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Students are encouraged to select an area to specialise in for their Report. The focus should be to define a narrow, but deep (detailed), area of research. It is anticipated that pursuing such a “narrow and deep” topic area is likely to provide students with high levels of autonomy, ownership, motivation and personalised learning outcomes.

The list is only intended as an idea prompt. Students are encouraged to identify alternative topic areas to pursue if they wish, but these must be submitted to their tutor for consideration and authority to proceed.

Each of the topics identified below contains ethical considerations. Each is open to be viewed for its relative strengths and weaknesses (positive and negative effects). Such a balanced consideration of strengths and weakness must be incorporated into the Report.

Areas which do not hold such “two-way” arguments are not suitable as a Report Topic area. For example, the incidence of malware (such as viruses) is not a suitable report topic area as it is “all bad” in its effect on society…unless a student can clearly argue some positive effects.

Introduction to Computer Control, Robotics, and Artificial Intelligence

This report aims to discuss the contemporary issues in IT. The research topic are Computer control, robotics, and artificial intelligence. A detailed discussion about these topics are provided in this report. An introduction about computer control, robotics and artificial intelligence is provided in this report. The benefits of computer control, robotics and artificial intelligence is discussed along with the limitations. Lastly, this report concludes with an appropriate conclusion of this report.

In these modern times, several facilities and works are managed by the system of automation and computers. There are several advantages of implementing automation system in a business such as increased efficiency, cost reduction, increase in the number of operators, increase the safety of workers, improvement of working performance and completion of work that cannot be achieved manually (Azar and Vaidyanathan 2015).

The term Artificial intelligence determines the complete concept of an intelligent machine in the terms of equally social and operational consequences. The trend towards shifting towards AI in swiftly increasing for the benefits of sophistication and accuracy of AI systems (Russell and Norvig 2016). The artificial systems can be useful in many sectors such as public services, transportation, business, medical, energy, engineering, and education. As per prediction, the next step in the field of artificial intelligence is the development and introduction of augmented intelligence (Michalski, Carbonell and Mitchell 2013). Wearable technologies and global sensing systems are growing towards systems embedded with intelligence. The implementation of AI in the systems has several benefits as well as disadvantages. It is vital to understand the harmonic and rational interaction that is essential between the projects that are application specific and the idealistic research ideas.

Operation of computer control system

  1. Microprocessor

One example for understanding the computer control system is microprocessor. Microprocessor is the small integrated circuit that is used for various purposes. It has the capacity to store huge amount of data. The usage of microprocessor varies from CPU in a computer to several other applications for broadening its usage (Miller and Rice 2013).

  1. The working principle of computer control system

The working principle of computer control system can be understood with the example of sensors.

Sensors are commonly used for collecting data and provide input in the computer. The various kinds of sensors are thermal sensors, pressure sensors, and light sensors. These kind of sensors has the ability of producing electrical signals as per the changes in external environment. In one case, light sensitive resistor has the ability to be used as a light sensor. It is due to the fact that the corresponding current and resistance will alter with the intensity of light in the environment. The input signals are processed by the computer. Then the output signal is transmitted to the components that are under the control of the computer, such as LED (Light Emitting Diode), and relay, for responding accordingly. Some input and output signals of a computer needs supplementary equipment and interface (Niewiadomska?Szynkiewicz et al. 2013).

Benefits and Limitations of Computer Control System

Benefits of computer control system

Some of the benefits of a computer control system are as follows:

  1. Swift speed of calculation: A huge number of calculations are carried out by the central processing unit of a modern computer in a small duration of time. The fast speed of calculation helps in controlling several facilities simultaneously under various conditions (LaFleur et al. 2013).
  2. Several systems of input and output devices: A computer possesses several input devices such as keyboard and mouse. Moreover, computer can utilise the electronic circuits for transforming signals into digital forms from the electronic sensors and then input them into the computer. The output signal is generated after processing the appropriate data. Computers can also use these electronic circuits to output digital signals, which is used for controlling different output devices, such as monitor and printer (Roffel and Chin 2017).
  3. High data storage capacity: Computers uses a huge number of storage devices for storing information like magnetic disk and floppy disk. These devices helps in performing the task of work control by using these stored information. Computers can be used to design complex design of work pieces. The designing process is executed by the transferring the input data in the storage devices of the CNC machine and allow the computer to control the shaping of the work pieces depending on the data (Alverson et al. 2014).
  4. Programmable control: a set of instructions is called a program. Computer generally operate depending on the program. This provides an opportunity to the operators to either input fresh program or alter the prevailing programs for changing the procedure and operation of the program depending on the needs. So, along with operating the repetitive work and the simple tasks, the computer control system performs the duty of operating the complex and complicated work under various conditions and then feedback depending on the program (Cao et al. 2014).
  5. Possibility of telecommunications: Computer control systems can be utilised for input or output of digital signals, using several wires for transmitting signals, establish communication or exchange information. The computerised information can be communicated to vast areas with the help of isolated cables, telephone connections, radio waves or internet. Machines or distant video cameras can be controlled using the internet; computers are also used for controlling spaceships in space via radio waves.

There are several limitations of the computer control system. The advantages makes this system essential for the implementation but the limitations of this system has to be considered.

  1. High cost: A huge amount of time has to be invested to make the design of a computer control system. The cost of the equipment that are required for the successful implementation of computer control system. The controlling of cutting machine can be considered as an example to understand the cost of implementation of computer control. A specified set of control program is required for controlling the movement path for the tools, speed of movement, speed of spinning, rate of feeding, alteration of tools, and addition of coolants (Matuszek et al. 2013).
  2. Maintenance difficulties: Integrated circuits and electronic circuits are produced in a mass scale with the help of computers. Replacement of damaged parts is a necessary aspect for the industries. As the computer technology is developing at a significant rate, the use of newly designed parts is not possible in the outmoded electronic circuit boards, and therefore, the maintenance of these computer systems is quite difficult.
  3. Intolerance under adversarial conditions: The working capability of the computer control system under adverse conditions is very low such as too hot or too cold conditions can hamper the working of the computer control system.
  4. Necessity of huge amount of supportive devices: Computers possess the capability of managing huge amount of data in a short duration, but there is a requirement of supporting devices for providing information.

Robotics is growing is becoming complicated by creating a foundation on the progress made in computing and electrical engineering, mechatronics. The basic sovereignty of a robot in an environment can be categorised into recognising, development, and implementation. The prominent idea of converging robotics and AI is to optimise the level of autonomy of learning (Siciliano and Khatib 2016).

Ethical issues in robotics

Privacy threat: The main component of AI is data, and it requires high level of security and proper management to properly maintain the information source to prevent security breaches. Preventive and protective technologies are required to be developed against these threats. Even though the issues may not be connected to the operations of AI, it is the most important responsibility of the AI operators to ensure the protection of data. In addition to these, the AI applications, which can compromise the privacy rights, must be preserved with distinct legislation that safeguards the individual (Geiger et al. 2013).

Security threats and AI weaponisation: With the propagation of security threats, like regional conflicts and terrorism, the modern world is in a race of global arms that developed a need of innovative weapons that are developed using AI, like missiles and autonomous drones, malicious software and virtual bots that are designed for executing sophisticated espionage. It might lead to the possibilities of an unexpected war escalation (Vertut 2013).

Economic and Employment issues: It has been predicted that the number of jobs occupied by the robots will rise at a significant rate in the near future (Lin, Abney, and Bekey 2014). It is also predicted that the development in the robotics and AI field will provide ability of interacting, evaluating, and executing complex decisions to the robots (Taylor et al. 2016). The implementation of Big Data in the branch of robotics will assist in creating formidable database for the robots to judge which algorithm is most suitable for a situation The increased growth in the technological field has replaced labour with machinery. The sole issue regarding the loss of jobs due to automation is that, the amount of jobs created with the introduction of automation in several industries is not proportionate to the amount of jobs that are created by automation. It is essential to implement proper guidelines to ensure the benefits of automation is gained by all the employees and not only by the employers.

Introduction to Robotics and Artificial Intelligence

Limitations of AI

Intelligence as fundamental model: A machine need to satisfy the needs of several abilities like reasoning, model building, real world understanding, and anticipation of the future actions to be considered intelligent. The notion of intelligence consists of these high-level mechanisms: insight, development, similarity, semantic, cognitive, and common sense (Nilsson 2014).

Hard generalisation and large datasets: After broad exercise on vast datasets, the machines of this new era can achieve striking results in identifying images or speech translation. These capabilities are obtained with the assist of statistical approximation on the accessible data. Moreover, the situation when the system has to deal with a new prospect, it is often identified that the system fails. The reason behind this failure is that, the recent AI systems are missing the level of generalizability and abstraction of humans (Cohen and Feigenbaum 2014).

Lack of analysis and Black box: The lack of interpretation can be considered as another issue in the recent AI system. For instance, profound neural networks possesses millions of restrictions and the understanding of the failure and opportunities of the network becomes impossible (Wenger 2014). Despite conducting some latest work on the effort of visualising some high-level features with the help of weight screens in convolution neural network, the gained competent models are not interpretable. The consequences are that several researchers use the method of recent AI approaches as Black box (Hovy, Navigli and Ponzetto 2013).

Robustness of AI: The recently developed AI systems are very easily deceived, which is the main problem that can affect almost all the machine learning (Bond and Gasser 2014).

Opportunities with AI

 Even though there are some issues in the concept of AI, it is still considered to be a major aspect in the recent times and it is breakthrough development for a prosperous future. The tendency of humans to depend on the systems of AI will increase in the near future as there is a significant growth in the availability and utilisation of AI systems (Yang and Xu 2013). This is the sole reason why so many large firms are investing huge capitals for the implementation of AI in their business. In some cases, AI systems are required to handle the data with increased complexities. The benefits of increased accuracy and sophistication of AI makes it ideal for the use in the sectors of economics, medical, energy, industrial, schooling, transportation, and public amenities (Charniak et al. 2014). Robust AI systems has the ability of replacing expensive human labour and generate different potential applications and work with humans for achieving better standards of service. As per prediction, the next stage of AI systems is the introduction of augmented intelligence (Gero 2014). Pervasive systems for sensing and easily accessible technologies are progressing towards the smart embedded systems, which are predicted to create a natural addition of the physical abilities of the human beings. The physical abilities of the human, information retrieval, and human sensing is limited for the humans but it has vast opportunities in the AI systems. The modified algorithms of AI with the addition of progressive sensing systems can track the various aspects around the humans and comprehend the intentions, therefore enabling seamless communication with each other (Ghahramani 2015).

Ethical Issues in Robotics

The recent developments of AI will act as a crucial factor in the imitation of the human brain. The recent advances in computation hardware and sensing will enable the linking of brain function with the behaviour of humans at such a level that the emotions and self-awareness can be observed and simulated in a more realistic way (Copeland 2015).

Conclusion

Therefore, it can be concluded that the use of AI in these recent times is gradually increasing. Several firms are progressing towards the implementation of AI in their system for increasing the revenue of their business and create more opportunities. The term Artificial intelligence determines the complete concept of an intelligent machine in the terms of equally social and operational consequences. The artificial systems can be useful in many sectors such as public services, transportation, business, medical, energy, engineering, and education. The implementation of AI in the systems has several benefits as well as disadvantages. There are several advantages of implementing automation system in a business such as increased efficiency, cost reduction, increase in the number of operators, and increase the safety of workers. Some of the benefits of computer control system are swift calculation speed, several systems of input and output devices, high data storage capacity, programmable control, and the possibility of telecommunications. Some of the limitations of computer control systems are high cost, maintenance difficulties, intolerance under adversarial conditions, and necessity of huge amount of supportive devices. Robotics is growing to be increasingly complicated by creating a foundation on the progress made in computing and electrical engineering, mechatronics. Some of the ethical issues of robotics are privacy threat, security threats and AI weaponisation, and economic and employment issues. Some of the limitations of AI are that a machine need to satisfy the needs of several abilities like reasoning, model building, real world understanding, and anticipation of the future actions to be considered intelligent. Some other limitations are hard generalisation and large datasets, lack of analysis and Black box, and robustness of AI.

Reference

Alverson, R., Callahan, D., Cummings, D., Koblenz, B., Porterfield, A. and Smith, B., 2014, June. The Tera computer system. In ACM International Conference on Supercomputing 25th Anniversary Volume (pp. 122-127). ACM.

Azar, A.T. and Vaidyanathan, S. eds., 2015. Chaos modeling and control systems design (Vol. 581). Germany: Springer.

Bond, A.H. and Gasser, L. eds., 2014. Readings in distributed artificial intelligence. Morgan Kaufmann.

Cao, L., Li, J., Ji, H. and Jiang, C., 2014. A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control. Journal of neuroscience methods, 229, pp.33-43.

Charniak, E., Riesbeck, C.K., McDermott, D.V. and Meehan, J.R., 2014. Artificial intelligence programming. Psychology Press.

Cohen, P.R. and Feigenbaum, E.A. eds., 2014. The handbook of artificial intelligence (Vol. 3). Butterworth-Heinemann.

Copeland, J., 2015. Artificial intelligence: A philosophical introduction. John Wiley & Sons.

Geiger, A., Lenz, P., Stiller, C. and Urtasun, R., 2013. Vision meets robotics: The KITTI dataset. The International Journal of Robotics Research, 32(11), pp.1231-1237.

Gero, J.S. ed., 2014. Artificial intelligence in design'91. Butterworth-Heinemann.

Ghahramani, Z., 2015. Probabilistic machine learning and artificial intelligence. Nature, 521(7553), p.452.

Hovy, E., Navigli, R. and Ponzetto, S.P., 2013. Collaboratively built semi-structured content and Artificial Intelligence: The story so far. Artificial Intelligence, 194, pp.2-27.

LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E. and He, B., 2013. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. Journal of neural engineering, 10(4), p.046003.

Lin, P., Abney, K. and Bekey, G.A., 2014. Robot ethics: the ethical and social implications of robotics. The MIT Press.

Matuszek, C., Herbst, E., Zettlemoyer, L. and Fox, D., 2013. Learning to parse natural language commands to a robot control system. In Experimental Robotics (pp. 403-415). Springer, Heidelberg.

Michalski, R.S., Carbonell, J.G. and Mitchell, T.M. eds., 2013. Machine learning: An artificial intelligence approach. Springer Science & Business Media.

Miller, E.J. and Rice, A.K., 2013. Systems of organization: The control of task and sentient boundaries. Routledge.

Niewiadomska?Szynkiewicz, E., Sikora, A., Arabas, P. and Ko?odziej, J., 2013. Control system for reducing energy consumption in backbone computer network. Concurrency and Computation: Practice and Experience, 25(12), pp.1738-1754.

Nilsson, N.J., 2014. Principles of artificial intelligence. Morgan Kaufmann.

Roffel, B. and Chin, P., 2017. Computer control in the process industries. CRC Press.

Russell, S.J. and Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.

Siciliano, B. and Khatib, O. eds., 2016. Springer handbook of robotics. Springer.

Taylor, R.H., Menciassi, A., Fichtinger, G., Fiorini, P. and Dario, P., 2016. Medical robotics and computer-integrated surgery. In Springer handbook of robotics (pp. 1657-1684). Springer, Cham.

Vertut, J. ed., 2013. Teleoperation and robotics: applications and technology (Vol. 3). Springer Science & Business Media.

Wenger, E., 2014. Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.

Yang, J.B. and Xu, D.L., 2013. Evidential reasoning rule for evidence combination. Artificial Intelligence, 205, pp.1-29.

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