Dive into the field of Artificial Intelligence in 10 weeks.

Prasad Nimantha Madusanka | MBA Data Analytics(UK)

6 min readMay 4, 2022

Pathway of becoming an AI Expert in 3 months | School of Computing | London Metropolitan University | Prasad Nimantha Madusanka, MBA(Data Analytics)

Artificial Intelligence — WEEK 1

Artificial intelligence refers to the intelligence exhibited by a machine, which is composed of human-like attributes (Verma et al, 2021 ). These attributes can be often equated to the activities like recognizing objects, understanding languages and having the capability to respond to a message of its own, decision making, and problem-solving, as such a combination of complex human-like cognitive capabilities (Russell & Norvig, 2016). The thought process, reasoning, and behaviours are often attempted to match the fidelity to human performance, measuring up to a certain idealism/rationality as shown in the below mind map.

Figure 1: Mindmap of General applications of AI

Intelligent Agents — WEEK 2

Intelligent agents are automated entities that are designed to observe using sensors and act upon the environment stimuli using the actuators while delivering the expected activity towards the goal in a rational manner. Agents may also have the ability to learn and use such knowledge to reach goals. The most used areas are economics, cognitive science, ethics, philosophy, socio-cognitive modelling, and computer simulations (Elmahalawy, 2015).

Figure 2: Mindmap of Intelligent Agents Engineering

Computer Vision — WEEK 3

Computer Vision is one of the main sub-fields under Artificial intelligence that focused on raining computer systems to detect and interpret and generate understanding of the visual world using digital signals. The field is an interdisciplinary area that is correlated with computers that can enrich information support to achieve a high-level understanding of seen objects and events in a scene using digital image analysis. The technology is predominantly used where monitoring is required for physical elements in engineering projects (Teizer, 2015).

Figure 3: Mindmap of Computer Vision Engineering

Natural Language Processing — WEEK 4

Natural Language Processing is a form of linguistic analysis commonly targeted at textual data such as boos, and documents using computational methodologies. The objective of NLP is to process natural language and construct a representation of the text in order to structure the unstructured natural language insights using linguistic statistics. The process is syntactic in nature and captures grammatical components and relationships among constituents of texts or sematic capturing conveyed by the text (Verspoor & Cohen , 2018)

Figure 4: Mindmap of Natural Language Processing

Pathfinding Algorithm — WEEK 5

Pathfinding algorithms are developed with the intention of dining the shortest possible path for solving a problem in graph theory. Given a starting and an endpoint and based on predefined criteria these algorithms are able to determine the shortest effective approach path (Chopra, 2019). Some of the use cases of Pathfinding Algorithms can be seen in Google Maps, Satellite navigation systems, routing via the internet, and also game development and space travel (Chopra, 2019).

Robotics — WEEK 6

Robotics is the study of purpose-built machines that can carry out actions automatically and exhibits intuitiveness and display intelligence using sensors and adapt to the actions based on the environment. These machines are programmable by a computer and can carry out sans-complex tasks using sensory information (Ben-Ari and Mondada, 2017).

Figure 6: Mindmap of Robotics Engineering

Adversarial Search — WEEK 7

Adversarial Search is the approach to studying the problems that arise during the planning stage, focused on the forces and agents that may encounter against the executioner (JavaPoint, n.d). While there are many approaches as shown in the mind map below the application of Adversarial Search can be seen in Business, Trading, War, and even in games such as chess. They mainly highlight the fact of unpredictability and hostility displayed by the opponent.

Knowledge-based System — WEEK 8

Knowledge base systems provide a range of possibilities representing the knowledge and reasoning capability with the knowledge possessed. These systems are comprised of three main components as “knowledge base”, the “inference engine” and the “user interface”. The functionalities of such systems are inspired by the research on Artificial Intelligence and the below mind map is designed to show its parameters and components of the field (Kingston, 2017).

Figure 8: Mindmap of Knowledge-based System Engineering

Human Augmentation — WEEK 9

Human augmentation is a research area that aims to enhance human capabilities using scientific approaches such as medicine and technology. The field practices enhancement methodologies based on chemicals, electronics, computer engineering, and design thinking up to apparel to augment the human in a non-invasive way ( Raisamo et al., 2019). The below mind map indicates its depth and practices in a dimensional way with its applications and various technologies used.

Uncertainty: Probability & Markov Chains — WEEK 10

The probability of something is dependent on the choices made, the medium used, time, or its complexity. The methodologies used in this subject are intended to analyze the correctness, reliability, and performance of systems in many finite states and their behaviour (Sen et al, 2006). The various approaches and elements and visualized in the below mind map for a better understanding of its approaches and use cases.

Figure 10: Uncertainty: Probability & Markov Chains

Sources

Ben-Ari, M. and Mondada, F., 2017. Robots and Their Applications. Elements of Robotics, [online] pp.1–20. Available at: <https://link.springer.com/chapter/10.1007%2F978-3-319-62533-1_1> [Accessed 12 May 2021].

Chopra, C., 2019. Pathfinding Algorithms. [online] Medium. Available at: <https://medium.com/swlh/pathfinding-algorithms-6c0d4febe8fd#:~:text=What%20are%20Pathfinding%20Algorithms%3F,based%20on%20some%20predefined%20criteria.> [Accessed 12 May 2021].

Elmahalawy, A., 2015. INTELLIGENT AGENT AND MULTI AGENT SYSTEMS. www.researchgate.net, [online] Available at: <https://www.researchgate.net/publication/274373995_INTELLIGENT_AGENT_AND_MULTI_AGENT_SYSTEMS> [Accessed 12 May 2021].

javatpoint.com. n.d. Artificial Intelligence | Adversarial Search — Javatpoint. [online] Available at: <https://www.javatpoint.com/ai-adversarial-search> [Accessed 12 May 2021].

J. Teizer. Advanced Engineering Informatics, 29 (2015), pp. 225–238

Kingston, J., 2017. Knowledge based system development tools. www.researchgate.net, [online] Available at: <https://www.researchgate.net/publication/321225118_Knowledge_based_system_development_tools> [Accessed 12 May 2021].

Raisamo, R., Rakkolainen, I., Majaranta, P., Salminen, K., Rantala, J. and Farooq, A., 2019. Human augmentation: Past, present and future. International Journal of Human-Computer Studies, 131, pp.131–143.

Sen, K., Viswanathan, M. and Agha, g., 2006. Model-Checking Markov Chains in the Presence of Uncertainties. Springer, [online] Available at: <https://link.springer.com/content/pdf/10.1007%2F11691372_26.pdf> [Accessed 12 May 2021].

S.J. Russell and P. Norvig ((3rd ed.)), Pearson Education Limited, Upper Saddle River, NJ (2016)

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal Of Information Management Data Insights, 1(1), 100002. doi: 10.1016/j.jjimei.2020.100002

Verspoor, K. and Cohen, K., 2018. Natural Language Processing. www.researchgate.net, [online] Available at: <https://www.researchgate.net/publication/291179558_Natural_Language_Processing#:~:text=De%EF%AC%81nition,to%20build%20a%20representation%20of> [Accessed 12 May 2021].