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• Artificial intelligence (AI) involves computer systems performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
• AI is a field of computer science focused on developing smart machines capable of motion or voice-activated commands.
• AI enables computers and IoT devices to mimic human behavior by learning from experiences and processing data for decision-making.
• AI allows businesses to make rational, data-driven decisions, reducing reliance on human emotions and biases.
• Examples of AI in daily life include apps providing virtual updates for commuters, helping them find alternative routes and interchange platforms.
• AI drive assist functions can automatically brake in emergencies and aid drivers in tight parking situations.
• Security systems use aerial drones that launch automatically when an alarm is triggered, streaming live video to a private security team.
• Some UK police forces use AI to predict crime locations and the likelihood of reoffending, potentially reducing pressure on officers and improving public safety.
• Online search engines, social media, streaming services, and e-commerce sites use AI to recommend content and products.
• Satellite navigation systems, such as Google Maps, Apple Maps, and Waze, use GPS to provide live travel assistance.
• Smart assistants (chatbots) offer help with banking, insurance, healthcare, travel, and tourism inquiries.
• ChatGPT is an AI language model developed by OpenAI that produces human-like text responses to questions and prompts.
• Smart home appliances like refrigerators and vacuum cleaners can operate without human input, using sensors to manage cleaning and defrosting.
• AI is used for predictive text functions that allow auto completion when typing a message on a smartphone, tablet, or computer.
• Artificial intelligence processes large data volumes faster and more accurately than humans, relying on big data and automated statistical analysis through specific coding and algorithms, using machine learning.
• Machine learning allows electronic devices to memorize and adapt without direct instructions.
• Machine learning lets computers learn and determine results based on patterns in large datasets, imitating human behavior and decision-making.
• Social media uses advanced machine learning to combat fake news, hate speech, online scams, and dishonest advertising in real time.
• AI and machine learning have transformed how businesses interact with customers by improving their ability to respond to evolving needs.
• Service providers use AI to track user data and customize content, but AI advancements may cause unemployment for customer service agents.
• AI is unlikely to replace roles requiring critical thinking and empathy.
• Former Google CEO Eric Schmidt raised concerns about AI's threat to democracy due to misinformation and its risks of bias, misinformation, and privacy rights infringement.
• in early 2024, a chatbot for DPD swore at and criticized a customer, gaining unwanted attention on social media.
• Also in early 2024, a car dealership chatbot agreed to sell a Chevrolet for just $1, highlighting the challenges of AI in customer service.

Benefits, Risks & Ethical Implications of MIS

• Management information systems (MIS) encompass advanced computer technologies and innovations that influence business decision-making and stakeholders.
• Technological innovation involves replacing an existing technology with one that improves a firm's productivity, product quality, and market competitiveness.
• Machine learning is an area within AI that enables computers to learn from data analysis and autonomously carry out tasks.
• MIS and technological innovations have benefits, risks, and ethical implications for business decisions and stakeholders.

Big Data

• Big data refers to large amounts of unprocessed and processed data from various sources.
• Big data requires sophisticated computer systems to capture, process, and analyze the data.
• Business decision-making improves with large amounts of meaningful data.
• The big data as a service market was valued at $12.74 billion in 2020 and is projected to reach $93.52 billion by 2028, with a compound annual growth rate of 28.2%.
• Big data aids businesses with more informed decisions based on facts, improved customer understanding, enhanced business activities, and increased revenues and profits.
• Big data involves high-volume, high-velocity, and high-variety information assets requiring cost-effective processing for better insight and process automation.
• Volume: The large amount of data generated from sources like smartphones, streaming services, and social media.
• Variety: The different types of data that provide multiple perspectives and comprehensive insights.
• Velocity: The speed at which data is generated and stored, often live.
• Veracity: The accuracy of the data, including the ability to remove inaccurate data.
• Value: The extent to which the data are useful for problem-solving and improving decision-making.
• Value is essential in the interconnected world, referring to the worth of data extracted from various sources.
• To be valuable it must be turned into data which can be something meaningful and useful for the business.
• Sheer amount of data generated constitutes data sets which are necessary for data analytics and data mining.
• Scientific decision-making is increasingly favored over intuition in the corporate world, due to the need for clear justifications.
• Given the huge volume of data available, big data has revolutionized traditional market research.
• Big data analytics can give the business a competitive advantage.
• Netflix claims a 93% retention rate because it knows users well using big data.
• Airline companies use big data for dynamic pricing.
• Amusement park operators, like Walt Disney World, use big data to improve visitor experiences.
• Social media marketers use big data for market research and promotion.
• Banks use big data for personalized services and fraud detection, using data from bank statements and transactions.
• Car manufacturers use big data (live GPS data) to improve traffic flow, predict maintenance needs, and inform insurance premiums.
• E-commerce businesses like Amazon and Netflix use big data for product recommendations, with Amazon earning 35% of sales revenue this way.
• Energy companies use big data to optimize energy generation, distribution, and consumption.
• Food delivery services like Uber Eats use big data for food delivery and meal recommendations.
• Healthcare providers use big data to track patient information, monitor pandemics, improve medical research, store electronic health records, and predict health risks.
• Wealth managers use big data to assess credit risk, inform investment decisions, and create personalized financial products.