INTRODUCTION TO ARTIFICIAL INTELLIGENCE FOR MANAGERS Introduction to Artificial Intelligence Artificial intelligence "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions." Why Artificial Intelligence? • With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. • With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc. • With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk. • AI opens a path for other new technologies, new devices, and new Opportunities. Groundwork for AI • 1921: Czech playwright Karel Čapek released a science fiction play “Rossum’s Universal Robots” which introduced the idea of “artificial people” which he named robots. This was the first known use of the word. • 1929: Japanese professor Makoto Nishimura built the first Japanese robot, named Gakutensoku. • 1949: Computer scientist Edmund Callis Berkley published the book “Giant Brains, or Machines that Think” which compared the newer models of computers to human brains. Birth of AI: • 1950: Alan Turing published “Computer Machinery and Intelligence” which proposed a test of machine intelligence called The Imitation Game. • 1952: A computer scientist named Arthur Samuel developed a program to play checkers, which is the first to ever learn the game independently. • 1955: John McCarthy held a workshop at Dartmouth on “artificial intelligence” which is the first use of the word, and how it came into popular usage. Artificial Intelligence: General 2012-present • 2012: Two researchers from Google (Jeff Dean and Andrew Ng) trained a neural network to recognize cats by showing it unlabeled images and no background information. • 2015: Elon Musk, Stephen Hawking, and Steve Wozniak (and over 3,000 others) signed an open letter to the worlds’ government systems banning the development of (and later, use of) autonomous weapons for purposes of war. • 2016: Hanson Robotics created a humanoid robot named Sophia, who became known as the first “robot citizen” and was the first robot created with a realistic human appearance and the ability to see and replicate emotions, as well as to communicate. • 2017: Facebook programmed two AI chatbots to converse and learn how to negotiate, but as they went back and forth they ended up forgoing English and developing their own language, completely autonomously. • 2018: A Chinese tech group called Alibaba’s language-processing AI beat human intellect on a Stanford reading and comprehension test. • 2019: Google’s AlphaStar reached Grandmaster on the video game StarCraft 2, outperforming all but .2% of human players. • 2020: OpenAI started beta testing GPT-3, a model that uses Deep Learning to create code, poetry, and other such language and writing tasks. While not the first of its kind, it is the first that creates content almost indistinguishable from those created by humans. • 2021: OpenAI developed DALL-E, which can process and understand images enough to produce accurate captions, moving AI one step closer to understanding the visual world. Philosophy of AI • • • • • Term “artificial intelligence” brings up philosophical questions whether intelligent behaviour implies or requires the existence of a mind, and to what extent is consciousness replicable as computation. The Turing test Is Eugene a computer or a person? The Chinese room argument Is a self-driving car intelligent? • How much does philosophy matter in practice? • John McCarthy pointed out, the philosophy of AI is “unlikely to have any more effect on the practice of AI research than philosophy of science generally has on the practice of science.” Thus, we’ll continue investigating systems that are helpful in solving practical problems without asking too much whether they are intelligent or just behave as if they were. Weak AI and Strong AI Types of AI Machine Learning • Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Deep Learning Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. Big Data Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can't manage them. But these massive volumes of data can be used to address business problems you wouldn't have been able Architects of Artificial Intelligence Machine Learning (ML): Machine learning is a subset of artificial intelligence that focuses on building algorithms and statistical models that enable computers to improve their performance on a specific task without being explicitly programmed. ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: •Supervised learning: In supervised learning, the algorithm is trained on labeled data. The goal is to learn a mapping function from input variables to output variables based on examples of input-output pairs. •Unsupervised learning: In unsupervised learning, the algorithm is trained on unlabeled data. The goal is to discover patterns or structures in the data without any prior knowledge of what to look for. •Reinforcement learning: In reinforcement learning, the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or punishments. The goal is to learn a policy that maximizes the cumulative reward over time. Applications of ML in real-world scenarios •Image and speech recognition: ML algorithms are used to recognize images and speech, which has led to the development of technologies such as facial recognition and speech-to-text. •Recommendation systems: ML algorithms are used to recommend products, services, and content to users based on their preferences and past behaviors. •Fraud detection: ML algorithms are used to detect fraudulent activities in financial transactions, such as credit card fraud and money laundering. •Natural language processing: ML algorithms are used to analyze and understand human language, which has led to the development of technologies such as chatbots and virtual assistants. •Predictive maintenance: ML algorithms are used to predict when machines and equipment will fail, allowing for proactive maintenance and reducing downtime. Natural language processing (NLP) • Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between humans and computers using natural language. It is the process of analyzing, understanding, and generating human language data in a way that is meaningful to computers. Importance of NLP in AI • • • • • The importance of NLP in AI lies in its ability to enable machines to understand and process human language, which is essential in various applications, such as: Chatbots and virtual assistants: NLP is used to create chatbots and virtual assistants that can understand and respond to human language, providing a more natural and intuitive user experience. Sentiment analysis: NLP is used to analyze the sentiment of text data, enabling businesses to monitor customer feedback and improve their products and services. Language translation: NLP is used to translate text from one language to another, enabling communication between people who speak different languages. Information retrieval: NLP is used to retrieve information from text data, such as search engine results and question-answering systems. Examples of NLP in action • Siri and Alexa: These virtual assistants use NLP to understand and respond to user queries. • Google Translate: This application uses NLP to translate text from one language to another. • Sentiment analysis tools: These tools use NLP to analyze the sentiment of text data, enabling businesses to monitor customer feedback and improve their products and services. • Spam filters: These filters use NLP to detect and filter out spam emails and messages. Computer vision (CV) • Computer Vision (CV) is a field of artificial intelligence that focuses on enabling machines to interpret and understand visual information from the world around them. CV algorithms are designed to analyze and make sense of digital images and video data, enabling machines to recognize patterns, objects, and even emotions. Types of CV • • • • • There are several types of computer vision, including: Image classification: This involves categorizing images into predefined classes, such as identifying whether an image contains a cat or a dog. Object detection: This involves identifying and locating objects within an image, such as detecting faces in a crowd or identifying obstacles in a self-driving car’s path. Image segmentation: This involves dividing an image into segments and assigning each segment a label, such as identifying the different components of a car engine. Object tracking: This involves tracking the movement of an object within a sequence of images or video data, such as following a person’s movement through a surveillance camera feed. Real-world applications of CV • • • • • Healthcare: CV is used to analyze medical images, such as X-rays and MRIs, to aid in the diagnosis and treatment of diseases. Autonomous vehicles: CV is used in self-driving cars to identify and track objects, such as pedestrians and other vehicles, in real time. Security and surveillance: CV is used in security and surveillance systems to monitor and analyze video data, such as identifying potential security threats in airports and public spaces. Retail: CV is used in retail to analyze customer behavior, such as tracking the movement of customers within a store to optimize store layouts and improve customer experiences. Manufacturing: CV is used in manufacturing to inspect products for defects and anomalies, such as identifying flaws in car parts on an assembly line. Robotics • Robotics is a field of artificial intelligence that focuses on the design, development, and implementation of robots, which are machines capable of performing tasks autonomously or semi-autonomously. Robotics involves the integration of various AI technologies, such as computer vision and natural language processing, to enable robots to interact with the world around them. Types of Robotics • There are several types of robotics, including: • Industrial robots: These are robots used in manufacturing and production environments to perform tasks such as welding, painting, and assembly. • Medical robots: These are robots used in healthcare settings to assist with surgeries, drug delivery, and patient care. • Service robots: These are robots designed to assist with tasks in various settings, such as cleaning robots used in homes and offices and delivery robots used in warehouses and retail stores. Examples of Robotics in action • Boston Dynamics: Boston Dynamics is a robotics company that designs and develops robots capable of walking, running, and performing acrobatic maneuvers. • Surgical robots: Surgical robots, such as the da Vinci surgical system, are used to assist with minimally invasive surgeries, enabling surgeons to perform complex procedures with greater precision and control. • Self-driving cars: Self-driving cars, such as those being developed by Tesla and Google, use robotics and AI technologies to navigate roads and interact with other vehicles and pedestrians. • Drones: Drones, or unmanned aerial vehicles (UAVs), are used in a variety of applications, including surveillance, delivery, and inspection of infrastructure such as bridges and power lines. Expert systems • Expert systems is a field of artificial intelligence that focuses on developing computer programs that can mimic the decision-making abilities of a human expert in a specific domain. Expert systems are designed to use knowledge and reasoning techniques to solve complex problems and provide recommendations to users. Applications of expert systems in realworld scenarios • Healthcare: Expert systems are used to assist with medical diagnoses, providing recommendations to doctors and medical professionals based on patient data and medical knowledge. • Financial services: Expert systems are used to assist with financial planning and investment decisions, providing recommendations based on economic data and market trends. • Manufacturing: Expert systems are used to optimize manufacturing processes and improve product quality, using data analysis and modeling to make recommendations for process improvements. • Customer service: Expert systems are used in customer service applications, such as chatbots, to provide personalized assistance and recommendations to customers. Examples of expert systems in action • MYCIN: MYCIN was one of the earliest expert systems, developed in the 1970s to assist with medical diagnoses and treatment recommendations for bacterial infections. • XCON: XCON was an expert system developed by Digital Equipment Corporation in the 1980s to configure and customize computer systems for customers. • Dendral: Dendral was an expert system developed in the 1960s to identify the structure of organic molecules, demonstrating the potential of expert systems in complex scientific domains. Interesting AI innovations as AI cases • Buzz off • Bumblebees use “buzz pollination,” a rapid vibrating motion, to pollinate fruits and vegetables such as strawberries, blueberries, tomatoes, potatoes, and sweet peppers. Tiny, AI-powered robots are now joining their insect friends to pollinate these crops in a more efficient and dependable way. An Israel-based company has commercialized these robots to produce the vibrations that shake off pollen from the flowers to fertilize the plants so they can bear fruit. • In other applications, robotic bees, at a tenth of a gram and half the size of a paper clip, are used in search-and-rescue missions, surveillance, and monitoring weather and environment. Some robot bees can swim underwater, opening up additional possibilities. AI cases • The whole tooth and nothing but the tooth • An AI toothbrush is programmed to take the data of thousands of labrecorded brushing actions, combine it with your personal brushing style, and come up with recommendations to make your pearly whites even whiter. • You’ll be alerted if you’ve missed spots when brushing, if you’re brushing with too much (or too little) pressure, or if you’re not brushing long enough. And, of course, there’s a smartphone app for that. Then animate this • Let’s take AI pictures to the next level. In a Harry-Potter-comes-true application, you can now use artificial intelligence to animate still photographs. If you’ve ever wished you’d known family members from a previous generation, you can upload their head-and-shoulders snapshots and then, through the magic of AI, see them gesture and move in a human way. Add a narrative about their life, and the AI platform can create your ancestor as a photo-realistic avatar that can tell you their story in a video. You can even change the language they speak for relatives from the home country. Picture this • When someone draws a cat, you recognize it by its long tail, its whiskers, its triangle nose, or its pointed ears. Actually, you may recognize it with just one or two of those features. QuickDraw, a browser video game, uses machine learning to gather cues like those to make an educated guess about what a player is drawing. It privately suggests a subject, then asks its artificial intelligence to guess what you’re drawing as you add features. Even if your sketches look more like scratches, your doodled feline is recognized based on the features the game has learned from thousands of other drawings of the same subject. The machine understands that some people will draw the body of the cat, some will draw the head and others will draw only the nose and whiskers in the time allowed. As the game becomes more intelligent (with more users drawing more sketches), it knows that distinct parts of a cat still equal a cat.
