ACS: Artificial Intelligence: a Starter Guide to the Future of Business
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
A Comprehensive Workplace Environment Based on a Deep
International Journal on Advances in Software, vol 11 no 3 & 4, year 2018, http://www.iariajournals.org/software/ 358 A Comprehensive Workplace Environment based on a Deep Learning Architecture for Cognitive Systems Using a multi-tier layout comprising various cognitive channels, reflexes and reasoning Thorsten Gressling Veronika Thurner Munich University of Applied Sciences ARS Computer und Consulting GmbH Department of Computer Science and Mathematics Munich, Germany Munich, Germany e-mail: [email protected] e-mail: [email protected] Abstract—Many technical work places, such as laboratories or All these settings share a number of commonalities. For test beds, are the setting for well-defined processes requiring both one thing, within each of these working settings a human being high precision and extensive documentation, to ensure accuracy interacts extensively with technical devices, such as measuring and support accountability that often is required by law, science, instruments or sensors. For another thing, processing follows or both. In this type of scenario, it is desirable to delegate certain a well-defined routine, or even precisely specified interaction routine tasks, such as documentation or preparatory next steps, to protocols. Finally, to ensure that results are reproducible, some sort of automated assistant, in order to increase precision and reduce the required amount of manual labor in one fell the different steps and achieved results usually have to be swoop. At the same time, this automated assistant should be able documented extensively and in a precise way. to interact adequately with the human worker, to ensure that Especially in scenarios that execute a well-defined series of the human worker receives exactly the kind of support that is actions, it is desirable to delegate certain routine tasks, such required in a certain context. -
Getting Started with Machine Learning
Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course -
Build Your Trust Advantage, Leadership in the Era of Data
Global C-suite Study 20th Edition Build Your Trust Advantage Leadership in the era of data and AI everywhere This report is IBM’s fourth Global C-suite Study and the 20th Edition in the ongoing IBM CxO Study series developed by the IBM Institute for Business Value (IBV). We have now collected data and insights from more than 50,000 interviews dating back to 2003. This report was authored in collaboration with leading academics, futurists, and technology visionaries. In this report, we present our key findings of CxO insights, experiences, and sentiments based on analysis as described in the research methodology on page 44. Build Your Trust Advantage | 1 Build Your Trust Advantage Leadership in the era of data and AI everywhere Global C-suite Study 20th Edition Our latest study draws on input from 13,484 respondents across 6 C-suite roles, 20 industries, and 98 countries. 2,131 2,105 2,118 2,924 2,107 2,099 Chief Chief Chief Chief Chief Chief Executive Financial Human Information Marketing Operations Officers Officers Resources Officers Officers Officers Officers 3,363 Europe 1,910 Greater China 3,755 North America 858 Japan 915 Middle East and Africa 1,750 Asia Pacific 933 Latin America 2 | Global C-suite Study Table of contents Executive summary 3 Introduction 4 Chapter 1 Customers: How to win in the trust economy 8 Action guide 19 Chapter 2 Enterprises: How to build the human-tech partnership 20 Action guide 31 Chapter 3 Ecosystems: How to share data in the platform era 32 Action guide 41 Conclusion: Return on trust 42 Acknowledgments 43 Related IBV studies 43 Research methodology 44 Notes and sources 45 Build Your Trust Advantage | 3 Executive summary More than 13,000 C-suite executives worldwide their data scientists, to uncover insights from data. -
A.I. Technologies Applied to Naval CAD/CAM/CAE
A.I. Technologies Applied to Naval CAD/CAM/CAE Jesus A. Muñoz Herrero, SENER, Madrid/Spain, [email protected] Rodrigo Perez Fernandez, SENER, Madrid/Spain, [email protected] Abstract Artificial Intelligence is one of the most enabling technologies of digital transformation in the industry, but it is also one of the technologies that most rapidly spreads in our daily activity. Increasingly, elements and devices that integrate artificial intelligence features, appear in our everyday lives. These characteristics are different, depending on the devices that integrate them, or the aim they pursue. The methods and processes that are carried out in Marine Engineering cannot be left out of this technology, but the peculiarities of the profession and the people that take part in it must be taken into account. There are many aspects in which artificial intelligence can be applied in the field of our profession. The management and access to all the information necessary for the correct and efficient execution of a naval project is one of the aspects where this technology can have a very positive impact. Access all the rules, rules, design guides, good practices, lessons learned, etc., in a fast and intelligent way, understanding the natural language of the people, identifying the most appropriate to the process that is being carried out and above all. Learning as we go through the design, is one of the characteristics that will increase the application of this technology in the professional field. This article will describe the evolution of this technology and the current situation of it in the different areas of application. -
Mlops: from Model-Centric to Data-Centric AI
MLOps: From Model-centric to Data-centric AI Andrew Ng AI system = Code + Data (model/algorithm) Andrew Ng Inspecting steel sheets for defects Examples of defects Baseline system: 76.2% accuracy Target: 90.0% accuracy Andrew Ng Audience poll: Should the team improve the code or the data? Poll results: Andrew Ng Improving the code vs. the data Steel defect Solar Surface detection panel inspection Baseline 76.2% 75.68% 85.05% Model-centric +0% +0.04% +0.00% (76.2%) (75.72%) (85.05%) Data-centric +16.9% +3.06% +0.4% (93.1%) (78.74%) (85.45%) Andrew Ng Data is Food for AI ~1% of AI research? ~99% of AI research? 80% 20% PREP ACTION Source and prepare high quality ingredients Cook a meal Source and prepare high quality data Train a model Andrew Ng Lifecycle of an ML Project Scope Collect Train Deploy in project data model production Define project Define and Training, error Deploy, monitor collect data analysis & iterative and maintain improvement system Andrew Ng Scoping: Speech Recognition Scope Collect Train Deploy in project data model production Define project Decide to work on speech recognition for voice search Andrew Ng Collect Data: Speech Recognition Scope Collect Train Deploy in project data model production Define and collect data “Um, today’s weather” Is the data labeled consistently? “Um… today’s weather” “Today’s weather” Andrew Ng Iguana Detection Example Labeling instruction: Use bounding boxes to indicate the position of iguanas Andrew Ng Making data quality systematic: MLOps • Ask two independent labelers to label a sample of images. -
Deep Learning I: Gradient Descent
Roadmap Intro, model, cost Gradient descent Deep Learning I: Gradient Descent Hinrich Sch¨utze Center for Information and Language Processing, LMU Munich 2017-07-19 Sch¨utze (LMU Munich): Gradient descent 1 / 40 Roadmap Intro, model, cost Gradient descent Overview 1 Roadmap 2 Intro, model, cost 3 Gradient descent Sch¨utze (LMU Munich): Gradient descent 2 / 40 Roadmap Intro, model, cost Gradient descent Outline 1 Roadmap 2 Intro, model, cost 3 Gradient descent Sch¨utze (LMU Munich): Gradient descent 3 / 40 Roadmap Intro, model, cost Gradient descent word2vec skipgram predict, based on input word, a context word Sch¨utze (LMU Munich): Gradient descent 4 / 40 Roadmap Intro, model, cost Gradient descent word2vec skipgram predict, based on input word, a context word Sch¨utze (LMU Munich): Gradient descent 5 / 40 Roadmap Intro, model, cost Gradient descent word2vec parameter estimation: Historical development vs. presentation in this lecture Mikolov et al. (2013) introduce word2vec, estimating parameters by gradient descent. (today) Still the learning algorithm used by default and in most cases Levy and Goldberg (2014) show near-equivalence to a particular type of matrix factorization. (yesterday) Important because it links two important bodies of research: neural networks and distributional semantics Sch¨utze (LMU Munich): Gradient descent 6 / 40 Roadmap Intro, model, cost Gradient descent Gradient descent (GD) Gradient descent is a learning algorithm. Given: a hypothesis space (or model family) an objective function or cost function a training set Gradient descent (GD) finds a set of parameters, i.e., a member of the hypothesis space (or specified model) that performs well on the objective for the training set. -
Ilearn Goals
Combining AI and collective intelligence to create a GPS for knowledge CRI Paris We experiment at frontiers of learning, life and digital From babies to lifelong learning LMD students Learning by doing Interdisciplinarity Sustainable Development goals A changing job market Too much! Fake news Recruting for skills on the digital job market Can we reinvent learning with artificial intelligence? What can you (almost) do with AI today? Deep Learning Classification Generation Who are these persons? www.thispersondoesnotexist.com Based on GAN (Generative Adversarial Networks) A.I. image generation From text to image From text to image Text generation Automatic Q&A generation from Wikipedia Debating with A.I. February 12th, 2019 IBM Project Debater vs. World Debating Champion Motion: “We should subsidise preschool” ● 15 mins to prepare arguments ● 4-minute opening statement ● 4-minute rebuttal ● 2-minute summary Predictive tools Pneumonia detection from thorax radiographies (2017) Deepfake A.I. lip reading Conversational interfaces May 2018 Google Duplex iLearn goals Map all learning resources available on the Internet Build learning profiles of our users Provide them maps of their own learnings Match learners with adapted resources Match learners with mentors or co-learners iLearn Knowledge maps + = + Collective Artificial intelligence intelligence Learning groups iLearn iLearn Tags an online Users improve document as a qualification Concepts extraction useful learning (concepts and resource difficulty) Artificial Collective Intelligence Intelligence -
Comments from More Cowbell Unlimited
June 10, 2019 Elham Tabassi National Institute of Standards and Technology 100 Bureau Drive, Stop 200 Gaithersburg, MD 20899 TM Enclosed: Technical Paper: FOCAL Information Warfare Defense Standard (v1.0 minus Appendix) Dear Ms. Tabassi, Thank you for the opportunity to submit comments in response to the National Institute of Standards and Technology’s (NIST) request for information on artificial intelligence (AI) standards. We assert that NIST should work collaboratively with Federal agencies and the private sector to develop a cross sector Information Warfare (IW) Defense Standard. The enclosed technical paper supports our assertion and TM describes our FOCAL IW Defense Standard , which is available for anyone to use. More Cowbell Unlimited, Inc. is a process mining and data science firm based in Portland OR. We are developing process technologies in support of national security and industry. Our mission is to help America remain a beacon of hope and strength on the world stage. Technological advancements are a double-edged sword. AI is a tool which promises great things for humanity, such reducing poverty and allowing creativity to flourish; however, there is a dark side which we believe must be the focal point of national security. Unsurprisingly, hunger for dominance and money are present in this discussion, too. Feeding large hordes of private information into an AI to create a “World Brain” is plausible and provides a vehicle to project power in various ways. One way to monetize and project power from this information is through advertisements. Another way--perhaps one we are already seeing-- is through IW. As the world becomes more reliant upon information, IW boosted with weaponized AI is a major threat. -
Face Recognition in Unconstrained Conditions: a Systematic Review
Face Recognition in Unconstrained Conditions: A Systematic Review ANDREW JASON SHEPLEY, Charles Darwin University, Australia ABSTRACT Face recognition is a biometric which is attracting significant research, commercial and government interest, as it provides a discreet, non-intrusive way of detecting, and recognizing individuals, without need for the subject’s knowledge or consent. This is due to reduced cost, and evolution in hardware and algorithms which have improved their ability to handle unconstrained conditions. Evidently affordable and efficient applications are required. However, there is much debate over which methods are most appropriate, particularly in the context of the growing importance of deep neural network-based face recognition systems. This systematic review attempts to provide clarity on both issues by organizing the plethora of research and data in this field to clarify current research trends, state-of-the-art methods, and provides an outline of their benefits and shortcomings. Overall, this research covered 1,330 relevant studies, showing an increase of over 200% in research interest in the field of face recognition over the past 6 years. Our results also demonstrated that deep learning methods are the prime focus of modern research due to improvements in hardware databases and increasing understanding of neural networks. In contrast, traditional methods have lost favor amongst researchers due to their inherent limitations in accuracy, and lack of efficiency when handling large amounts of data. Keywords: unconstrained face recognition, deep neural networks, feature extraction, face databases, traditional handcrafted features 1 INTRODUCTION The development of accurate and efficient face recognition systems for use in unconstrained conditions is an area of high research interest. -
Leading Edge Technologies from the Slide Rule to the Cloud
Liquidtool Systems AG Leading Edge Technologies From the Slide Rule to the Cloud Rudolf Meyer 2021-03-24 Table of Contents 1 Introduction .............................................................................................................................. 2 2.1 Industry 0.0 – Industrial prerequisites ..................................................................... 3 2.2 Industry 1.0 – Industrial production .......................................................................... 3 2.3 Industry 2.0 – Industrial mass-production ............................................................. 4 2.4 Industry 3.0 – Industrial automation ........................................................................ 4 2.5 Industry 3.5 – Industrial globalization ...................................................................... 5 3 A closer look – Where do we stand now? ..................................................................... 6 3.1 Industry 4.0 – Industrial digitization ......................................................................... 7 3.2 Leading edge technologies for Industry 4.0 ....................................................... 10 4 Looking ahead – Industry 4.0, 5.0, 6.0, 7.0… ................................................................ 15 Impact on the labor market .............................................................................................. 15 Impending developments .................................................................................................16 4.1 Industry 5.0 -
Creating the Engine for Scientific Discovery
www.nature.com/npjsba PERSPECTIVE OPEN Nobel Turing Challenge: creating the engine for scientific discovery ✉ Hiroaki Kitano 1 Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the “science of science” needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by “AI Scientists” may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond. npj Systems Biology and Applications (2021) 7:29 ; https://doi.org/10.1038/s41540-021-00189-3 1234567890():,; NOBEL TURING CHALLENGE AS AN ULTIMATE GRAND EURISKO6,8. -
Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens Natalie
Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens by Natalie Lao S.B., Massachusetts Institute of Technology (2016) M.Eng., Massachusetts Institute of Technology (2017) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 2020 ○c Massachusetts Institute of Technology 2020. All rights reserved. Author................................................................ Department of Electrical Engineering and Computer Science August 28, 2020 Certified by. Harold (Hal) Abelson Class of 1922 Professor of Electrical Engineering and Computer Science Thesis Supervisor Accepted by . Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students 2 Reorienting Machine Learning Education Towards Tinkerers and ML-Engaged Citizens by Natalie Lao Submitted to the Department of Electrical Engineering and Computer Science on August 28, 2020, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Science Abstract Artificial intelligence (AI) and machine learning (ML) technologies are appearing in everyday contexts, from recommendation systems for shopping websites to self- driving cars. As researchers and engineers develop and apply these technologies to make consequential decisions in everyone’s lives, it is crucial that the public under- stands the AI-powered world, can discuss AI policies, and become empowered to engage in shaping these technologies’ futures. Historically, ML application devel- opment has been accessible only to those with strong computational backgrounds working or conducting research in highly technical fields. As ML models become more powerful and computing hardware becomes faster and cheaper, it is now tech- nologically possible for anyone with a laptop to learn about and tinker with ML.