AI Project Life Cycle PDF

The project management life cycle provides a framework for managing any type of project. It is a sequence of phases that a project goes through from its initiation to its deployment. The AI project cycle involves various stages: Stage 1: problem scoping: This includes identifying a problem and havi...

The project management life cycle provides a framework for managing any type of project. It is a sequence of phases that a project goes through from its initiation to its deployment. The AI project cycle involves various stages: Stage 1: problem scoping: This includes identifying a problem and having a vision to solve it. It involves a series of steps to narrow down to a problem statement using your AI knowledge. Understanding the problem scoping through 4 W’s Canvas 4 W’s Problem Canvas : helps us in identifying 4 crucial parameters we need to know for solving a problem. The 4Ws refer to :. Who?- who is facing a problem and who are the stakeholders of the problem. What?- what is the nature of problem and how you know about the problem. When/ Where?- It is related to the context or situation or location of the problem. Why?- Why we need to solve the problem and what are the benefits to the stakeholders after solving the problem. Is it worth solving ? Stage 2: Data Acquisition What is data? Data can be a piece of information/ facts/ figures and statistics collected together for reference or analysis. To enable an AI project to predict an output, we need to train the model first, using the data. Data is the lifeblood of Artificial Intelligence. Types of Data: Two types of data are: Training data(80%) and testing data(20%). 1. Training data: The collected data through the system is known as training data. In other words the input given by the user in the system can be considered as training data. 2. Testing data - The result data set or processed data is known as testing data. In other words, the output of the data is known as testing data. Data can be collected through –  Sensors (data collected from various sensors/ biometric)  Surveys (Customer feedback & review)  Observations (reading & analysing trends)  Cameras (Data from Webcam/ CCTV)  API -Application Programming Interface (Data  Web scraping (taken from diff. web pages) from various apps generated on servers) Some of the open-sourced govt. portals are  data.gov.in, india.gov.in STAGE 3: Data Exploration : It means finding the patterns and trends in the data. , interpreting some useful information out of the acquired data and put it uniformly for a better understanding of the data. Spending time with your data will help get a set of trends, relationships and patterns in the data. Data Visualization: It is the graphical representation of data and information. By using graphical tools like charts and graphs, it is easy to understand the trends and patterns of data. It is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. Stage 4: Modelling Difference between machine learning and deep learning Data Modelling techniques Rule Based Approach : It refers to the AI modelling where the relationship or patterns in data are defined by the developer. The machine follows the rules or instructions mentioned by the developer, and performs its task accordingly. Learning Based Approach AI modelling where the relationship or patterns in data are not defined by the developer. In this approach, random data is fed to the machine and it is left on the machine to figure out patterns and trends out of it. Generally this approach is followed when the data is unlabeled and too random for a human to make sense out of it. Thus, the machine looks at the data, tries to extract similar features out of it and clusters same datasets together.  In the end as output, the machine tells us about the trends which it observed in the training data. Decision tree Decision tree is the most powerful and popular tool for classification and prediction. It is made up of several nodes with top-down approach A Decision tree is a flowchart like tree structure. The basic structure of a Decision Tree starts from the root, where the decision tree starts. From there, the tree diverges into multiple directions with the help of arrows called branches. Each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label. Stage 5: Evaluation In this stage we properly test the system to find out the efficiency and performance of the AI model on the basis of parameters such as – It enables continuous improvement of the model to achieve the project goal. The reliability is checked using test data from Data Acquisition stage. Deployment It is the process of integrating a newly created AI model into existing production environment to make practical implementation of the model with actual data as input to give the desired output. It also involves certain hardware and software settings so that AI model can be efficiently used by end-users. AI ETHICAL ISSUES AND CONCERNS As we are human beings and we are following some moral principles for doing certain activities, Similarly certain ethics are also associated with AI systems and tools. Let us review some of the concerns around AI - AI Ethics Principles Identifying the principles To make AI better, we need to identify the factors responsible for it.  The following principles in AI Ethics affect the quality of AI solutions ▪ Human Rights ▪ Bias ▪ Privacy ▪ Inclusion Let’s look at the AI Ethics principles in detail: Human Rights When building AI solutions, we need to ensure that they follow human rights. Here are a few things that you should take care of ▪ Does your AI take away Freedom? ▪ Does your AI discriminate against People? ▪ Does your AI deprive people of jobs? ▪ What are some other human rights which need to be protected when it comes to AI? Job Loss - The integration of AI into the workplace has the potential to revolutionize industries by boosting efficiency and unleashing creativity. Tasks previously performed by humans are often taken over by machines that can operate more efficiently, leading to concerns about unemployment. It's essential for policymakers and businesses to address these ethical concerns by considering retraining programs and new job creation to mitigate the negative effects on the workforce. Bias Bias (partiality or preference for one over the other) often comes from the collected data. The bias in training data also appears in the results. Here are a few things that you should take care of ▪ Does your data equally represent all the sections of the included populations? ▪ Will your AI learn to discriminate against certain groups of people? ▪ Does your AI exclude some people? ▪ What are some other biases that might appear in an AI? Some of the sources of AI bias are –  i) Data – AI systems are result of data that is fed to them. The dataset sould be realistic and of sufficient size. However the huge dataset my at times reflect subjectivity and underlying social bias. ii) Algorithm – The algorithm in itself does not add bias but it amplifies the bias on the basis of data. iii) Developers – Ethics and bias of developers are reflected in their models. Privacy We need to have rules which keep our individual and private data safe. The gadgets and apps used on a daily basis are AI enabled. The data is being gathered regularly from your social networking sites and the apps you use. Here are a few things that you should take care of ▪ Does your AI collect personal data from people? ▪ What does it do with the data? ▪ Does your AI let people know about the data that it is collecting for its use? ▪ Will your AI ensure a person’s safety? Or will it compromise it? ▪ What are some other ways in which AI can breach someone’s privacy? Inclusion AI MUST NOT discriminate against a particular group of population, causing them any kind of disadvantage. Here are a few things you should take care of ▪ Does your AI leave out any person or a group? ▪ Is a rich person and a poor person benefitted equally from your AI? ▪ How easy is it to use your AI? ▪ Who does your AI help? ▪ How can we make AI more inclusive? AI Access AI access can be acquired by two means –  Data availability – AI models need huge datasets to analyse, draw conclusions and learn from it.  Abilities – AI needs access to capable hardware to turn its learning into useful action.