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Artificial Intelligence 1. Introduction 1.1. Definition AI (Artificial Intelligence) is a machine’s ability to perform cognitive functions as humans do, such as perceiving, learning, reasoning, and solving problems. The benchmark for AI is the human level concerning in teams of reasoning, speech, and vision. 1.2. History AI was a term first coined at Dartmouth College in 1956. Cognitive scientist Marvin Minsky was optimistic about the technologys future. The 19741980 saw government funding in the field drop, a period known as AI winter, when several criticised progress in the field. However, the fervour was revived afterwards in the 1980s when the British government started funding the technology again, especially because they were worried about competition with the Japanese. In 1997, IBMs Deep Blue began the first computer to beat a Russian Grandmaster, making history.
Technical Writing And Presentation Table of Contents POINT OF VIEW “Some people call this artificial intelligence, but the reality is this technology will enhance us So instead of artificial intelligence, I think we’ll augment our intelligence.” - Binh “One day the word "Soon" will be replaced by "Finally".” - Hoa “Software is eating the world, but AI is going to eat software.” - Nam “AI is neither good nor evil It’s a tool It’s a technology for us to use.” - Dung - Technical Writing And Presentation Artificial Intelligence Introduction 1.1 Definition AI (Artificial Intelligence) is a machine’s ability to perform cognitive functions as humans do, such as perceiving, learning, reasoning, and solving problems The benchmark for AI is the human level concerning in teams of reasoning, speech, and vision 1.2 History AI was a term first coined at Dartmouth College in 1956 Cognitive scientist Marvin Minsky was optimistic about the technology's future The 1974-1980 saw government funding in the field drop, a period known as "AI winter", when several criticised progress in the field However, the fervour was revived afterwards in the 1980s when the British government started funding the technology again, especially because they were worried about competition with the Japanese In 1997, IBM's Deep Blue began the first computer to beat a Russian Grandmaster, making history Types of AI Using these characteristics for reference, all artificial intelligence systems - real and hypothetical - fall into one of three types: • Artificial narrow intelligence (ANI), which has a narrow range of abilities • Artificial general intelligence (AGI), which is on par with human capabilities • Artificial superintelligence (ASI), which is more capable than a human 2.1 Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI Artificial narrow intelligence (ANI), also referred to as weak AI or narrow AI, is the only type of artificial intelligence we have successfully realized to date Narrow AI is goal-oriented, designed to perform singular tasks and is very intelligent at completing the specific task it is programmed to While these machines may seem intelligent, they operate under a narrow set of constraints and limitations, which is why this type is commonly referred to as weak AI Narrow AI doesn’t mimic or replicate human intelligence, it merely simulates human behaviour based on a narrow range of parameters and contexts Narrow AI has experienced numerous breakthroughs in the last decade, powered by achievements in machine learning and deep learning Technical Writing And Presentation Narrow AI’s machine intelligence comes from the use of natural language processing (NLP) to perform tasks NLP is evident in chatbots and similar AI technologies By understanding speech and text in natural language, AI is programmed to interact with humans in a natural, personalised manner Narrow AI can either be reactive, or have a limited memory Reactive AI is incredibly basic; it has no memory or data storage capabilities, emulating the human mind’s ability to respond to different kinds of stimuli without prior experience Limited memory AI is more advanced, equipped with data storage and learning capabilities that enable machines to use historical data to inform decisions Examples of narrow AI: • Siri by Apple, Alexa by Amazon, Cortana by Microsoft and other virtual assistants • Image / facial recognition software • Entertainment or marketing content recommendations based on watch/listen/purchase behaviour • Self-driving cars 2.2 Artificial General Intelligence (AGI) / Strong AI / Deep AI Artificial general intelligence (AGI), also referred to as strong AI or deep AI, is the concept of a machine with general intelligence that mimics human intelligence and/or behaviours, with the ability to learn and apply its intelligence to solve any problem AGI can think, understand, and act in a way that is indistinguishable from that of a human in any given situation AI researchers and scientists have not yet achieved strong AI To succeed, they would need to find a way to make machines conscious, programming a full set of cognitive abilities Machines would have to take experiential learning to the next level, not just improving efficiency on singular tasks, but gaining the ability to apply experiential knowledge to a wider range of different problems Strong AI uses a theory of mind AI framework, which refers to the ability to discern needs, emotions, beliefs and thought processes of other intelligent entitles Theory of mind level AI is not about replication or simulation, it’s about training machines to truly understand humans Fujitsu-built K, one of the fastest supercomputers, is one of the most notable attempts at achieving strong AI, but considering it took 40 minutes to simulate a single second of neural activity, it is difficult to determine whether or not strong AI will be achieved in our foreseeable future 2.3 Artificial Superintelligence (ASI) Artificial super intelligence (ASI), is the hypothetical AI that doesn’t just mimic or understand human intelligence and behaviour, ASI is where machines become self-aware and surpass the capacity of human intelligence and ability Technical Writing And Presentation In addition to replicating the multi-faceted intelligence of human beings, ASI would theoretically be exceedingly better at everything we do; math, science, emotional relationships, … ASI would have a greater memory and a faster ability to process and analyse data and stimuli Consequently, the decision-making and problem solving capabilities of super intelligent beings would be far superior than those of human beings The potential of having such powerful machines at our disposal may seem appealing, but the concept itself has a multitude of unknown consequences If self-aware super intelligent beings came to be, they would be capable of ideas like self-preservation The impact this will have on humanity, our survival, and our way of life, is pure speculation Subfields of Artificial Intelligence Major sub-fields of AI now include: Machine Learning, Neural Networks, Evolutionary Computation, Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and Planning But in this chapter we will discuss about basic sub-fields of AI: Deep Learning and Machine Learning 3.1 Deep Learning - Definition Deep learning is a computer software that mimics the network of neurons in a brain It is a subset of machine learning and is called deep learning because it makes use of deep neural networks In deep learning, the learning phase is done through a neural network A neural network is an architecture where the layers are stacked on top of each other - How does Deep Learning work? Deep learning networks learn by discovering intricate structures in the data they experience By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data - Types of Deep Neural Networks • Convolutional Neural Network (CNN) - CNN is a class of deep neural networks most • • • commonly used for image analysis Recurrent Neural Network (RNN) - RNN uses sequential information to build a model It often works better for models that have to memorize past data Generative Adversarial Network (GAN) - GAN are algorithmic architectures that use two neural networks to create new, synthetic instances of data that pass for real data Deep Belief Network (DBN) - DBN is a generative graphical model that is composed of multiple layers of latent variables called hidden units Each layer is interconnected, but the units are not 3.2 Machine Learning - Definition Machine Learning is a type of AI in which a computer is trained to automate tasks that are exhaustive or impossible for human beings It is the best tool to analyze, understand, and identify Technical Writing And Presentation patterns in data based on the study of computer algorithms Machine learning can make decisions with minimal human intervention - How does Machine Learning work? Machine learning accesses vast amounts of data (both structured and unstructured) and learns from it to predict the future It learns from the data by using multiple algorithms and techniques Below is a diagram that shows how a machine learns from data - Types of Machine Learning • Supervised Learning In supervised learning, the data is already labeled, which means you know the target variable Using this method of learning, systems can predict future outcomes based on past data • Unsupervised Learning Unsupervised learning algorithms employ unlabeled data to discover patterns from the data on their own The systems are able to identify hidden features from the input data provided Once the data is more readable, the patterns and similarities become more evident • Reinforcement Learning The goal of reinforcement learning is to train an agent to complete a task within an uncertain environment The agent receives observations and a reward from the environment and sends actions to the environment The reward measures how successful action is with respect to completing the task goal - Machine Learning Processes Machine Learning involves seven steps: Data gathering, Data Pre-Processing, Choose Model, Train Model, Test Model, Tune Model, Prediction - Machine Learning Applications • Sales forecasting for different products • Fraud analysis in banking • Product recommendations • Stock price prediction 3.3 Difference between Machine Learning and Deep Learning Parameter Machine Learning Deep Learning Data Excellent performances Excellent performance on a big dataset Dependencies on a small/medium dataset Hardware Work on a low-end Requires powerful machine, preferably dependencies machine with GPU: DL performs a significant amount of matrix multiplication Technical Writing And Presentation Application of AI 4.1 AI Application in E-Commerce - Personalized Shopping Artificial Intelligence technology is used to create recommendation engines through which you can engage better with your customers These recommendations are made in accordance with their browsing history, preference, and interests It helps in improving your relationship with your customers and their loyalty towards your brand - AI-powered Assistants Virtual shopping assistants and chatbots help improve the user experience while shopping online Natural Language Processing is used to make the conversation sound as human and personal as possible Moreover, these assistants can have real-time engagement with your customers - Fraud Prevention Credit card frauds and fake reviews are two of the most significant issues that E-Commerce companies deal with By considering the usage patterns, AI can help reduce the possibility of credit card frauds taking place Many customers prefer to buy a product or service based on customer reviews AI can help identify and handle fake reviews 4.2 Applications Of Artificial Intelligence in Education - Administrative Tasks Automated to Aid Educators Artificial Intelligence can help educators with non-educational tasks like facilitating and automating personalized messages to students, arranging and facilitating parent and guardian interactions, HR-related topics,… - Creating Smart Content Artificial Intelligence helps create a rich learning experience by generating and providing audio and video summaries and integral lesson plans - Voice Assistants Without even the direct involvement of the lecturer or the teacher, a student can access extra learning material or assistance through Voice Assistants - Personalized Learning Using AI technology, hyper-personalization techniques can be used to monitor students’ data thoroughly, and habits, lesson plans, reminders, study guides, flash notes, frequency or revision, etc., can be easily generated 4.3 Applications of Artificial Intelligence in Lifestyle - Autonomous Vehicles Technical Writing And Presentation Automobile manufacturing companies like Toyota, Audi, Volvo, and Tesla use machine learning to train computers to think and evolve like humans when it comes to driving in any environment and object detection to avoid accidents - Spam Filters The email that we use in our day-to-day lives has AI that filters out spam emails sending them to spam or trash folders, letting us see the filtered content only The popular email provider, Gmail, has managed to reach a filtration capacity of approximately 99.9% - Facial Recognition Our favorite devices like our phones, laptops, and PCs use facial recognition techniques by using face filters to detect and identify in order to provide secure access Apart from personal usage, facial recognition is a widely used Artificial Intelligence application even in high securityrelated areas in several industries - Recommendation System Various platforms that we use in our daily lives like e-commerce, entertainment websites, social media, video sharing platforms, like youtube, etc., all use the recommendation system to get user data and provide customized recommendations to users to increase engagement This is a very widely used Artificial Intelligence application in almost all industries 4.4 Applications of Artificial Intelligence in Robotics Robotics is another field where artificial intelligence applications are commonly used Robots powered by AI use real-time updates to sense obstacles in its path and pre-plan its journey instantly It can be used for: • Carrying goods in hospitals, factories, and warehouses • Cleaning offices and large equipment • Inventory management 4.5 Applications of Artificial Intelligence in Social Media - Instagram On Instagram, AI considers your likes and the accounts you follow to determine what posts you are shown on your explore tab - Facebook Artificial Intelligence is also used along with a tool called DeepText With this tool, Facebook can understand conversations better It can be used to translate posts from different languages automatically - Twitter Technical Writing And Presentation AI is used by Twitter for fraud detection, removing propaganda, and hateful content Twitter also uses AI to recommend tweets that users might enjoy, based on what type of tweets they engage with 4.6 Applications of Artificial Intelligence in Marketing Artificial intelligence (AI) applications are popular in the marketing domain as well • Using AI, marketers can deliver highly targeted and personalized ads with the help of behavioral analysis, pattern recognition, etc It also helps with retargeting audiences at the right time to ensure better results and reduced feelings of distrust and annoyance • AI can help with content marketing in a way that matches the brand's style and voice It can be used to handle routine tasks like performance, campaign reports, and much more • Chatbots powered by AI, Natural Language Processing, Natural Language Generation, and Natural Language Understanding can analyze the user's language and respond in the ways humans • AI can provide users with real-time personalizations based on their behavior and can be used to edit and optimize marketing campaigns to fit a local market's needs The Evolution of AI 1950’s: Starting Period - In 1956, John McCarthy first used the term Artificial Intelligence Demonstration of the first running AI program at Carnegie Mellon University In the same year, Los Alamos chess is the first program to play a chess-like game, developed by Paul Stein and Mark Wells 1960’s: Rising Period - In 1964, Danny Bobrow’s dissertation at MIT showed how computers could understand natural language In 1965, ELIZA, an interactive computer program that could functionally converse in English with a person, was created at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum 1970’s to 1980’s: Low Development Period - In 1972, WABOT-1, the first anthropomorphic robot was built in Japan at Waseda University In 1980, WABOT-2, which can converse with a person, read a normal musical score with is eye and play tunes of average difficulty on an electronic organ, was built and became the first milestone in developing a "personal robot" 1980’s to 1990’s: Steady Development Period - In 1995, computer scientist Richard Wallace developed the software chatbot ALICE inspired by Eliza Technical Writing And Presentation - In 1997, The Deep Blue Chess Program beat the then world chess champion, Garry Kasparov 21st Century: Blooming Period - - - In 2000, interactive robot pets have become commercially available MIT displays Kismet, a robot with a face that expresses emotions In 2009, Google secretly developed a driverless car and by 2014 it passed Nevada selfdriving test In 2010, Microsoft launched Kinect for Xbox 360 the first gaming device that tracks human body In 2011, Apple released Siri, a virtual assistant on Apple iOS operating systems In 2012, Google researchers trained a large neural network of sixteen thousand processors to recognize images of cats by showing it 10 million unlabeled images from YouTube videos In 2014, Microsoft released Cortana their version of a virtual assistant, also Amazon created Amazon Alexa, a home assistant that developed into smart speakers that function as personal assistants In 2016 a humanoid robot named Sophia was created by Hanson Robotics She's the first robot citizen with her ability to see make facial expressions and communicate through AI In 2018, Google developed BERT - the first deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus Future of AI Artificial intelligence is shaping the future of humanity across nearly every industry It is already the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future So let’s see how artificial intelligence is poised to fundamentally restructure broader swaths of our economy and society over the next decade with bold predictions that are informed by experts and scientists in the field 6.1 AI and Machine Learning will transform the scientific method AI enables an unprecedented ability to analyze enormous data sets and computationally discover complex relationships and patterns AI, augmenting human intelligence, is primed to transform the scientific research process, unleashing a new golden age of scientific discovery in the coming years 6.2 AI will enable next-gen consumer experiences Next-generation consumer experiences like the metaverse and cryptocurrencies have garnered much buzz These experiences and others like them will be critically enabled by AI AI algorithms have the potential to learn much more quickly in a digital world (e.g., virtual driving to train autonomous vehicles) These are natural catalysts for AI to bridge the feedback loops between the digital and physical realms For instance, blockchain, cryptocurrency and distributed finance, at their core, are all about integrating frictionless capitalism into the economy Technical Writing And Presentation 6.3 Addressing the climate crisis will require AI As a society we have much to in mitigating the socioeconomic threats posed by climate change Carbon pricing policies, still in their infancy, are of questionable effectiveness One potential new approach involves prediction markets powered by AI that can tie policy to impact, taking a holistic view of environmental information and interdependence This would likely be powered by digital "twin Earth" simulations that would require staggering amounts of real-time data and computation to detect nuanced trends imperceptible to human senses Other new technologies such as carbon dioxide sequestration cannot succeed without AI-powered risk modeling, downstream effect prediction and the ability to anticipate unintended consequences 6.4 AI will enable truly personalized medicine Simply put, AI is uniquely suited to construct and analyze "digital twin" rubrics of individual biology and is able to so in the context of the communities an individual lives in The human body is mind-boggling in its complexity, and it is shocking how little we know about how drugs work (paywall) Without AI, it is impossible to make sense of the massive datasets from an individual’s physiology, let alone the effects on individual health outcomes from environment, lifestyle and diet AI solutions have the potential not only to improve the state of the art in healthcare, but also to play a major role in reducing persistent health inequities Pros and Cons 7.1 Pros - Diminishes Human Error Since AI decisions come from compiled data with the aid of designed algorithms, errors are reduced, accuracy is increased, and precision is possible Ultimately, fewer mistakes equal savings in both time and resources, so AI becomes a win-win for your company - Facilitates Faster Decision-Making Finding ways to save time by making faster decisions you feel confident in is always valuable AI can this for you The more decisions AI makes, the more it has to pull from for future decision-making, improving the process - Offers Continual 24/7 Availability AI never rests or requires sleep, while the human body and mind need rest to continue to function at optimal levels This continual availability 24 hours a day, days a week with no gap in coverage, can make a huge impact on increases in your company’s productivity - Provides Digital Assistants Digital assistants can significantly reduce the need for excessive customer service staff For example, the rise in the use of chatbots already proves how useful they can be in directing customers to the information needed Another is the rising voice bot to help with queries - Excels at Working with Large Sets of Data 10 Technical Writing And Presentation The more data available, the more AI is needed to make sense of it all in less time Artificial intelligence is highly beneficial in making sense of the large sets of data available these days It can both acquire and extract data rapidly, but that’s not all From there, AI takes the data further with interpretation and transformation 7.2 Cons - Requires Higher Overall Costs It’s no secret AI is expensive The initial set-up alone requires a high investment - Reduces Employment While replacing repetitive jobs and other types of work with AI is beneficial to a company, it will undoubtedly also affect employment Traditional job roles will be phased away, leading to unemployment of those who them - Lacks Creative Ability One of the drawbacks of using AI, particularly when playing a role in your content marketing strategy, is its inability to be creative and innovative - Absence of Emotional Range While AI-enhanced machines can work faster and continually, they cannot factor emotion into decisions - Increases Potential for Human Laziness Automating tasks and utilizing more and more digital assistants can lead to increased machine dependency and even human laziness Relying on AI can cause us to use our brains less to memorize, strategize, and solve issues on our own The effects this may have on future generations may be vast if left unacknowledged Conclusion - - Through AI, computers have the ability to harness massive amounts of data and use their learned intelligence to make optimal decisions and discoveries in fractions of the time that it would take humans AI is already the main driver of emerging technologies and it will continue to act as a technological innovator for the foreseeable future AI has good and bad impact but one thing is certain: AI will be available to every category human in the future 11 ...Technical Writing And Presentation Artificial Intelligence Introduction 1.1 Definition AI (Artificial Intelligence) is a machine’s ability to perform cognitive functions as humans do, such as