John Thompson from Client Solutions Ireland presents a brief history of Artificial Intelligence, why it's such a hot topic, the benefits it can bring and the challenges it presents.
Discuss about Secret invention -> patentability
The principle secret -> build working models -> introduce how to review patentability -> Including 4 legal requirements -> statutory class (focus on class 1: process)
The document discusses an open approach to increasing customer retention and lifetime value through wearable devices and data. It introduces the speaker, Jeff Katz, and covers topics like the recent history of wearables being kept in a drawer, kindergarten lessons of sharing, and three big ideas - interoperability, data stewardship, and transparency. The presentation concludes by introducing Geeny, a platform for building compelling solutions through an open and transparent approach to wearable data and consumer choice.
Automation of knowledge work involves using computers to perform tasks previously done by knowledge workers such as data analysis, problem solving, and decision making. As computers get better at mimicking human reasoning, they are taking over more high-paying jobs. Trends include increased automation of workflows, accessibility of information, remote and distributed work, and exponential increases in computer processing power. While automation shortens time to acquire knowledge and provides accurate information, it also risks human manipulation and could displace human jobs. It may help industries like medicine, manufacturing, law, education and finance by enabling faster research, consistent learning, and identification of existing information. However, there are also concerns about its impact on the economy and jobs.
Usama Fayyad talk at Silicon Slopes Technology Summit in Salt Lake City January 31, 2019. The title is "Deploying #AI Technology that Works - #AI Hype vs. Reality: Lessons Learned for Pragmatic AI in the Enterprise. I cover my own version of a brief history of AI and how #BigData is strongly related to making AI work. I cover 5 lessons from the front lines for making AI work in the Enterprise. I conclude with a brief overview of what we are doing at OODA Health, Inc.
This document discusses IoT adoption and provides context about the presenter. It begins with credentials and disclaimer from the presenter. Then, it defines IoT and discusses trends supporting IoT adoption such as decreasing hardware costs and increasing computational power and internet availability. It notes that adoption does not guarantee usage. Challenges to IoT adoption are then outlined as long implementation times, scalability issues, and customization difficulties. Finally, it discusses theoretical models of technology acceptance including the theory of reasoned action, technology acceptance model, and technology acceptance model 2.
The document provides 10 reasons for opting for a career in computer science. It discusses how computer science is involved in many aspects of modern life and enables one to solve complex problems and make a positive impact. Computer science careers offer high pay and job satisfaction with many open positions available. Expertise in computer science is valuable for other careers as well by providing problem-solving skills. Computer science allows for both individual creativity and team collaboration. It is seen as an essential part of a well-rounded education, and future opportunities in the field are vast due to its ability to drive innovation.
Getting to AI ROI: Finding Value in Your Unstructured Contentindico data
Artificial Intelligence is definitely having its moment, but if you’re like most companies, you haven’t yet been able to capture ROI from these exciting technologies. It seems complicated, expensive, requires specialized talent, crazy data requirements, and more. Your boss may have dropped a vague missive onto your desk asking you to “figure out how AI can help enhance our business.” You have piles and piles of unstructured content—contracts, documents, feedback, but you haven’t been able to drive value from your data. Where to even start?
We’ll show you how.
Hear Indico’s CEO Tom Wilde and Intellyx’s Jason Bloomberg's perspectives in this valuable and practical webinar to start your AI journey to success. In this webinar you will learn:
- An understanding of the “alphabet soup” of AI and which technology is right for you—including Machine Learning, Deep Learning, Transfer Learning, and more
- A framework for developing use cases that can benefit from AI
- The building blocks for AI success
- A methodology for designing in ROI from the outset
The document discusses the 5 steps of an AI/ML innovation project:
1. Empathize and analyze to understand user needs and objectives.
2. Define and synthesize insights about key decisions and potential variables and metrics.
3. Ideate potential solutions by choosing a suitable machine learning model.
4. Prototype and tune by creating mockups to study user interactions and identify insights.
5. Test and validate prototypes using user testing and evaluating the machine learning model on test data.
Discuss about Secret invention -> patentability
The principle secret -> build working models -> introduce how to review patentability -> Including 4 legal requirements -> statutory class (focus on class 1: process)
The document discusses an open approach to increasing customer retention and lifetime value through wearable devices and data. It introduces the speaker, Jeff Katz, and covers topics like the recent history of wearables being kept in a drawer, kindergarten lessons of sharing, and three big ideas - interoperability, data stewardship, and transparency. The presentation concludes by introducing Geeny, a platform for building compelling solutions through an open and transparent approach to wearable data and consumer choice.
Automation of knowledge work involves using computers to perform tasks previously done by knowledge workers such as data analysis, problem solving, and decision making. As computers get better at mimicking human reasoning, they are taking over more high-paying jobs. Trends include increased automation of workflows, accessibility of information, remote and distributed work, and exponential increases in computer processing power. While automation shortens time to acquire knowledge and provides accurate information, it also risks human manipulation and could displace human jobs. It may help industries like medicine, manufacturing, law, education and finance by enabling faster research, consistent learning, and identification of existing information. However, there are also concerns about its impact on the economy and jobs.
Usama Fayyad talk at Silicon Slopes Technology Summit in Salt Lake City January 31, 2019. The title is "Deploying #AI Technology that Works - #AI Hype vs. Reality: Lessons Learned for Pragmatic AI in the Enterprise. I cover my own version of a brief history of AI and how #BigData is strongly related to making AI work. I cover 5 lessons from the front lines for making AI work in the Enterprise. I conclude with a brief overview of what we are doing at OODA Health, Inc.
This document discusses IoT adoption and provides context about the presenter. It begins with credentials and disclaimer from the presenter. Then, it defines IoT and discusses trends supporting IoT adoption such as decreasing hardware costs and increasing computational power and internet availability. It notes that adoption does not guarantee usage. Challenges to IoT adoption are then outlined as long implementation times, scalability issues, and customization difficulties. Finally, it discusses theoretical models of technology acceptance including the theory of reasoned action, technology acceptance model, and technology acceptance model 2.
The document provides 10 reasons for opting for a career in computer science. It discusses how computer science is involved in many aspects of modern life and enables one to solve complex problems and make a positive impact. Computer science careers offer high pay and job satisfaction with many open positions available. Expertise in computer science is valuable for other careers as well by providing problem-solving skills. Computer science allows for both individual creativity and team collaboration. It is seen as an essential part of a well-rounded education, and future opportunities in the field are vast due to its ability to drive innovation.
Getting to AI ROI: Finding Value in Your Unstructured Contentindico data
Artificial Intelligence is definitely having its moment, but if you’re like most companies, you haven’t yet been able to capture ROI from these exciting technologies. It seems complicated, expensive, requires specialized talent, crazy data requirements, and more. Your boss may have dropped a vague missive onto your desk asking you to “figure out how AI can help enhance our business.” You have piles and piles of unstructured content—contracts, documents, feedback, but you haven’t been able to drive value from your data. Where to even start?
We’ll show you how.
Hear Indico’s CEO Tom Wilde and Intellyx’s Jason Bloomberg's perspectives in this valuable and practical webinar to start your AI journey to success. In this webinar you will learn:
- An understanding of the “alphabet soup” of AI and which technology is right for you—including Machine Learning, Deep Learning, Transfer Learning, and more
- A framework for developing use cases that can benefit from AI
- The building blocks for AI success
- A methodology for designing in ROI from the outset
The document discusses the 5 steps of an AI/ML innovation project:
1. Empathize and analyze to understand user needs and objectives.
2. Define and synthesize insights about key decisions and potential variables and metrics.
3. Ideate potential solutions by choosing a suitable machine learning model.
4. Prototype and tune by creating mockups to study user interactions and identify insights.
5. Test and validate prototypes using user testing and evaluating the machine learning model on test data.
What is information technology ?
The application of computers and telecommunications equipment to store, retrieve, transmit and manipulate data
Industries like computer hardware, software, electronics, semiconductors, intern et, telecom equipment, e-commerce and computer services, etc are examples of IT ventures
This document discusses data science and the growing field of big data. It notes that data science uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. It provides some key facts about the massive amount of data being generated every day from various sources like social media, internet transactions, sensors and devices. The document also discusses the differences between data science and computer science, with data science focusing more on analyzing large datasets to answer questions and find insights, while computer science focuses more on software development and engineering.
Data science is a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from large amounts of structured and unstructured data. As the amount of data in the world grows exponentially, the need for data scientists to analyze this big data and discover useful patterns will also grow dramatically. By 2025, it is estimated that there will be over 200 zettabytes of cloud data storage worldwide and data science jobs are projected to be the highest paid and most in-demand jobs of the future.
This workshop covered major trends in IT such as cloud computing, big data, and machine learning in order to help Tunisian entrepreneurs identify opportunities. Cloud computing can make computing more efficient and affordable by outsourcing hardware maintenance to large data centers. While cloud computing may reduce some IT jobs, it also creates new opportunities for developers. Big data refers to the vast amounts of data collected online, which poses security and privacy issues when stored remotely. Machine learning uses algorithms to automate tasks and processes based on patterns in data.
The document discusses the impact of social media and cloud computing on education and the environment. It notes that social media allows for more interactive and collaborative online learning for students, and helps teachers encourage autonomy and receive feedback. Important issues with social media include e-safety and distraction. Cloud computing reduces the need for local data storage and computing power, lowering energy costs for data processing and storage. However, security and reliability of cloud services are concerns.
The document discusses the Internet of Things (IoT), which connects physical objects through electronics, software and sensors to collect and exchange data. It provides examples of IoT applications like the Nest thermostat and an ETC transportation system in Taiwan. IoT combines multiple disciplines and can improve various aspects of life beyond smart homes, with machine learning playing a key role in current IoT applications.
This document summarizes a 25 minute presentation by Jeff Katz on smart homes. The presentation discusses how smart home technology has not lived up to expectations, outlines Katz's background in technology and engineering, and presents three big ideas for improving the smart home experience: interoperability and data stewardship, working together to build solutions, and consumer transparency and choice. The presentation concludes with a question and answer session.
1) AI has the potential to transform industries just as electricity did 100 years ago.
2) For AI to have widespread impact, several key technologies like data acquisition, computing power, and machine learning algorithms had to converge.
3) AI is now being applied across many domains like healthcare, finance, transportation and more to automate processes, augment human work, and create personalized experiences for consumers.
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, IoT/big data, and semantic computing concepts. Semantic computing transforms unstructured data into structured triples that can be queried using ontologies to add context and meaning. It discusses how semantic computing supports applications in various domains like finance, government, and healthcare by integrating diverse data sources and enabling expanded analytics. The US Navy case study shows how semantic computing helped the Navy reduce energy costs.
This document provides an introduction to a micro MOOC on career technologies. It discusses how technology has evolved to include more than just electronic devices and also encompasses tools, machines, and resources used to solve problems. The MOOC will focus on cutting-edge technologies used in various career fields and will explore career information related to those fields. Students will study technologies and careers in three clusters: communications technologies, physical technologies, and bio technologies. The course requirements include journal entries, blog posts, discussion boards, and modules for each career cluster.
While technology provides many benefits like increased efficiency and access to information, it also comes with disadvantages. As society becomes more dependent on technology, people rely on it for everyday tasks and can feel disabled when systems fail. Additionally, technological advances reduce the need for human workers as machines can perform the same tasks. This decreases the value of human labor and can negatively impact employment globally. However, technology also allows for cost-savings for businesses.
Book Reading - Does IT Matter - Nicholas CarrRitesh Nayak
The document discusses the history and evolution of information technology from the development of the microprocessor in 1969 to modern technologies like the World Wide Web. It argues that IT is becoming a commodity rather than a source of competitive advantage as hardware, software, and infrastructure become standardized. As IT diffuses and its strategic benefits vanish, companies must manage IT costs carefully while leveraging it to enable new business strategies and reduce risks through collaboration. The future impact of IT depends on how well companies adapt their businesses rather than simply investing to stay "cutting edge".
Technology is defined as the application of scientific knowledge for practical purposes, especially in industry. It includes tools, machines, materials, and systems that extend human abilities. Technology is used in many areas of life like work, communication, transportation, learning, manufacturing and more. An example of technology is computers and digital devices that have changed education by allowing remote learning and collaboration in real time.
Semantic Software is an Australian cognitive computing company that has developed a comprehensive semantic computing platform to help analyze large amounts of data. Their platform uses various AI techniques like deep learning, machine learning, natural language processing, and semantic computing with reasoning and inferencing to help computers analyze data without needing as much human input. They believe semantic computing is key to truly cognitive applications and that their platform is one of the most advanced for these purposes.
Nicholas Carr argued in a 2003 Harvard Business Review article that information technology no longer provides a competitive advantage for most companies and should be viewed as a commodity. Carr draws parallels between the development of IT and previous infrastructural technologies like electricity and railroads that were initially proprietary but became standardized and shared. While early adoption of new technologies can offer temporary advantages, Carr advises companies to follow rather than lead technological innovation as technologies mature and competitive benefits diminish.
This document discusses learner owned devices and the role of IT departments. It notes that while consumer technology is advancing rapidly, institutions have responsibilities around security, compliance, and infrastructure impact. One strategy is to secure the institutional network and not restrict devices until they connect. The role of IT has shifted from provider to facilitator of technology use. Compromise is needed - engaging learners in dialogue about benefits and limitations of new technologies. IT must understand technology potential while users understand institutional constraints.
The document discusses information systems and their components. It states that information systems integrate various components like data collection, storage, processing and delivery of information. It then discusses how businesses, governments and individuals rely on information systems to carry out operations and interact with others. Some key points made include how corporations use information systems for marketing, finance and HR functions while governments use it to provide efficient public services. It also discusses different types of information like statistical, tactical and operational, as well as resources required for information systems like hardware, software, data, networks and people.
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Hector Guerrero- Road to Business AnalyticsErika Marr
This document provides an overview of key concepts in business analytics including:
- Definitions of data science, data scientist, and analytics which involve extracting insights from data.
- A process map of data science including data collection, cleaning, modeling, and communication.
- A brief history and timeline of developments in computer technology, statistics, and analytics from the 1960s to present.
- Emerging areas like artificial intelligence, autonomous systems, and the impact of technology on jobs and society.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins with definitions of AI and discusses how people commonly interact with AI systems like search engines and virtual assistants. It then describes the three phases of computing and the shift to the current AI computing era. The document outlines why AI is important for automation, decision making, personalization and other applications. It also discusses the main types of AI as strong/narrow AI and weak/general AI. The relationship between AI, machine learning, and deep learning is explained. The document concludes with introductions to machine learning and its key concepts like data, features, labels and common Python libraries. It also covers the main types of machine learning as supervised, unsupervised and
Deep Learning for AI - Yoshua Bengio, MilaLucidworks
Deep Learning for AI
The keynote address covered several topics related to deep learning for AI:
1. Deep learning is based on the assumption that intelligence arises from general learning mechanisms that can acquire knowledge from data and experience.
2. Recent breakthroughs using deep learning have improved computer performance in areas like perception, language processing, games, and medical imaging analysis.
3. Deep learning exploits hierarchical feature learning through neural network architectures to allow machines to learn higher levels of abstraction from data, enabling better generalization.
4. While deep learning has achieved success, fully human-level AI still requires progress in unsupervised learning and constructing intuitive models from interacting with the world like humans do from a young age.
What is information technology ?
The application of computers and telecommunications equipment to store, retrieve, transmit and manipulate data
Industries like computer hardware, software, electronics, semiconductors, intern et, telecom equipment, e-commerce and computer services, etc are examples of IT ventures
This document discusses data science and the growing field of big data. It notes that data science uses scientific methods and processes to extract knowledge and insights from structured and unstructured data. It provides some key facts about the massive amount of data being generated every day from various sources like social media, internet transactions, sensors and devices. The document also discusses the differences between data science and computer science, with data science focusing more on analyzing large datasets to answer questions and find insights, while computer science focuses more on software development and engineering.
Data science is a multi-disciplinary field that uses scientific methods and processes to extract knowledge and insights from large amounts of structured and unstructured data. As the amount of data in the world grows exponentially, the need for data scientists to analyze this big data and discover useful patterns will also grow dramatically. By 2025, it is estimated that there will be over 200 zettabytes of cloud data storage worldwide and data science jobs are projected to be the highest paid and most in-demand jobs of the future.
This workshop covered major trends in IT such as cloud computing, big data, and machine learning in order to help Tunisian entrepreneurs identify opportunities. Cloud computing can make computing more efficient and affordable by outsourcing hardware maintenance to large data centers. While cloud computing may reduce some IT jobs, it also creates new opportunities for developers. Big data refers to the vast amounts of data collected online, which poses security and privacy issues when stored remotely. Machine learning uses algorithms to automate tasks and processes based on patterns in data.
The document discusses the impact of social media and cloud computing on education and the environment. It notes that social media allows for more interactive and collaborative online learning for students, and helps teachers encourage autonomy and receive feedback. Important issues with social media include e-safety and distraction. Cloud computing reduces the need for local data storage and computing power, lowering energy costs for data processing and storage. However, security and reliability of cloud services are concerns.
The document discusses the Internet of Things (IoT), which connects physical objects through electronics, software and sensors to collect and exchange data. It provides examples of IoT applications like the Nest thermostat and an ETC transportation system in Taiwan. IoT combines multiple disciplines and can improve various aspects of life beyond smart homes, with machine learning playing a key role in current IoT applications.
This document summarizes a 25 minute presentation by Jeff Katz on smart homes. The presentation discusses how smart home technology has not lived up to expectations, outlines Katz's background in technology and engineering, and presents three big ideas for improving the smart home experience: interoperability and data stewardship, working together to build solutions, and consumer transparency and choice. The presentation concludes with a question and answer session.
1) AI has the potential to transform industries just as electricity did 100 years ago.
2) For AI to have widespread impact, several key technologies like data acquisition, computing power, and machine learning algorithms had to converge.
3) AI is now being applied across many domains like healthcare, finance, transportation and more to automate processes, augment human work, and create personalized experiences for consumers.
The document discusses semantic computing and its benefits. It provides an agenda for introducing semantic software, IoT/big data, and semantic computing concepts. Semantic computing transforms unstructured data into structured triples that can be queried using ontologies to add context and meaning. It discusses how semantic computing supports applications in various domains like finance, government, and healthcare by integrating diverse data sources and enabling expanded analytics. The US Navy case study shows how semantic computing helped the Navy reduce energy costs.
This document provides an introduction to a micro MOOC on career technologies. It discusses how technology has evolved to include more than just electronic devices and also encompasses tools, machines, and resources used to solve problems. The MOOC will focus on cutting-edge technologies used in various career fields and will explore career information related to those fields. Students will study technologies and careers in three clusters: communications technologies, physical technologies, and bio technologies. The course requirements include journal entries, blog posts, discussion boards, and modules for each career cluster.
While technology provides many benefits like increased efficiency and access to information, it also comes with disadvantages. As society becomes more dependent on technology, people rely on it for everyday tasks and can feel disabled when systems fail. Additionally, technological advances reduce the need for human workers as machines can perform the same tasks. This decreases the value of human labor and can negatively impact employment globally. However, technology also allows for cost-savings for businesses.
Book Reading - Does IT Matter - Nicholas CarrRitesh Nayak
The document discusses the history and evolution of information technology from the development of the microprocessor in 1969 to modern technologies like the World Wide Web. It argues that IT is becoming a commodity rather than a source of competitive advantage as hardware, software, and infrastructure become standardized. As IT diffuses and its strategic benefits vanish, companies must manage IT costs carefully while leveraging it to enable new business strategies and reduce risks through collaboration. The future impact of IT depends on how well companies adapt their businesses rather than simply investing to stay "cutting edge".
Technology is defined as the application of scientific knowledge for practical purposes, especially in industry. It includes tools, machines, materials, and systems that extend human abilities. Technology is used in many areas of life like work, communication, transportation, learning, manufacturing and more. An example of technology is computers and digital devices that have changed education by allowing remote learning and collaboration in real time.
Semantic Software is an Australian cognitive computing company that has developed a comprehensive semantic computing platform to help analyze large amounts of data. Their platform uses various AI techniques like deep learning, machine learning, natural language processing, and semantic computing with reasoning and inferencing to help computers analyze data without needing as much human input. They believe semantic computing is key to truly cognitive applications and that their platform is one of the most advanced for these purposes.
Nicholas Carr argued in a 2003 Harvard Business Review article that information technology no longer provides a competitive advantage for most companies and should be viewed as a commodity. Carr draws parallels between the development of IT and previous infrastructural technologies like electricity and railroads that were initially proprietary but became standardized and shared. While early adoption of new technologies can offer temporary advantages, Carr advises companies to follow rather than lead technological innovation as technologies mature and competitive benefits diminish.
This document discusses learner owned devices and the role of IT departments. It notes that while consumer technology is advancing rapidly, institutions have responsibilities around security, compliance, and infrastructure impact. One strategy is to secure the institutional network and not restrict devices until they connect. The role of IT has shifted from provider to facilitator of technology use. Compromise is needed - engaging learners in dialogue about benefits and limitations of new technologies. IT must understand technology potential while users understand institutional constraints.
The document discusses information systems and their components. It states that information systems integrate various components like data collection, storage, processing and delivery of information. It then discusses how businesses, governments and individuals rely on information systems to carry out operations and interact with others. Some key points made include how corporations use information systems for marketing, finance and HR functions while governments use it to provide efficient public services. It also discusses different types of information like statistical, tactical and operational, as well as resources required for information systems like hardware, software, data, networks and people.
Similar to Robotics and Artificial Intelligence - The good the bad and the data - John Thompson Client Solutions Ireland - DAMA Ireland March 2019 Event
IBM Academy of Technology & Cognitive ComputingNico Chillemi
I delivered this presentation at University at Chieti-Pescara in Abruzzo (Italy) in September 2015, introducing IBM Academy of Technology and talking about Cognitiva Computing and Analytics with IBM Watson and IBM IT Operations Analytics Log Analysis (ITOA). The video in Italian is available on YouTube, please contact me if you are interested. Thanks to Amanda Tenedini for the help with Social Media and to Piero Leo for the help with IBM Watson.
Hector Guerrero- Road to Business AnalyticsErika Marr
This document provides an overview of key concepts in business analytics including:
- Definitions of data science, data scientist, and analytics which involve extracting insights from data.
- A process map of data science including data collection, cleaning, modeling, and communication.
- A brief history and timeline of developments in computer technology, statistics, and analytics from the 1960s to present.
- Emerging areas like artificial intelligence, autonomous systems, and the impact of technology on jobs and society.
[DSC Europe 22] On the Aspects of Artificial Intelligence and Robotic Autonom...DataScienceConferenc1
Autonomy in targeting is a function that could be applied to any intelligent system, in particular the rapidly expanding array of robotic systems, in the air, on land and at sea – including swarms of small robots. This is an area of significant investment and emphasis for many armed forces, and the question is not so much whether we will see more intelligent robots, but whether and by what means they will remain under human control. Today’s remote-controlled weapons could become tomorrow’s autonomous weapons with just a software upgrade. The central element of any future autonomous weapon system will be the software. Military powers are investing in AI for a wide range of applications10 and significant efforts are already underway to harness developments in image, facial and behavior recognition using AI and machine learning techniques for intelligence gathering and “automatic target recognition” to identify people, objects or patterns. Although not all autonomous weapon systems incorporate AI and machine learning, this software could form the basis of future autonomous weapon systems.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins with definitions of AI and discusses how people commonly interact with AI systems like search engines and virtual assistants. It then describes the three phases of computing and the shift to the current AI computing era. The document outlines why AI is important for automation, decision making, personalization and other applications. It also discusses the main types of AI as strong/narrow AI and weak/general AI. The relationship between AI, machine learning, and deep learning is explained. The document concludes with introductions to machine learning and its key concepts like data, features, labels and common Python libraries. It also covers the main types of machine learning as supervised, unsupervised and
Deep Learning for AI - Yoshua Bengio, MilaLucidworks
Deep Learning for AI
The keynote address covered several topics related to deep learning for AI:
1. Deep learning is based on the assumption that intelligence arises from general learning mechanisms that can acquire knowledge from data and experience.
2. Recent breakthroughs using deep learning have improved computer performance in areas like perception, language processing, games, and medical imaging analysis.
3. Deep learning exploits hierarchical feature learning through neural network architectures to allow machines to learn higher levels of abstraction from data, enabling better generalization.
4. While deep learning has achieved success, fully human-level AI still requires progress in unsupervised learning and constructing intuitive models from interacting with the world like humans do from a young age.
Data science is a multidisciplinary field that uses statistics, programming, and machine learning to extract knowledge and insights from large amounts of data. It has various applications like email spam detection, medical diagnosis, predicting stock prices, and self-driving cars. The document discusses how the size of data is rapidly increasing and will continue to do so, with an estimated 463 exabytes of new data generated daily by 2025. It also outlines common tasks performed by data scientists like understanding business problems, analyzing and visualizing data, making recommendations, and predicting future values. Theoretical and practical aspects of data science are also covered, along with examples of how it relates to other fields.
AI-SDV 2021: Nils Newmann - AI – Who is in control and why is that important?Dr. Haxel Consult
The document discusses artificial intelligence (AI) systems for categorizing articles and the importance of training data. It provides an example of a university building an AI system to categorize articles as related to AI or not and further categorize AI articles into subdomains. The system was trained on tens of thousands of manually tagged records to understand the categories. The document emphasizes that effective AI requires extensive training data and algorithms developed for small datasets, as subject matter experts typically work with small amounts of specialized data, not large public datasets. New techniques like few-shot learning, zero-shot learning, and transfer learning allow building AI that supports experts working with small, specialized datasets.
The document discusses the role and responsibilities of an information system manager. An information system manager may oversee standalone PCs, networks, or multi-user systems. Key responsibilities include understanding how hardware and software function, configuring systems for optimal performance, evaluating performance, training users, and maintaining the system. To be effective, an information system manager needs patience, communication skills, hands-on experience, and a working knowledge of hospitality operations. The role can be reactive, proactive, or coordinative in integrating business needs with technology. Well-managed systems provide accurate, accessible data to efficiently support operations.
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Diego Oppenheimer discusses the rise of algorithm marketplaces and the new "algorithm economy". Key points include:
- Advances in machine learning, computer vision, speech recognition and natural language processing are enabling algorithms to interpret unstructured data at scale.
- Algorithm marketplaces allow algorithms to be hosted, discovered, monetized and composed modularly to address a wide range of use cases across many industries.
- The algorithm economy will lower barriers to applying machine intelligence and foster innovation as algorithms become reusable assets that creators and users can both benefit from.
IBM's Watson is a question answering computer system developed by IBM to answer questions posed in natural language. It was named after IBM's founder Thomas J. Watson and was initially created to compete on the game show Jeopardy! where it defeated human champions in 2011. Watson uses advanced natural language processing, semantic analysis, and machine learning to defeat human opponents. It is capable of answering complex questions with nuanced language and is being developed by IBM for commercial applications in fields like healthcare, finance and education.
IBM's Watson is a question answering computer system developed by IBM to answer questions posed in natural language. It was named after IBM's founder Thomas J. Watson and was initially created to compete on the game show Jeopardy! where it defeated human champions in 2011. Watson uses advanced natural language processing, semantic analysis, and machine learning to defeat human opponents. It is capable of answering complex questions with nuanced language and is being developed by IBM for commercial applications in fields like healthcare, finance and education.
IBM's Watson is a question answering computer system developed by IBM to answer questions posed in natural language. It was named after IBM's founder Thomas J. Watson and was initially created to compete on the game show Jeopardy! where it defeated human champions in 2011. Watson uses advanced natural language processing, semantic analysis, and machine learning to defeat human opponents. It is capable of answering complex questions with nuanced language and is being developed by IBM for commercial applications in fields like healthcare, finance and education.
IBM's Watson is a question answering computer system developed by IBM to answer questions posed in natural language. It was named after IBM's founder Thomas J. Watson and was initially created to compete on the game show Jeopardy! where it defeated human champions in 2011. Watson uses advanced natural language processing, semantic analysis, and machine learning to defeat human opponents. It is capable of answering complex questions with nuanced language and is being developed by IBM for commercial applications in fields like healthcare, finance and education.
Susan Etlinger is an industry analyst who focuses on data and analytics. She has authored two reports on social media ROI and social analytics. She advises clients on measurement strategies and extracting insights from social data. She also works with technology companies to refine their strategies.
Big data refers to very large data sets that cannot be analyzed using traditional techniques. It is characterized by volume, velocity, and variety. Analyzing big data can help solve problems and generate value. The amount of data is growing exponentially from various sources like customer transactions, photos, and genome sequencing. This growth is driving changes in analytics approaches and capabilities.
Data science brings together techniques from computer science, statistics, mathematics, and domain knowledge to extract insights from
20240104 HICSS Panel on AI and Legal Ethical 20240103 v7.pptxISSIP
20240103 HICSS Panel
Ethical and legal implications raised by Generative AI and Augmented Reality in the workplace.
Souren Paul - https://www.linkedin.com/in/souren-paul-a3bbaa5/
Event: https://kmeducationhub.de/hawaii-international-conference-on-system-sciences-hicss/
- The document discusses navigating the Python ecosystem for data science. It outlines the various areas data science teams deal with like reporting, data processing, machine learning modeling, and application development.
- It describes the different tools and libraries in the Python ecosystem that support these areas, including machine learning, cluster computing, and scientific computing.
- The talk aims to help understand what the Python data science ecosystem offers and common gaps, so people don't reinvent solutions or get stuck looking for answers. It covers how tools fit into the machine learning workflow and work together.
This document outlines the structure and content of a module on big data. It includes 3 sessions:
1) An introduction to big data, what it is, why it matters, and the big data ecosystem. Students are assigned a proposal on using big data for analysis.
2) A discussion of MapReduce concepts, architecture, and the big data ecosystem. Students submit research and references.
3) Student presentations of use cases. This session also covers the data science process, a SWOT analysis of big data, the internet of things, and a Cisco IoT use case using predictive analytics.
My AAAI 2015 Fall Symposium presentation in the Track on "Cognitive Assistance" on November 13, 2015.
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Robotics and Artificial Intelligence - The good the bad and the data - John Thompson Client Solutions Ireland - DAMA Ireland March 2019 Event
1. Robotics and Artificial Intelligence
A brief history…
John Thompson
Managing Partner
Business Intelligence and Analytics
john.thompson@clientsolutions.ie
3. Slide 3
What is it?
• AI - enabling machines to perform functions equivalent to logic, reasoning, planning,
learning, and perception.
• The ‘key’ ingredient in making systems flexibly autonomous – Robotics
• Supervised Learning – Algorithms applied to training data to generate
‘models’
• Originated in the 1950s, with key theoretical developments in 70s and 80s
– Neural Nets, k-Means Clustering, Decision Trees, Support Vector Machines, Bayes etc.
– Some machines create their own models and/or training data – Machine Learning, Deep Learning
“All models are wrong but some are useful”
4. Slide 4
Why is it such a hot topic?
• It has always been a topic of great interest
• Reaching a tipping point
– Combination of improvements in processing power, availability of data, digitalisation of daily activities
– Low entry barriers
– Generic, user-friendly Technology
– Increasingly broad real-world applications
– Commercial viability
– Internet, Big Data, Amazon, Facebook, Google, Watson, Deep Learning, Autonomous Vehicles, Chat-
bots, Virtual Assistants etc.
5. Slide 5
What are the issues
• Ethics – Not can we, but should we?
– control, privacy, society, freedom
• Quality
– Of Model
– Of Data
6. Thank You
Presented By:
John Thompson
Managing Partner
Business Intelligence and Analytics
john.thompson@clientsolutions.ie