Artificial Intelligence Explained - Definition, Examples, and Benefits - PDF

Artificial intelligence (AI) is defined in the Oxford English Dictionary as "The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." It is a way...

Artificial intelligence (AI) is defined in the Oxford English Dictionary as "The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." It is a way that computers can be programmed to learn from data to perform certain tasks, such as facial and voice recognition. AI is an area of computer science that develops the ability of smart machines to perform tasks rather than natural or human intelligence, such as motion or voice activated commands on smart devices. AI enables computers and IoT devices (the Internet of things) to mimic human behaviour and actions, such as becoming familiar with different situations, learning from experiences, processing information to solve problem, and using data to inform decision making. In theory, AI enables businesses to make rational decisions based on data, rather than relying on human emotions and biases that can result in irrational choices and outcomes. Examples of artificial intelligence in daily life include: Apps that support commuters with virtual updates and alternative bus and train routes, showing people where to interchange and which platform to use. Drive assist functions in motor vehicles can break automatically in emergency situations and assist drivers with parking in tight spaces (a feature of self-driving cars). Facial and voice recognition systems to access online banking, to complete online purchases, and even to open security doors. Security systems can use aerial drones that are automatically launched when an alarm is triggered, streaming live video to a private security team. In the UK, some police forces have tested predictive policing tools, i.e., used AI to predict where crimes are likely to happen as well as the probability of people reoffending. The AI technology could help to reduce pressure on police officers and improve public safety. Online search engines, social media platforms (such as YouTube), streaming service providers (such as Netflix), and e-commerce businesses (such as Amazon) provide recommendations that users are likely to be interested in, including social media feeds. Satellite navigation systems, using the global position system (GPS) to provide live travel assistance to motorists and travellers using a smartphone or other satellite navigation device. Examples include Google Maps, Apple Maps, Bing Maps, and Waze (which is owned by Google). Smart assistants (often referred to as \"chatbots\") that provide help with enquiries about banking, insurance, healthcare, as well as travel and tourism. This also covers the use of marketing chatbots. ChatGPT (Chat Generative Pre-Trained Transformer) is a language model developed by OpenAI, designed to generate human-like text responses to questions and prompts. Smart home appliances, such as a smart fridge freezers (that auto clean and auto defrost when needed) and smart vacuum cleaner (that use sensors to detect when and where rooms need to be cleaned), without human input. The use of predictive text functions when typing a message on a smartphone, tablet, or computer. Artificial intelligence uses other aspects of management information systems (such as critical infrastructures and data analytics) to process large volumes of data in faster and more accurate ways than humans can. It relies on big data and automated statistical analysis, enabling machines to collate, analyze, understand, and learn from data through specifically designed coding and algorithms. Therefore, AI relies on the use of machine learning. Machine learning is the use of computer systems, algorithms, and statistical models to enable electronic devices to memorize and adapt on their own without following direct instructions. As a dimension of artificial intelligence, it enables computers to learn and determine results based on patterns in large data sets to imitate intelligent human behaviour and decision making. For example, advanced machine learning is being used by social media businesses to tackle the issues related to fake news, hate speech, online scams, and dishonest advertising - all in real time. The use of AI has revolutionised and will continue to transform the way in which businesses conduct their activities and develop their relationship with customers. In general, AI and machine learning have enabled businesses to know more about customers and to improve their ability to respond to the evolving needs of their customers. For example, AI enables service providers such as Amazon, Instagram, Netflix, Spotify, TikTok, and Twitter to track data of users to determine their preferences and likes in order to adapt content more accordingly. However, as AI technology rapidly advances, there are possible negative impacts too. For example, analysts expect AI could cause mass unemployment for customer service agents in multiple industries. The AI technology and machine learning would deal with customer queries, so businesses may well cut call centre staff in order to reduce costs. For now at least, it is unlikely that AI will replace people in roles that require critical thinking and empathy. Former Google CEO Eric Schmidt also raised concerns about IA and its potential threat to democracy because of the misinformation that could be spread on social media platforms. Furthermore, there are concerns associated with AI and the risks of exacerbating bias, the widespread of misinformation, and the potential of infringing privacy rights. Furthermore, AI is still in its infancy for commercial purposes. In early 2024, the BBC reported that a chatbot for parcel delivery company DPD swore at and criticized a customer. The incident gained unwanted attention on social media, with the customer\'s post being viewed 800,000 times in just 24 hours. The BBC also reported that a car dealership\'s chatbot agreed to sell a Chevrolet to a customer for just \$1! These examples highlight the challenges of implementing AI in customer service, with chatbots occasionally providing unexpected or inappropriate responses.\ \ **Benefits, risks, & ethical implications of MIS** The term management information systems (MIS) is a collective term used to describe the advanced computer technologies and technological innovations that influence business decision-making and stakeholders of a business. Technological innovation refers to the partial or full replacement of an existing technology by one that improves a firm\'s productivity, its product quality, and competitiveness in the market. For example, machine Learning (which is a discipline within the field of artificial intelligence) allows computers to learn for themselves, such as learning from data analysis, and carry out tasks autonomously. Management information systems (MIS) and technological innovations have benefits, risks, and ethical implications on business decision-making and stakeholders. The points are outlined in the sections below. **Big data** Big data refers to access to extensive amounts of unprocessed (raw) and processed (structured) data from a broad range of sources. The data are often complex, due to the huge volume available, so sophisticated computer systems are used to capture, process, and analyze the data. Such tasks would be beyond the ability of humans without the use of technology to manage the process. In general, business decision-making can be improved when there are large amounts of meaningful data available. Market analyses show that big data as a service market was valued at \$12.74 billion in 2020 but is forecast to increase to \$93.52 billion by 2028 (which represents a compound annual growth rate of 28.2%). The reason for this projected growth is that big data can help businesses in numerous interrelated ways, including: Making more informed business decisions, based on facts, trends, and logic. Understanding their customers in better ways, thereby supplying goods and services that meet their changing needs. Improving business activities and operational efficiency. Generating additional revenues and profits. \"Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.\" Volume - the large amount of data generated. Volume (and variety) can come from numerous sources, such as smartphones, tablet computers, streaming services, e-commerce databases, and social media platforms. Variety - the diversity or different types of data, enabling multiple perspectives and comprehensive insights to an issue. Velocity - the speed at which data are generated and stored, often live. Two additional Vs were later added to Daney\'s original model: Veracity - the extent to which the data are accurate, including the ability to separate out inaccurate data. Value - the extent to which the data are useful for supporting problem solving and improving decision making. Value is a particularly important aspect of big data in today\'s highly digital and interconnected world. In this context, value refers to the worth of the data being extracted from the variety of data sources. Having endless volumes of data is of no real value to a business unless the data can be turned something meaningful and useful for the business. Hence, it is important to understand the costs and benefits of collecting, processing, and analyzing the volumes of data to ensure that they can be used by the organization.The sheer amount of data generate constitute the data sets that are necessary for data analytics and data mining. In the modern and competitive corporate world, there is a growing expectation to use more scientific decision making, rather than methods that rely on intuition and gut feelings. Given the increasing complexities and widespread impacts of many business decisions, strategies based on scientific methods are easier to document and account for, especially if clear justifications are required. Similarly, given the huge volume of data available, big data has revolutionized traditional market research. Implementing big data analytics can give businesses a competitive advantage as they gain information to improve strategic decision making. Using big data analytics also helps to boost customer acquisition and improve customer retention. For example, Netflix claims that it knows users so well that the company\'s retention rate is about 93%. Some other examples of how businesses use big data in the real world include the following: Airline companies use big data to determine different prices to charge passengers on each day of the year, using dynamic pricing. Amusement park operators, such as Walt Disney World Theme Parks, use big data to understand visitor behaviour at its theme parks and hotels, so that it can offer an even more \"magical\" experience for its guests. Social media marketers can access large amounts of data for market research and market planning purposes in order to better inform their sales practices and improve promotional techniques. Banks use big data to deliver improved and more personalised services for their customers. Using data from bank statements and transactions enables the banks to knows a lot more about their customers, from what they like to buy, and how often, to where they go on holiday most frequently. Big data also enables banks to detect fraud. Car manufacturers use big data such as live GPS data from motor vehicles to improve traffic flow and reduce congestion. It is also used to predict and warn drivers about maintenance needs for their vehicles, such as repair and servicing schedules. Many insurance companies also use big data from a car\'s black box to make more informed decisions about risk management and insurance premiums. E-commerce businesses, such as Amazon, and online streaming services, such as Netflix, use big data for product recommendations. Amazon earns about 35% of its sales revenues from product recommendations. Energy companies use big data to optimize the generation, distribution, and consumption of energy in homes and places of work. This includes analyzing big data from power plants as well as monitoring and examining data from smart metres in residential homes to improve energy efficiency. Food delivery service providers, such as Uber Eats, use big data to make accurate forecasts of food delivery times for their customers, as well as meal recommendations. Healthcare providers use big data to track patient information, monitor pandemics, and improve medical research, e.g., big data is used by medical clinics to store and analyze electronic health records to identify patterns and predict health risks. Wealth managers and financial advisers use big data and data analytics to assess credit risk and inform investment decisions. It can also enable them to create personalized financial products and services for their clients.