Introduction to Artificial Intelligence (AI)

The correct sequence of events showing the development progress of Artificial Intelligence, from oldest to most recent, is as follows:

1. The term 'Artificial Intelligence' was coined.
2. The program ELIZA was developed.
3. AI winter started.
4. Expert systems were developed.
5. Machine learning became popular.
6. The concept of deep learning was introduced.

Two applications of Artificial Intelligence in healthcare that can be used to handle pandemic situations are:

1. Diagnosis of diseases
2. Monitoring the progression of contagious diseases

Two online shopping websites where the use of Artificial Intelligence can be observed are:

1. Amazon
   Observation: Amazon uses AI to suggest similar or related products based on a user’s previous searches and purchases. It also recommends products that other customers have bought together, which improves the shopping experience.

2. Flipkart
   Observation: Flipkart uses AI to show personalised product recommendations and targeted advertisements. It analyses customer behaviour, purchase history, and preferences to suggest suitable products to users.

The completed table is given below:

True

Reason — Deep learning includes specialised artificial neural networks, which are inspired by the structure of the human brain.

False

Reason — Machine learning methodologies became popular in the 1990s, not in 1970. The 1970s mainly saw the development of natural language processing and the program ELIZA, followed by the AI winter period.

False

Reason — The Turing test was proposed by Alan Turing in 1950.

True

Reason — Cortana is an AI-based voice assistant developed by Microsoft. It uses Artificial Intelligence to understand and respond to voice commands, similar to other voice assistants like Amazon Alexa, Google Assistant, and Apple Siri.

True

Reason — AI-enabled chatbots help in sales and marketing by interacting with customers, handling their queries, providing product recommendations, and collecting feedback, which helps improve sales and marketing activities.

False

Reason — AI can be used to develop autonomous machines such as robots and self-driving vehicles that work with little or no human intervention. Therefore, AI is not limited to developing only semi-automatic machines.

True

Reason — Predictive analytics in AI involves analysing a large amount of relevant and historical data to identify patterns, understand trends, and make predictions for better decision-making.

False

Reason — Natural Language Processing (NLP) is used to process and understand human language, such as text and speech. The analysis of image data is done using other AI techniques, not NLP.

True

Reason — AI can analyse historical and real-time financial transaction data to identify anomalies or suspicious patterns, which helps in detecting fraudulent or abnormal transactions.

False

Reason — AI can be used in natural disaster management for analysing data from satellite images, sensors, and other sources to predict disasters, assess damage, and support speedy disaster recovery.

John McCarthy

Reason — The term "Artificial Intelligence" was first coined by John McCarthy in 1956, and he is also known as the Father of Artificial Intelligence.

Human brain structure

Reason — Artificial neural networks are inspired by the structure of the human brain.

Siri

Reason — Siri is an AI-enabled virtual assistant developed by Apple that uses Artificial Intelligence to understand and respond to voice commands.

An AI machine and a human

Reason — In the Turing test, a human judge (evaluator) interacts with both a human and a machine simultaneously and compares their responses to determine whether the machine shows human-like intelligence.

Fraud detection

Reason — In the banking sector, AI is used to analyse historical and real-time financial data to identify fraudulent or abnormal transactions, which helps banks prevent financial losses.

Handling repetitive tasks

Reason — Process automation helps in handling repetitive and rule-based tasks efficiently, which reduces errors and frees employees from extensive and laborious work.

Face recognition

Reason — Face recognition is an AI-based technique used to identify a person by analysing facial features, and it is commonly used in smartphones to unlock the mobile phone screen.

DeepMind AlphaFold

Reason — DeepMind AlphaFold is an AI system used to solve complex protein structure problems by predicting the three-dimensional structure of proteins, which is a challenging biological problem.

All of these

Reason — AI can filter incoming emails, prioritise emails, and correct language-related errors using techniques such as Natural Language Processing. Hence, AI can perform all these functions in an email system.

Explanations for how AI system derives the conclusion

Reason — Explainability in AI refers to providing clear and understandable explanations for how an AI system arrives at its decisions or conclusions. It helps users understand and trust the reasoning behind AI outputs.

1. The program ELIZA, developed by Joseph Weizenbaum, was able to perform conversation between a user and a computer.
2. In the Turing test, the machine tries to convince the judge that it is a Human.
3. Remote patient monitoring can be done through AI and Smart devices.
4. Knime Analytics Platform is an example of an AI platform for analysing research and development data.
5. AI can keep an eye on the wellbeing and behaviour of animals via Wearables and sensors.

AI helps in enhancing cyber security in defence by threat detection, anomaly detection, and mitigation. Machine learning algorithms analyse a huge amount of data to identify patterns indicative of cyber threats or attacks, which helps in protecting defence systems from cyber threats.

AI is used in generating choice-based recommendation of online entertainment videos by analysing user preferences, viewing patterns, and historical data. AI algorithms study information such as watching history, ratings, and genre preferences to recommend movies and TV series according to the user’s interests. This helps in providing personalised content suggestions on entertainment platforms.

AI is helpful in assisting humans by providing personalised assistance through technologies such as chatbots and virtual assistants. These AI systems help users by responding to queries, providing recommendations, and assisting with tasks, which improves customer service and user experience.

AI-powered virtual assistants such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana help users in creating reminders, answering queries, delivering information, and managing smart home devices.

AI helps in reducing human errors in many ways. Some examples are:

• Human errors induced by weariness, supervision, or carelessness are reduced by eliminating human involvement in dull and repetitive processes.
• AI algorithms may validate data to assure its accuracy and integrity. Inconsistencies, abnormalities, or missing values in datasets can be identified and flagged. This helps in minimising errors.
• Natural language processing systems can understand context, remove spelling errors, and recommend acceptable replies.

Prejudice is an unfavourable view or assumption that is formed without knowledge, reason, or justification. It is a judgement or opinion formed without knowing the facts, particularly against an ethnic, racial, socio-economic, or religious group.

Example: An AI system is designed to predict the chances of diabetes. If the data is collected by assuming that obesity leads to diabetes, the data may include only the weight of people and ignore other factors such as family history, blood pressure, anxiety, etc. In this case, the prediction generated by the AI model will be wrong in most cases due to prejudice in data collection.

AI has impacted many industries and organisations. Some important applications of AI in commercial field are:

1. 24 x 7 Customer service: AI-enabled chat bots can handle customer queries 24 x 7. These chatbots can also help with product recommendations and collecting feedback.

2. Sales and marketing: AI helps improve the sale of a product by sending personalised messages to customers by understanding their buying patterns and choices.

3. Supply chain management: The supply chain system can be managed by understanding product demand and supply. This can be done by analysing product reviews through AI-enabled tools.

4. Fraud detection: Financial data can be analysed with the help of AI tools. It will help in detecting fraudulent activities and handling financial losses.

5. Product development: AI tools can be used to understand customer requirements and feedback that will help in developing optimised products. It will also help in understanding product shortcomings and future consequences.

6. Risk management: Financial risks can be analysed through AI tools by observing historical data. These tools also suggest risk-handling solutions.

AI has several applications in the medical and healthcare domain. Some common applications of AI in healthcare are:

1. Understanding medical images: AI tools are used to understand medical images such as X-rays, CT scans, and MRIs. This helps medical professionals diagnose diseases like cancer, cardiovascular conditions, and other abnormalities.

2. Diagnosis of diseases: AI algorithms help in diagnosing diseases by analysing patient history, symptoms, and medical records. AI-enabled tools can generate warning messages regarding diseases based on a person’s symptoms and lifestyle.

3. Drug discovery: AI helps in analysing a huge amount of biomedical data to identify potential drug combinations and predict the effectiveness or side effects of drugs.

4. Personalised medicine: AI helps in prescribing personalised medical treatments by analysing a patient’s genetic information, medical history, and lifestyle data.

5. Virtual assistants: AI-assisted virtual assistants help in handling patient's queries and provide full-time support to monitor the physical and mental health of patients.

6. Robotic surgery: AI-enabled robots are used to perform complex surgeries, which enhance surgical accuracy, reduce invasiveness, and improve results.

7. Predictive analytics: AI algorithms analyse patient data to predict disease conditions, identify risks, and recommend preventive measures, especially for chronic diseases.

8. Healthcare administration: AI is used to manage medical records, appointment scheduling, billing, and other administrative tasks, improving hospital administration.

Some of the important applications of AI in transport are:

1. Traffic management: AI analyses real-time traffic data to improve traffic flow and reduce congestion. It helps in forecasting traffic patterns, identifying jams, and dynamically changing traffic signals to reduce travel time, fuel use, and carbon emissions.

2. Maintenance prediction: AI monitors the condition of vehicles and predicts repair requirements by examining sensor data and previous performance.

3. Intelligent Transportation Systems (ITS): AI is used in ITS to improve the effectiveness and security of transportation networks. It analyses data from traffic cameras, GPS systems, and sensors to provide real-time traffic information.

4. Autonomous vehicles: AI is used in self-driving vehicles to process data from sensors, radar, and cameras, enabling vehicles to assess surroundings, make quick decisions, and navigate safely.

5. Logistics: AI analyses data on transit routes to optimise logistics operations, enhance route planning, reduce delivery time, and save costs.

6. Customer service: AI-enabled virtual assistants help customers with ticket booking, answering queries, providing personalised suggestions, and real-time travel information.

7. Safety and security: AI systems analyse video feeds from surveillance cameras to identify suspicious activities, monitor crowd behaviour, and enhance security at transportation hubs.

8. Demand prediction: AI systems forecast transportation demand by analysing historical data, current events, and other variables, helping in capacity management and service optimisation.

AI may be used to track the spread of infectious diseases by analysing data from diverse sources, such as social media, news stories, medical records, and public health databases. Some examples of AI in monitoring contagious diseases are:

• AI techniques help in identifying keywords, phrases, or patterns related to symptoms, sickness, or infection.
• AI may anticipate prospective disease outbreaks or follow the progress of an existing infectious disease by recognising similar content or geolocation data.
• The use of AI technologies during COVID-19 is a recent example of the application of AI to monitor contagious diseases. During this pandemic, AI tools were used to spread awareness, identify the virus, identify disease through X-Ray and CT scans, develop vaccines, anticipate and track the spread of the virus, and monitor patients.

Transparency in AI refers to the transparency and openness for the decision-making processes of AI systems. Users and stakeholders should understand how AI systems work, make choices, and what data they utilise. Transparent AI systems allow for inspection, the identification and correction of biases, and the development of user trust.

Example: An example of transparency in AI may be reviewing loan applications and deciding eligibility. Transparency in this sense means providing explicit information about how the AI system reviews loan applications and decides creditworthiness. This involves revealing the elements and criteria taken into account, such as credit history, income, debt-to-income ratio, and other important characteristics. Transparent AI solutions also improve applicants' access to the decision-making process. If a loan application is declined, the AI system may deliver an explanation to the applicant, outlining the particular reasons for the decision and the elements that led to it.

Some activities in our daily routine that use Artificial Intelligence and the AI technology used in them are:

A smart device helps in health monitoring by continuously tracking and analysing an individual's health-related data using sensors and AI technology. The working of a smart device for health monitoring can be explained in the following steps:

1. Data collection: Smart devices such as wearable fitness trackers or smartwatches are equipped with sensors that collect health-related data like heart rate, sleep patterns, steps walked, calories burnt, and activity levels.
2. Continuous monitoring: The device continuously monitors the vital signs and physical activities of the individual throughout the day, even when the person is not in a hospital.
3. Data analysis using AI: AI algorithms analyse the collected data to understand patterns, detect abnormalities, and assess the health condition of the individual.
4. Alerts and reminders: Based on the analysis, smart devices send alerts, notifications, or reminders to encourage users to stay active, manage stress, and maintain healthy habits.
5. Remote access by healthcare providers: The monitored data can be shared with healthcare professionals, enabling remote patient monitoring and timely medical support if required.

Artificial Intelligence can play an important role in space research because space missions involve large amounts of data, complex decisions, and environments where humans cannot easily go. Some probable applications are:

1. Analysis of Space Data
   Space telescopes, satellites, and probes collect huge amounts of data (images, signals, readings). AI systems can quickly analyse this data, find patterns, and identify important information, such as new stars, planets, or changes in climate on Earth.
2. Autonomous Spacecraft and Satellites
   AI can help spacecraft and satellites make decisions on their own without waiting for instructions from Earth. For example, AI can help a satellite adjust its path, avoid obstacles, or manage power efficiently.
3. Space Robotics
   AI-powered robots can be used on the Moon, Mars, or other planets to explore the surface, collect samples, and conduct experiments. Since AI allows machines to learn and respond to situations, robots can work safely in places that are dangerous for humans .
4. Prediction and Problem Detection
   AI can predict possible faults or failures in spacecraft systems by learning from past data. This helps scientists fix problems early and increases the success rate of space missions.