| can ai do engineering: Applications of Artificial Intelligence in Electrical Engineering Khalid, Saifullah, 2020-03-27 Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies. Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students. |
| can ai do engineering: Artificial Intelligence in Mechanical and Industrial Engineering Kaushik Kumar, Divya Zindani, J. Paulo Davim, 2021-06-20 Artificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains. The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals. The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains. |
| can ai do engineering: WIPO Technology Trends 2019 - Artificial Intelligence World Intellectual Property Organization, 2019-01-21 The first report in a new flagship series, WIPO Technology Trends, aims to shed light on the trends in innovation in artificial intelligence since the field first developed in the 1950s. |
| can ai do engineering: Artificial Intelligence in Construction Engineering and Management Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski, 2021-06-18 This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally. |
| can ai do engineering: Applications of Artificial Intelligence in Process Systems Engineering Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong, 2021-06-05 Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering |
| can ai do engineering: Systems Engineering and Artificial Intelligence William F. Lawless, Ranjeev Mittu, Donald A. Sofge, Thomas Shortell, Thomas A. McDermott, 2021-11-02 This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges. |
| can ai do engineering: Artificial Intelligence in Chemical Engineering Thomas E. Quantrille, Y. A. Liu, 2012-12-02 Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. - Allows the reader to learn AI quickly using inexpensive personal computers - Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions - Includes a computer diskette for an illustrated case study - Demonstrates an expert system for separation synthesis (EXSEP) - Presents a detailed review of published literature on expert systems and neural networks in chemical engineering |
| can ai do engineering: Human-Centered AI Ben Shneiderman, 2022 The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity. |
| can ai do engineering: Machine Learning Engineering in Action Ben Wilson, 2022-05-17 Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. |
| can ai do engineering: Artificial Intelligence and Robotics Huimin Lu, 2020-11-10 This book provides insights into research in the field of artificial intelligence in combination with robotics technologies. The integration of artificial intelligence and robotic technologies is a highly topical area for researchers and developers from academia and industry around the globe, and it is likely that artificial intelligence will become the main approach for the next generation of robotics research. The tremendous number of artificial intelligence algorithms and big data solutions has significantly extended the range of potential applications for robotic technologies, and has also brought new challenges for the artificial intelligence community. Sharing recent advances in the field, the book features papers by young researchers presented at the 4th International Symposium on Artificial Intelligence and Robotics 2019 (ISAIR2019), held in Daegu, Korea, on August 20–24, 2019. |
| can ai do engineering: What Is Artificial Intelligence?: A Conversation Between An Ai Engineer And A Humanities Researcher Suman Gupta, Peter H Tu, 2020-06-22 'A light-hearted, but engaging conversation about one of the key technologies of our age.I recommend this book to anyone interested in the broader issues around Artificial Intelligence.'Richard HartleyAustralian National University, Australia This book engages with the title question: what is artificial intelligence (AI)? Instead of reiterating received definitions or surveying the field from a disciplinary perspective, the question is engaged here by putting two standpoints into conversation. The standpoints are different in their disciplinary groundings — i.e. technology and the humanities — and also in their approaches — i.e. applied and conceptual. Peter is an AI engineer: his approach is in terms of how to make AI work. Suman is a humanities researcher: his approach is in terms of what people and academics mean when they say 'AI'.A coherent argument, if not a consensus, develops by putting the two standpoints into conversation. The conversation is presented in 32 short chapters, in turn by Suman and Peter. There are two parts: Part 1, Questioning AI, and Part 2, AI and Government Policy. The first part covers issues such as the meaning of intelligence, automation, evolution, artificial and language. It outlines some of the processes through which these concepts may be technologically grounded as AI. The second part addresses policy considerations that underpin the development of AI and responds to the consequences. Themes taken up here include: rights and responsibilities; data usage and state-level strategies in the USA, UK and China; unemployment and policy futures. |
| can ai do engineering: Mechatronics 2019: Recent Advances Towards Industry 4.0 Roman Szewczyk, Jiří Krejsa, Michał Nowicki, Anna Ostaszewska-Liżewska, 2019-08-28 This book gathers papers presented at Mechatronics 2019, an international conference held in Warsaw, Poland, from September 16 to 18, 2019. The contributions discuss the numerous, multidisciplinary technological advances in the field of applied mechatronics that the emerging Industry 4.0 has already yielded. Each chapter presents a particular example of interdisciplinary theoretical knowledge, numerical modelling and simulation, or the application of artificial intelligence techniques. Further, the papers show how both software and physical devices can be incorporated into mechatronic systems to increase production efficiency and resource savings. The results and guidelines presented here will benefit both scientists and engineers looking for solutions to specific industrial and research problems. |
| can ai do engineering: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| can ai do engineering: Artificial Intelligence Methods For Software Engineering Meir Kalech, Rui Abreu, Mark Last, 2021-06-15 Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s) |
| can ai do engineering: Reinventing the Product Eric Schaeffer, David Sovie, 2019-03-03 Create the personalized and compelling experiences that today's customers expect by harnessing AI and digital technologies to create smart connected products, with this cutting-edge guide from senior leaders at Accenture. Digital technology is both friend and foe: highly disruptive, yet it cannot be ignored. As traditional products transform into smart connected products faster than ever before, companies that fail to make use of it now put themselves in the firing line for disintermediation or even eradication. However, digital technology is also the biggest opportunity for product-making businesses to create the next generation of goods in the marketplace. In Reinventing the Product, Eric Schaeffer and David Sovie, both Senior Managing Directors at Accenture, show how this reinvention is made possible, to deliver truly intelligent, and often even autonomous, products. Reinventing the Product makes the case for companies to rethink their product strategy, innovation and engineering processes, including: - How to harness the opportunities of AI and digital technologies, such as IoT sensors, blockchain, advanced analytics, cloud and edge computing - Practical advice on transforming their entire culture to build the future of successful 'living products' - Features case studies from global organizations such as Faurecia, Signify, Symmons and Haier and interviews with thought leaders from top companies including Amazon, ABB, Tesla, Samsung and Google This book provides the only advice any product-making company needs as it embarks on, or accelerates, its digitization journey. |
| can ai do engineering: Artificial Intelligence in Process Engineering Michael Mavrovouniotis, 2012-12-02 Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers. |
| can ai do engineering: Real-World Natural Language Processing Masato Hagiwara, 2021-12-14 Voice assistants, automated customer service agents, and other cutting-edge human-to-computer interactions rely on accurately interpreting language as it is written and spoken. Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you''ll explore the core tools and techniques required to build a huge range of powerful NLP apps. about the technology Natural language processing is the part of AI dedicated to understanding and generating human text and speech. NLP covers a wide range of algorithms and tasks, from classic functions such as spell checkers, machine translation, and search engines to emerging innovations like chatbots, voice assistants, and automatic text summarization. Wherever there is text, NLP can be useful for extracting meaning and bridging the gap between humans and machines. about the book Real-world Natural Language Processing teaches you how to create practical NLP applications using Python and open source NLP libraries such as AllenNLP and Fairseq. In this practical guide, you''ll begin by creating a complete sentiment analyzer, then dive deep into each component to unlock the building blocks you''ll use in all different kinds of NLP programs. By the time you''re done, you''ll have the skills to create named entity taggers, machine translation systems, spelling correctors, and language generation systems. what''s inside Design, develop, and deploy basic NLP applications NLP libraries such as AllenNLP and Fairseq Advanced NLP concepts such as attention and transfer learning about the reader Aimed at intermediate Python programmers. No mathematical or machine learning knowledge required. about the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009, focusing on Natural Language Processing and machine learning. He has interned at Google and Microsoft Research, and worked at Baidu Japan, Duolingo, and Rakuten Institute of Technology. He now runs his own consultancy business advising clients, including startups and research institutions. |
| can ai do engineering: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems Ashish Kumar Luhach, Atilla Elçi, 2020-11-13 This book focuses upon the recent advances in the realization of Artificial Intelligence-based approaches towards affecting secure Cyber-Physical Systems. It features contributions pertaining to this multidisciplinary paradigm, in particular, in its application to building sustainable space by investigating state-of-art research issues, applications and achievements in the field of Computational Intelligence Paradigms for Cyber-Physical Systems-- |
| can ai do engineering: Artificial Intelligence in Structural Engineering Ian Smith, 1998-07-15 This book presents the state of the art of artificial intelligence techniques applied to structural engineering. The 28 revised full papers by leading scientists were solicited for presentation at a meeting held in Ascona, Switzerland, in July 1998. The recent advances in information technology, in particular decreasing hardware cost, Internet communication, faster computation, increased bandwidth, etc., allow for the application of new AI techniques to structural engineering. The papers presented deal with new aspects of information technology support for the design, analysis, monitoring, control and diagnosis of various structural engineering systems. |
| can ai do engineering: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| can ai do engineering: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| can ai do engineering: Human + Machine Paul R. Daugherty, H. James Wilson, 2018-03-20 AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that think in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a leader’s guide with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence. |
| can ai do engineering: Robotics and AI Andrew C. Staugaard, 1987 Very Good,No Highlights or Markup,all pages are intact. |
| can ai do engineering: The Essence of Artificial Intelligence Alison Cawsey, 1998 A concise, practical introduction to artificial intelligence, this title starts with the fundamentals of knowledge representation, inference, expert systems, natural language processing, machine learning, neural networks, agents, robots, and much more. Examples and algorithms are presented throughout, and the book includes a complete glossary. |
| can ai do engineering: Ask a Manager Alison Green, 2018-05-01 From the creator of the popular website Ask a Manager and New York’s work-advice columnist comes a witty, practical guide to 200 difficult professional conversations—featuring all-new advice! There’s a reason Alison Green has been called “the Dear Abby of the work world.” Ten years as a workplace-advice columnist have taught her that people avoid awkward conversations in the office because they simply don’t know what to say. Thankfully, Green does—and in this incredibly helpful book, she tackles the tough discussions you may need to have during your career. You’ll learn what to say when • coworkers push their work on you—then take credit for it • you accidentally trash-talk someone in an email then hit “reply all” • you’re being micromanaged—or not being managed at all • you catch a colleague in a lie • your boss seems unhappy with your work • your cubemate’s loud speakerphone is making you homicidal • you got drunk at the holiday party Praise for Ask a Manager “A must-read for anyone who works . . . [Alison Green’s] advice boils down to the idea that you should be professional (even when others are not) and that communicating in a straightforward manner with candor and kindness will get you far, no matter where you work.”—Booklist (starred review) “The author’s friendly, warm, no-nonsense writing is a pleasure to read, and her advice can be widely applied to relationships in all areas of readers’ lives. Ideal for anyone new to the job market or new to management, or anyone hoping to improve their work experience.”—Library Journal (starred review) “I am a huge fan of Alison Green’s Ask a Manager column. This book is even better. It teaches us how to deal with many of the most vexing big and little problems in our workplaces—and to do so with grace, confidence, and a sense of humor.”—Robert Sutton, Stanford professor and author of The No Asshole Rule and The Asshole Survival Guide “Ask a Manager is the ultimate playbook for navigating the traditional workforce in a diplomatic but firm way.”—Erin Lowry, author of Broke Millennial: Stop Scraping By and Get Your Financial Life Together |
| can ai do engineering: Artificial Intelligence in Engineering Robert A. Adey, Duvvuru Sriram, 1987-08-01 |
| can ai do engineering: Rebooting AI Gary Marcus, Ernest Davis, 2019-09-10 Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a robust artificial intelligence that can make our lives better. “Finally, a book that tells us what AI is, what AI is not, and what AI could become if only we are ambitious and creative enough.” —Garry Kasparov, former world chess champion and author of Deep Thinking Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy! does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence. The real world, in contrast, is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Taking inspiration from the human mind, Marcus and Davis explain what we need to advance AI to the next level, and suggest that if we are wise along the way, we won't need to worry about a future of machine overlords. If we focus on endowing machines with common sense and deep understanding, rather than simply focusing on statistical analysis and gatherine ever larger collections of data, we will be able to create an AI we can trust—in our homes, our cars, and our doctors' offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how a new generation of AI can make our lives better. |
| can ai do engineering: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| can ai do engineering: Deploying Machine Learning Robbie Allen, 2019-05 Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage. But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects. Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully. You'll learn: What machine learning is, how it compares to big data and artificial intelligence, and why it's suddenly so important What machine learning can do for you: solutions for computer vision, natural language processing, prediction, and more How to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new products Separating hype from reality: identifying pitfalls, limitations, and misconceptions upfront Knowing enough about the technology to work effectively with your technical team Getting the data right: sourcing, collection, governance, security, and culture Solving harder problems: exploring deep learning and other advanced techniques Understanding today's machine learning software and hardware ecosystem Evaluating potential projects, and addressing workforce concerns Staffing your project, acquiring the right tools, and building a workable project plan Interpreting results -- and building an organization that can increasingly learn from data Using machine learning responsibly and ethically Preparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications. For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way. |
| can ai do engineering: Designing Autonomous AI Kence Anderson, 2022-06-14 Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodology in this book to design autonomous AI. This book examines: Differences between and limitations of automated, autonomous, and human decision-making Unique advantages of autonomous AI for real-time decision-making, with use cases How to design an autonomous AI from modular components and document your designs |
| can ai do engineering: The AI-Powered Workplace Ronald Ashri, 2019-12-09 We are entering the next wave of digital transformation. Artificial intelligence has an ever-increasing significance in our daily lives, and there is no difference when it comes to our workplaces. It is up to you to choose how to utilize these new tools to sharpen your organization’s competitive advantage, improve your team’s well-being, and help your business thrive. In The AI-Powered Workplace, author Ronald Ashri provides a map of the digital landscape to guide you on this timely journey. You’ll understand how the combination of AI, data, and conversational collaboration platforms—such as Slack, Microsoft Teams, and Facebook Workplace—is leading us to a radical shift in how we communicate and solve problems in the modern workplace. Our ability to automate decision-making processes through the application of AI techniques and through modern collaboration tools is a game-changer. Ashri skillfully presents his industry expertise and captivating insights so you have a thorough understanding of how to best combine these technologies with execution strategies that are optimized to your specific needs. The AI-Powered Workplace is an essential technical, cultural, and business handbook that arms you with clear steps to redefine and improve how you get work done. Software is now a proactive workplace partner revolutionizing all aspects of our professional lives from how we collaborate in the digital sphere to the literal physical environments in which we operate our business. This book not only ensures that you do not get left behind, but that you are consistently light years ahead of the pack. What You'll Learn Learn how the introduction of AI-powered applications in the workplace replaces or augments our capabilities and enables activities that were not possible beforeRealize how the combination of AI, data, and messaging platforms (Slack, Microsoft Teams, Skype, WhatsApp) leads to a radical shift in how we communicate, collaborate, and solve problemsDevelop strategies for the digital transformation of organizations through the use of AI-powered applications (from simple chatbots to more complex conversational applications) that operate within messaging environments we use to collaborate with our colleagues dailyKnow the dangers and ethical questions that the introduction of these technologies can cause in the workplace Who This Book is For Professionals at all levels interested in learning how AI, conversational platforms, and data can change organizations, including but not limited to team leaders, managers, and CxOs |
| can ai do engineering: Don't Fear AI. , 2019 Over the last decade, Europe and most advanced economies experienced a decline in productivity, leading to political unrest and rising uncertainty about the future. A new production revolution, enabled in part by artificial intelligence (AI), is now emerging, bringing a new wave of technologies, but there are widespread fears that these changes also will bring a big rise in unemployment as machines replace human beings in big numbers. History tells us that we should not be afraid of industrial change. AI will take over some tasks, but this will not happen all of a sudden and there will be plenty of work left for humans. Restricting or slowing down new technology will not help the world economy. Instead, nations need to help people adjust to more technically advanced jobs, while education should focus more on 21st century skills such as teamwork and critical thinking. These are our next real challenges. |
| can ai do engineering: Applications of Artificial Intelligence Techniques in Engineering Hasmat Malik, Smriti Srivastava, Yog Raj Sood, Aamir Ahmad, 2018-09-28 The book is a collection of high-quality, peer-reviewed innovative research papers from the International Conference on Signals, Machines and Automation (SIGMA 2018) held at Netaji Subhas Institute of Technology (NSIT), Delhi, India. The conference offered researchers from academic and industry the opportunity to present their original work and exchange ideas, information, techniques and applications in the field of computational intelligence, artificial intelligence and machine intelligence. The book is divided into two volumes discussing a wide variety of industrial, engineering and scientific applications of the emerging techniques. |
| can ai do engineering: Software Engineering for Robotics Ana Cavalcanti, Brijesh Dongol, Rob Hierons, Jon Timmis, Jim Woodcock, 2021-07-05 The topics covered in this book range from modeling and programming languages and environments, via approaches for design and verification, to issues of ethics and regulation. In terms of techniques, there are results on model-based engineering, product lines, mission specification, component-based development, simulation, testing, and proof. Applications range from manufacturing to service robots, to autonomous vehicles, and even robots than evolve in the real world. A final chapter summarizes issues on ethics and regulation based on discussions from a panel of experts. The origin of this book is a two-day event, entitled RoboSoft, that took place in November 2019, in London. Organized with the generous support of the Royal Academy of Engineering and the University of York, UK, RoboSoft brought together more than 100 scientists, engineers and practitioners from all over the world, representing 70 international institutions. The intended readership includes researchers and practitioners with all levels of experience interested in working in the area of robotics, and software engineering more generally. The chapters are all self-contained, include explanations of the core concepts, and finish with a discussion of directions for further work. Chapters 'Towards Autonomous Robot Evolution', 'Composition, Separation of Roles and Model-Driven Approaches as Enabler of a Robotics Software Ecosystem' and 'Verifiable Autonomy and Responsible Robotics' are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| can ai do engineering: Artificial Intelligence for Science and Engineering Applications Shahab D. Mohaghegh, 2024-04-01 Artificial Intelligence (AI) is defined as the simulation of human intelligence through the mimicking of the human brain for analysis, modeling, and decision‐making. Science and engineering problem solving requires modeling of physical phenomena, and humans approach the solution of scientific and engineering problems differently from other problems. Artificial Intelligence for Science and Engineering Applications addresses the unique differences in how AI should be developed and used in science and engineering. Through the inclusion of definitions and detailed examples, this book describes the actual and realistic requirements as well as what characteristics must be avoided for correct and successful science and engineering applications of AI. This book: Offers a brief history of AI and covers science and engineering applications Explores the modeling of physical phenomena using AI Discusses explainable AI (XAI) applications Covers the ethics of AI in science and engineering Features real‐world case studies Offering a probing view into the unique nature of scientific and engineering exploration, this book will be of interest to generalists and experts looking to expand their understanding of how AI can better tackle and advance technology and developments in scientific and engineering disciplines. |
| can ai do engineering: Building Intelligent Systems Geoff Hulten, 2018-03-06 Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems. Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world. What You’ll Learn Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success Design an intelligent user experience: Produce data to help make the Intelligent System better over time Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice Create intelligence: Use different approaches, including machine learning Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want Who This Book Is For Software engineers, machine learning practitioners, and technical managers who want to build effective intelligent systems |
| can ai do engineering: Embedded Systems Kiyofumi Tanaka, 2012-03-16 Nowadays, embedded systems - computer systems that are embedded in various kinds of devices and play an important role of specific control functions, have permeated various scenes of industry. Therefore, we can hardly discuss our life or society from now onwards without referring to embedded systems. For wide-ranging embedded systems to continue their growth, a number of high-quality fundamental and applied researches are indispensable. This book contains 13 excellent chapters and addresses a wide spectrum of research topics of embedded systems, including parallel computing, communication architecture, application-specific systems, and embedded systems projects. Embedded systems can be made only after fusing miscellaneous technologies together. Various technologies condensed in this book as well as in the complementary book Embedded Systems - Theory and Design Methodology, will be helpful to researchers and engineers around the world. |
| can ai do engineering: Information Technology Innovation National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Panel on Artificial Intelligence, Committee on Depicting Innovation in Information Technology, 2020-11-30 Information technology (IT) is widely understood to be the enabling technology of the 21st century. IT has transformed, and continues to transform, all aspects of our lives: commerce and finance, education, energy, health care, manufacturing, government, national security, transportation, communications, entertainment, science, and engineering. IT and its impact on the U.S. economyâ€both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)â€continue to grow in size and importance. IT’s impacts on the U.S. economyâ€both directly (the IT sector itself) and indirectly (other sectors that are powered by advances in IT)â€continue to grow. IT enabled innovation and advances in IT products and services draw on a deep tradition of research and rely on sustained investment and a uniquely strong partnership in the United States among government, industry, and universities. Past returns on federal investments in IT research have been extraordinary for both U.S. society and the U.S. economy. This IT innovation ecosystem fuels a virtuous cycle of innovation with growing economic impact. Building on previous National Academies work, this report describes key features of the IT research ecosystem that fuel IT innovation and foster widespread and longstanding impact across the U.S. economy. In addition to presenting established computing research areas and industry sectors, it also considers emerging candidates in both categories. |
| can ai do engineering: AI Superpowers Kai-Fu Lee, 2018 AI Superpowers is Kai-Fu Lee's New York Times and USA Today bestseller about the American-Chinese competition over the future of artificial intelligence. |
| can ai do engineering: Artificial Intelligence in Engineering Design Bozzano G Luisa, 2012-12-02 Artificial Intelligence in Engineering Design is a three-volume edited collection of key papers from the field of AI and design, aimed at providing a state-of-the art description of the field, and focusing on how ideas and methods from artificial intelligence can help engineers in the design of physical artifacts and processes. The books survey a wide variety of applications in the areas of civil, chemical, electrical, computer, VLSI, and mechanical engineering. |
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