G11 Unit 2 Emerging Technologies PDF

UNIT 2 EMERGING TECHNOLOGIES Learning Outcomes At the end of this unit, students will be able to:  Explain Emerging Technologies  Define Artificial Intelligence and state its branches  Describe common Artificial Intelligence applications  Differentiate the concepts of Augmented Reality and Vir...

UNIT 2 EMERGING TECHNOLOGIES Learning Outcomes At the end of this unit, students will be able to:  Explain Emerging Technologies  Define Artificial Intelligence and state its branches  Describe common Artificial Intelligence applications  Differentiate the concepts of Augmented Reality and Virtual Reality  Describe Data Science and its applications  Unit Overview  Emerging technology can be understood as a technology that is developing and evolving fast with a prominent socio-economic impact.  There are several examples of emerging technologies that are covered in this unit such as artificial intelligence, virtual reality, augmented reality, and data science.  The application areas of emerging technologies are also discussed in the unit.  2.1. Artificial Intelligence  Artificial Intelligence (AI) is a branch of computer science that deals with the design of systems that mimic human intelligence.  AI systems are designed to have features that only humans are capable of such as reasoning and generalization.  An example of reasoning is, “My friend is either at home or in her workplace. My friend is not in her workplace. Therefore, my friend is at home”.  Generalizing is about applying  past experiences to current problems of similar nature. For example, “I know how to change a wheel on my bicycle and therefore I can use this knowledge to change the wheel on my friend’s bicycle.”  Even though the history of AI goes back to the 1940s, it was in the 1950s that the term was coined.  AI has become so popular and a powerful tool today due mainly to significant growth in computer capacity and the availability of a large amount of data.  AI algorithms are now extensively used in a wide range of areas including business, medicine, and entertainment.  2.1.1 Branches of AI a. Machine Learning  Machine Learning is the study of computer algorithms that enable the computer to learn from data and improve itself without being explicitly programmed.  Machine learning algorithms learn from experience.  Unlike conventional computer algorithms with explicit instructions that are followed by the computer to solve a problem, machine learning algorithms allow the computer to train on sample data inputs.  A model that is built from the sample data will then be used to make predictions that are useful for decision-making.  some of the examples of applications of machine learning are: Fraud detection, recommendation systems, email spam detector, and speech recognition Figure 2.1 Fraud detection using machine learning  b. Robotics  Robotics is the study of machines called robots that substitute tasks that are traditionally done by human beings.  Over the past few decades, robots have been used mainly in replacing human labor in routine and mundane activities such as those that are found in car manufacturing assembly lines.  Robots are also used in environments that are considered hazardous to human beings like handling radioactive wastes, underwater, and space explorations. Figure 2.2 Robots in car manufacturing assembly line  Similarly, the application of robots in the household is also becoming common. Nowadays, robots are used at home to do chores, assist the elderly, monitor young children, and more  A robot vacuum cleaner, is a well-known example of the application of robots in homes.  robot vacuum cleaner is a machine that sweeps carpets and cleans floors without human intervention. Figure 2.3 Robot vacuum cleaner  c. Natural Language Processing  Natural Language Processing (NLP) is a branch of AI that deals with enabling computers to understand written and spoken human language in the same way human beings do.  Computers’ understanding of natural language is what powers the services we get from a wide range of systems around us.  Some of the major application areas of NLP:  Spam Detection: Email service providers, like Yahoo, analyze a text in anemail to check if it is spam or not using NLP. Machine Translation: translation of text from one language to another that systems, like Google Translate, do is supported by NLP. Virtual Assistant: voice-operated virtual assistants such as Amazon’s Alexa and Apple’s Siri use NLP. Sentiment Analysis: by extracting and analyzing users’ data on social media,it is possible to learn users’ opinions (which could be negative, positive, orneutral) about something using NLP. Fake News Identification: identifying whether or not news is trustworthy and checking the reliability of its source can be done using NLP. Text Summarization: summary of a large piece of document like academic articles can be automatically done using NLP. Figure 2.4 Applications of natural language processing  d. Expert Systems  Expert systems are computer programs that emulate human experts.  They are designed to solve problems that are normally solved by human experts.  Expert systems are one of the earliest attempts to put the field of artificial intelligence to the test.  Some of the fields that benefit from expert systems include medical diagnosis, petroleum engineering, and financial investing.  An expert system consists of three components: User Interface, Knowledge Base, and Inference Engine.  User Interface: this is part of the expert system that users use to interact with the expert system.  It accepts queries from the user and forwards them to the inference engine.  It is also the means through which users see the recommendation that the expert system provides.  Knowledge Base: this is the repository of knowledge of experts in the domain area.  The knowledge is organized in the form of if-then-else rules.  The knowledge contained is of both factual and heuristic (experiential or common-sense rule) types.  The completeness, accuracy, and precision of the knowledge captured in the knowledge base are central to the performance of expert systems.  Inference Engine: this is the brain of the expert system that produces the answer by referencing the knowledge base.  One of the earliest and most well-known examples of expert systems is MYCIN. MYCIN is an expert system that emulates infectious disease experts. It identifies bacteria that cause infections and recommends antibiotics. Figure 2.5 Expert system  Application of AI I. Online Shopping  AI algorithms are used to dive into and process the massive user data available in various systems to extract personalized data about shoppers.  The data is then used to present users with items that they are more likely interested in, which makes the whole shopping experience efficient and enjoyable.  ii. Autonomous Cars  By collecting data from sources such as GPS, vehicle radar, and cameras, autonomous cars can function without the help of a human agent.  Although fully automated autonomous cars are not yet on the streets, autopilot features are already implemented in cars produced by companies like Tesla.  The trend and development in the area indicate that the era of fully automated cars is not far away.  Multiple reports indicate that autonomous cars have great potential for improving road safety.  Autonomous cars are believed to significantly reduce the number of road accidents due to their features such as the ability to exchange safety-critical information with one another, the 360-degree view of the surrounding, the power to flag hazards on the road ahead, and many more.  iii. Social Media  The size of data available on social media makes it practically impossible to process the data using human beings. This is why AI has become a fundamental part of how social networks operate today.  Social media companies, like Facebook, apply AI in various areas such as advertising, delivery of personalized content, facial recognition, and many more.  iv. Surveillance  Surveillance systems can benefit significantly from AI. For example, AI systems can automatically detect suspicious behavior in real-time.  This can be implemented in places like supermarkets, military and security stations, or any place with restricted access.  v. Agriculture  Global phenomena like climate change, food security, and population growth are making the application of innovative approaches in the area of agriculture more important than ever.  AI systems are at the core of such type of innovative approaches.  Examples of applications of AI in agriculture include precision farming, detection of pests, monitoring the health of soils and crops, and the like.  AI-based agricultural advisory systems can be built that farmers use to be climate-ready and adjust their farming activities accordingly.  vi. Customer Service  Due to advancements in multiple areas of AI such as Natural Language Processing (NLP), chatbots are nowadays used to provide customer support.  A chatbot is a system that simulates human conversation through text or voice commands.  It is a fast and cheaper way for companies to provide support and assistance to their customers.  vii. Healthcare  Healthcare is one of the areas with immense potential for the application of AI.  Disease diagnosis, drug development, and hospital care are some of the examples of applications of AI in the healthcare sector.  viii. Space Exploration  Space exploration is characterized by the processing of a massive amount of data.  AI and Machine Learning, which is one of the branches of AI, are used to process data that assists space exploration activities.  Operations like space mission planning, data collection, navigation, and maneuvering are supported by AI.  NASA,for example, uses a robot known as Robonaut to work on the International Space Station.  Robonaut uses many of the tools that an astronaut uses and can perform tasks that are normally done by human beings.  ix. Smart Homes  AI systems are abundant in what is known as smart homes.  The AI systems assist in home security, household chores, alerting of smokes, and many more.  x. Banks  Credit cards and other forms of fraud prevalent in the banking industry can be prevented using fraud prevention AI.  AI systems are used to trace the pattern in credit card transactions and detect frauds.  xi. Search Engine  Search engines use AI systems such as NLP to better understand user search queries.  They also use AI in ranking algorithms, which determine in what order responses are presented to users in reply to their search query.  2.2.1 Augmented Reality  Augmented Reality (AR) is a technology that enhances the real world by overlaying computer-generated digital data on real-world objects.  By blending the real world with computer-generated information, AR creates an engaging and dynamic user experience.  The hardware of AR includes a processor, sensors, input devices, and a display.  Mobile devices do have all four components to run AR applications.  Depending on the application, however, the display could be tablets, smartphones, head-mounted displays (HMD), or smart glasses. Such displays are used to locate objects of focus as well as show the computer-generated overlaid information.  AR works by first capturing the picture of the real-world object through a camera.  Then relevant information about the image like measurement of the object, as well as other objects that are present in the image and their relative distance from the object in focus, will be processed by the software.  Virtual information will be finally generated and overlaid on the object.  Virtual Reality  Virtual reality (VR) is a technology that creates a three-dimensional computer generated simulated environment.  A person can interact with VR using electronic devices such as goggles, headsets, gloves, or bodysuits.  VR attempts to create an illusory environment that can be presented to our senses with artificial information, making our minds believe that it is (almost) a reality.  Unlike AR, VR creates a completely immersive experience for the user.  Using sensors of various types, a VR environment responds to the user’s movements and adjusts views and perspectives in real-time.  VR also enables users to touch and manipulate virtual objects by way of data gloves equipped with force-feedback devices.  This is how a VR creates the illusion of being in a real environment.  Application of VR and AR The potential areas of application of AR and VR are very wide. The interactive nature of these technologies, however, makes them more attractive to areas such as education, healthcare, and entertainment. The following are examples of the application of AR and VR within the three areas. Education: AR and VR have a wide array of uses in education and enable to learn-on-the go using real-time instructions. They also make learning interactive and fun. Among the reasons why such technologies are very useful in education include: o They allow students to travel in time and space and see historical events in a very interactive manner. o AR allows students to learn various things more concretely. For example, students can see the different organ systems visually and interactively. o The complexity of some concepts can be reduced when presented in a three- dimensional interactive manner. o They are a good alternative in an environment where resources are scarce such as laboratory equipment. o Learning will be made possible from anywhere using relatively  Healthcare: there are numerous AR and VR applications implemented in the medical industry. Some of the applications in use today include: o Physicians and medical students are trained and taught using AR/VR technologies. The technologies allow them to learn life-saving operations like surgery in a risk-free environment. o AR improves disease diagnosis in a manner that is less invasive and pain-free for the patient. o By helping surgeons visualize the patient’s muscles, bones, and internal organs, AR supports a surgical procedure in a low-risk and much more accurate way. o Tele-surgery, which is conducted with the patient and the surgeon being in different locations, is supported by VR. o VR can be used to distract patients from stressful experiences caused by prolonged recuperation in hospitals. Such patients can use VR headsets to focus on entirely different things and make their medical treatment less stressful.  Entertainment: this is another area where the use of AR and VR has become very popular. The following are some examples of the application of AR and VR in entertainment: o By adding computer-generated data on artifacts in art galleries and museums, AR creates a very interactive and enjoyable experience for visitors. o VR movie theatres equipped with individual armchairs that have extra gear, glasses, helmets, or tools for aroma injections can create a completely new and immersive experience for movie fans. o VR and AR components can be added to amusement parks to help visitors enjoy their visit more. o Different manufacturers are introducing numerous AR applications which present gamers with new ways to interact with the real world. The rise in the number of mobile gamers has also created a demand for augmented reality games.  Data Science  The amount of data that is being generated by organizations from different sources is growing exponentially.  In order to properly utilize such an enormous amount of data in a manner that ensures competitiveness, an appropriate type of approach or method on how to use data is required.  Data science provides the tools and techniques that are used by organizations to take advantage of the vast amount of data that they own.  Data Science is a blend of tools, algorithms, and machine learning (ML) principles used to discover and extract hidden patterns.  It is an interdisciplinary field that includes areas such as statistics, mathematics, and computer science.  Data science employs various tools at different stages in finding valuable solutions.  A professional who is charged with the responsibilities of the activities involved in data science is known as a data scientist.  Data scientists use different types of algorithms to turn data into useful insights.  The concept of big data is strongly associated with data science.  Big data refers to a huge volume of data that cannot be processed using traditional methods, and it is characterized by volume, variety, and velocity.  For example, in 2020 Facebook generated 4 petabytes of data every day — that is a million gigabytes.  Data science applies mathematical and statistical approaches and computer tools for processing big data.  The insights generated through data science tools and techniques apply to almost all fields.  In manufacturing, for example, data science can be used to forecast product demand that will be used to determine the precise amount of raw material that needs to be ordered.  The personalization of information on social media is achieved through the use of data science tools on the massive amount of data that social media companies collect from their users.  Weather predictions in the agricultural sector, preventive medicine in health care, and risk management in business are but a few examples to mention about the areas where data science has improved results to a great extent.  The application of data science in sites like social networking sites involves the collection of large amounts of user data.  But this sometimes creates tension with the issue of privacy, especially in countries where there are strong privacy regulations.  Data anonymization and data generalization are some of the ways suggested for tackling issues of data protection and privacy.  Data anonymization refers to removing personally identifiable information from data, while data generalization is about bunching data into broad categories such as age groups and geographical areas.  As these approaches limit the level of insight that companies generate from data, privacy remains to be a contentious issue.