demonstrate that they are impossible.

Twin Earth

The Twin Earth thought experiment, introduced by Hilary Putnam, is responsible for one of the main arguments used against functionalism, although it was originally intended as an argument against semantic internalism. The thought experiment is simple and runs as follows. Imagine a Twin Earth which is identical to Earth in every way but one: water does not have the chemical structure H₂O, but rather some other structure, say XYZ. It is critical, however, to note that XYZ on Twin Earth is still called "water" and exhibits all the same macro-level properties that H₂O exhibits on Earth (i.e., XYZ is also a clear drinkable liquid that is in lakes, rivers, and so on). Since these worlds are identical in every way except in the underlying chemical structure of water, you and your Twin Earth doppelgänger see exactly the same things, meet exactly the same people, have exactly the same jobs, behave exactly the same way, and so on. In other words, since you share the same inputs, outputs, and relations between other mental states, you are functional duplicates. So, for example, you both believe that water is wet. However, the content of your mental state of believing that water is wet differs from your duplicate's because your belief is of H₂O, while your duplicate's is of XYZ. Therefore, so the argument goes, since two people can be functionally identical, yet have different mental states, functionalism cannot sufficiently account for all mental states.

Most defenders of functionalism initially responded to this argument by attempting to maintain a sharp distinction between internal and external content. The internal contents of propositional attitudes, for example, would consist exclusively in those aspects of them which have no relation with the external world and which bear the necessary functional/causal properties that allow for relations with other internal mental states. Since no one has yet been able to formulate a clear basis or justification for the existence of such a distinction in mental contents, however, this idea has generally been abandoned in favor of externalist causal theories of mental contents (also known as informational semantics). Such a position is represented, for example, by Jerry Fodor's account of an "asymmetric causal theory" of mental content. This view simply entails the modification of functionalism to include within its scope a very broad interpretation of input and outputs to include the objects that are the causes of mental representations in the external world.

The twin earth argument hinges on the assumption that experience with an imitation water would cause a different mental state than experience with natural water. However, since no one would notice the difference between the two waters, this assumption is likely false. Further, this basic assumption is directly antithetical to functionalism; and, thereby, the twin earth argument does not constitute a genuine argument: as this assumption entails a flat denial of functionalism itself (which would say that the two waters would not produce different mental states, because the functional relationships would remain unchanged).

Meaning holism

Another common criticism of functionalism is that it implies a radical form of semantic holism. Block and Fodor referred to this as the damn/darn problem. The difference between saying "damn" or "darn" when one smashes one's finger with a hammer can be mentally significant. But since these outputs are, according to functionalism, related to many (if not all) internal mental states, two people who experience the same pain and react with different outputs must share little (perhaps nothing) in common in any of their mental states. But this is counterintuitive; it seems clear that two people share something significant in their mental states of being in pain if they both smash their finger with a hammer, whether or not they utter the same word when they cry out in pain.

Another possible solution to this problem is to adopt a moderate (or molecularist) form of holism. But even if this succeeds in the case of pain, in the case of beliefs and meaning, it faces the difficulty of formulating a distinction between relevant and non-relevant contents (which can be difficult to do without invoking an analytic–synthetic distinction, as many seek to avoid).

Triviality arguments

According to Ned Block, if functionalism is to avoid the chauvinism of type-physicalism, it becomes overly liberal in "ascribing mental properties to things that do not in fact have them". As an example, he proposes that the economy of Bolivia might be organized such that the economic states, inputs, and outputs would be isomorphic to a person under some bizarre mapping from mental to economic variables.

Hilary Putnam, John Searle, and others have offered further arguments that functionalism is trivial, i.e. that the internal structures functionalism tries to discuss turn out to be present everywhere, so that either functionalism turns out to reduce to behaviorism, or to complete triviality and therefore a form of panpsychism. These arguments typically use the assumption that physics leads to a progression of unique states, and that functionalist realization is present whenever there is a mapping from the proposed set of mental states to physical states of the system. Given that the states of a physical system are always at least slightly unique, such a mapping will always exist, so any system is a mind. Formulations of functionalism which stipulate absolute requirements on interaction with external objects (external to the functional account, meaning not defined functionally) are reduced to behaviorism instead of absolute triviality, because the input-output behavior is still required.

Peter Godfrey-Smith has argued further that such formulations can still be reduced to triviality if they accept a somewhat innocent-seeming additional assumption. The assumption is that adding a transducer layer, that is, an input-output system, to an object should not change whether that object has mental states. The transducer layer is restricted to producing behavior according to a simple mapping, such as a lookup table, from inputs to actions on the system, and from the state of the system to outputs. However, since the system will be in unique states at each moment and at each possible input, such a mapping will always exist so there will be a transducer layer which will produce whatever physical behavior is desired.

Godfrey-Smith believes that these problems can be addressed using causality, but that it may be necessary to posit a continuum between objects being minds and not being minds rather than an absolute distinction. Furthermore, constraining the mappings seems to require either consideration of the external behavior as in behaviorism, or discussion of the internal structure of the realization as in identity theory; and though multiple realizability does not seem to be lost, the functionalist claim of the autonomy of high-level functional description becomes questionable.

AI effect

From Wikipedia, the free encyclopedia

The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.

Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'." Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'"

"The AI effect" tries to redefine AI to mean: AI is anything that has not been done yet

A view taken by some people trying to promulgate the AI effect is: As soon as AI successfully solves a problem, the problem is no longer a part of AI.

Pamela McCorduck calls it an "odd paradox" that "practical AI successes, computational programs that actually achieved intelligent behavior, were soon assimilated into whatever application domain they were found to be useful in, and became silent partners alongside other problem-solving approaches, which left AI researchers to deal only with the "failures", the tough nuts that couldn't yet be cracked."

When IBM's chess playing computer Deep Blue succeeded in defeating Garry Kasparov in 1997, people complained that it had only used "brute force methods" and it wasn't real intelligence. Fred Reed writes:

"A problem that proponents of AI regularly face is this: When we know how a machine does something 'intelligent,' it ceases to be regarded as intelligent. If I beat the world's chess champion, I'd be regarded as highly bright."

Douglas Hofstadter expresses the AI effect concisely by quoting Larry Tesler's Theorem:

"AI is whatever hasn't been done yet."

When problems have not yet been formalised, they can still be characterised by a model of computation that includes human computation. The computational burden of a problem is split between a computer and a human: one part is solved by computer and the other part solved by a human. This formalisation is referred to as human-assisted Turing machine.

AI applications become mainstream

Software and algorithms developed by AI researchers are now integrated into many applications throughout the world, without really being called AI.

Michael Swaine reports "AI advances are not trumpeted as artificial intelligence so much these days, but are often seen as advances in some other field". "AI has become more important as it has become less conspicuous", Patrick Winston says. "These days, it is hard to find a big system that does not work, in part, because of ideas developed or matured in the AI world."

According to Stottler Henke, "The great practical benefits of AI applications and even the existence of AI in many software products go largely unnoticed by many despite the already widespread use of AI techniques in software. This is the AI effect. Many marketing people don't use the term 'artificial intelligence' even when their company's products rely on some AI techniques. Why not?"

Marvin Minsky writes "This paradox resulted from the fact that whenever an AI research project made a useful new discovery, that product usually quickly spun off to form a new scientific or commercial specialty with its own distinctive name. These changes in name led outsiders to ask, Why do we see so little progress in the central field of artificial intelligence?"

Nick Bostrom observes that "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labelled AI anymore."

Legacy of the AI winter

Many AI researchers find that they can procure more funding and sell more software if they avoid the tarnished name of "artificial intelligence" and instead pretend their work has nothing to do with intelligence at all. This was especially true in the early 1990s, during the second "AI winter".

Patty Tascarella writes "Some believe the word 'robotics' actually carries a stigma that hurts a company's chances at funding"

Saving a place for humanity at the top of the chain of being

Michael Kearns suggests that "people subconsciously are trying to preserve for themselves some special role in the universe". By discounting artificial intelligence people can continue to feel unique and special. Kearns argues that the change in perception known as the AI effect can be traced to the mystery being removed from the system. In being able to trace the cause of events implies that it's a form of automation rather than intelligence.

A related effect has been noted in the history of animal cognition and in consciousness studies, where every time a capacity formerly thought as uniquely human is discovered in animals, (e.g. the ability to make tools, or passing the mirror test), the overall importance of that capacity is deprecated.

Herbert A. Simon, when asked about the lack of AI's press coverage at the time, said, "What made AI different was that the very idea of it arouses a real fear and hostility in some human breasts. So you are getting very strong emotional reactions. But that's okay. We'll live with that."

 

Romanization (cultural)

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