Cutting Edge Artificial Intelligence

Cutting Edge Artificial Intelligence
Author: Anna Leigh
Publsiher: Searchlight Books (TM) -- Cutt
Total Pages: 32
Release: 2018-08
Genre: Juvenile Nonfiction
ISBN: 9781541527737

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Filled with cool photos and information about the latest in artificial intelligence technology, this book shows readers how AI has developed, how it is used, and what might be in store for the future of AI.

Artificial Intelligence in Financial Markets

Artificial Intelligence in Financial Markets
Author: Christian L. Dunis,Peter W. Middleton,Andreas Karathanasopolous,Konstantinos Theofilatos
Publsiher: Springer
Total Pages: 344
Release: 2016-11-21
Genre: Business & Economics
ISBN: 9781137488800

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As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.

The AI Advantage

The AI Advantage
Author: Thomas H. Davenport
Publsiher: MIT Press
Total Pages: 244
Release: 2019-08-06
Genre: Business & Economics
ISBN: 9780262538008

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Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage. In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient. Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise. Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI. A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.

The Artificial Intelligence Infrastructure Workshop

The Artificial Intelligence Infrastructure Workshop
Author: Chinmay Arankalle,Gareth Dwyer,Bas Geerdink,Kunal Gera,Kevin Liao,Anand N.S.
Publsiher: Packt Publishing Ltd
Total Pages: 732
Release: 2020-08-17
Genre: Computers
ISBN: 9781800206991

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Explore how a data storage system works – from data ingestion to representation Key Features Understand how artificial intelligence, machine learning, and deep learning are different from one another Discover the data storage requirements of different AI apps using case studies Explore popular data solutions such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3) Book Description Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You'll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you'll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You'll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you'll gain hands-on experience with PyTorch. Finally, you'll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you'll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods. What you will learn Get to grips with the fundamentals of artificial intelligence Understand the importance of data storage and architecture in AI applications Build data storage and workflow management systems with open source tools Containerize your AI applications with tools such as Docker Discover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS) Use the AWS CLI and AWS SDK to perform common data tasks Who this book is for If you are looking to develop the data storage skills needed for machine learning and AI and want to learn AI best practices in data engineering, this workshop is for you. Experienced programmers can use this book to advance their career in AI. Familiarity with programming, along with knowledge of exploratory data analysis and reading and writing files using Python will help you to understand the key concepts covered.

Cutting Edge Artificial Intelligence

Cutting Edge Artificial Intelligence
Author: Anna Leigh
Publsiher: Lerner Publications
Total Pages: 32
Release: 2018-08
Genre: Juvenile Nonfiction
ISBN: 9781541523487

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Filled with cool photos and information about the latest in artificial intelligence technology, this book shows readers how AI has developed, how it is used, and what might be in store for the future of AI.

Artificial Intelligence and Deep Learning for Decision Makers

Artificial Intelligence and Deep Learning for Decision Makers
Author: Dr. Jagreet Kaur,Navdeep Singh Gill
Publsiher: BPB Publications
Total Pages: 248
Release: 2019-12-28
Genre: Computers
ISBN: 9789389328684

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Learn modern-day technologies from modern-day technical giants DESCRIPTION The aim of this book is to help the readers understand the concept of artificial intelligence and deep learning methods and implement them into their businesses and organizations. The first two chapters describe the introduction of the artificial intelligence and deep learning methods. In the first chapter, the concept of human thinking process, starting from the biochemical responses within the structure of neurons to the problem-solving steps through computational thinking skills are discussed. All chapters after the first two should be considered as the study of different technological and Artificial Intelligence giants of current age. These chapters are placed in a way that each chapter could be considered a separate study of a separate company, which includes the achievements of intelligent services currently provided by the company, discussion on the business model of the company towards the use of the deep learning technologies, the advancement of the web services which are incorporated with intelligent capability introduced by company, the efforts of the company in contributing to the development of the artificial intelligence and deep learning research. KEY FEATURES Real-world success and failure stories of artificial intelligence explained Understand concepts of artificial intelligence and deep learning methods Learn how to use artificial intelligence and deep learning methods Know how to prepare dataset and implement models using industry leading Python packages You’ll be able to apply and analyze the results produced by the models for prediction WHAT WILL YOU LEARN How to use the algorithms written in the Python programming language to design models and perform predictions in general datasets Understand use cases in different industries related to the implementation of artificial intelligence and deep learning methods Learn the use of potential ideas in artificial intelligence and deep learning methods to improve the operational processes or new products and how services can be produced based on the methods WHO THIS BOOK IS FOR This book is targeted to business and organization leaders, technology enthusiasts, professionals, and managers who seek knowledge of artificial intelligence and deep learning methods. Table of Contents Artificial Intelligence and Deep Learning Data Science for Business Analysis Decision Making Intelligent Computing Strategies By Google Cognitive Learning Services in IBM Watson Advancement web services by Baidu Improved Social Business by Facebook Personalized Intelligent Computing by Apple Cloud Computing Intelligent by Microsoft

Cutting Edge Evolutions of Information Technology

Cutting Edge Evolutions of Information Technology
Author: Dr.Kashif Qureshi
Publsiher: Booksclinic Publishing
Total Pages: 566
Release: 2019-06-14
Genre: Electronic Book
ISBN: 9789388797276

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"Just some years before, there have been no throngs of Machine Learning, scientists developing intelligent merchandise and services at major corporations and startups. Once the youngest folks (the authors) entered the sector, machine learning didn’t command headlines in daily newspapers. Our oldsters had no plan what machine learning was, including why we would like it to a career in medication or law. Machine learning was an advanced tutorial discipline with a slender set of real-world applications. And people applications, e.g. speech recognition and pc vision, needed most domain data that they were usually thought to be separate areas entirely that machine learning was one tiny part. Neural networks, the antecedents of the deep learning models that we tend to specialize in during this book, were thought to be out-of-date tools. In simply the previous five years, deep learning has taken the world by surprise, using fast progress in fields as diverse as laptop vision, herbal language processing, computerized speech recognition, reinforcement learning, and statistical modelling. With these advances in hand, we can now construct cars that power themselves (with increasing autonomy), clever reply structures that anticipate mundane replies, assisting humans to dig out from mountains of email, and software program retailers that dominate the world’s first-class people at board video games like Go, a feat once deemed to be a long time away. Already, these equipment are exerting a widening impact, changing the way films are made, diseases are…diagnosed, and enjoying a developing role in simple sciences – from astrophysics to biology. This e-book represents our attempt to make deep learning approachable, instructing you each the concepts, the context, and the code."

The The Reinforcement Learning Workshop

The The Reinforcement Learning Workshop
Author: Alessandro Palmas,Emanuele Ghelfi,Dr. Alexandra Galina Petre,Mayur Kulkarni,Anand N.S.,Quan Nguyen,Aritra Sen,Anthony So,Saikat Basak
Publsiher: Packt Publishing Ltd
Total Pages: 822
Release: 2020-08-18
Genre: Computers
ISBN: 9781800209961

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With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning’s core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.

Artificial Intelligence in Economics and Finance Theories

Artificial Intelligence in Economics and Finance Theories
Author: Tankiso Moloi,Tshilidzi Marwala
Publsiher: Springer Nature
Total Pages: 125
Release: 2020-05-07
Genre: Computers
ISBN: 9783030429621

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As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.

AI and IoT for Sustainable Development in Emerging Countries

AI and IoT for Sustainable Development in Emerging Countries
Author: Zakaria Boulouard,Mariyam Ouaissa,Sarah El Himer
Publsiher: Springer Nature
Total Pages: 135
Release: 2022
Genre: Artificial intelligence
ISBN: 9783030906184

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This book comprises a number of state-of-the-art contributions from both scientists and practitioners working in a large pool of fields where AI and IoT can open up new horizons. Artificial intelligence and Internet of Things have introduced themselves today as must-have technologies in almost every sector. Ranging from agriculture to industry and health care, the scope of applications of AI and IoT is as wide as the horizon. Nowadays, these technologies are extensively used in developed countries, but they are still at an early stage in emerging countries. AI and IoT for Sustainable Development in Emerging CountriesChallenges and Opportunities is an invaluable source to dive into the latest applications of AI and IoT and how they have been used by researchers from emerging countries to solve sustainable development-related issues by taking into consideration the specifities of their countries. This book starts by presenting how AI and IoT can tackle the challenges of sustainable development in general and then focuses on the following axes: AI and IoT for smart environment and energy Industry 4.0 and intelligent transportation A vision towards an artificial intelligence of medical things AI, social media, and big data analytics. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in these particular areas or those interested in grasping its diverse facets and exploring the latest advances on their respective fields and the role of AI and IoT in them.

Deep Learning with PyTorch

Deep Learning with PyTorch
Author: Luca Pietro Giovanni Antiga,Eli Stevens,Thomas Viehmann
Publsiher: Simon and Schuster
Total Pages: 520
Release: 2020-07-01
Genre: Computers
ISBN: 9781638354079

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“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

What Was Artificial Intelligence

What Was Artificial Intelligence
Author: Sue Curry Jansen
Publsiher: mediastudies.press
Total Pages: 74
Release: 2022-04-01
Genre: Computers
ISBN: 9781951399054

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When it was originally published in 2002, Sue Curry Jansen’s “What Was Artificial Intelligence?” attracted little notice. The long essay was published as a chapter in Jansen’s Critical Communication Theory, a book whose wisdom and erudition failed to register across the many fields it addressed. One explanation for the neglect, ironic and telling, is that Jansen’s sheer scope as an intellectual had few competent readers in the communication studies discipline into which she published the book. “What Was Artificial Intelligence?” was buried treasure. In this mediastudies.press edition, Jansen’s prescient autopsy of AI self-selling—the rhetoric of the masculinist sublime—is reprinted with a new introduction. Now an open access book, “What Was Artificial Intelligence?” is a message in a bottle, addressed to Musk, Bezos, and the latest generation of AI myth-makers.

Chinese Power and Artificial Intelligence

Chinese Power and Artificial Intelligence
Author: William C. Hannas,Huey-Meei Chang
Publsiher: Taylor & Francis
Total Pages: 320
Release: 2022-07-29
Genre: History
ISBN: 9781000619409

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This book provides a comprehensive account of Chinese AI in its various facets, based on primary Chinese-language sources. China’s rise as an AI power is an event of importance to the world and a potential challenge to liberal democracies. Filling a gap in the literature, this volume is fully documented, data-driven, and presented in a scholarly format suitable for citation and for supporting downstream research, while also remaining accessible to laypersons. It brings together 15 recognized international experts to present a full treatment of Chinese artificial intelligence. The volume contains chapters on state, commercial, and foreign sources of China’s AI power; China’s AI talent, scholarship, and global standing; the impact of AI on China’s development of cutting-edge disciplines; China’s use of AI in military, cyber, and surveillance applications; AI safety, threat mitigation, and the technology’s likely trajectory. The book ends with recommendations drawn from the authors’ interactions with policymakers and specialists worldwide, aimed at encouraging AI’s healthy development in China and preparing the rest of the world to engage with it. This book will be of much interest to students of Chinese politics, science and technology studies, security studies and international relations.

The Deep Learning Revolution

The Deep Learning Revolution
Author: Terrence J. Sejnowski
Publsiher: MIT Press
Total Pages: 352
Release: 2018-10-23
Genre: Computers
ISBN: 9780262038034

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How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

Trusted Artificial Intelligence in Manufacturing

Trusted Artificial Intelligence in Manufacturing
Author: John Soldatos,Dimosthenis Kyriazis
Publsiher: Unknown
Total Pages: 135
Release: 2021-09-30
Genre: Electronic Book
ISBN: 1680838768

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The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities.