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Bird’s Eye View of AI for Business Chapter 1: Introducing AI for Business Module 1 Chapter 2: Understanding Data Interpretation by Machines Chapter 3: Industrial and Household Robotics Chapter 4: Managing AI Projects  Introducing...

Bird’s Eye View of AI for Business Chapter 1: Introducing AI for Business Module 1 Chapter 2: Understanding Data Interpretation by Machines Chapter 3: Industrial and Household Robotics Chapter 4: Managing AI Projects  Introducing AI for Business What is AI? How one defines AI can vary greatly. Philosophically, what is “intelligence?” How one perceives intelligence in turn defines its artificial counterpart. A broad and optimistic definition of the field of AI could be: “the area of computer science that studies how machines can perform tasks that would normally require a sentient agent.” It could be argued from such a definition that something as simple as a computer multiplying two numbers is “artificial intelligence.” This is because we have designed a machine capable of taking an input and independently producing a logical output that usually would require a living entity to process. A more skeptical definition might be more narrow, for example: “the area of computer science that studies how machines can closely imitate human intelligence.” From such definition skeptics may argue that what we have today is not artificial intelligence. Up until now, they have been able to point to examples of tasks that computers cannot perform, and therefore claim that computers cannot yet “think” or exhibit artificial intelligence if they cannot satisfactorily perform such functions. USAI ® 2 Chapter 1  Introducing AI for Business In our aforementioned multiplication task, a computer will certainly be faster and more accurate than a human if the two numbers are large enough. There are other areas where humans can currently perform much better than computers. For example, a human can recognize, label, and classify objects with a few examples, whereas currently a computer might require thousands of examples to perform at the same level of accuracy. Research and improvement continue relentlessly, and we will continue to see computers solving more and more problems that just a few years ago we could only dream of them solving. An interesting way to consider the field of AI is that AI is in some ways one more branch of science that is studying the most fascinating computer we know: the brain. With AI, we are attempting to reflect some of the systems and mechanics of the brain within computing, and thus find ourselves borrowing from, and interacting with, fields such as neuroscience. Why do we need to study AI? AI can impact every aspect of our lives. The field of AI tries to understand patterns and behaviors of entities. With AI, we want to build smart systems and understand the concept of intelligence as well. The intelligent systems that we construct are very useful in understanding how an intelligent system like our brain goes about constructing another intelligent system. Let’s look at how our brain processes information: Figure 1: Basic brain components Compared to some other fields such as mathematics or physics that have been around for centuries, AI is relatively in its infancy. Over the last couple of decades, AI has produced some spectacular products such as self-driving cars and intelligent robots that can walk. Based on the direction in which we are heading, it’s obvious that ® 3 Chapter 1  Introducing AI for Business achieving intelligence will have a great impact on our lives in the coming years. We can’t help but wonder how the human brain manages to do so much with such effortless ease. We can recognize objects, understand languages, learn new things, and perform many more sophisticated tasks with our brain. How does the human brain do this? We don’t yet have many answers to that question. When you try to replicate tasks that the brain performs, using a machine, you will see that it falls way behind! Our own brains are far more complex and capable than machines, in many respects. ® 4 Chapter 1  Introducing AI for Business When we try to look for things such as extraterrestrial life or time travel, we don’t know if those things exist; we’re not sure if these pursuits are worthwhile. The good thing about AI is that an idealized model for it already exists: our brain is the holy grail of an intelligent system! All we have to do is to mimic its functionality to create an intelligent system that can do something similarly to, or better than, our brain. Let’s see how raw data gets converted into intelligence through various levels of processing: Figure 2: Conversion of data into intelligence One of the main reasons we want to study AI is to automate many things. We live in a world where: We deal with huge and insurmountable amounts of data. The human brain can’t keep track of so much data. Data originates from multiple sources simultaneously. The data is unorganized and chaotic. Knowledge derived from this data must be updated constantly because the data itself keeps changing. The sensing and actuation must happen in real-time with high precision. ® 5 Chapter 1  Introducing AI for Business Even though the human brain is great at analyzing things around us, it cannot keep up with the preceding conditions. Hence, we need to design and develop intelligent machines that can do this. We need AI systems that can: Handle large amounts of data in an efficient way. With the advent of Cloud Computing, we are now able to store huge amounts of data. Ingest data simultaneously from multiple sources without any lag. Index and organize data in a way that allows us to derive insights. Learn from new data and update constantly using the right learning algorithms. Think and respond to situations based on the conditions in real time. Continue with tasks without getting tired or needing breaks. AI techniques are actively being used to make existing machines smarter so that they can execute faster and more efficiently. ® 6 Chapter 1  Introducing AI for Business Branches of AI It is important to understand the various fields of study within AI so that we can choose the right framework to solve a given real-world problem. There are several ways to classify the different branches of AI: Supervised learning vs. unsupervised learning vs. reinforcement learning Artificial general intelligence vs. narrow intelligence By human function: ° Machine vision ° Machine learning ° Natural language processing ° Natural language generation Following, we present a common classification: Machine learning and pattern recognition: This is perhaps the most popular form of AI out there. We design and develop software that can learn from data. Based on these learning models, we perform predictions on unknown data. One of the main constraints here is that these programs are limited to the power of the data. If the dataset is small, then the learning models would be limited as well. Let’s see what a typical machine learning system looks like: ® 7 Chapter 1  Introducing AI for Business Figure 3: A typical computer system When a system receives a previously unseen data point, it uses the patterns from previously seen data (the training data) to make inferences on this new data point. For example, in a facial recognition system, the software will try to match the pattern of eyes, nose, lips, eyebrows, and so on in order to find a face in the existing database of users. Logic-based AI: Mathematical logic is used to execute computer programs in logic-based AI. A program written in logic-based AI is basically a set of statements in logical form that expresses facts and rules about a problem domain. This is used extensively in pattern matching, language parsing, semantic analysis, and so on. ® 8 Chapter 1  Introducing AI for Business Search: Search techniques are used extensively in AI programs. These programs examine many possibilities and then pick the most optimal path. For example, this is used a lot in strategy games such as chess, networking, resource allocation, scheduling, and so on. Knowledge representation: The facts about the world around us need to be represented in some way for a system to make sense of them. The languages of mathematical logic are frequently used here. If knowledge is represented efficiently, systems can be smarter and more intelligent. Ontology is a closely related field of study that deals with the kinds of objects that exist. It is a formal definition of the properties and relationships of the entities that exist in a domain. This is usually done with a taxonomy or a hierarchical structure of some kind. The following diagram shows the difference between information and knowledge: Figure 4: Information vs. Knowledge Planning: This field deals with optimal planning that gives us maximum returns with minimal costs. These software programs start with facts about the situation and a statement of a goal. These programs are also aware of the facts of the world, so that they know what the rules are. From this information, they generate the most optimal plan to achieve the goal. Heuristics: A heuristic is a technique used to solve a given problem that’s practical and useful in solving the problem in the short term, but not guaranteed to be optimal. This is more like an educated guess on what approach we should ® 9 Chapter 1  Introducing AI for Business take to solve a problem. In AI, we frequently encounter situations where we cannot check every single possibility to pick the best option. Thus, we need to use heuristics to achieve the goal. They are used extensively in AI in fields such as robotics, search engines, and so on. Genetic programming: Genetic programming is a way to get programs to solve a task by mating programs and selecting the fittest. The programs are encoded as a set of genes, using an algorithm to get a program that can perform the given task well. ® 10 Chapter 1  Introducing AI for Business The five tribes of machine learning Machine learning can be further classified in a variety of ways. One of our favorite classifications is the one provided by Pedro Domingos in his book The Master Algorithm. In his book, he classifies machine learning by the field of science that sprouted the ideas. For example, genetic algorithms sprouted from Biology concepts. Here are the full classifications, the name Domingos uses for the tribes, and the dominant algorithms used by each tribe, along with noteworthy proponents: Tribe Origins Dominant algorithm Proponents Tom Mitchell Logic and Symbolists Inverse deduction Steve Muggleton Philosophy Ross Quinlan Yan LeCun Connectionists Neuroscience Backpropagation Geoffrey Hinton Yoshua Bengio John Koza Evolutionaries Biology Genetic programming John Holland Hod Lipson David Heckerman Bayesians Statistics Probabilistic inference Judea Pearl Michael Jordan Peter Hart Analogizers Psychology Kernel machines Vladimir Vapnik Douglas Hofstadter Symbolists – Symbolists use the concept of induction or inverse deduction as their main tool. When using induction, instead of starting with a premise and looking for conclusions, inverse deduction starts with a set of premises and conclusions and works backwards to fill in the missing pieces. An example of deduction: Socrates is human + All humans are mortal = What can be deduced? (Socrates is mortal) An example of induction: Socrates is human + ?? = Socrates is mortal (Humans are mortal?) Connectionists – Connectionists use the brain, or at least our very crude understanding of the brain, as their primary tool – mainly neural networks. Neural networks are a type of algorithm, modeled loosely after the brain, which are designed to recognize patterns. They can recognize numerical patterns contained in vectors. In order to use them, all inputs, be they images, sound, text, or time series need to be translated into these numerical vectors. It is hard to open a magazine or a news site and not read about examples of “deep learning.” Deep learning is a specialized type of a neural network. Evolutionaries – Evolutionaries focus on using the concepts of evolution, natural selection, genomes, and DNA mutation and applying them to data processing. Evolutionary algorithms will constantly mutate, evolve, and adapt to unknown conditions and processes. ® 11 Chapter 1  Introducing AI for Business Bayesians – Bayesians focus on handling uncertainty using probabilistic inference. Vision learning and spam filtering are some of the problems tackled by the Bayesian approach. Typically, Bayesian models will take a hypothesis and apply a type of “a priori” reasoning, assuming that some outcomes will be more likely. They then update a hypothesis as they see more data. Analogizers – Analogizers focus on techniques that find similarities between examples. The most famous analogizer model is the k-nearest neighbor algorithm. Making machines think like humans For decades, we have been trying to get the machine to think more like humans. In order to make this happen, we need to understand how humans think in the first place. How do we understand the nature of human thinking? One way to do this would be to note down how we respond to things. But this quickly becomes intractable, because there are too many things to note down. Another way to do this is to conduct an experiment based on a predefined format. We develop a certain number of questions to encompass a wide variety of human topics, and then see how people respond to it. Once we gather enough data, we can create a model to simulate the human process. This model can be used to create software that can think like humans. Of course, this is easier said than done! All we care about is the output of the program given an input. If the program behaves in a way that matches human behavior, then we can say that humans have a similar thinking mechanism. The following diagram shows different levels of thinking and how our brain prioritizes things: ® 12 Chapter 1  Introducing AI for Business Figure 6: The levels of thought ® 13 Chapter 1  Introducing AI for Business Within computer science, there is a field of study called Cognitive Modeling that deals with simulating the human thinking process. It tries to understand how humans solve problems. It takes the mental processes that go into this problem-solving process and turns it into a software model. This model can then be used to simulate human behavior. Cognitive modeling is used in a variety of AI applications such as deep learning, expert systems, natural language processing, robotics, and so on. Building rational agents A lot of research in AI is focused on building rational agents. What exactly is a rational agent? Before that, let us define the word rationality within the context of AI. Rationality refers to observing a set of rules and following their logical implications in order to achieve a desirable outcome. This needs to be performed in such a way that there is maximum benefit to the entity performing the action. An agent, therefore, is said to act rationally if, given a set of rules, it takes actions to achieve its goals. It just perceives and acts according to the information that’s available. This system is used a lot in AI to design robots when they are sent to navigate unknown terrains. How do we define what is desirable? The answer is that it depends on the objectives of the agent. The agent is supposed to be intelligent and independent. We want to impart the ability to adapt to new situations. It should understand its environment and then act accordingly to achieve an outcome that is in its best interests. The best interests are dictated by the overall goal it wants to achieve. Let’s see how an input gets converted to action: ® 14 Chapter 1  Introducing AI for Business Figure 7: Converting input into action How do we define the performance measure for a rational agent? One might say that it is directly proportional to the degree of success. The agent is set up to achieve a task, so the performance measure depends on what percentage of that task is complete. But we must think as to what constitutes rationality in its entirety. If it’s just about results, we don’t consider the actions leading up to the result. Making the right inferences is a part of being rational, because the agent must act rationally to achieve its goals. This will help it draw conclusions that can be used successively. ® 15 Chapter 1  Introducing AI for Business But, what about situations where there are no provably right things to do? There are situations where the agent doesn’t know what to do, but it still must do something. Let’s set up a scenario to make this last point clearer. Imagine a self-driving car that’s going at 60 miles an hour and suddenly someone crosses its path. For the sake of the example, assume that given the speed the car is going, it only has two choices. Either the car crashes against a guard rail knowing that it will kill the car occupant, or it runs over the pedestrian and kills them. What’s the right decision? How does the algorithm know what to do? If you were driving, would you know what to do? We now are going to learn about one of the earliest examples of a rational agent – the General Problem Solver. As we’ll see, despite the lofty name, it really wasn’t capable of solving any problem, but it was a big leap in the field of computer science nonetheless. General Problem Solver The General Problem Solver (GPS) was an AI program proposed by Herbert Simon, J.C. Shaw, and Allen Newell. It was the first useful computer program that came into existence in the AI world. The goal was to make it work as a universal problem-solving machine. Of course, there were many software programs that existed before, but these programs performed specific tasks. GPS was the first program that was intended to solve any general problem. GPS was supposed to solve all the problems using the same base algorithm for every problem. As you must have realized, this is quite an uphill battle! To program the GPS, the authors created a new language called Information Processing Language (IPL). The basic premise is to express any problem with a set of well-formed formulas. These formulas would be a part of a directed graph with multiple sources and sinks. In a graph, the source refers to the starting node and the sink refers to the ending node. In the case of GPS, the source refers to axioms and the sink refers to the conclusions. Even though GPS was intended to be a general purpose, it could only solve well-defined problems, such as proving mathematical theorems in geometry and logic. It could also solve word puzzles and play chess. The reason was that these problems could be formalized to a reasonable extent. But in the real world, this quickly becomes intractable because of the number of possible paths you can take. If it tries to brute force a problem by counting the number of walks in a graph, it becomes computationally infeasible. Solving a problem with GPS Let’s see how to structure a given problem to solve it using GPS: 1. The first step is to define the goals. Let’s say our goal is to get some milk from the grocery store. 2. The next step is to define the preconditions. These preconditions are in reference to the goals. To get milk from the grocery store, we need to have a mode of transportation and the grocery store should have milk available. 3. After this, we need to define the operators. If my mode of transportation is a car and if the car is low on fuel, then we need to ensure that we can pay the fueling station. We need to ensure that you can pay for the milk at the store. ® 16 Chapter 1  Introducing AI for Business An operator takes care of the conditions and everything that affects them. It consists of actions, preconditions, and the changes resulting from taking actions. In this case, the action is giving money to the grocery store. Of course, this is contingent upon you having the money in the first place, which is the precondition. By giving them the money, you are changing your money condition, which will result in you getting the milk. GPS will work if you can frame the problem like we did just now. The constraint is that it uses the search process to perform its job, which is way too computationally complex and time consuming for any meaningful real-world application. In this section we learned what a rational agent is. Now let’s learn how to make these rational agents more intelligent and useful. Building an intelligent agent There are many ways to impart intelligence to an agent. The most commonly used techniques include machine learning, stored knowledge, rules, and so on. In this section, we will focus on machine learning. In this method, the way we impart intelligence to an agent is through data and training. Let’s see how an intelligent agent interacts with the environment: ® 17 Chapter 1  Introducing AI for Business Figure 8: An intelligent agent interaction with its environment With machine learning, sometimes we want to program our machines to use labeled data to solve a given problem. By going through the data and the associated labels, the machine learns how to extract patterns and relationships. In the preceding example, the intelligent agent depends on the learning model to run the inference engine. Once the sensor perceives the input, it sends it to the feature extraction block. Once the relevant features are extracted, the trained inference engine performs a prediction based on the learning model. This learning model is built using machine learning. The inference engine then takes a decision and sends it to the actuator, which then takes the required action in the real world. ® 18 Chapter 1  Introducing AI for Business There are many applications of machine learning that exist today. It is used in image recognition, robotics, speech recognition, predicting stock market behavior, and so on. In order to understand machine learning and build a complete solution, you will have to be familiar with many techniques from different fields such as pattern recognition, artificial neural networks, data mining, statistics, and so on. Types of models There are two types of models in the AI world: Analytical models and learned models. Before we had machines that could compute, people used to rely on analytical models. Analytical models were derived using a mathematical formulation, which is basically a sequence of steps followed to arrive at a final equation. The problem with this approach is that it was based on human judgment. Hence, these models were simplistic and often inaccurate, with just a few parameters. Think of how Newton and other scientists of old made calculations before they had computers. Such models often involved prolonged derivations and long periods of trial and error before a working formula was arrived at. We then entered the world of computers. These computers were good at analyzing data. So, people increasingly started using learned models. These models are obtained through the process of training. During training, the machines look at many examples of inputs and outputs to arrive at the equation. These learned models are usually complex and accurate, with thousands of parameters. This gives rise to a very complex mathematical equation that governs the data that can assist in making predictions. Machine learning allows us to obtain these learned models that can be used in an inference engine. One of the best things about this is the fact that we don’t need to derive the underlying mathematical formula. You don’t need to know complex mathematics, because the machine derives the formula based on data. All we need to do is create the list of inputs and the corresponding outputs. The learned model that we get is just the relationship between labeled inputs and the desired outputs. Stages of AI 1. Artificial Narrow Intelligence (ANI): The most common form of AI you would find in the market is ANI. These Artificial Intelligence systems are intended to solve a single problem and could perform a single task well. They have limited capabilities, such as recommending a product for an e-commerce user or predicting the weather. This is the only type of Artificial Intelligence that exists today. They can approach human functioning in particular contexts and even exceed them in many cases, but they only excel in highly controlled environments with a limited set of parameters. Examples of ANI: Smartphone apps, Chess and AlphaGo, image identification tools, speech recognition tools, self-driving systems, Google Translate, spam filters, etc. 2. Artificial General Intelligence (AGI): An artificial intelligence reaches the general state wherein it can perform any intellectual task with the same level of precision as a human. It is defined as AI, which has a cognitive function at the human level in a wide variety of domains, for instance, image processing, language processing, computational functioning, and reasoning. You are still far behind building an AGI system, and this is still a theoretical concept. An ® 19 Chapter 1  Introducing AI for Business AGI system would need to comprise thousands of Narrow Artificial Intelligence systems working together and communicating with each other to imitate human reasoning. Even with the most advanced infrastructure and computer systems, like IBM’s Watson, it has taken 40 minutes to simulate a single second of neural activity. This gives you vital information about the interconnectedness and high complexity of the human brain, and about the extent of challenges you would face while building an AI with your existing resources. A perfect AI will be able to integrate multipurpose systems and will be able to drive them with human- level intelligence (reasoning, thinking, and decision-making). It will be able to analyze diverse information and make decisions accordingly. 3. Artificial Super Intelligence (ASI): An AI is super when it can beat humans in many tasks. In other words, machines are smarter than humans or surpass human intelligence. It would include making rational decisions and functions, like creating better art and building emotional relationships. Examples. Super Intelligent AI agents, systems that are masters at every skill, subject, or discipline and are faster than the smartest humans. Why Artificial Intelligence? AI can affect all aspects of our lives. The AI field tries to understand the patterns and ® 20 Chapter 1  Introducing AI for Business behaviors of entities. With AI, you would not only want to build intelligent systems but also understand the concept of intelligence. The smart systems that are made are extremely helpful in understanding how an intelligent system like your brain creates another intelligent system. Unlike other fields like mathematics or physics that have been around for centuries, AI is still in its infancy. In the past few decades, AI has produced some spectacular products, such as autonomous cars and smart robots that can walk. Based on the direction where the world is heading, it is quite evident that gaining intelligence will have a significant impact on humans for years to come. You can’t resist the urge to think about how the human mind figures out how to accomplish such a great deal without any problem. You can perceive objects, get dialects, learn new things, and perform a lot progressively refined assignments with your minds. How does the human mind do this? At the point when you attempt to do ® 21 Chapter 1  Introducing AI for Business these activities with a machine, you will see it falls behind. For example, when you try to search for things like extra-terrestrial life or time travel, you don’t know if those things exist. The good thing about AI’s holy grail is that you know it exists. Your brain is the holy grail! It is a spectacular illustration of an intelligent system. All you have to do is imitate its functionality to create an intelligent system that can do something similar, possibly even more. These days, artificial intelligence is used in almost every industry, giving a technological advantage to all companies that integrate artificial intelligence at scale. According to McKinsey, artificial intelligence has the potential to create USD 600 billion in retail, providing 50 percent more incremental value in banking compared to other analytical techniques. In transportation and logistics, the potential revenue increase is 89 percent or more. Specifically, if an organization uses artificial intelligence for its marketing team, it can automate routine and monotonous tasks, allowing the sales representative to focus on tasks such as building relationships, raising leads, etc. A “Gong” company provides a conversation intelligence service. Every time a sales representative makes a phone call, the machine records transcripts and analyzes the chat. The VP can use AI analysis and recommendations to formulate a winning strategy. In a nutshell, AI provides cutting-edge technology to handle complex data, which is impossible for a human to manage. AI automates redundant jobs, allowing workers to focus on high-level, essential tasks. When AI is applied at scale, it leads to reduced costs and increases revenue. Let’s see how raw data is converted to wisdom through various levels of processing: ® 22 Chapter 1  Introducing AI for Business One of the foremost reasons everyone wants to study AI is to automate many tasks. In the present world, organizations handle massive and impossible amounts of data. The human brain cannot track that much data. The data originates from multiple sources simultaneously and is disorganized and chaotic. The knowledge derived from this data must be continually updated as the data itself changes continuously. The sensing and analysis must happen in real-time with high precision. ® 23 Chapter 1  Introducing AI for Business Although the human brain is excellent at analyzing things around it, but it cannot keep up with other conditions such as to store a large amount of information, make decisions in adverse conditions, etc. Therefore, there is a need to design and develop smart machines, which can store a massive amount of data. There is a need for artificial intelligence systems that can efficiently handle large amounts of data. With the advent of Cloud Computing, the organizations can now: Store massive amounts of data, Ingest data simultaneously from multiple sources without delay, Index and organize data in a way that allows us to derive insights. Learn from new data and update continuously using the correct learning algorithms. Think and respond to situations based on real-time conditions. AI techniques are being actively used to make existing machines smarter so they can execute tasks faster and more efficiently. ® 24 Chapter 1  Introducing AI for Business Artificial Intelligence vs. Machine Learning vs. Data Science Artificial Intelligence, much like data science, is a wide field of applications, systems, and more that aims at replicating human intelligence through machines. Artificial Intelligence represents action-planned feedback for perception. Perception > Planning > Action > Feedback of Perception Perception > Planning > Action > Feedback of Perception Data science uses different parts of this loop to solve specific problems. For example, in the first step (that is, perception), data scientists try to use data to identify patterns. Similarly, in the next step (i.e., planning), they try to: find all possible solutions find the best solution ® 25 Chapter 1  Introducing AI for Business After planning, the next step is action, and so on. Data Science creates a system that interrelates both the above points and helps businesses move forward. Data Science isn’t precisely a subset of machine learning, but it uses ML to analyze data and make predictions. It integrates ML with other disciplines, like big data analytics and cloud computing. Data Science completely focuses on real-world problems and is a practical application of ML. Even though the terms Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS) follow in the same domain and are connected, but they have their specific applications and meaning. Artificial Intelligence systems mimic or replicate human intelligence. ML gives the systems the ability to learn and improve from experiences without performing them. Data science is an umbrella term that incorporates data analytics, data mining, machine learning, artificial intelligence, and other several related disciplines. The below infographic explains the relationship between AI, ML, and DS. ® 26 Chapter 1  Introducing AI for Business Artificial Intelligence (AI) vs. Machine Learning (ML) Most of your smartphones, everyday devices, and even the Internet use artificial intelligence. Large companies that want to announce their latest innovations often use AI and machine learning interchangeably, though machine learning and AI are different in some ways. ® 27 Chapter 1  Introducing AI for Business Artificial Intelligence is a computer with human-like characteristics. Grab your brain; it can easily and seamlessly calculate the world around us. A computer can accomplish the same job through artificial intelligence. Artificial Intelligence is a broad science that mimics the human ability to create intelligent machines. ML (a subset of AI) refers to systems that can learn from experience. Machine learning models look for patterns in the data and then try to draw conclusions. In a nutshell, there is no need to program the machine manually. The programmer gives some examples from which the computer learns how to adapt. Machine Learning (ML) vs. Data Science (DS) Data Science and Machine Learning are closely related. Data Science helps to evaluate the data of machine learning algorithms. Data Science covers the entire range of data processing and machine learning in terms of algorithms or statistics aspects. Data science is the use of statistical methods to find patterns in the data. Data science includes techniques like statistical modeling, visualization, and pattern recognition. Machine learning focuses on developing algorithms from the data provided. Machine learning practices similar kinds of techniques as data science. Machine Learning Techniques Classification: Separating into groups having definite values, e.g., 0 or 1, cat or dog, orange, or apple, etc. It is used to classify new observations, predict discrete responses, ® 28 Chapter 1  Introducing AI for Business and predict qualitative targets. Categorization: This is a technique to organize data into categories for its most effective and efficient use. ® 29 Chapter 1  Introducing AI for Business Clustering: Cluster analysis or clustering is the task of grouping a group of objects so that objects in the same group are more similar to each other (in a sense) than the objects in other groups. Clustering is a method of unsupervised learning and is a common technique used for statistical data analysis in many fields. Trend Analysis: Represents variations of low frequency in a time series; the high and medium frequency fluctuations being out. Anomaly Detection: This is a technique of identifying rare events or observations, which can raise suspicions by being statistically different from the rest of the observations. Such “anomalous” behavior typically translates to some kind of a problem like a credit card fraud, machine failure in a server, a cyber-attack, etc. Data Visualization: Data visualization creates patterns or images from data by using algorithms so that users can effectively understand and respond to the data. Decision Tree: A decision tree is a decision support instrument that uses a tree- like decision model and its likely consequences, including chance event outcomes, resource costs, and utility. This is a method of displaying algorithms that contain ® 30 Chapter 1  Introducing AI for Business only conditional control statements. Decision trees are commonly used in operational research, especially in decision analysis. They help in identifying the strategy most likely to succeed and are a popular tool in machine learning. Application of AI Artificial Intelligence has numerous applications in today’s society. Nowadays, it has become crucial because it can solve complex problems in many industries, such as medical, entertainment, finance, and education, and can make your daily life more comfortable and faster. AI can be used in various forms across multiple fields and continues to expand rapidly. 1. Healthcare In the last ten years, AI has been increasingly used in the healthcare industry. It has proved to be a game-changer, improving every part of the industry virtually. In the future, it is expected to have a significant impact on this industry. ® 31 Chapter 1  Introducing AI for Business Healthcare organizations/institutions are using AI for better and faster diagnosis. AI can help doctors make a diagnosis and can tell patients when their condition will be deteriorated so that they can provide medical assistance to patients before being hospitalized. AI-enabled workflow assistants are aiding doctors to free-up their schedules. These assistants reduce time and cost by streamlining processes and opening up new avenues for the industry. Also, AI-powered technology helps pathologists analyze tissue samples and make a more accurate diagnosis. A clinical decision support system developed by Cambio Health Care helps in stroke prevention. It can give physicians a warning when the patient is at risk of having a stroke. Coala, a real-time heart monitoring system, introduced a digitalized device that can find cardiac diseases. AI in healthcare doesn’t need us to develop a new medication. Just by using an existing drug in the right way, you can save lives. 2. Marketing Marketing is a method of sugar-coating products to fascinate more customers. Humans are good at sugar coating, but what if you only build algorithms or robots to market brands or companies? In the early 2000s, if you would search online stores to find products without knowing its exact name, it might have become a nightmare. But now, when you search for products in any e-commerce store, you get all possible results related to the topic. Like these search engines are reading your minds. After a few seconds, you will get a list of all related items. For example, ‘find the right movie on Netflix’. As discussed in the previous chapter, one of the reasons users are addicted to Netflix is its highly accurate prediction technology, which uses “customer response to movies.” To recommend shows and movies, it checks millions of records. You may like it based on previous actions and movie choices. This technology becomes smarter every day with the growing data set. Soon AI will enable online consumers to buy products by clicking photos. 3. Banking AI-based systems are helping banks deliver customer support, detect anomalies, and prevent credit card frauds. An Electronic Virtual Assistant (EVA) is assisting a leading bank by processing customer queries. To date, EVA has processed more than 3,000,000 customer queries, interacted with more than 500,000 unique users, and conducted more than 1,000,000 conversations. EVA can gather information from thousands of sources and provide answers in less than 0.4 seconds. Using AI for fraud prevention is not new. AI solutions can be used to upgrade the security of many business sectors, including retail and finance. By tracking card usage and endpoint access, security experts can prevent fraud more effectively. Organizations use AI to monitor and analyze trading behavior. Companies like MasterCard and RBS WorldPay have been using AI and deep learning technologies, and it saved millions of dollars for them. ® 32 Chapter 1  Introducing AI for Business 4. Gaming Most video games -including “racing” games, shooting games, or strategy games – have different components supported by AI or related applications. Enemy robots and neutral characters are common examples. The primary purpose of using AI in games is to provide players with realistic gaming experience, enabling them to contend with each other on a virtual platform. Also, the AI in the game can improve players’ interest and satisfaction in the long run. AI is being used extensively in the gaming industry. These days, games are not just for entertainment and passing the time, but it can design intelligent agents that can compete with humans. AlphaGo is a computer program that can play the strategy game “Go.” Today, the gaming industry is among the most profitable sectors, and according to experts by 2022, the revenue of the gaming industry will be USD 196 million. As it continues to grow, the demands of the gaming community and the game itself are also evolving. The need for realism in games is higher than ever. Artificial intelligence plays a vital role in making games more interactive, immersive, and intelligent. With the rapid development of new AI technologies and algorithms, this is an exciting time for game developers to show their full potential. 5. Finance The financial sector relies heavily on real-time reporting, accuracy, and processing of large amounts of quantitative data to make key decisions. AI-enabled systems outperform all these aspects. Because of the accuracy and efficiency of AI, the financial sector is rapidly implementing ML, adaptive intelligence, algorithmic trading, chatbots, automation, etc. into many processes, e.g., identify fraud, detect anti-money laundering pattern and many more. Automated advisors, powered by AI, can predict the best portfolio or stock based on preferences by scanning the market data. AI can also generate actionable reports based on relevant financial data by scanning millions of crucial data points and saving analysts numerous hours of work. 6. Data Security/Cyber Security: Emerging technologies put cybersecurity at risk. Even new developments in security, professionals’ defense strategies sometimes fail. Also, with the continuous development of offense and defense strategies and innovation, the complexity and number of cyber- attacks are increasing. Security experts combine the advantages of artificial intelligence (AI) with network security and have additional resources to defend against vulnerable networks and data from cyber attackers. Compared with what traditional security methods usually anticipate or require, artificial intelligence security involves using artificial intelligence to identify and stop cyber threats with less manual intervention. AI can save time and money by quickly browsing structured data and thoroughly reading and learning unstructured data, statistical information, words, and phrases. It can be used to provide proactive and quick responses to new challenges, which may be before humans discover new challenges. Artificial Intelligence-driven security tools can reduce risks and even manage many threats to data security. They can do this through automation and inspection of themselves, or by providing enhanced functionality for the security team and security operations center (SOC). It helps reduce SQL injection, denial of service (DOS) attacks, data leakage, ransomware, etc. AEG bot and AI2 Platforms are used to detect software bugs and cyber-attacks. ® 33 Chapter 1  Introducing AI for Business 7. Security and surveillance Imagine a single person monitoring multiple video cameras—indeed, a stressful and tedious job to do. But AI can help to automate it. The video surveillance system powered by AI can detect crimes before they happen. They track unusual behavior of people (standing motionless for a long time, stumbling, or napping on benches, etc.). The system can warn its human attendants that they can take action to avoid accidents. Moreover, when such activities are reported and counted as accurate, they will further help improve monitoring services through back-end machine learning. Technologies such as facial recognition and speech recognition are getting better with each passing day. Image processing technology uses data science to assist artificial intelligence. Surveillance, a targeted form of monitoring, is usually carried out to obtain specific data or evidence, and often occurs when people do not know that they are being monitored, such as collecting data from applications without knowing it. The US, China, and Germany have the best surveillance cameras in the world. China is the undisputed leader, and the number of surveillance cameras installed in its territory is four times that of the United States. 8. Smart home devices Artificial intelligence can make a “smart home” a reality. Most smart home devices you buy use AI to learn your behavior, so they can automatically adjust settings to make your experience as frictionless as possible. Smart voice assistants control these smart home devices, again an AI. Some of the best Smart Home Devices are Echo Plus – Best Smart Speaker, Nest Learning Thermostat – Best Energy-Saving Smart Thermostat, Amazon Echo Dot – Best Smart Home Hub, and many more. Smart lights can change intensity and color based on time, and thermostats can adjust the temperature based on user preference. 9. Social Media Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a super-efficient manner. AI can organize and manage a large amount of data and can analyze it to identify the latest trends, hashtags, and requirements of different users. From the notifications received to the feeds seen in the timeline, all content is managed by AI. AI will understand your past web searches, interactions, behaviors, and all other things you have done while visiting these websites, and tailor ® 34 Chapter 1  Introducing AI for Business your experience based on these observations. The role and complexity of artificial intelligence grow with increasing content in the platforms. Because then, it has to fight spam and improve user experience. ® 35 Chapter 1  Introducing AI for Business 10. Travel & Transport AI can make end-to-end travel arrangements: suggesting hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots for better and faster response to customers. In both the travel and transportation industries, AI is steadily becoming one of the vital service tools for both service providers and users. Travel companies are capitalizing on the ubiquitous usage of smart devices. Most users, with the help of AI-powered travel assistants, view trips, research landmarks, and dining options locally, and book trips on these devices. Chatbots assist in travel recommendations, better booking prices, and faster response times. Google Maps scan road information and utilizes algorithms to identify the optimal route to take, by bike, car, bus, train, or on foot. 11. Autonomous vehicles and drones For the last few decades, self-driving cars have been the buzzword in the AI industry. The development of autonomous vehicles is revolutionizing the transportation system. Waymo conducted several test drives at Phoenix before deploying its first AI-based public ride service. The AI system collects data from the “vehicle’s radar,” cloud services, cameras, and GPS to build control signals that operate the vehicle. Advanced deep learning algorithms can accurately predict things than come “near the vehicle.” Therefore, Waymo’s cars are more efficient and safer. A large segment of autonomous vehicles is connected and, thus, can share the information. In Tesla’s self-driving car AI implements computer vision, image detection, and deep learning to operate cars that can automatically detect objects and drive without human intervention. Large companies like Wal-Mart and Amazon have already invested heavily in drone delivery plans. Expect more investments in the future. With self-driving vehicles traveling on the road and self-driving drones delivering goods, many transportations and service-related challenges can be resolved faster and more effectively. 12. Robotics Usually, robots are programmed to perform some repetitive tasks. Still, with the help of AI, you can create intelligent robots that can perform tasks with their own experiences without being pre-programmed. Robots have sensors and actuators that can-do different things. These sensors can see things in front of them and measure the temperature, heat, movements, and so on. They have processors on board that compute various information in real-time. They are also capable of adapting to new environments. Humanoid robots Erica and Sophia can talk and behave like humans. 13. Media & Entertainment industry The way content is distributed is changing the world over, and the global media and entertainment industry is a witness to this rapid transformation. With the help of ubiquitous content creation tools like high-resolution cameras, smartphones, and content creation software, pretty much anyone can create, publish, and distribute different forms of content (write-ups, audios, and videos). ® 36 Chapter 1  Introducing AI for Business AI is helping the companies in the media and entertainment industry improve their services and upgrade their customer experience. Here are a few AI use cases: 13.1. Metadata tagging: AI-based video intelligence tools can analyze the contents of a video frame by frame and attach appropriate tags to them. Prominent media creators and distributors like CBS interactive are using this. This can be a painful manual task to perform. 13.2. Content personalization: Popular music and video streaming platforms like Spotify and Netflix are using AI and ML algorithms to study user behavior and demographics. The algorithms recommend what their consumers may be most interested in listening to or watching next and help in keeping them engaged. These AI-based platforms are providing customers the content that caters to their unique likings, thus offering them a highly personalized experience. 13.3. Reporting automation: Aside from automating day-to-day or minute-by-minute operations, AI is helping media companies in strategic decision-making. Big media and broadcasting companies are using natural language generation and ML and to create channel performance reports from raw data. AI-enabled data analysis and natural language generation-based reporting automation tools, help business leaders create performance reports. These algorithms have easy-to-understand language commentaries, and they arrive at accurate insights that help in making informed data-driven decisions. 13.4. Subtitle generation: International media publishing companies need their content fit for consumption by multi-region audiences. To do so, they need to provide accurate multilingual subtitles for their video content. Human translators can take hundreds or thousands of years to write subtitles for various movies and shows manually. 14. Agriculture Artificial Intelligence in agriculture is helping farmers increase efficiency and productivity and reduce the negative impact on the environment. Artificial Intelligence technology can help the agricultural sector produce healthier crops, control pests, and monitor the soil. Under growing conditions, it can organize data for farmers, provide workload assistance, and advance a wide range of agriculture-related tasks throughout the food supply chain. These innovations in agriculture are driven by climate change, population growth, and food security issues. Therefore, the use of AI in agriculture can make farmers around the world more efficient, enabling farms of all sizes to operate and maintain the world’s food supply. Using artificial intelligence in agriculture can help farmers understand data insights such as temperature, precipitation, wind speed, and solar radiation. Data analysis of historical value can better understand the required results. The best part of implementing AI in agriculture is that it “will not eliminate the work of human farmers.” On the ® 37 Chapter 1  Introducing AI for Business contrary, it will improve their processes. Artificial Intelligence provides a more effective way to produce, harvest, and sell essential crops in the following ways: Checking defective crops and improving healthy crop production. Strengthening agro-based businesses to run more efficiently. Automating machine adjustments for weather forecasting and disease or pest identification. Improving crop management practices, thus, helping many tech businesses invest in algorithms useful in agriculture. AI-driven technologies assist farmers in optimizing planning and create more generous yields by determining crop choices, the best hybrid seed, and resource utilization. Helping farmers address climate variation, pests, and weeds infestation. AI can identify a disease with 98% accuracy. Farmers can use AI to manage weeds by implementing computer vision, robotics, and machine learning. With the help of the AI, data are gathered to keep a check on weeds, which helps the farmers spray chemicals only where the weeds are present—thus limiting the amount of chemical spray Identifying or monitoring defects and nutrient deficiencies in the soil. With image recognition, AI identifies possible defects through captured images, helping farmers remain updated with weather forecasting. The forecasted/predicted data help farmers increase yields and profits without risking the crop. 15. Personalized shopping experience (E-commerce) AI is helping shoppers to find related products with recommended sizes, colors, and even brands. To this end, the online shopping platform you use collects and stores much information about your usage, whether you like it or not. With AI, online stores can use the smallest piece of data about every link you follow or hover to and use it to personalize your experience on a deeper level. This information is used to give you timely alerts, messages, visuals that may interest you, and dynamic content that modifies according to demand and supply. Online stores can also automatically change currency and interface, send notifications about discounts on best-selling products, and offer time limits on desirable items. Google Analytics can provide online store information like your location, your browser, device, etc., along with the time you spend on each page. 16. Education: The education world is becoming more convenient and personalized because of the numerous AI applications. They have changed the way people learn because educational materials are becoming accessible to all with the help of smart devices and computers. Now, students don’t need to attend physical classes to study if they have a personal computer and an internet connection. AI is also automating administrative tasks, allowing educators to spend more time with students. AI in education usually focuses on identifying what a student does and ‘doesn’t know. It does so with the help of diagnostic testing and by developing personalized curricula for each student’s specific needs. Derek Li, the founder of Chinese EdTech unicorn Squirrel AI, says: “In three hours you understand students more than the three years spent by the best teachers.” ® 38 Chapter 1  Introducing AI for Business These are the benefits brought by AI in education: 16.1. Automating Administrative Tasks: AI can automate the administrative tasks of educators and academic institutions. At present, teachers spend a lot of time on grading exams, responding to students, and assessing homework. AI can grade multiple tests. 16.2. Smart Content: Smart content includes virtual content like video lectures and video conferencing. AI systems use traditional syllabuses to create customized books for specific subjects. With AI, textbooks are getting digitized, and new learning interfaces are being designed to help students across academic grades and ages. 16.3. Personalized Learning: AI may not necessarily replace teachers, but they can perform better by offering personalized recommendations. AI customizes in-class assignments and final exams, ensuring the best possible assistance to students. 16.4. Global Learning: AI can help eliminate boundaries. It can facilitate the learning of any course from anywhere across the globe and at any time. 16.5. New Efficiencies: AI improves IT processes and brings out new efficiencies. Schools can use it to prevent students from getting lost in crowds when they run in corridors. AI can also be used to model complex data enabling the operations to create data-driven forecasts. 17. Automated customer support Today, ‘you’d hardly find a good online store that ‘doesn’t offer at least one form of high-tech customer support in addition to the traditional support channel that can be reached via email or phone. Traditional support channels cost companies a huge amount of waste of capital and human resources, which could have been used directly for more intelligent and creative tasks. AI-enabled customer assistants can answer simple questions, such as letting you know the status of your order and help you find specific products based on descriptions. Chatbots can enhance the shopping experience in the following ways: Increasing user retention by sending reminders and notifications. Offering instant answers, thus reducing response time. Providing upselling opportunities through a personalized approach. 18. Chatbots Almost every modern household today has a virtual assistant to control the appliances at home. Siri and Cortana are gaining popularity because of the user experience they provide. Amazon’s Echo uses speech recognition and NLP to perform a wide range of tasks on your command. Other than playing your favorite songs, it can be used to control home devices, book cabs, make phone calls, order food, check the weather conditions, and so on. ‘Google’s virtual assistant, Google Duplex, can not only respond to calls and book appointments for you, but it can also add a human touch. ® 39 Chapter 1  Introducing AI for Business ® 40 Chapter 1  Introducing AI for Business With the help of Natural Language Processing (NLP) and machine learning, the device can process human language and perform tasks such as managing schedules, making a reservation, controlling a smart home, and so on. 19. Creative arts Artificial intelligence technology is inspiring new songs. Based on information gathered from millions of newspaper headlines, conversations, and speeches, insights can be collected that can help musicians create topics. Watson BEAT can help composers by proposing different musical elements. By harnessing the power of artificial intelligence, musicians can understand the needs of listeners and create songs based on these insights. IBM’s AI-powered Chef Watson is part of IBM’s mission to develop cognitive computing applications that can improve medical research by creating amazing new formulations to help people discover new ideas. MuseNet can now write classical music to echo the classical legends, Bach and Mozart. It is a deep neural network that can generate 4 minutes of musical composition with ten different instruments and can combine the musical styles of the country, Mozart, and Beetle. MuseNet discovers patterns of harmony, style, and rhythm through self- learning. “Wordsmith” is a natural language generation platform that can transform data into insightful narratives. ® 41