Presentation by Dr Jason Zagami to the Information Communication Technology Educators New South Wales (ICTENSW) conference on 15 March 2014 in Sydney, NSW.
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
Digital Technologies: What now?
Presentation by Dr Jason Zagami to the Queensland Studies Authority: Australian Curriculum conference on 22 March 2014 in Brisbane, QLD.
Australian Digital Technologies LeadersJason Zagami
Australian Digital Technologies Leaders
Presentation by Dr Jason Zagami to the Australian Digital Technologies Leaders (EdTechSA) on 13 April 2014 in Adelaide, SA.
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but what’s the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
The upsurge of deep learning for computer vision applicationsIJECEIAES
Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process.
This presentation provides an overview of artificial intelligence (AI) and deep learning. It begins with introductions to AI and deep learning, explaining that AI allows machines to perform tasks typically requiring human intelligence through machine learning. Deep learning is a type of machine learning using artificial neural networks inspired by the human brain. The presentation then discusses why AI has grown recently, citing increased computing power, data storage, and data availability. It also covers deep learning model development and concepts like underfitting and overfitting. The presentation describes different types of learning approaches like supervised, unsupervised, and reinforcement learning. It concludes with popular applications of deep learning like precision agriculture, computer vision, and recommendations.
THIS IS AN INTRODUCTORY PPT OF EMERGING TECHNOLOGIES AND NEED IN REAL LIFE. THIS WIL EXPLAIN BSICS ABOUT ALL EMERGING TECHNOLOGY AND THEIR APPLICATION IN VARIOUS SECTOR
Michael Sharp has extensive experience in user experience research. Some of his past roles include conducting usability testing on 3D rendering applications at the University of Colorado, studying readers' preferences for technical illustrations through eye tracking research at Rensselaer Polytechnic Institute, and managing UX labs. More recently, he has led UX work for companies like Qwest.com, Electronic Ink, Androgames, and Radiometrics.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Jordan Ryan Molina offers a class syllabus covering fundamentals of computer science including history of computing, programming, algorithms, data storage, operating systems, networking, the internet, and social issues. The class progresses from basic concepts like binary and hardware to object-oriented programming in Java, and considers both technical topics and how technology impacts society. Students will learn through explanations, examples, and hands-on programming exercises.
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...Chetan Khatri
This document summarizes a presentation about open source AI and machine learning technologies for product development. The presentation discusses key concepts like artificial intelligence, machine learning, deep learning and neural networks. It also provides examples of using computer vision, natural language processing and other AI techniques for applications like self-driving cars, visual search, sentiment analysis and more. Challenges in scaling models and frameworks are discussed along with solutions like ONNX for model interoperability across platforms.
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Cognitive computing systems use machine learning algorithms to mimic human cognition. They are able to perform complex tasks through adaptive, interactive, and iterative processes that allow them to continually acquire knowledge from data. Major examples of cognitive computing include IBM's Watson, which can understand natural language questions and provide justified answers by analyzing vast amounts of data in seconds. Cognitive computing has applications in healthcare, agriculture, transportation, security, and more.
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
Artificial intelligence (AI) is the broad field of creating intelligent machines, while machine learning (ML) is a subset of AI where systems can learn from large amounts of data to perform tasks like image recognition. Deep learning (DL) is a subset of ML that uses artificial neural networks (ANN) modeled after the human brain to identify patterns. Natural language processing (NLP) allows computers to understand human language through techniques like machine translation, question answering and sentiment analysis. The top uses of AI and ML are data security, real-time analytics, personalized dashboards, data management, sales forecasting and personal security. Automatic speech recognition (ASR) converts speech to text to power voice assistants.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
A Seminar Report on Artificial IntelligenceAvinash Kumar
This is a seminar report on Artificial Intelligence. This is mainly concerned for engineering projects & reports. This is actually done for presentation purpose.
Artificial intelligence (AI) is an area of computer science that aims to design machines that can think and act intelligently, like humans. The document discusses several key aspects of AI including:
- The goals of AI such as learning, reasoning, understanding language.
- Examples of modern AI applications like defeating chess champions, driving vehicles autonomously, and assisting with medical diagnoses.
- The history and development of AI from its origins in the 1950s to modern areas like neural networks.
- Challenges in developing truly intelligent machines that can match all aspects of human intelligence like creativity and common sense.
Digital imaging involves the creation, processing, compression, storage, printing and display of digital images. Key hardware considerations for digital imaging include the processor, RAM, monitor, video card, image capture device and file storage. Important software considerations are the graphics editing software, pixels, tones/levels, bit depth, color modes, resolution, and common file formats like JPEG, TIFF and PSD.
The document discusses computer ethics and copyright issues for educators. It addresses common misconceptions around sharing and copying digital materials. It also provides guidance on what uses are permissible, such as downloading materials for classroom lessons, as well as what requires permission, like posting student work online. Resources for educators to learn about computer ethics and copyright law are also listed.
THIS IS AN INTRODUCTORY PPT OF EMERGING TECHNOLOGIES AND NEED IN REAL LIFE. THIS WIL EXPLAIN BSICS ABOUT ALL EMERGING TECHNOLOGY AND THEIR APPLICATION IN VARIOUS SECTOR
Michael Sharp has extensive experience in user experience research. Some of his past roles include conducting usability testing on 3D rendering applications at the University of Colorado, studying readers' preferences for technical illustrations through eye tracking research at Rensselaer Polytechnic Institute, and managing UX labs. More recently, he has led UX work for companies like Qwest.com, Electronic Ink, Androgames, and Radiometrics.
This document provides an introduction and overview of the CS3243 Foundations of Artificial Intelligence course for AY2003/2004 Semester 2. The summary includes:
1) Key course details such as the textbook, lecturer, grading breakdown, and outline of topics covered.
2) A brief history of AI, including early milestones and the state of the art, such as Deep Blue defeating Kasparov in chess in 1997.
3) An overview of different views of AI, including acting humanly (Turing test), thinking humanly (cognitive modeling), thinking rationally (logic), and the textbook's approach of acting rationally as a rational agent.
It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.
Jordan Ryan Molina offers a class syllabus covering fundamentals of computer science including history of computing, programming, algorithms, data storage, operating systems, networking, the internet, and social issues. The class progresses from basic concepts like binary and hardware to object-oriented programming in Java, and considers both technical topics and how technology impacts society. Students will learn through explanations, examples, and hands-on programming exercises.
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...Chetan Khatri
This document summarizes a presentation about open source AI and machine learning technologies for product development. The presentation discusses key concepts like artificial intelligence, machine learning, deep learning and neural networks. It also provides examples of using computer vision, natural language processing and other AI techniques for applications like self-driving cars, visual search, sentiment analysis and more. Challenges in scaling models and frameworks are discussed along with solutions like ONNX for model interoperability across platforms.
CS6659 Artificial Intelligence
Slides in the features of Artificial Intelligence, Definition of Artificial Intelligence
Can be used by undergraduate students
Cognitive computing systems use machine learning algorithms to mimic human cognition. They are able to perform complex tasks through adaptive, interactive, and iterative processes that allow them to continually acquire knowledge from data. Major examples of cognitive computing include IBM's Watson, which can understand natural language questions and provide justified answers by analyzing vast amounts of data in seconds. Cognitive computing has applications in healthcare, agriculture, transportation, security, and more.
The document discusses artificial intelligence (AI) and pattern recognition. It defines AI as the intelligence demonstrated by machines, and the branch of computer science which creates it. Pattern recognition is described as assigning labels or classifications to input values based on identifying patterns. The history of AI from its origins in the 1950s is briefly outlined, along with major branches like logical AI, planning, and applications like game playing, speech recognition, robotics, and computer vision.
Introduction to artificial intelligence lecture 1REHAN IJAZ
This document provides an introduction to artificial intelligence. It defines intelligence as the ability to solve problems, think, plan, learn, recognize patterns, and handle ambiguous situations. The document then asks if machines can exhibit these intelligent behaviors such as searching meshes, solving sequences, developing plans, diagnosing issues, answering questions, recognizing fingerprints, understanding concepts, and perceiving the world. It states that the goal of AI is to create systems that can learn, think, perceive, analyze and act like humans. Early work in AI included the development of programs that used logic to solve problems like humans. Current areas of AI research include computer vision, natural language processing, expert systems, robotics, and creating human-like robots.
Artificial intelligence (AI) is the broad field of creating intelligent machines, while machine learning (ML) is a subset of AI where systems can learn from large amounts of data to perform tasks like image recognition. Deep learning (DL) is a subset of ML that uses artificial neural networks (ANN) modeled after the human brain to identify patterns. Natural language processing (NLP) allows computers to understand human language through techniques like machine translation, question answering and sentiment analysis. The top uses of AI and ML are data security, real-time analytics, personalized dashboards, data management, sales forecasting and personal security. Automatic speech recognition (ASR) converts speech to text to power voice assistants.
Artificial intelligence (AI) is the human-like intelligence exhibited by machines or software. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology. Major AI researchers and textbooks define the field as "the study and design of intelligent agents",[1] where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.[2] John McCarthy, who coined the term in 1955,[3] defines it as "the science and engineering of making intelligent machines".[4]
AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence (or "strong AI") is still among the field's long term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.
This document summarizes an IT seminar about artificial intelligence. It defines intelligence and AI, discussing early pioneers like Alan Turing. It provides examples of modern AI applications, including facial and speech recognition, learning, planning, and problem solving. Bees' ability to recognize faces from different angles is discussed, as well as conversational bots like Buddhabot. Research into building cognitive computers that mimic the brain is also summarized. The document concludes with discussing limitations of AI and potential future applications.
AI(Full name Artificial Intelligence)is a new technological science that studies and develops theories, methods, techniques, and application systems used to simulate, extend, and expand human intelligence.
Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence.
A Seminar Report on Artificial IntelligenceAvinash Kumar
This is a seminar report on Artificial Intelligence. This is mainly concerned for engineering projects & reports. This is actually done for presentation purpose.
Artificial intelligence (AI) is an area of computer science that aims to design machines that can think and act intelligently, like humans. The document discusses several key aspects of AI including:
- The goals of AI such as learning, reasoning, understanding language.
- Examples of modern AI applications like defeating chess champions, driving vehicles autonomously, and assisting with medical diagnoses.
- The history and development of AI from its origins in the 1950s to modern areas like neural networks.
- Challenges in developing truly intelligent machines that can match all aspects of human intelligence like creativity and common sense.
Digital imaging involves the creation, processing, compression, storage, printing and display of digital images. Key hardware considerations for digital imaging include the processor, RAM, monitor, video card, image capture device and file storage. Important software considerations are the graphics editing software, pixels, tones/levels, bit depth, color modes, resolution, and common file formats like JPEG, TIFF and PSD.
The document discusses computer ethics and copyright issues for educators. It addresses common misconceptions around sharing and copying digital materials. It also provides guidance on what uses are permissible, such as downloading materials for classroom lessons, as well as what requires permission, like posting student work online. Resources for educators to learn about computer ethics and copyright law are also listed.
This document discusses the photo editing and management software Aperture. It provides an overview of Aperture's capabilities for capturing, organizing, adjusting, publishing, and integrating digital images. Key features highlighted include comparing multiple images, non-destructive editing, tethered shooting, bulk exporting and labeling, and integration with iLife and iWork. Several useful plug-ins for Aperture are also listed. The document concludes by giving examples of how Aperture is used in the classroom by students and teachers, such as for editing photos, creating prints, websites and galleries, and collaborating and publishing work.
Photoshop was created by Thomas Knoll in 1987 and published by Adobe Systems. It is the current market leader for image manipulation software. Photoshop allows users to brighten images, enhance photos, and add special effects. Photo Pos Pro is also image editing software that is now free. It offers step-by-step help for beginners. GIMP was created by Spencer Kimball and Peter Mattis as a free image editor for Linux/Unix. It has a customizable interface and supports photo enhancement, retouching, and file formats.
The document discusses different teaching philosophies including teacher-centered, learner-centered, and ICT-centered philosophies. It outlines the skills and tools needed for effective technology integration in education, including computers, the internet, and digital techniques. The document argues that technology can enhance learning by allowing students more flexibility and control over what, when, where, and how they learn.
The document provides a history of educational technology from ancient times to modern day:
- Educational technology has its roots in ancient Greece where knowledge was systematically organized and instructional methods were developed.
- Major advances included the development of visual aids in the 19th century, educational films in the 1920s, instructional television in the 1930s, and the introduction of computers and the internet in recent decades.
- Today, educational technology encompasses a variety of tools and approaches aimed at addressing educational needs through the application of current technologies like computers and networks.
The document provides an overview of the Digital Technologies curriculum in Australia to demystify it for teachers. It discusses how a digital economic future is inevitable and schools need to prepare students with skills like being entrepreneurial, adaptive to change, and digitally discerning. The curriculum focuses on developing skills in areas like computational thinking, design thinking, data representation, and digital systems. It differentiates Digital Technologies, which teaches specific computer science concepts, from general ICT capability. It provides examples of what ICT capability and computational thinking look like at different year levels. The goal is to provide practical opportunities for students to develop innovative solutions through design thinking and information systems knowledge.
Lecture 2 Teaching Digital Technologies 2016Jason Zagami
This document provides an overview of key concepts related to teaching digital technologies, including computational thinking, systems thinking, design thinking, and futures thinking. It discusses important problems in the world like global warming, armed conflicts, and overpopulation that could be addressed through computational thinking. The document also outlines key concepts for different year levels, including creating interactive games, databases, and computer systems. It provides examples of concepts like algorithms, binary search, and the travelling salesman problem.
This is the lecture delivered at Jadavpur University for the engineering students. The lecture was organised by the JU Entrepreneurship Cell and Alumni Association, Singapore Chapter.
The document discusses preparing students for the digital future and age of technological disruption by focusing on developing deep learning competencies like creativity, collaboration, and critical thinking through project-based and inquiry-led learning that leverages digital tools and technologies. It provides examples of deep learning pedagogies and assessments as well as resources for professional learning around digital learning and teaching.
The document summarizes a tutorial on Opentech AI given by Jim Spohrer and Daniel Pakkala, discussing trends in lowering the cost of AI technologies, benchmarks for measuring AI progress, and types of cognitive systems ranging from tools to mediators. It also provides an outline for Daniel Pakkala's presentation on the Opentech AI architecture, ecosystem, and roadmap, discussing frameworks for understanding intelligence evolution and the need for an architecture framework for AI systems.
Sharing & Scaling The Language Of Digital LearningDr. Daniel Downs
Embedding Digital Citizenship, Computer Science and Makerspaces across your district provides amazing opportunities for students and teachers but it also requires that everyone is on the same page in terms of academic vocabulary related to educational technology. The presentation will detail the process the North Reading Digital Learning Team uses K-12 to scale a broader understanding of key digital learning terms into shared co-teaching lesson plans, digital learning curriculum sequence development and scaling teacher's knowledge base in the areas of digital learning and innovative teaching. Strategies for sequencing digital learning lessons based on refining key student vocabulary will be discussed.
AI Powered Campus Resource Assistance using Google Dialog FlowYaswantAY
The "AI Powered Campus Resource Assistance" project leverages cutting-edge technologies such as Natural Language Processing (NLP) and Generative AI to enhance campus experiences through an intelligent and conversational interface. Developed on the Google Dialog flow platform, our solution aims to revolutionize the way students and faculty access and interact with campus resources.
The system employs NLP to understand and interpret user queries, allowing for seamless communication between users and the AI assistant. Through the integration of Generative AI, the platform goes beyond predefined responses, generating contextually relevant and personalized information tailored to the user's needs. This dynamic approach enables the AI to adapt to various scenarios, providing a more human-like interaction and enhancing user engagement.
Key features include real-time assistance with campus navigation, event information, academic queries, and other resource-related inquiries. Users can effortlessly seek information, receive updates, and engage in meaningful conversations, fostering a more connected and informed campus community.
By harnessing the power of Google Dialog flow, our project not only streamlines information retrieval but also ensures scalability, accessibility, and ease of integration with existing campus systems. This AI-driven solution represents a significant step toward creating an intelligent, user-centric campus environment, ultimately enhancing the overall educational experience for all stakeholders.
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
This document provides an agenda and overview for a deep learning course. The agenda includes an introduction to program and course learning outcomes, the syllabus, class management tools, and an introduction to week 1 of deep learning. The syllabus outlines 15 weekly topics on deep learning concepts and algorithms. Example student projects are provided showing applications of deep learning to areas like computer vision, natural language processing, and games. The introduction to week 1 discusses artificial intelligence, machine learning, and deep learning definitions and provides an overview of programming assignments and deep learning in action.
The document provides information about Nettech India's data science course. It discusses the high demand for data scientists and what data science entails, including organizing, packaging and delivering data. It also defines what a data scientist does. The course covers topics like natural language processing, OpenCV, deep learning, and Tableau. It provides overviews of each topic and what students will learn, such as applying deep learning models to tasks like machine translation and using OpenCV for image processing, recognition and detection.
The document provides an overview of the computing curriculum in England, including aims, key stages, and learning objectives. At key stage 1, students will learn about algorithms, basic programming, data storage and retrieval, and online safety. At key stage 2, they will design and write programs, use logical reasoning, understand computer networks and the internet, use search engines effectively, and collect/analyze data. At key stage 3, topics include computational modeling, algorithms, programming languages, Boolean logic, computer systems, and data representation.
This document provides an overview of artificial intelligence (AI), machine learning (ML), and data science. It discusses how these fields are booming technologies that are taking engineering to the next level. The document outlines some real-world applications of AI, ML, and data science, as well as important skills needed in 2022 for these fields, such as programming languages, algorithms, data analytics, and machine learning models. It also lists some free and open-source tools that students can use to learn and work with data science.
Presentation of the challenges facing IT departments when digital natives invade universities. Presented at Forskningsnet Konference 2009
http://forskningsnettet.dk/konferencer/2009/
This document discusses key concepts related to computational thinking and systems thinking. It covers abstraction, data collection and representation, algorithms, specification, and implementation. Digital systems including hardware, software, and networks are explored. Interactions between people and digital systems and various impacts are also examined. The goal is for students to develop computational thinking skills to solve problems through project-based learning.
Teacher Professional Development for ICT Integration into CurriculumMohan Robert
Presentation used in Teacher PD at Indus International School for ICT Integrating into curriculum. This presentation consists of education Philosophy’s, tools and techniques that helps integrate technology into curriculum.
Similar to Digital Technologies 2014 (ICTENSW) (20)
Zagami, J. (2016, October). Digital Solutions Response. Presentation at the accessIT - ACS Qld State Conference 2016, Brisbane, Australia. Retrieved from http://www.slideshare.net/j.zagami/digital-solutions-response
This document discusses moonshot projects, xThinking labs, and inquiry-based project-based learning (iPBL) led by Dr. Jason Zagami of Griffith University. Dr. Zagami's email and website are provided for further contact.
Zagami, J. & Becker, S. (2016, September). ACCE Leadership Forum Summary. Presentation at the Australian Council for Computers in Education Conference, Brisbane, Australia.
Zagami, J. & Becker, S. (2016, September). ACCE Leadership Forum. Forum conducted at the Australian Council for Computers in Education Conference, Brisbane, Australia.
Three key trends are discussed in the document:
1. Redesigning learning spaces to be more hands-on and support new models like flipped classrooms. Wireless bandwidth and large displays are being upgraded.
2. Rethinking how schools work by making them more flexible, project-based, and multidisciplinary to prepare students for the real world.
3. Increasing collaborative learning both in person and online to improve engagement and allow global collaboration between students and teachers.
Horizon Report K12: What are the trends, challenges and developments in techn...Jason Zagami
Zagami, J. (2016, June) Horizon Report K12: What are the trends, challenges and developments in technology. Keynote presentation presented to Digital Technologies Summit 2016: Initial Teacher Education, Brisbane, Queensland, Australia. https://www.griffith.edu.au/conference/digital-technologies-summit-2016
This document discusses teaching computational thinking through technologies education. It emphasizes developing students' thinking skills like design thinking, computational thinking, systems thinking and futures thinking through project-based learning. The document outlines curriculum outcomes, contexts, challenges and expectations for developing solutions across different year levels. It also discusses integrating different models of thinking, evaluating solutions, and the importance of creativity, innovation and accepting failure in the learning process.
This document discusses teaching design thinking, computational thinking, systems thinking, strategic thinking, and futures thinking through challenge-based learning. It outlines approaches like the Stanford d.school design process and Daylight Design Thinking process. Key aspects covered include organizing learning environments, contextualizing challenges, the design process, solution types, assessments, expectations for students, and sample contexts in engineering, food production, and materials technologies. Competitions and 2-4 activities/projects are suggested to teach these various thinking approaches.
Lecture 4 Teaching Futures, Systems and Strategic Thinking 2016Jason Zagami
The document provides an overview of different types of thinking that can be taught, including systems thinking, computational thinking, design thinking, futures thinking, strategic thinking, and solutions thinking. It then focuses on futures thinking, outlining why studying the future is important and some tools used in futures thinking like environmental scans, trend analysis, scenarios, and backcasting. Finally, it discusses systems thinking and key concepts like stocks, flows, feedback loops, causal loops, and system dynamics modeling. The document aims to introduce various thinking approaches and tools that can be taught to help students develop important skills for understanding complex systems and creating preferred futures.
This document provides an overview of teaching design technologies. It discusses key concepts like systems thinking, design thinking, and contexts. Engineering principles and systems, food and fibre production, food specializations, and materials technologies are presented as contexts. The design process of investigating problems, generating solutions, producing solutions, evaluating solutions, and collaborating is explained. Types of designed solutions like products, services, and environments are also summarized. Overall, the document outlines the main approaches and concepts used for teaching design technologies.
This document outlines a university course on teaching technologies education. It discusses key topics like what technology and educational technologies are, and introduces the technologies learning area. The course covers teaching digital technologies, design technologies, and systems, futures, and strategic thinking over 10 weeks. Students complete a log of learning activities and portfolio of their work which is due at the end. Tutorials involve exploring the Australian curriculum and hands-on challenges in design and programming.
Trends, challenges and developments in technologies that will influence the f...Jason Zagami
Keynote presentation by Dr Jason Zagami to the ASLA conference on 29 September 2015 at Brisbane, Queensland.
Zagami, J. (2015, September) Trends, challenges and developments in technologies that will influence the future of libraries. Keynote presentation presented to ASLA conference, Brisbane, Queensland, Australia. http://www.slideshare.net/j.zagami/trends-challenges-and-developments-in-technologies-that-will-influence-the-future-of-libraries
Teaching the Technologies learning area using a thinking skills approachJason Zagami
Presentation by Dr Jason Zagami to the QSITE2015 conference on 24 September 2015 at Townsville, Queensland.
Zagami, J. (2015, September) Teaching the Technologies learning area using a thinking skills approach. Presentation presented to QSITE2015 conference, Townsville, Queensland, Australia. http://www.slideshare.net/j.zagami/teaching-the-technologies-learning-area-using-a-thinking-skills-approach
The Technologies learning area provides an opportunity to develop in students five distinct but complementary ways of thinking about and understanding the world: Systems Thinking, Design Thinking, Computational Thinking, Futures Thinking, and Strategic Thinking. This session will explore approaches to teaching the Technologies learning area through problem-solving activities that develop these thinking approaches.
The document discusses key concepts in systems thinking. It explains that systems thinking views phenomena holistically by considering large numbers of interactions, rather than isolating smaller parts. Mental models are used to understand complex systems, and dynamic models with stocks, flows, and feedback loops can simulate how systems change over time. Several examples are given to illustrate systems thinking concepts like balancing and reinforcing feedback, and how systems can be viewed from different perspectives.
Teaching the Technologies learning area using a thinking skills approachJason Zagami
This document outlines an approach to teaching digital technologies and design and technologies using thinking skills such as systems thinking, computational thinking, design thinking, futures thinking, and strategic thinking. It discusses each of these thinking skills in detail and provides examples of how they can be applied across the curriculum areas of digital technologies and design and technologies. The overall approach is to engage students in challenge-based learning projects that focus on solving complex problems using various thinking skills and collaborative processes.
Developing a Preferred Futures perspectiveJason Zagami
The document discusses developing a preferred futures perspective in technologies education. It aims to conceptualize more just and sustainable human and planetary futures by developing knowledge and skills in exploring probable and preferred futures scenarios. Students learn to understand the dynamics of human, social, and ecological systems and their influence on alternative futures, while also developing a sense of responsibility and action toward creating better futures through techniques like trend analysis, environmental scanning, visioning, and backcasting.
This document discusses creativity, failure, and innovation in technology education. It provides information about how students at different primary school levels (early, middle, upper) approach design tasks and develop their design thinking. For early primary students, design processes are flexible and initial designs may differ significantly from final products. Middle primary students recognize processes used and how they could be improved. They draw on resources to inform design. Upper primary students identify issues and research alternative designs. The document also covers models of the creative process, techniques to inspire creativity like brainstorming, and how innovation involves new solutions rather than just improvements. Failure is presented as an opportunity to learn.
The document discusses teaching technologies education and pedagogical diversity. It covers organizing learning environments, design challenges, contextualizing, personalizing, localizing, and modernizing learning. It also addresses assessing student achievement, cooperative learning models, persistence, unit planning, and common unit planning problems. Key aspects of design thinking are defined, including investigating problems, generating designs, producing solutions, evaluating, and collaborating in an iterative process.
This document discusses several thinking approaches that can be applied to education including design thinking, systems thinking, computational thinking, futures thinking, and strategic thinking. It notes some of the big global problems they could help address such as global warming, food scarcity, and health issues. It also provides an overview of design thinking processes, challenge-based learning approaches, and integrating curriculum into classroom projects and competitions.
This document discusses design thinking and the design process in technologies education. It defines design thinking as using strategies to understand problems, generate creative ideas, and evaluate solutions. It outlines key concepts like contexts, design briefs, and types of designed solutions (products, services, environments). The design process involves investigating problems, generating solutions, producing a solution, evaluating it, and collaborating. Each step of the process is explained in more detail. The document also discusses engineering, food/fiber production, food specializations, and materials/technologies as contexts for design projects.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
7. Queensland Society for Information Technology in Education
Immediate Past President
Australian Council for
Computers in Education
Editor
Australian Educational Computing
Australian College of
Educators
(Gold Coast Region)
President
17. UK dis-application
!
ICT as a subject name carries negative
connotations of a dated and unchallenging
curriculum that does not serve the needs and
ambitions of pupils. Changing the subject name
of ICT to computing will not only improve the
status of the subject but also more accurately
reflect the breadth of content included in the
proposed new programmes of study
18. !
!
!
I remember being at school and using early
computers. Yes, I was in computer club - and I
loved it. I think we’ve lost some of that sense of
joy and excitement in computing, and have just
become focused on just training kids to use
Windows. We want to bring some of that
excitement back.
September 2013
!
Elizabeth Truss
Parliamentary
Under Secretary
of State for Education and Childcare
19. !
!
!
Coding - one of the essential skills of the 21st
century - will now start at age 5. We are aiming to
develop one of the most rigorous computing
curricula in the world, where pupils will learn to
handle detailed, abstract computing processes
and over-11s will learn 2 programming
languages (one of which must be textual).
September 2013
!
Elizabeth Truss
Parliamentary
Under Secretary
of State for Education and Childcare
22. Processes and
production skills
Collecting, managing and analysing data /
Creating digital solutions by:
!
defining
designing
implementing
evaluating
collaborating and managing
23. Knowledge and
understanding
Digital systems
the components of digital systems:
hardware, software and networks and their use
Representation of data
how data are represented and structured
symbolically
24. Abstraction
!
Data Collection, Data Representation and Data
Interpretation
!
Specification, Algorithms and Implementation
!
Digital Systems
!
Interactions and Impacts
25. Abstraction
which underpins all content, particularly the
content descriptions relating to the concepts of
data representation and specification,
algorithms and implementation
26. Computational
Thinking
which underpins all content, particularly the
content descriptions relating to the concepts of
data representation and specification,
algorithms and implementation
58. Computational Thinking
!
"Computational thinking is a
fundamental skill for everyone, not just
for computer scientists. To reading,
writing, and arithmetic, we should add
computational thinking to every child’s
analytical ability."
!
Jannette Wing
66. Computational Fairy Tales
The Ant and the Grasshopper: A Fable of Algorithms (Algorithms)
!
Bullies, Bubble Sort, and Soccer Tickets (Bubble Sort)
!
Hunting Dragons with Binary Search (Binary Search)
!
Binary Searching for Cinderella (Binary Search)
!
Goldilocks and the Two Boolean Bears (Boolean)
!
The Tortoise, the Hare, and 50000 Ants (Parallel Algorithms)
67. Computational Fairy Tales
The ant paused for a moment while he thought. "It is the algorithm
that we use," he finally replied.
!
"Algorithm?" asked the grasshopper.
!
"A set of steps or instructions for accomplishing a task," explained
the ant. "Like when a carpenter builds a chair, he uses an algorithm
that includes measuring, cutting, smoothing, and hammering."
!
"What task does your algorithm solve?" asked the grasshopper.
"Does it solve the problem of having too much time during the
summer?" He chuckled out loud at his own joke.