AI is transforming a range of industries by enabling more accurate medical diagnoses, personalized treatment protocols, faster drug discovery, and assisted surgery. AI also allows for autonomous vehicles, predictive maintenance, optimized supply chains, fraud detection, personalized financial services, and smart cities. Deep learning and large datasets are being applied to problems in healthcare, transportation, manufacturing, retail, agriculture, finance, and government.
The document provides an introduction to artificial intelligence (AI) and its history. It defines key AI terms like artificial intelligence, machine learning, and deep learning. It explains how deep learning helps solve limitations of classic machine learning by determining representations of data. The summary highlights major developments in AI history including early algorithms, expert systems, neural networks, and breakthroughs with deep learning starting in 2006. It differentiates modern AI using deep learning from prior approaches and provides examples of AI applications.
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
This presentation is present the history and invention of the artificial intelligence . This presentation is express the power of artificial intelligence in present and future. In this presentation we explain the natural language programming, speech recognition, computer vision,robotic, automatic programming, quantum computing.This presentation also express the power of neural network power in present and future
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines two categories of AI methods - symbolic AI and computational intelligence - and discusses applications of AI in domains like finance, medicine, gaming, and robotics. It also notes some achievements of AI and predicts that AI will continue growing exponentially and potentially change the world.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
The document provides an introduction to artificial intelligence (AI) and its history. It defines key AI terms like artificial intelligence, machine learning, and deep learning. It explains how deep learning helps solve limitations of classic machine learning by determining representations of data. The summary highlights major developments in AI history including early algorithms, expert systems, neural networks, and breakthroughs with deep learning starting in 2006. It differentiates modern AI using deep learning from prior approaches and provides examples of AI applications.
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
This presentation is present the history and invention of the artificial intelligence . This presentation is express the power of artificial intelligence in present and future. In this presentation we explain the natural language programming, speech recognition, computer vision,robotic, automatic programming, quantum computing.This presentation also express the power of neural network power in present and future
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines two categories of AI methods - symbolic AI and computational intelligence - and discusses applications of AI in domains like finance, medicine, gaming, and robotics. It also notes some achievements of AI and predicts that AI will continue growing exponentially and potentially change the world.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Computational Intelligence: concepts and applications using AthenaPedro Almir
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence (AI) and is concentrated in the study of adaptive mechanisms to enable or facilitate intelligent behavior
in complex and changing environments. This presentation presents the key concepts of this area and how to use Athena to create intelligent systems. Athena is a visual tool developed aiming at offering a simple approach to the development of CI-based software systems, by dragging and dropping components in a visual environment, creating a new concept, that we call CI as a Service (CIaaS).
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
The document provides an overview of deep learning, including its history, key concepts, applications, and recent advances. It discusses the evolution of deep learning techniques like convolutional neural networks, recurrent neural networks, generative adversarial networks, and their applications in computer vision, natural language processing, and games. Examples include deep learning for image recognition, generation, segmentation, captioning, and more.
Introduction to Computational Intelligent
Motivation
Main umbrella: Natural Computing
Computational options: Levels of Abstraction
Definition: CI
Basic Properties of CI
CI Main Paradigms
Examples of Natural phenomenas
Computational Intelligence: Modeling Methodology
Applications of CI
Recommended References
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
This presentation provides an overview of robotics and AI. It defines a robot as a machine that can sense its environment, think to follow instructions, and act. Current developments include robots that can perform surgery, explore hazardous areas, and recognize faces and objects. Industrial and manufacturing robots are widely used today. Issues include robots being unable to handle unexpected situations and potentially increasing unemployment, though future developments may focus on greater intelligence, learning ability, and human-friendly design.
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
The document discusses artificial intelligence and defines it as the science and engineering of making intelligent machines, especially intelligent computer programs. It notes two main approaches to AI: engineering and cognitive modeling. Intelligence is defined as the ability to learn and solve problems, specifically the ability to solve novel problems, act rationally, and act like humans. The document also discusses various applications and techniques in AI, including search algorithms, expert systems, fuzzy logic, robotics, and genetic algorithms.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
Artificial intelligence and machine learning technologies are transforming key industries like manufacturing, finance, retail, and healthcare. Edge computing and federated learning are emerging approaches that can help address challenges around data privacy, bandwidth constraints, and latency. Edge AI runs optimized models directly on devices to analyze data and only send results rather than raw data. Federated learning leverages local AI models across edge devices to improve performance while keeping sensitive data private. Together these approaches help make AI more scalable, responsive and privacy-preserving for industries.
Computational Intelligence and ApplicationsChetan Kumar S
Slides used at IEEE Computational Intelligence Society, Bangalore Chapter:
Winter School On Emerging Topics in Computational Intelligence -Theory and Applications
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
The document introduces artificial intelligence, machine learning, and deep learning. It discusses supervised, unsupervised, and reinforced learning techniques. Examples of applications discussed include image recognition, natural language processing, and virtual assistants. The document also notes that some AI systems have developed their own internal languages when interacting without human supervision.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
The Role of Artificial Intelligence in Manufacturing : 15 High Impacted AI Us...CANOPY ONE SOLUTIONS
1) AI has the potential to greatly disrupt and transform manufacturing when applied to areas like predictive maintenance, supply chain management, quality control, and customization.
2) AI enhances robotic tasks by enabling better human-robot interaction and allowing robots to perform more complex tasks.
3) The use of sensors and IoT combined with AI provides real-time data that improves maintenance scheduling, reduces downtime, and increases productivity.
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
best presentation Artitficial Intelligencejennifer joe
This document provides an overview of artificial intelligence (AI), including its history, how it works, applications, and drawbacks. It discusses key aspects of AI such as speech recognition, machine learning, computer vision, pattern recognition, and the relationship between cognition and AI. The document also explores differences between human and artificial intelligence as well as examples of AI in robotics.
As AI becomes more and more prevalent in our lives, the decisions it makes for us are becoming more and more impactful on our lives and those of others.
How can we help people to have trust in the models we're building? The field of Explainable AI focuses on making any machine learning model interpretable by non experts.
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
This document provides a summary of the state of artificial intelligence (AI) research and developments over the past year. It covers key areas like research breakthroughs, talent, industries utilizing AI, and public policy issues related to AI. The document is produced by two authors in East London as a way to capture the progress of AI and spark discussion about its implications. It includes sections on research breakthroughs in areas like transfer learning, advances in hardware that have enabled progress, and the use of video datasets to help machines understand scenes and actions to gain a level of common sense.
The document provides an overview of artificial intelligence (AI), including its history, how it works, branches of AI such as ontology, heuristics, genetic programming and epistemology, goals of AI, and uses of AI. It discusses how AI was founded in 1956 and aims to make computers intelligent like humans by applying knowledge through scientific theorems and neural networks. The goals of AI include solving knowledge-intensive tasks, replicating human intelligence, and enhancing human and computer interactions. AI has applications in various fields such as finance, healthcare, transportation, gaming and more.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
This document provides information about an AI certification course for the Microsoft Azure AI Fundamentals exam (AI-900). It outlines the intended audience, prerequisites, language, content included, exam details, and types of artificial intelligence. The course is intended for anyone interested in learning the basics of AI or clearing the AI-900 exam. It includes over 8 hours of video content, practice tests, quizzes and other study materials. Upon completion, students will receive a certificate and lifetime access to the course content.
Introduction to Computational Intelligent
Motivation
Main umbrella: Natural Computing
Computational options: Levels of Abstraction
Definition: CI
Basic Properties of CI
CI Main Paradigms
Examples of Natural phenomenas
Computational Intelligence: Modeling Methodology
Applications of CI
Recommended References
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
This presentation provides an overview of robotics and AI. It defines a robot as a machine that can sense its environment, think to follow instructions, and act. Current developments include robots that can perform surgery, explore hazardous areas, and recognize faces and objects. Industrial and manufacturing robots are widely used today. Issues include robots being unable to handle unexpected situations and potentially increasing unemployment, though future developments may focus on greater intelligence, learning ability, and human-friendly design.
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
The document discusses artificial intelligence and defines it as the science and engineering of making intelligent machines, especially intelligent computer programs. It notes two main approaches to AI: engineering and cognitive modeling. Intelligence is defined as the ability to learn and solve problems, specifically the ability to solve novel problems, act rationally, and act like humans. The document also discusses various applications and techniques in AI, including search algorithms, expert systems, fuzzy logic, robotics, and genetic algorithms.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
AI for Manufacturing (Machine Vision, Edge AI, Federated Learning)byteLAKE
Artificial intelligence and machine learning technologies are transforming key industries like manufacturing, finance, retail, and healthcare. Edge computing and federated learning are emerging approaches that can help address challenges around data privacy, bandwidth constraints, and latency. Edge AI runs optimized models directly on devices to analyze data and only send results rather than raw data. Federated learning leverages local AI models across edge devices to improve performance while keeping sensitive data private. Together these approaches help make AI more scalable, responsive and privacy-preserving for industries.
Computational Intelligence and ApplicationsChetan Kumar S
Slides used at IEEE Computational Intelligence Society, Bangalore Chapter:
Winter School On Emerging Topics in Computational Intelligence -Theory and Applications
Differences Between Machine Learning Ml Artificial Intelligence Ai And Deep L...SlideTeam
"You can download this product from SlideTeam.net"
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/325zI9o
The document introduces artificial intelligence, machine learning, and deep learning. It discusses supervised, unsupervised, and reinforced learning techniques. Examples of applications discussed include image recognition, natural language processing, and virtual assistants. The document also notes that some AI systems have developed their own internal languages when interacting without human supervision.
This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.
The Role of Artificial Intelligence in Manufacturing : 15 High Impacted AI Us...CANOPY ONE SOLUTIONS
1) AI has the potential to greatly disrupt and transform manufacturing when applied to areas like predictive maintenance, supply chain management, quality control, and customization.
2) AI enhances robotic tasks by enabling better human-robot interaction and allowing robots to perform more complex tasks.
3) The use of sensors and IoT combined with AI provides real-time data that improves maintenance scheduling, reduces downtime, and increases productivity.
Every single security company is talking about how they are using machine learning—as a security company you have to claim artificial intelligence to be even part of the conversation. However, this approach can be dangerous when we blindly rely on algorithms to do the right thing. Rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and, in turn, discovering wrong insights.
In this session, we will discuss:
• Limitations of machine learning and issues of explainability
• Where deep learning should never be applied
• Examples of how the blind application of algorithms can lead to wrong results
best presentation Artitficial Intelligencejennifer joe
This document provides an overview of artificial intelligence (AI), including its history, how it works, applications, and drawbacks. It discusses key aspects of AI such as speech recognition, machine learning, computer vision, pattern recognition, and the relationship between cognition and AI. The document also explores differences between human and artificial intelligence as well as examples of AI in robotics.
As AI becomes more and more prevalent in our lives, the decisions it makes for us are becoming more and more impactful on our lives and those of others.
How can we help people to have trust in the models we're building? The field of Explainable AI focuses on making any machine learning model interpretable by non experts.
The State of Artificial Intelligence in 2018: A Good Old Fashioned ReportNathan Benaich
This document provides a summary of the state of artificial intelligence (AI) research and developments over the past year. It covers key areas like research breakthroughs, talent, industries utilizing AI, and public policy issues related to AI. The document is produced by two authors in East London as a way to capture the progress of AI and spark discussion about its implications. It includes sections on research breakthroughs in areas like transfer learning, advances in hardware that have enabled progress, and the use of video datasets to help machines understand scenes and actions to gain a level of common sense.
The document provides an overview of artificial intelligence (AI), including its history, how it works, branches of AI such as ontology, heuristics, genetic programming and epistemology, goals of AI, and uses of AI. It discusses how AI was founded in 1956 and aims to make computers intelligent like humans by applying knowledge through scientific theorems and neural networks. The goals of AI include solving knowledge-intensive tasks, replicating human intelligence, and enhancing human and computer interactions. AI has applications in various fields such as finance, healthcare, transportation, gaming and more.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
This document provides information about an AI certification course for the Microsoft Azure AI Fundamentals exam (AI-900). It outlines the intended audience, prerequisites, language, content included, exam details, and types of artificial intelligence. The course is intended for anyone interested in learning the basics of AI or clearing the AI-900 exam. It includes over 8 hours of video content, practice tests, quizzes and other study materials. Upon completion, students will receive a certificate and lifetime access to the course content.
Artificial intelligence and robotics.pptxSumant Saini
The document discusses how artificial intelligence is transforming the pharmaceutical industry. It describes how AI is accelerating drug discovery by analyzing vast datasets to identify promising drug candidates faster. AI is also improving clinical trials by helping select optimal patients and design virtual trials. Additionally, AI optimizes manufacturing through predictive maintenance, quality control, and process optimization. The future of AI in pharma includes personalized medicine, drug repurposing, and continuous innovation.
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Richard van der Velde, Technical Support Lead for Cookiebot @CMP – “Artificia...Associazione Digital Days
The training of artificial intelligence systems is just the latest use of users’ personal data that companies collect online. But the information on how the data is used, what consent is needed or how it will be regulated is not always clear. Strong concerns have already been raised about data privacy and consent.
This document discusses emerging technology trends and provides an overview of several key trends: smart machines, artificial intelligence, 3D printing, augmented reality, predictive analytics, the internet of things, big data, and wearables. The author's goal is to help the audience understand these rapidly changing technologies and how they will impact how people interact with technology. Each trend is defined and examples are given to illustrate real-world applications and leaders in each field.
Staring with an brief overview of the changing role of the CIO between 2018 and 2020, then moving into the technology landscape, here are 10 use cases across the new three: AI, IoT and Blockchain (and in many cases an overlap of them)
Cristene Gonzalez-Wertz is the Leader for the IBM Institute for Business Value in Electronics as well as an alumni of IBM's Watson Group. She speaks on the intersection of technology, software, offerings, platforms and new business models.
Artificial intelligence submitted by shivShiv Bindal
This 100-hour project report on artificial intelligence provides an overview of the topic in 3 sentences:
It discusses the history and development of AI from the 1950s to the present, covering major figures, technologies and applications such as expert systems, game playing, and robotics. The report also examines different branches, categories and goals of AI as well as examples like smart cars, smartphones and its use in movies and the military. In conclusion, the report addresses both the advantages and disadvantages of AI while acknowledging its potential though also noting it has not yet fulfilled all expectations.
Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...Ansgar Koene
The document discusses bias in algorithmic decision-making and governance standards. It introduces Ansgar Koene and several projects aimed at addressing algorithmic bias, including developing an IEEE standard on algorithmic bias considerations and a governance framework for algorithmic accountability. It then discusses the concept of algorithmic literacy and the need for awareness raising, accountability standards for public sector algorithms, regulatory oversight, and global coordination on algorithm governance.
"In 'Unleashing the Power of AI,' we delve into the transformative potential of artificial intelligence (AI) across industries and its profound impact on society. From revolutionizing healthcare with personalized medicine to optimizing transportation with autonomous vehicles, AI is reshaping how we live, work, and interact with technology. Join us as we explore the latest advancements, real-world applications, and ethical considerations driving the AI revolution forward, unlocking new opportunities and shaping the future of innovation."
Protecting Data Privacy in Analytics and Machine LearningUlf Mattsson
In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to use open source tools to put these technologies to work for databases and other data sources.
When we think about developing AI responsibly, there’s many different activities that we need to think about. In this session, we will discuss technologies that help protect people, preserve privacy, and enable you to do machine learning confidentially.
This session discusses industry standards and emerging privacy-enhanced computation techniques, secure multiparty computation, and trusted execution environments. We will discuss Zero Trust philosophy fundamentally changes the way we approach security since trust is a vulnerability that can be exploited particularly when working remotely and increasingly using cloud models. We will also discuss the “why, what, and how” of techniques for privacy preserving computing.
We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud.
We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.
This document provides an overview of computer vision including its definition, applications, working concepts, popular models and datasets, advantages, and disadvantages. Computer vision is a field that uses computer algorithms to gain a high-level understanding from digital images or videos. It has applications in areas like face detection, object detection and tracking, developing social distancing tools, and medical image analysis. Popular computer vision models include ResNet, YOLO, and MobileNet, and datasets include COCO, ImageNet, and CIFAR10. Advantages are faster and more reliable processing while disadvantages include needing specialists and potential failures in image processing. The document also discusses uses of computer vision for COVID-19 response and in areas like healthcare, automotive, and retail
The Revolutionary Progress of Artificial Inteligence (AI) in Health CareSindhBiotech
This Lecture is presented by our 2k23 volunteer Hina Nawaz, she is from Karachi, Pakistan, and she is covering "The Revolutionary Progress of Artificial Inteligence (AI) in Health Care".
Youtube: https://youtu.be/vhJRCj5ZgJc
Artificial intelligence and Internet of Things.pptxSriLakshmi643165
The document discusses artificial intelligence (AI) and the Internet of Things (IoT). It defines AI as using machines to simulate human intelligence through learning, reasoning and self-correction. IoT is defined as the network of physical devices connected through software and sensors to exchange data. The document outlines key applications of AI in healthcare, retail, education and more. It also discusses applications of IoT in healthcare, traffic monitoring, agriculture and fleet management. Finally, it discusses the future integration of AI and IoT, noting their potential to optimize systems, provide personalized recommendations and enable predictive maintenance through analysis of data collected by IoT devices.
This document summarizes a presentation on machine learning models, adversarial attacks, and defense strategies. It discusses adversarial attacks on machine learning systems, including GAN-based attacks. It then covers various defense strategies against adversarial attacks, such as filter-based adaptive defenses and outlier-based defenses. The presentation also addresses issues around bias in AI systems and the need for explainable and accountable AI.
Artificial intelligence ,robotics and cfd by sneha gaurkar Sneha Gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
The document presents an overview of the history and current applications of artificial intelligence. It discusses early pioneers in AI research like Alan Turing and his work developing the Turing machine. Examples of modern AI applications discussed include IBM's Watson system for healthcare and finance, driverless vehicles, surgical robotics, and automated assembly lines. The document also explores ideas about the future of AI such as the concept of a technological singularity and developing strong artificial general intelligence.
This document provides an overview of deep learning and common deep learning concepts. It discusses that deep learning uses complex neural networks to determine representations of data, rather than requiring humans to engineer features. It also describes convolutional neural networks and how they are better than fully connected networks for tasks like image recognition. Additionally, it covers transfer learning and how pre-trained models can be adapted to new tasks by retraining final layers, reducing data and computation needs. Common deep learning architectures mentioned include AlexNet, VGG16, Inception and MobileNets.
This document provides legal notices and disclaimers for an informational presentation by Intel. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration. Finally, it specifies that the sample source code in the presentation is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.
This document contains legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance depends on system configuration and that sample source code is released under an Intel license agreement. Finally, it provides basic copyright information.
This legal document provides several notices and disclaimers regarding the information presented. Specifically:
- The presentation is for informational purposes only and Intel makes no warranties regarding the information or summaries of the information.
- Any performance claims depend on system configuration and hardware/software/service activation. Performance varies depending on system configuration.
- The sample source code is released under the Intel Sample Source Code License Agreement.
- Intel and the Intel logo are trademarks of Intel Corporation in the U.S. and other countries. Other names may belong to other owners.
- Copyright of the content is held by Intel Corporation and all rights are reserved.
The document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. The document further states that sample source code is released under the Intel Sample Source Code License Agreement and that Intel and its logo are trademarks.
The document discusses various techniques for edge detection and line detection in images, including:
- Canny edge detection, which uses thresholds to detect and link edges.
- Hough transforms, which detect shapes like lines and circles by counting points that agree with a shape model.
- RANSAC for line detection, which forms line hypotheses from random samples and counts supporting points.
- Techniques for thinning thick edges and detecting edge contours.
The document discusses image processing techniques including image derivatives, integral images, convolution, morphology operations, and image pyramids.
It explains that image derivatives detect edges by capturing changes in pixel intensity, and provides an example calculation. Integral images allow fast computation of box filters by precomputing pixel sums. Convolution is used to calculate probabilities as the sliding overlap of distributions. Morphology operations like erosion and dilation modify images based on pixel neighborhoods. Image pyramids create multiple resolution layers that aid in object detection across scales.
This document provides a 3-sentence summary of a legal notice and disclaimer document:
The document states that any Intel technologies discussed are for informational purposes only and Intel makes no warranties regarding the information. It notes that performance may vary depending on system configuration and that source code samples are released under an Intel license agreement. The document also provides legal notices and disclaimers regarding Intel trademarks and copyright.
This document provides legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance can vary depending on system configuration and that sample source code is released under an Intel license agreement. Finally, it lists various trademarks.
This document provides an overview of Word2Vec, a model for generating word embeddings. It explains that Word2Vec uses a neural network to learn vector representations of words from large amounts of text such that words with similar meanings are located close to each other in the vector space. The document outlines how Word2Vec is trained using either the Continuous Bag-of-Words or Skip-gram architectures on sequences of words from text corpora. It also discusses how the trained Word2Vec model can be used for tasks like word similarity, analogy completion, and document classification. Finally, it provides a Python example of loading a pre-trained Word2Vec model and using it to find word vectors, similarities, analogies and outlier words.
Text similarity measures are used to quantify the similarity between text strings and documents. Common text similarity measures include Levenshtein distance for word similarity and cosine similarity for document similarity. To apply cosine similarity, documents first need to be represented in a document-term matrix using techniques like count vectorization or TF-IDF. TF-IDF is often preferred as it assigns higher importance to rare terms compared to common terms.
The document provides an overview of using Markov chains and recurrent neural networks (RNNs) for text generation. It discusses:
- How Markov chains can model text by treating sequences of words as "states" and predicting the next word based on conditional probabilities.
- The limitations of Markov chains for complex text generation.
- How RNNs address some limitations by incorporating memory via feedback connections, allowing them to better capture sequential relationships.
- Long short-term memory (LSTM) networks, which help combat the "vanishing gradient problem" to better learn long-term dependencies in sequences.
- How LSTMs can be implemented in Python using Keras to generate text character-by-character based on
This document discusses natural language processing (NLP) toolkits and preprocessing techniques. It introduces popular Python NLP libraries like NLTK, TextBlob, spaCy and gensim. It also covers various text preprocessing methods including tokenization, removing punctuation/characters, stemming, lemmatization, part-of-speech tagging, named entity recognition and more. Code examples demonstrate how to implement these techniques in Python to clean and normalize text data for analysis.
This document provides an overview of matrix decomposition techniques for dimensionality reduction and topic modeling, specifically principal component analysis (PCA), singular value decomposition (SVD), latent semantic analysis (LSA), and non-negative matrix factorization (NMF). PCA and SVD are introduced as mathematical techniques to reduce dimensions while preserving variance/information. LSA and NMF are described as applying SVD and NMF respectively to text data to derive topic models from the latent semantic space. Examples of implementing these techniques in Python are also provided.
This document discusses machine learning and natural language processing (NLP) techniques for text classification. It provides an overview of supervised vs. unsupervised learning and classification vs. regression problems. It then walks through the steps to perform binary text classification using logistic regression and Naive Bayes models on an SMS spam collection dataset. The steps include preparing and splitting the data, numerically encoding text with Count Vectorization, fitting models on the training data, and evaluating model performance on the test set using metrics like accuracy, precision, recall and F1 score. Naive Bayes classification is also introduced as an alternative simpler technique to logistic regression for text classification tasks.
1. LDA represents documents as mixtures of topics and topics as mixtures of words.
2. It assumes documents are generated by first choosing a topic distribution, then choosing words from that topic.
3. The algorithm estimates topic distributions for each document and word distributions for each topic that are most likely to have generated the observed document-word matrix.
This document provides an introduction to natural language processing (NLP). It defines NLP as teaching computers to process human language. The two main components of NLP are natural language understanding (NLU), which is deriving meaning from language, and natural language generation (NLG), which is generating language from meaning representations. The document discusses the history of NLP from early rule-based systems to current deep learning methods. It also outlines several applications of NLU like classification and summarization and applications of NLG like machine translation and caption generation.
Regularization and feature selection techniques can help prevent overfitting in machine learning models. Regularization adds a penalty term to the cost function that shrinks coefficient magnitudes, while feature selection aims to identify and remove unnecessary features. Both approaches reduce model complexity to improve generalization. Ridge regression performs L2 regularization by adding a penalty term that shrinks all coefficients. Lasso regression uses L1 regularization to drive some coefficients to exactly zero, performing embedded feature selection. Elastic net is a compromise that allows for both L1 and L2 regularization. Recursive feature elimination (RFE) removes features, using a model to recursively eliminate the weakest features.
The document discusses dimensionality reduction techniques. It begins by explaining the curse of dimensionality, where adding more features can hurt performance due to the exponential increase in the number of examples needed. It then introduces dimensionality reduction as a solution, where the data can be represented using fewer dimensions/features through feature selection, linear/non-linear transformations, or combinations. Principal component analysis (PCA) and singular value decomposition (SVD) are described as common linear dimensionality reduction methods. The document also discusses nonlinear techniques like kernel PCA and multi-dimensional scaling, as well as uses of dimensionality reduction like in image and natural language processing applications.
Three sentences summarizing the document:
Unsupervised learning techniques like clustering can be used to identify hidden patterns in unlabeled data. Hierarchical agglomerative clustering works by iteratively merging the closest pairs of clusters until the stopping criteria is reached. Different linkage methods like single, complete, average and Ward linkage determine how the distance between clusters is calculated during the merging process.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
3. Learning Objectives
You will be able to:
• Explain why AI is transforming a range of industries
• Give specific examples of how AI technology affects industries
3
4.
5. Review Prior Lesson Learning Objectives
5
▪ Defined AI, Machine Learning (ML), and Deep Learning (DL)
▪ Discussed AI’s key historical developments
▪ Showed the cyclical nature of AI’s public perception, funding, and interest
▪ Differentiated modern AI from prior AI
▪ Illustrated various applications of AI
6. How Is This Era of AI Different?
6
Cutting Edge
Results in a
Variety of Fields
Bigger
Datasets
Faster
Computers
Neural Nets
7.
8. Healthcare: Medical Diagnosis
8
Traditionally : Medical Diagnosis was a challenging process.
• Many symptoms are nonspecific
• Process of elimination was used
to determine root cause
(neither efficient nor exact)
9. Healthcare: Medical Diagnosis
9
Now with AI : Doctors can provide diagnoses more efficiently
and accurately, with the availability of:
• Large medical datasets
• Computer vision algorithms
10. Healthcare: Medical Diagnosis
10
Example: Breast Cancer, 2016, Harvard
Medical School researchers
▪ Used DL to identify cancer in lymph node
images
▪ Used Convolutional Neural Nets and custom
hardware
▪ AI model combined with humans achieved
lower error than either one individually
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
Pathologist AI model Pathologist +
AI model
Error Rate
11. Healthcare: Treatment Protocol
11
Traditionally : Doctors would diagnose a condition and recommend a
treatment based on what historically worked for most people.
• Some considerations for
population/demographics
• Difficult to create custom treatments
without extensive research/cost
12. Healthcare: Treatment Protocol
12
Now with AI : Doctors can tailor treatments to individual patients.
• Large medical datasets
• ML and DL algorithms
• Population/demographics
analysis/simulations
13. Healthcare: Treatment Protocol
13
Example: ICU Intervene, MIT Computer
Science and Artificial Intelligence Laboratory.
• Uses ICU data, from vitals, labs, notes, to
determine how to treat specific symptoms.
• Makes real-time predictions from DL models,
to provide recommendations for patients.
• Forecasts predictions into the future
(a few hours) compared to traditional methods
(a few minutes).
• Predictions can be run on common GPU and
CPU hardware.
14. Healthcare: Drug Discovery
14
Traditionally : Each new drug approval costs over a billion dollars in
Research and Development.
▪ The cost has been doubling every
9 years since 1970
▪ The drug discovery process can take
decades
▪ 9 out of 10 drug approval attempts fail
▪ There are currently only 1,500
approved drugs
15. Healthcare: Drug Discovery
15
Now With AI : Companies are leveraging structured and unstructured data
with AI, to establish a pipeline of new drug discovery.
▪ There are 1020 possible drug-like molecules
▪ Massive space for potential discovery
16. Healthcare: Drug Discovery
16
Example: HetioNet drug discovery model, 2016, UCSF,
Himmelstein and Baranzini.
• Developed a graph network to encode
millions of biomedical reports.
• Used ML to predict probability of
treatment efficacy for ~209,000
compound-disease pairs.
• Provided clear pharmacological
insights for epilepsy drug discovery
and treatment.
17. Healthcare: Surgery
17
Traditionally : Every type of surgery poses possible risks to the patient.
▪ Adverse anesthesia effects
▪ Operational complications
18. Healthcare: Surgery
18
Now with AI : Semi-intelligent computer systems predict surgical steps,
identify complications, and warn surgeons about pending challenges.
• Computer “vision” leverages data from
laparoscopic and arthroscopic cameras
• Smart systems automate dictation by
generating notes during the surgery
• Surgeons can send point-of-view live feeds
of the operative site to experts anywhere in
the world for real-time advice.
20. Genomics: Genome Sequencing
20
In 2003: High-throughput
sequencing made the process
more efficient by leveraging a
technique called “shotgun
strategy”.
▪ The data produced from this
technique is imperfect and
errors can be introduced at
each step in the process
21. Genomics: Genome Sequencing
21
Now with AI : Sequence
companies are employing AI
techniques to reduce cost and
increase accuracy.
▪ Illumina claims that within the
near future sequencing will only
take 1 hour and cost only $100
22. Genomics: AI for Genome Sequencing
22
Example: Google’s DeepVariant* sequencing:
▪ Leverages massive data sets together with DL
to identify all variants
▪ Accuracy on genome classification: 99.958 %
▪ DeepVariant* is computationally expensive, but
the framework can run on GPU hardware,
allowing for a faster learning process
▪ Availability as open source code promises to
revolutionize the industry
*Other names and brands may be claimed as the property of others.
23.
24. Transportation: Autonomous Cars
24
Traditionally : Despite having safer cars, the number of deadly car
accidents have been on the rise the last few years.
• The leading cause of automobile
accidents is human error
• One of the primary sources of traffic
jams is each driver acting out of
self-interest, that prevents traffic flow
• Part of the population who can’t drive:
children, the elderly, and the disabled
25. Transportation: Autonomous Cars
25
Now with AI : Self-driving cars are enabled by the latest AI
breakthroughs in computer vision.
▪ Cars identify stop signs, lane lines, and
other landmarks via DL tools
▪ Mapping technology can use computer
vision to detect addresses
▪ Cars triangulate and can use other 3D-
sensing technologies, such as LIDAR
and RADAR
26. Transportation: Autonomous Cars
26
Example: Waymo, the autonomous vehicle division of Alphabet Inc.
• Waymo has been operating
self-driving minivans without a safety
driver since October 2017
• Waymo’s Carcraft* software
accelerated the car’s development,
with 2.5 billion simulated miles driven
in 2016
• The system used DL together with
massive data sets collected from
self-driving cars on public roads
*Other names and brands may be claimed as the property of others.
27. Transportation: Automated Trucking
27
Traditionally : There is a shortage of 48,000 drivers nationwide.
▪ Driver turnover rates at some
companies reach 300%
▪ Truck drivers are twice as likely as
other workers to be obese and/or
have diabetes
▪ Truckers are half as likely to have
health insurance
▪ The number of accidents and fatalities
have increased in recent years
28. Transportation: Automated Trucking
28
Now with AI : Autonomous trucks can coordinate movements with other
trucks.
▪ Save on fuel, and reduce wind-drag
and the chance of a collision
▪ Video, LIDAR, and accelerometers are
used to collect detailed data about
the truck’s surroundings
▪ Guidance algorithms provide feedback
for braking, steering, and throttling
commands, based on incoming and
historical data
29.
30. Retail : AI in Supply Chain and Customer Experience
30
Traditionally : Americans are shifting their spending from material goods to
experiences.
▪ The “Amazon effect”: there have been
nine major retail bankruptcies in 2017
▪ Retailers need to become competitive
or risk obsolescence
▪ Balancing “out-of-stock” with “over-stock”
trade-off requires great finesse
31. Retail : AI in Supply Chain and Customer Experience
31
Now with AI : Companies bring experience and optimization to retail shopping.
▪ AI-powered gift concierge learns your
preferences as you engage, and can help
predict the appropriate gift to buy
▪ Leveraging ML-trained agents, companies
are providing recommendations via
natural language
▪ Companies using AI via Watson* to monitor
factors from weather to consumer behavior,
to optimize consumption rate predictions
*Other names and brands may be claimed as the property of others.
32. Retail : AI in Customer Experience
32
Example: The North Face and Watson* are combining massive datasets
and AI, to bring the brick-and-mortar experience to e-commerce.
• The North Face, with Fluid and IBM
Watson*, has launched XPS* - an AI-
enabled digital expert that uses a natural
language interface to help shoppers.
• XPS curates and filters the available
options, so shoppers are more likely
to make a purchase
*Other names and brands may be claimed as the property of others.
33. Food Retail: AI to Manage the Supply Chain
33
Traditionally : Restaurants use historical data or “gut-feeling”
approach to supply chain.
▪ This can result in excessive waste
or food unavailability
34. Food Retail: AI to Manage the Supply Chain
34
Now with AI : Many companies have started to leverage sophisticated
algorithms to forecast demand.
▪ Agents can adjust orders with
trading partners in real time, as
required for business need
35. Food Retail: AI to Manage Supply Chain
35
Example: Vivanda’s FlavorPrint* program.
• Based on recipes and consumer-provided
data, Vivanda maps data to create
“digital-taste” identifiers for
each consumer
• Providing ML-based recommendations
to customers may influence demand
• Shares data with food industry customers,
enabling them to improve demand
forecasts
*Other names and brands may be claimed as the property of others.
36.
37. Finance: Fraud Detection
37
Traditionally : Fraud is on the rise, but fraud detection is a challenging
problem to solve correctly.
▪ Historically, a predefined rule-set was
used for fraud identification, but this
approach misses much of the nuance
that surrounds fraud
▪ 1/3 of falsely identified fraud events
result in lost customers
▪ In the US, this loss is worth 13 times
the cost of actual fraud
38. Finance: Fraud Detection
38
Now with AI : With ML techniques, banks can predict fraud based on a
behavioral baseline to compare against.
▪ Uses historical shopping data and
shopping habits of customers
▪ Compares new data to baseline
to determine likelihood of fraud
39. Finance: Fraud Detection
39
Example: Sift Science
• Established a fraud data consortium
developed from over 6000 websites to
leverage large-scale real-time ML
• Autonomously learns new fraud
patterns based on billions of user
actions
40. Finance: Risk Management
40
Traditionally : New regulations force tighter control on financial institutions.
▪ New business model disruptions
▪ Increasing pressure on costs and
returns
41. Finance: Risk Management
41
Now with AI : ML can help discern the credit worthiness of potential customers
▪ Tailor a financial portfolio to fit the
goals of the user using ML algorithms.
▪ Financial institutions can develop
early warning systems for automated
reporting, portfolio management,
and recommendations based on ML.
42. Finance: Management
42
Example: ZestFinance
• Traditional underwriting systems make
decisions using few data points.
• Those with a limited credit history are often
denied credit, ultimately leading to loss of
revenue for lenders.
• ZestFinance leverages thousands of
data sources together with ML to more
accurately score borrowers, even people
with a small credit history.
43. Finance: Stock Trading
43
Traditionally : The speed and volume of information is daunting.
▪ The market is reactionary.
▪ It’s difficult to remain competitive
while relying on traditional trading
methods.
▪ Fundamental analysis is unable to
show the entire financial picture.
44. Finance: Stock Trading
44
Now with AI : Companies use massive datasets together with DL methods
for better forecasting.
▪ Data pulled from financial, political,
and social media
▪ Analyst reports combined.
45. Finance: Stock Trading
45
Example: Sentient Technologies, and Learning Evolutionary Algorithm
Framework (LEAF*)
• Manages millions of data points to find trends
and make successful stock trades.
• AI algorithms identify and combine
successful trading patterns.
• Successful strategies are tested in the real
world, evolving autonomously with LEAF.
• Sentient has received more funding than
any other AI company.
*Other names and brands may be claimed as the property of others.
46.
47. Agriculture: AgTech
47
Traditionally : The world population is
estimated to reach 9 billion by 2050.
▪ Food production will have to increase by
70% to meet the projected demand.
▪ Most land suitable for farming is already
being used, hence the needed increase
must come from higher yields.
▪ Agriculture must feed the world while not
over-straining Earth’s resources.
source: www.card.iastate.edu
48. Agriculture: AI in AgTech
48
Now with AI : Autonomous robots use computer vision and a produce
vacuum system for produce harvest.
• DL-enabled robots are being used to identify
and kill weeds.
• Companies have shown 90% herbicide
reduction due to “targeted” spray application.
• AI-driven genome sequencing advancements
enables crop “genome” editing.
49. Agriculture: AI in AgTech
49
Example: TellusLabs yield predictions.
• Uses ML together with weather and
other historical data to forecast yields.
• Leverages cloud-based GPUs for DL on
satellite images.
• TellusLab’s predictions have shown to be
consistently more accurate than the
USDA.
• Came within 1% of predicting corn and
soybean yields in 2017.
50. Manufacturing: Preventative/Predictive Maintenance
50
Traditionally : Relied on historical data to provide basis for preventative
maintenance schedule.
▪ Conservative approach: parts were
replaced well before failure, and
thus financially inefficient.
▪ Flawed due to inability to predict
new failure modes.
51. Manufacturing: Preventative/Predictive Maintenance
51
Now with AI : Internet of Things (IoT) sensors help to optimize
maintenance scheduling.
▪ Part replacement schedule is
optimized by assessing
anomalies and failure patterns.
▪ Safety and productivity can
increase exponentially.
52. Manufacturing: Preventative/Predictive Maintenance
52
Example: AI with General Electric.
• GE is the industry leader for Internet of
Things (IoT) sensor installations on engines
and turbines, and plans to have 60,000
engines connected to the internet by 2020.
• Computer vision cameras and
reinforcement learning algorithms find tiny
cracks or damage.
• Sensor data and AI allows GE to track
performance and optimize part
replacement.
53. Manufacturing: Fault Detection
53
Example: Computer vision for fault detection on solar panels.
▪ DL algorithm trained on labelled data of
correctly manufactured vs. flawed panels
▪ Reduced the need for human inspection
by 66% compared to historical need
54. Manufacturing: Automate Garment Industry
54
Example: SoftWear Automation’s ”sewbots”.
▪ Computer vision is used to track fabric
at the thread level.
▪ Eliminates need for human
seamstress / seamster.
▪ Allows designers to create garments
that were previously thought to be too
complicated or specialized to construct.
55.
56. Government: Smart Cities
56
Traditionally : As of 2008, for the first time in history, half of
the world’s population resides in cities.
▪ There are heightened demands on
scarce resources.
▪ Simultaneously, a large part of
existing infrastructure is underutilized
or not being used efficiently.
57. Government: Smart Cities
57
Now with AI : AI techniques are used to analyze photo and video data to
perform studies of pedestrian and traffic trends.
▪ Adaptive signal control: allows traffic
lights to tailor their timing based on
real-time data.
▪ With license plate recognition, and DL
technology, cities can not only optimize
parking but can also track criminals.
58. Government: Smart Cities
58
Example: AT&T reimagines smart cities
• AT&T developed a framework to help
cities integrate Internet of Things (IoT)
sensors with AI.
• Remotely monitor the condition of
roads, bridges, buildings.
• Assist with public safety.
• Notify police if gunfire has gone off,
by using sound detection.
59. Government : Cybersecurity
59
Example: Deep Instinct
• Uses GPU-based neural network to
achieve 99% detection rates for even the
most advanced cyber attacks.
• DeepInstinct’s DL models have the ability
to detect patterns - mostly designed by
humans - enabling the prediction of
pending cyber attack.
60. Government : Education
60
Example: Adaptive learning systems, and grading.
• Learning analytics track student
performance and provide tailored
educational programs.
• Using natural language processing
and ML models, AI programs can be
used for long answer and essay
grading.
61.
62. Oil and Gas : AI to Optimize Operations
62
Traditionally : Shrinking oil reserves force companies to operate in
remote and possibly hostile areas.
▪ Price has fallen dramatically in
recent years.
▪ Forcing company layoffs and
drastic budget cuts.
▪ Ultimately, companies are in
great need of optimizing
operations and cost.
63. Oil and Gas : AI to Optimize Operations
63
Now with AI : AI uses economic, political and weather data to forecast
optimum production locations.
▪ Drilling is still an expensive and
risk-prone endeavor.
▪ ML, with seismic, thermal and
strata data, can help optimize
the drilling process.
64. Oil and Gas : AI for Oil and Gas Exploration
64
Example: ExxonMobile and MIT developing “submersible” robots
for exploration.
• AI robots are used in ocean exploration
to detect “natural seep”.
• Robots are trained via DL techniques
and learn from their mistakes.
• Simultaneously protect the ecosystem
and detect new energy resources.
65. AI and Customer Service
65
Example: Bot assistants and customer service agents
• AI Augmented messaging.
• AI for sorting and routing inquiries.
• AI enhanced customer phone calls.
• Some companies have used AI to
fully automate customer service.
66. Music: AI for Music Generation
66
Example: “I AM AI”, first album released in 2017 to be generated by AI –
with professional musicians and DL technology.
• Music generation is possible due to special
DL algorithms that are designed for
sequential data.
• The models learn musical patterns based
on learning from large musical datasets.
• Raw music files can be processed on cloud-
based computer power, making DL on these
datasets possible.
67. Gaming: AI and the Next Generation of Games
67
Now with AI : Forza 5 Motorsport* uses its “Drivatar” AI system to
learn how to drive in the style of other players in the game.
▪ Neural networks are used to train
characters to walk and run realistically.
▪ Reinforcement Learning (RL) is a technique
used throughout gaming.
*Other names and brands may be claimed as the property of others.
68. Learning Objectives Recap
In this session, we worked to:
• Explain why AI is transforming a range of industries.
• Give specific examples of how AI technology affects industries.
68
69.
70. Sources for information used in this presentation (listed by slide number)
Slide 7 https://commons.wikimedia.org/wiki/File:Stethoscope_and_Laptop_Computer_-_Nci-vol-9713-
300.jpg
Slide 8 https://www.pexels.com/photo/black-and-white-blood-pressure-kit-220723
Slide 9 https://pixnio.com/science/medical-science/syringe-needle-medicine-injection-health-hospital
Slide 10 https://www.pexels.com/photo/view-of-operating-room-247786/
Slide 11 https://www.pexels.com/photo/white-pink-and-yellow-blister-packs-163944/
Slide 13 https://www.goodfreephotos.com/business-and-technology/graphics-and-charts-on-
tablet.jpg.php
23 - https://www.pexels.com/photo/crowded-street-with-cars-passing-by-708764/
26 https://www.pexels.com/photo/white-dump-truck-near-pine-tress-during-daytime-93398/
27 https://www.pexels.com/photo/action-automotive-cargo-container-diesel-590839/
29 https://www.pexels.com/photo/assorted-color-box-lot-on-rack-811101/
32 https://www.pexels.com/photo/food-salad-restaurant-person-5317/
33 https://www.pexels.com/photo/basil-delicious-food-ingredients-459469/
38 https://www.pexels.com/photo/marketing-iphone-smartphone-notebook-34069/
40 https://www.pexels.com/photo/coding-computer-data-depth-of-field-577585/
41 https://www.pexels.com/photo/blur-cash-close-up-dollars-545065/
71. Sources for information used in this presentation (listed by slide number)
41 https://www.pexels.com/photo/blur-cash-close-up-dollars-545065/
42 https://www.pexels.com/photo/administration-articles-bank-black-and-white-261949/
43 https://www.pexels.com/photo/airport-bank-board-business-534216/
44 https://www.pexels.com/photo/abstract-art-blur-bright-373543/
47 https://www.pexels.com/photo/green-tractor-175389/
50 https://www.pexels.com/photo/close-up-of-telephone-booth-257736/
52 https://www.pexels.com/photo/blur-computer-connection-electronics-442150/
53 https://www.pexels.com/photo/black-and-silver-solar-panels-159397/
57 https://www.pexels.com/photo/aerial-photography-of-concrete-bridge-681347
58 https://www.pexels.com/photo/photo-of-guy-fawkes-mask-with-red-flower-on-top-on-hand-38275/
60 https://www.pexels.com/photo/girls-on-desk-looking-at-notebook-159823/
62 https://www.pexels.com/photo/landscape-sunset-architecture-platform-87236/
65 https://www.pexels.com/photo/people-laptop-industry-internet-132700/
Editor's Notes
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare
Nonspecific: (ex: redness of skin is a sign of many different disorders)
https://en.wikipedia.org/wiki/Artificial_intelligence_in_healthcare
Nonspecific: (ex: redness of skin is a sign of many different disorders)
https://hms.harvard.edu/news/better-together
JL: updated chart based on :
https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-diagnosis/
For example, it was previously thought that Tamoxifen was 80% effective for breast cancer patients, but now with machine learning analysis, specialists know that it is actually 100% effective on 80% of the population.
http://www.oreilly.com/data/free/files/datascience-transforming-healthcare.pdf
For example, it was previously thought that Tamoxifen was 80% effective for breast cancer patients, but now with machine learning analysis, specialists know that it is actually 100% effective on 80% of the population.
http://www.oreilly.com/data/free/files/datascience-transforming-healthcare.pdf
https://www.techemergence.com/machine-learning-in-pharma-medicine/
https://www.wired.com/story/google-is-giving-away-ai-that-can-build-your-genome-sequence/
(see ‘DeepVariant works by:’ ..
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
(it takes a gallon of water to grow 1 almond!)
https://www.forbes.com/sites/themixingbowl/2017/09/05/can-artificial-intelligence-help-feed-the-world/#6ebf25bf46db
http://www.economist.com/technology-quarterly/2016-06-09/factory-fresh
Img:https://www.card.iastate.edu/iowa_ag_review/summer_08/article4.aspx
https://www.infoq.com/articles/machine-learning-techniques-predictive-maintenance
https://www.engineering.com/AdvancedManufacturing/ArticleID/15798/How-Predictive-Maintenance-Fits-into-Industry-40.aspx
More: Industries where we are seeing this: GE engines and turbines, manufacturing plant examples?
https://www.infoq.com/articles/machine-learning-techniques-predictive-maintenance
https://www.engineering.com/AdvancedManufacturing/ArticleID/15798/How-Predictive-Maintenance-Fits-into-Industry-40.aspx
More: Industries where we are seeing this: GE engines and turbines, manufacturing plant examples?
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
Where does AI apply?
Computer vision (highlight industries)
Self Driving Cars
Healthcare
Image Search
Natural language processing (highlight industries)
Chatbots
Social Media
AirBNB listing recommendation and similarity
Industries
Self Driving Cars
Automated Driving, Healthcare, Manufacturing, IOT, Gaming
iOT “The goal is that your doctor would receive notification if a certain condition was met – your heart rate increased to an unsafe level, or even stopped, for example”
Gaming - intelligent agents
Companies
Self Driving car companies
Finance
For example, Starbucks has launched a virtual barista platform to take orders via voice commands.
https://www.techemergence.com/ai-for-customer-service-use-cases/