Brief about Artificial Intelligence and Machine Learning

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Brief about Artificial Intelligence and Machine Learning

Executive Summary

This report attempts to introduce the concepts of Artificial Intelligence and Machine learning to the managers of AECOM, which is a large corporation. This brief introduces the basic concepts of machine learning, its importance, and the benefits of using it.

1.1 Status of Emerging Technologies

Humans develop technology to solve their problems, and we have reached a position in which we are capable of creating machines equivalent to humans. A company such as AECOM, which has multiple business interests, can utilize emergent technologies to meet business requirements. Artificial intelligence and machine learning is one such technology that can add value to the projects of AECOM, especially the management of sports venues.  This is a brief about the emerging technology and its benefits to the organization.

Artificial Intelligence (AI) has been an emerging branch of computer science for the last fifty years, which promises varied applications in business (Buchanan 2005). Many cutting-edge applications have already entered the marketplace based on developments in Artificial intelligence, and the company must make use of its benefits. The advances in information technology are driven by extraordinary capabilities in computing and a set of emerging disciplines ranging from simple data processing to machine learning and neural networks. Now, these AI applications have begun to pervade businesses and homes in the form of semi-automatic gadgets, self-driving cars, smartphones, interactive games, etc. AI machines will likely impact our communities, governments, and institutions in unimaginable ways (Thompson, S., 2008).

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1.2 Nature of Ordinary Machines

As all of us know, machines as such are not intelligent and cannot think. Traditionally, machines are created to perform specific tasks, such as grinding grams, cutting metal, pumping liquid, digging holes in the ground, etc. It is a known fact that machines do their tasks faster and with more precision in comparison with humans. Machines have helped humans to lead a life with ease.

The distinction between humans and machines is marked by the use of intelligence in performing the work. The former works based on intelligence and the latter does not have it. The human brain constantly receives signals from the environment through the five senses of vision, audition, smell, taste, and touch. The human brain accumulates data from inside gathers data from outside, and processes them using its neural network system for understanding the world and taking action. In the perceptual process, the environmental signals are decoded, recognized, and organized based on the memories and experiences preserved in the brain. However, the machines do to have such ability to make sense of and process the signals from the environment.

1.3 Introduction to Machine Learning

Machine learning is a discipline and branch of the vast subject matter of artificial intelligence. As we have described earlier a machine cannot process the signals or the data available to it, intelligently. It cannot recognize, analyze, or classify the data and cannot use the previous experience to adjust to the present conditions. Machines simply do not learn, and because of this, they cannot think, understand, and make decisions on their own (Mitchell, 2006). Humans have to create programs to project intelligence to machines and make them smart.

Humans have been successful in creating semi-intelligent machines called computers (Mohammed, Khan, & Bashier 2016). In computers, a set of instructions is coded into the machine to accomplish a specific task. For example, if we want to total a few numbers, the specific program will carry out the process and give us the total sum. A typical computer has a part similar to the brain called the Central Processing Unit (CPU), which carries out complex computing. Computer scientists develop algorithms to solve specific problems and write them in the CPU. When an input is given to the computer, with the help of the algorithm, the computer processes the data and provides an output. Algorithms are the methods used to solve a problem. Different people use different algorithms to reach the same output. The input and output of the computers may remain the same and the algorithms can change. The efficiency and effectiveness of computers depend on the algorithms embedded in their processors. Till now, though computers have become powerful processors, they still cannot learn by themselves and behave as human beings.

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1.4 Beginning of Machine Learning

Due to advances in rapid computing, innovative networking designs, and modeling of human behaviors, scientists can make the machine smart by giving it the capacity to learn independently in limited ways (Thompson, S., 2008). The machines achieve learning by using intelligent software or algorithms.  The goal of machine learning is to make it respond to the signal independently in a desired way.  Sometimes computer scientists can understand the model such as by identifying a specific color, but they cannot understand the mechanism behind reading a handwritten message. However, the learning algorithm creators make use of complex statistical processes such as Markov chains, decision trees, rule-based classifiers, discriminant component analysis, etc. to provide learning capability to the machines. Smart machines and robots are the results of the technologists working in the domain of machine learning.

1.5 Machine Learning Techniques

There are four machine learning techniques i.e. supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning (Mohammed, Khan, & Bashier 2016). In supervised learning, the machine is exposed to a set of signals and the target is to make the machine recognize a variety of combinations of data and classify the signal for further action. For example, an automobile that crosses the line will be noted and identified at a later period.

In unsupervised learning, the machine is allowed to process the data and reach a unique output. For example, data mining the databases and reaching correlations that are hidden in the pool of data. Semi-supervised learning makes use of the labeled data as well as the unlabeled data to generate outputs and responses. For example, identifying a customer and proposing new services to him/her based on the buying history or interests.

Reinforcement learning is the most complex concerning machine learning. In this method, the scientists are attempting to make the machine learn from the observations mustered from its interaction with the environment. The algorithms are written in such a way that the machine can take action that would maximize its reward or minimize the threats i.e. similar to humans and animals.

For producing intelligent programs for reinforcement learning the following steps are used (Mohammed, Khan, & Bashier 2016).

  1. The first step is to expose the algorithm to the input state
  2. Provide a range of decisions to the algorithms to take actions
  3. When the action is performed, the algorithm receives a reward or reinforcement from the environment.
  4. The algorithm maintains a store of interpretive information about the reward.

Depending on the nature of rewards, further policy for the respective state in terms of action is regulated.

These four facets of machine learning have opened various avenues of application for commercial use cost-effectively.

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1.6 Applications of Machine learning for AECOM in managing Sports venues

For AECOM, AI and machine learning gadgets for face recognition, automobile recognition, and security in sports venues will be highly useful. In the current scenario, security in sports venues is critical and the venue managers have to pay keen attention to it. Machine learning has already demonstrated that it can provide solutions to many real-world issues. Pictures from smartphones and CCTV cameras are used at an unprecedented rate and can be integrated with AI machines. For example, Face recognition Machines can help the company in cost-saving solutions of security because the machines are available in the market at a low cost. The face recognition gadget can help the sports venue to track the movements of the visitors and keep records of the time in and time outs. This technological equipment can help security solutions of the company to use large data from different sources and generate meaningful information automatically which is very difficult for humans to do manually.

1.7 Cost benefits of Machine learning

A company such as AECOM can immensely benefit from using equipment with machine learning in the security of the sports venue (Domingo 2012). First by employing automatic visitor recognition gadgets, it can avoid costly employment to the security observers and also the risks associated with the injury and loss of life in case of accidents. Second it can avoid human errors due to fatigue and other personal conditions if AI-based security systems are used in crowded venues. Third, the security systems can do multiple tasks other than face recognition, such as tracking visitor movements, data processing, provision of real-time data, etc. Though the initial cost of installing the AI equipment is high, in the long run, the cost of ownership and the risks are reduced for the company

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1.8 Conclusion

Machine learning algorithms can understand the environment and perform important tasks by adapting to the situation in limited ways.  This feature makes it attractive to industries and institutions as a cost-effective technological solution.  As more reliable and tested equipment becomes available, more variety of problems can be managed. Machine learning and AI-based instruments are going to be critical for companies like AECOM in the future.


AECOM, 2017. About AECOM, available at Accessed on 20th March 2017.

Buchanan, B.G., 2005. A (very) brief history of artificial intelligence. Ai Magazine, 26(4), p.53.

Domingos, P., 2012. A few useful things to know about machine learning. Communications of the ACM, 55(10), pp.78-87.

Mitchell, T.M., 2006. The discipline of machine learning (Vol. 9). Carnegie Mellon University, School of Computer Science, Machine Learning Department.

Mohammed, M., Khan, M.B. and Bashier, E.B.M., 2016. Machine Learning: Algorithms and Applications. CRC Press.

Thompson, S., 2008. Artificial intelligence has come of age. ICT futures: Delivering pervasive real-time and secure services, pp.153-164.



AECOM is a large organization with multiple interests and expertise. They help large clients, communicates, governments, and institutions to provide solutions to complex challenges. (AECOM 2017)

The company takes projects that can deliver clean water and energy, build high-rise buildings with green parameters, plan and create townships and cities, restore damaged environments, and build public facilities such as roads, bridges, transit systems, tunnels, stadiums, parks, etc.

The company’s expertise is available across markets and geographies to offer transformative results. The stadiums at the Rio Olympics in 2016 were built by this company.

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