artificial intelligence

AN OVERVIEW OF AI COMPONENTS

The two main components of AI that will have the greatest impact on

project management are machine learning and natural language

processing. A machine learning algorithm uses a mathematical

formula based in calculus that attempts to find the least error between

correlations in the data. This is also known as minimizing the cost

function. Based on the correlations, the AI tool creates a model that

can be used to classify or make a prediction based on a new set of

data. A machine learning tool is written in software code and often

uses utilities, or precoded functional blocks of code, to create

decision-making algorithms, the most popular and effective one being

a neural network. Learning occurs in several ways, but the most

common are as follows.

Supervised learning is when a dataset is labeled and the algorithm is

trained to correlate each dataset with the labeled result. The algorithm

is capable of modifying itself until it has the most accurate model. It is

then used on test data to verify the accuracy of the model. For

projects, we can also label our datasets. There are successful

projects, well-designed risk plans, and communication plans that

result in high stakeholder satisfaction. There are also negative results

for each example. Supervised learning is commonly used in the field

of health care to diagnose X-ray results and can provide higher

accuracy than a trained technician.

5 The algorithm is trained on X-ray

images labeled as either clear or showing evidence of a condition. A

new X-ray image with a unique pattern is used as input to the

algorithm and it diagnoses or predicts the result.

Unsupervised learning is when the data is not labeled but has a

sufficient number of clues so that the algorithm is able to classify the

data effectively. If the data indicates that an object has leaves, has a

trunk, and has branches, then the algorithm will correctly classify it as

a tree. The main benefit of unsupervised learning is clustering. The

algorithm has no labels and simply groups similar items together. How

can this be applied to projects? Risks can be clustered or grouped so

that when one of the risks from a cluster occurs, there is a strong

possibility that a similar risk will occur.

Reinforcement learning is when the algorithm learns through trial and

error to make proper predictions. A common example is typing on a

smartphone, with the full word appearing after you’ve entered only two

or three letters. The program learns your pattern through a series of

repetitions in which you did not select the word it suggested because

that was not the word you wanted. How can this apply to project

management? All algorithms must be updated on a regular basis to

stay accurate and this is one of the methods to do so.

There are several different methods used to create a machine learning

algorithm, including a neural network, random forest, support vector

machines (SVM), and a Naive Bayes classifier.

6 The algorithm creates

a model based on historical data and then uses the model to make a

classification or prediction based on a new set of data. Another basic

capability of AI is NLP, which is a computer program’s ability to

interpret human language and classify communication into a meaning

or, as it is called in NLP, an intent. This includes the ability to interpret

emotion behind the words, which is a skill known as sentiment

analysis. NLP is also used to search documents and extrapolate

meaning and to determine correlations and anomalies. NLP-based

algorithms can be integrated into project management tools. For

example, NLP can be used to search the text of a document and

compare it to documents from similar projects. If the documents from

similar projects are labeled as either accurate or incomplete, NLP

results can be used by a machine learning algorithm to classify a

current document based on the labels. NLP can also be used in scope

documents or project proposals to search for errors or inconsistencies.

NLP is used to create models that allow a computer to interact

with a human who is speaking. When humans talk or when they

create written text, such as an instant message or an email, it is called

an utterance. NLP uses utterances to determine a positive or negative

sentiment with regard to a project environment. NLP can also be used

to uncover utterances that reveal the threshold levels of stakeholders.

In other words, the project status in terms of a late schedule, for

example, can be above the tolerance level for the project customer or

project sponsor who has the authority to ask for a complete

justification and analysis before further funding is provided.

An intriguing benefit of a machine learning algorithm is that the

same basic algorithm can be used with different data to achieve a new

objective. Pedro Domingos uses an analogy to explain this in his book

one opposable thumb—and yet it can make and use an infinite variety

of tools.”

7 Machine learning tools allow us to deliver increasingly larger

and more complex projects and still achieve a successful result. There

is a significant difference between regular programming code and

machine learning algorithms. Normal programming code must ensure

that all possibilities are taken into account in the code. Typically, if a

particular event happens, then the code performs an action. Regular

software programs are filled with logic that is required for every

possible event; if something unforeseen happens, the code will stop.

With machine learning code, the program uses data to arrive at a

probability and then uses that result to perform an action. If a new

event occurs, the code uses the model based on data to make a

decision and continues. There is logic in the code but far less is

required than for non-AI-based programs.

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