Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
Congress on Evolutionary Computation (CEC 2016) - Plenary TalkGraham Kendall
Title: Is Evolutionary Computation Evolving Fast Enough?
Abstract: Evolutionary Computation (EC) has been part of the research agenda for at least 60 years but if you ask the average EC researcher to name three examples of EC being used in real world applications they might struggle. Other technologies have seen much wider adoption. 3D printing is changing the way that manufacturing is done, moving some of that functionality into the home. Immersive reality is on the verge of changing society, in ways that are not totally clear yet. Ubiquitous computing is becoming more prevalent, enabling users to access computing resources in ways that were unimaginable even just a few years ago. It might be argued that EC has not had the same penetration as other technologies. In this talk, we will look back at what EC promised, see if it has delivered on that promise and compare its progress with other technologies. Finally, we will suggest some challenges that might further advance EC and enable its wider adoption.
Ilya Sutskever at AI Frontiers : Progress towards the OpenAI missionAI Frontiers
OpenAI has made progress towards its mission of developing beneficial artificial general intelligence (AGI). This includes developing OpenAI Five, which reached the world's top level at the game Dota 2 through reinforcement learning techniques. OpenAI has also achieved dexterous in-hand manipulation with Dactyl and photorealistic image generation with generative adversarial networks. While challenges remain, deep learning has made rapid progress in areas like computer vision, translation and reinforcement learning. With continued growth in computing power, near-term AGI should be considered a serious possibility and risks should be proactively addressed.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Computational Thinking: Why It is Important for All StudentsNAFCareerAcads
Given the importance of computing and computer science in most career paths, computational thinking must be a part of every curriculum. This session explores
how computational thinking is related to computer science and information technology and how it might affect K-12 education. Participants will look at curricula examples and learn about new resources produced by a joint ISTE/
CSTA NSF group.
Presenter: Joe Kmoch, Milwaukee Public Schools
A Technological Revolution in Automated Software DevelopmentGraham Kendall
This presentation argues that sofrware development has lagged behind other engineering disciplines (such as 3D printing) and that there is a need for a significant breakthrough so that software development can be done by a home user who does not posses a high level of technical programming knowledge
The document discusses plans to test a Structured Process Modeling Theory (SPMT) through an experiment. It proposes measuring various concepts from the theory, including cognitive load and process modeling behavior. Participants would complete cognitive tests before a modeling session where their actions are recorded. Outliers would be interviewed afterwards. The experiment aims to analyze relationships between modeler characteristics, modeling process, and model quality as specified in the SPMT. Feedback is requested on the research plans and feasibility of the proposed methodology for theory testing.
MixTaiwan 20170222 清大電機 孫民 AI The Next Big ThingMix Taiwan
講師簡介:
孫民助理教授│清華大學電機系
孫民博士目前任教於國立清華大學電機系,他畢業於國立交通大學電子工程學系後,取得史坦福電機碩士、密西根安雅堡電機系統組博士、以及西雅圖華盛頓大學計算機工程博士後的經歷。他的研究興趣在電腦視覺、機器學習、以及人機互動領域,近年來基於深度學習在電腦視覺的突破,他致力於開發橫跨人工智慧不同子領域的系統,如自動影片文字描述(視覺x自然語言)、以及與人類行為互動的智慧機器(視覺 x 控制)。
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
Congress on Evolutionary Computation (CEC 2016) - Plenary TalkGraham Kendall
Title: Is Evolutionary Computation Evolving Fast Enough?
Abstract: Evolutionary Computation (EC) has been part of the research agenda for at least 60 years but if you ask the average EC researcher to name three examples of EC being used in real world applications they might struggle. Other technologies have seen much wider adoption. 3D printing is changing the way that manufacturing is done, moving some of that functionality into the home. Immersive reality is on the verge of changing society, in ways that are not totally clear yet. Ubiquitous computing is becoming more prevalent, enabling users to access computing resources in ways that were unimaginable even just a few years ago. It might be argued that EC has not had the same penetration as other technologies. In this talk, we will look back at what EC promised, see if it has delivered on that promise and compare its progress with other technologies. Finally, we will suggest some challenges that might further advance EC and enable its wider adoption.
Ilya Sutskever at AI Frontiers : Progress towards the OpenAI missionAI Frontiers
OpenAI has made progress towards its mission of developing beneficial artificial general intelligence (AGI). This includes developing OpenAI Five, which reached the world's top level at the game Dota 2 through reinforcement learning techniques. OpenAI has also achieved dexterous in-hand manipulation with Dactyl and photorealistic image generation with generative adversarial networks. While challenges remain, deep learning has made rapid progress in areas like computer vision, translation and reinforcement learning. With continued growth in computing power, near-term AGI should be considered a serious possibility and risks should be proactively addressed.
by Samantha Adams, Met Office.
Originally purely academic research fields, Machine Learning and AI are now definitely mainstream and frequently mentioned in the Tech media (and regular media too).
We’ve also got the explosion of Data Science which encompasses these fields and more. There’s a lot of interesting things going on and a lot of positive as well as negative hype. The terms ML and AI are often used interchangeably and techniques are also often described as being inspired by the brain.
In this talk I will explore the history and evolution of these fields, current progress and the challenges in making artificial brains
From the FreshTech 2017 conference by TechExeter
www.techexeter.uk
Computational Thinking: Why It is Important for All StudentsNAFCareerAcads
Given the importance of computing and computer science in most career paths, computational thinking must be a part of every curriculum. This session explores
how computational thinking is related to computer science and information technology and how it might affect K-12 education. Participants will look at curricula examples and learn about new resources produced by a joint ISTE/
CSTA NSF group.
Presenter: Joe Kmoch, Milwaukee Public Schools
A Technological Revolution in Automated Software DevelopmentGraham Kendall
This presentation argues that sofrware development has lagged behind other engineering disciplines (such as 3D printing) and that there is a need for a significant breakthrough so that software development can be done by a home user who does not posses a high level of technical programming knowledge
The document discusses plans to test a Structured Process Modeling Theory (SPMT) through an experiment. It proposes measuring various concepts from the theory, including cognitive load and process modeling behavior. Participants would complete cognitive tests before a modeling session where their actions are recorded. Outliers would be interviewed afterwards. The experiment aims to analyze relationships between modeler characteristics, modeling process, and model quality as specified in the SPMT. Feedback is requested on the research plans and feasibility of the proposed methodology for theory testing.
MixTaiwan 20170222 清大電機 孫民 AI The Next Big ThingMix Taiwan
講師簡介:
孫民助理教授│清華大學電機系
孫民博士目前任教於國立清華大學電機系,他畢業於國立交通大學電子工程學系後,取得史坦福電機碩士、密西根安雅堡電機系統組博士、以及西雅圖華盛頓大學計算機工程博士後的經歷。他的研究興趣在電腦視覺、機器學習、以及人機互動領域,近年來基於深度學習在電腦視覺的突破,他致力於開發橫跨人工智慧不同子領域的系統,如自動影片文字描述(視覺x自然語言)、以及與人類行為互動的智慧機器(視覺 x 控制)。
Jacob Sherson (University of Aarhus) - Science@homeCitizenCyberlab
This document discusses citizen science and game-based education. It describes how citizen science projects can engage billions of volunteers to assist with or lead research. Game-based learning is presented as a way to teach topics in a fun and engaging manner by embedding curriculum within research-relevant games and simulations. Long term goals include developing a global crowd computing platform and remote laboratory to further mass collaboration on scientific problems and experiments.
Matching Game Mechanics and Human Computation Tasks in Games with a PurposeCUbRIK Project
The document discusses using game mechanics to design Games with a Purpose (GWAPs) to solve human computation tasks, outlines a development process for GWAPs including defining the task and matching it to appropriate game mechanics, and provides an example of using line drawing mechanics to segment fashion images and identify trends.
Matching Game Mechanics and Human Computation Tasks in Games with a PurposeLuca Galli
Presentation held at ACM Workshop on Serious Games at ACM Multimedia 2014 to introduce techniques that can be used to match Human Computation Tasks to Game Mechanics.
The development process of a traditional digital game is described to put the basis on which the development of GWAP can be described. The concept of Human Computation Task is provided along with the most common multimedia refinement tasks that can be found in literature and everyday scenarios. Finally game mechanics that could be matched to particular task instances are shown, along with examples. The entire process has been applied to a real Game with a Purpose scenario.
How to do science in a large IT company (ICPC World Finals 2021, Moscow)Alexander Borzunov
The talk covers:
- Why do companies need research?
- What researchers do?
- Research at Yandex
- How did I get there?
- Our group's research: Distributed deep learning over the Internet
1. The document discusses learning application security through participating in hacking competitions called Capture the Flag (CTF) events.
2. CTF events involve trying to break into toy applications to retrieve flags as a fun way to improve skills in areas like reverse engineering, cryptography, and forensics.
3. The author's team organizes the PoliCTF event which had over 1,000 registered teams in 2015 and involved designing challenges that tested skills without being impossible to solve.
When the Heart BD2K grant was originally written. We proposed to build something called “Big Data World” to help advance citizen science, scientific crowdsourcing and science education – especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.
AI Based Game Design - Teaching how to expand designers' artistic palette wit...Mirjam Eladhari
Talk given at the Game Developers Conference (GDC) in San Francisco on the 3rd of March 2015.
One approach to game design innovation is AI-based game design (AIGD), in which the game mechanics are inspired and enabled by AI systems. This case study describes AIGD as an educational approach along with best practices of using it in teaching, illustrated by example student games demonstrating both design and technical innovation. Teaching AI and design in tandem enables students to take a role where they can use different technological approaches as part of their artistic palette as game developers.
This document discusses artificial intelligence techniques for non-player characters (NPCs) in computer games. It summarizes a survey of research papers from 2005 to 2017 that identified 26 AI methods used for NPCs, including fuzzy state machines, Monte Carlo tree search, A* pathfinding algorithms, reinforcement learning, and neural networks with genetic algorithms. The document also discusses some notable AI competitions in computer games like the Mario AI competition and techniques like genetic algorithms and neural networks that were applied to create intelligent Mario agents. It concludes by proposing future research areas like applying improved pathfinding algorithms like HA* and using virtual reality to create more immersive open world game experiences.
Science and Videogames. Computational intelligence in videogamesAntonio Mora
This document discusses using video games for scientific purposes. It describes how video game controllers like the Wii remote and Kinect have been used for robot control, pattern recognition, and other scientific applications. It also discusses how video games incorporate scientific principles like physics and how artificial intelligence is an important area of research for game development. Some specific examples of research include evolving bot AI in Unreal Tournament using genetic algorithms and using games like Pac-Man and StarCraft for AI research challenges.
Robotics is a promising path towards developing artificial general intelligence (AGI) according to the author. Robotics presents a complex environment without an obvious reward function, requiring general problem solving abilities. Transfer learning techniques can be used to train robots in simulation and transfer policies to real robots. Self-play, where an AI agent competes against copies of itself, may be a way to develop general dexterity and complex strategies as seen with games like Go and Dota 2. The author believes self-play could help train AGI by incentivizing the evolution of general intelligence through social and competitive problems in an open-ended environment.
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Players operate virtual train switches and speeds to prevent collisions. Researchers developed a VR train simulator to study train traffic control. Through mixing realities, embedded training is expanding to provide integrated training anytime, anywhere. Advancements are transferring to other industries like business and education. Integrated research in tracking, rendering, and scenario delivery are expanding VR simulation possibilities and command/control visualizations.
The document discusses various aspects of game jams and game development. In 3 sentences:
Game jams bring together educators, students, and industry professionals to rapidly prototype games under tight constraints like short time limits. This iterative process simulates real-world game development and teaches important lessons about teamwork, communication, scoping projects, and embracing failures. Several games from past jams have been successful and signed publishing deals, demonstrating how jams can be an educational activity and potential pathway to the game industry for participants.
Interactive Video Search: Where is the User in the Age of Deep Learning?klschoef
Interactive video retrieval tools are commonly evaluated using user studies, log file analysis, and indirect task-based evaluations like competitions. User studies directly observe users performing tasks with a tool and provide qualitative feedback. Log file analysis examines quantitative interaction patterns. Competitions like TRECVID and Video Browser Showdown pose search tasks to quantitatively compare tools. A combination of methods is often used to fully understand a tool's effectiveness from different perspectives.
Our shelter just collapsed, who didn’t calculate correctly Eric D. Milks
Our shelter just collapsed, who didn’t calculate correctly? Design challenges of building an educational video game discusses the challenges faced in developing an educational video game called Survival Master. It had a large team from different universities but faced issues with too many opinions slowing progress. Designing fun elements was difficult when related to math and science concepts. Technical challenges included choosing software before fully designing and many jumps between platforms. Lessons learned included needing better communication, assigning a project lead, creating decision models, and avoiding too many changes late.
Retro games like Snakes, Pac-Man and Bomberman were implemented in a client-server architecture and used to teach introductory AI concepts in a more engaging way for students. The games were made accessible by representing the state in JSON and allowing agents to submit actions. A public leaderboard and open source code on GitHub gamified the experience and motivated students to improve their agents' performance. This approach increased attention in class and more students have pursued further study in AI and game development as a result.
Modern machine learning is immensely powerful but also has very significant limitations that don't always get the attention they deserve. In this talk, I tried to contrast machine learning against AI and the original goals of that field, give some context and discuss a potential path forward.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Jacob Sherson (University of Aarhus) - Science@homeCitizenCyberlab
This document discusses citizen science and game-based education. It describes how citizen science projects can engage billions of volunteers to assist with or lead research. Game-based learning is presented as a way to teach topics in a fun and engaging manner by embedding curriculum within research-relevant games and simulations. Long term goals include developing a global crowd computing platform and remote laboratory to further mass collaboration on scientific problems and experiments.
Matching Game Mechanics and Human Computation Tasks in Games with a PurposeCUbRIK Project
The document discusses using game mechanics to design Games with a Purpose (GWAPs) to solve human computation tasks, outlines a development process for GWAPs including defining the task and matching it to appropriate game mechanics, and provides an example of using line drawing mechanics to segment fashion images and identify trends.
Matching Game Mechanics and Human Computation Tasks in Games with a PurposeLuca Galli
Presentation held at ACM Workshop on Serious Games at ACM Multimedia 2014 to introduce techniques that can be used to match Human Computation Tasks to Game Mechanics.
The development process of a traditional digital game is described to put the basis on which the development of GWAP can be described. The concept of Human Computation Task is provided along with the most common multimedia refinement tasks that can be found in literature and everyday scenarios. Finally game mechanics that could be matched to particular task instances are shown, along with examples. The entire process has been applied to a real Game with a Purpose scenario.
How to do science in a large IT company (ICPC World Finals 2021, Moscow)Alexander Borzunov
The talk covers:
- Why do companies need research?
- What researchers do?
- Research at Yandex
- How did I get there?
- Our group's research: Distributed deep learning over the Internet
1. The document discusses learning application security through participating in hacking competitions called Capture the Flag (CTF) events.
2. CTF events involve trying to break into toy applications to retrieve flags as a fun way to improve skills in areas like reverse engineering, cryptography, and forensics.
3. The author's team organizes the PoliCTF event which had over 1,000 registered teams in 2015 and involved designing challenges that tested skills without being impossible to solve.
When the Heart BD2K grant was originally written. We proposed to build something called “Big Data World” to help advance citizen science, scientific crowdsourcing and science education – especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.
AI Based Game Design - Teaching how to expand designers' artistic palette wit...Mirjam Eladhari
Talk given at the Game Developers Conference (GDC) in San Francisco on the 3rd of March 2015.
One approach to game design innovation is AI-based game design (AIGD), in which the game mechanics are inspired and enabled by AI systems. This case study describes AIGD as an educational approach along with best practices of using it in teaching, illustrated by example student games demonstrating both design and technical innovation. Teaching AI and design in tandem enables students to take a role where they can use different technological approaches as part of their artistic palette as game developers.
This document discusses artificial intelligence techniques for non-player characters (NPCs) in computer games. It summarizes a survey of research papers from 2005 to 2017 that identified 26 AI methods used for NPCs, including fuzzy state machines, Monte Carlo tree search, A* pathfinding algorithms, reinforcement learning, and neural networks with genetic algorithms. The document also discusses some notable AI competitions in computer games like the Mario AI competition and techniques like genetic algorithms and neural networks that were applied to create intelligent Mario agents. It concludes by proposing future research areas like applying improved pathfinding algorithms like HA* and using virtual reality to create more immersive open world game experiences.
Science and Videogames. Computational intelligence in videogamesAntonio Mora
This document discusses using video games for scientific purposes. It describes how video game controllers like the Wii remote and Kinect have been used for robot control, pattern recognition, and other scientific applications. It also discusses how video games incorporate scientific principles like physics and how artificial intelligence is an important area of research for game development. Some specific examples of research include evolving bot AI in Unreal Tournament using genetic algorithms and using games like Pac-Man and StarCraft for AI research challenges.
Robotics is a promising path towards developing artificial general intelligence (AGI) according to the author. Robotics presents a complex environment without an obvious reward function, requiring general problem solving abilities. Transfer learning techniques can be used to train robots in simulation and transfer policies to real robots. Self-play, where an AI agent competes against copies of itself, may be a way to develop general dexterity and complex strategies as seen with games like Go and Dota 2. The author believes self-play could help train AGI by incentivizing the evolution of general intelligence through social and competitive problems in an open-ended environment.
Deep Learning and Reinforcement Learning summer schools summary
26th June-6th July 2017, Montreal, Quebec
Things I learned. What was your favourite lesson?
Players operate virtual train switches and speeds to prevent collisions. Researchers developed a VR train simulator to study train traffic control. Through mixing realities, embedded training is expanding to provide integrated training anytime, anywhere. Advancements are transferring to other industries like business and education. Integrated research in tracking, rendering, and scenario delivery are expanding VR simulation possibilities and command/control visualizations.
The document discusses various aspects of game jams and game development. In 3 sentences:
Game jams bring together educators, students, and industry professionals to rapidly prototype games under tight constraints like short time limits. This iterative process simulates real-world game development and teaches important lessons about teamwork, communication, scoping projects, and embracing failures. Several games from past jams have been successful and signed publishing deals, demonstrating how jams can be an educational activity and potential pathway to the game industry for participants.
Interactive Video Search: Where is the User in the Age of Deep Learning?klschoef
Interactive video retrieval tools are commonly evaluated using user studies, log file analysis, and indirect task-based evaluations like competitions. User studies directly observe users performing tasks with a tool and provide qualitative feedback. Log file analysis examines quantitative interaction patterns. Competitions like TRECVID and Video Browser Showdown pose search tasks to quantitatively compare tools. A combination of methods is often used to fully understand a tool's effectiveness from different perspectives.
Our shelter just collapsed, who didn’t calculate correctly Eric D. Milks
Our shelter just collapsed, who didn’t calculate correctly? Design challenges of building an educational video game discusses the challenges faced in developing an educational video game called Survival Master. It had a large team from different universities but faced issues with too many opinions slowing progress. Designing fun elements was difficult when related to math and science concepts. Technical challenges included choosing software before fully designing and many jumps between platforms. Lessons learned included needing better communication, assigning a project lead, creating decision models, and avoiding too many changes late.
Retro games like Snakes, Pac-Man and Bomberman were implemented in a client-server architecture and used to teach introductory AI concepts in a more engaging way for students. The games were made accessible by representing the state in JSON and allowing agents to submit actions. A public leaderboard and open source code on GitHub gamified the experience and motivated students to improve their agents' performance. This approach increased attention in class and more students have pursued further study in AI and game development as a result.
Modern machine learning is immensely powerful but also has very significant limitations that don't always get the attention they deserve. In this talk, I tried to contrast machine learning against AI and the original goals of that field, give some context and discuss a potential path forward.
Similar to The Role of Evolutionary Computation in Game AI (20)
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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.
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.
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.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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 Setup Warehouse & Location in Odoo 17 Inventory
The Role of Evolutionary Computation in Game AI
1. Discover the world at Leiden UniversityDiscover the world at Leiden University
The Role of
Evolutionary Computation
in Game AI
Mike Preuss @ S3 ACM GECCO summer school, Praha
July 10, 2019
2. Discover the world at Leiden University
● my roots in Evolutionary Computation
● game AI today: a forming field
● Procedural Content Generation
● Balancing
● GVGAI/competitions/rolling horizon EA
roadmap
2
3. Discover the world at Leiden University
Evolutionary Computation and me
●most likely the last PhD student of Hans-Paul
Schwefel (co-inventor of the Evolution Strategy)
●most important message: expect the unexpected
●focus on multi-modal optimization (detect
several good, different solutions simultaneously)
●a bit on multi-objective optimization
●some work on experimental methodology
3
4. Discover the world at Leiden University
PhD (2013) and book (2015)
most important results:
●the advantage of niching (if any) can only come from
coordinated placement
●in contrast: SLS is randomized placement
●simulations/theory: unfortunately, we cannot speed
up finding the global optimum
●but if we go for coverage (find most if not all good
optima) we can gain a lot
●invented NEA2 as top algorithm (2015), lesson
learned: for niching, also use the objective values
modern name for this stuff: quality diversity
4
5. Discover the world at Leiden University
GECCO multimodal optimization competition
• builds on a fixed selection of 20 problems made
from 12 functions in different dimensions
• averages over 3 different rankings:
- average PR (peak rate) values
- static F1 (takes the amount of points in the
answer set into account)
- dynamic F1 (same but as integral over time
for the whole runtime)
• only global optima are sought
• the number of optima is low for most problems
http://www.epitropakis.co.uk/gecco2019/
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what we did not expect so early
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bla
a forming field
a forming field
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what about
game AI?
current game AI is:
• playing well
• generating content
• modeling players
see 2018 book:
Artificial Intelligence
and Games, by
Yannakakis/Togelius
http://gameaibook.org/book.pdf
video: fully generated
game worlds
in No Man’s Sky (2016)
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when game AI becomes… artificially intelligent
ongoing discussion in game AI: how intelligent is current game AI?
see recent gamasutra post:
http://www.gamasutra.com/view/news/253974/When_artificial_intelligence_in_video_games_becomesartificially_intellige
nt.php
"My prediction is a little bit controversial.
I think the next giant leap of game AI is actually artificial intelligence."
- Alex J. Champandard at GDC 2013
Ken de Jong (at GECCO 2017, Berlin) on the question:
What do you see as dominant research direction in Evolutionary Computation for
the next years?
“It is time to apply our methods to real problems now.”
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classical AI techniques in games
even in the early 2000s, lots of standard techniques in use (and developers showing
little interest for a change):
• finite state machines (FSM)
• hierarchical FSM
• scripting
basically mostly used for Non-Player Character (NPC) control, also:
• cellular automata
• influence maps
• basic optimization approaches
• game theory
(still useful for the right problem, but we have so much more now)
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modern approach
• we treat all aspects of games, now also game design/production
• we use what fits best for a specific problem
everything is an agent:
AI director, opponent AI controller, content generator, ....
game AI
specific algorithms
classic AI /
machine learning
in-game AI use
computational intelligence
game design/production
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game ai overall view
• game AI traditionally encompasses mostly NPC behavior and bots
• control happens during the game, design support before deployment
• design support is rather new, but a highly dynamic research area
game AI
design supportcontrol
directors
strategic (bots)
/supportive AI
PCGNPC behavior balancing
NPC = non-player character, PCG = procedural content generation
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panoramic view
game AI components, player centric view, from:
Yannakakis, G.N.; Togelius, J., "A Panorama of Artificial and Computational Intelligence in Games," in IEEE
TCIAIG, 2014
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why apply CI methods to games?
contrary to board games,
• game trees often not applicable
• incomplete information
• concurrency: during planning phase, game situation
changes
• quantifying a game situation is not trivial
➔ good and fast approximations are needed
evolutionary optimization is
• versatile, flexible, still works (somehow)
• copes with noise and strange search spaces
• can be asked to deliver a result at any time
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Evolutionary and AI
•not fully up to date, but clearly shows relations
•boundaries much more fuzzy nowadays
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CI and games: a field forming
• 1999: Blondie24, learning checkers with CI
• 2005 first Computational Intelligence in Games (CIG) conference
• IEEE TCIAIG Journal (Transactions on CI and Artificial
Intelligence in Games) since 2009
• EvoGames track in Evo*, DETA track in GECCO since 2009/2011
• many “neighbor” conferences, etc. AIIDE, FDG, gameai/nucl.ai
conf. (not strictly CI, but CI welcome)
• general approach target oriented, not technique oriented
• 2012: first Dagstuhl seminar on AI and CI in Games
• 2015: second Dagstuhl seminar (integration)
• 2017: third Dagstuhl seminar (AI-supported design)
• 2018/2019: journal and conference renamed to IEEE Transactions
on Games and IEEE Conference on Games
• 2019: first Shonan seminar on AI for Games
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one small step for [a] man….
what recently happened:
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AlphaGo: Go
(MCTS, expert knowledge, self-play)
AlphaGoZero: Go
(MCTS-, self-play)
Deep Reinforcement Learning: ALE
(pixel input, self-play)
AlphaStar: StarCraft
(MCTS?, pixel input, self-play, expert knowledge)
AlphaZero: Go, Chess, Shogi
(MCTS-, self-play)
Go-Explore: Montezuma
(population based exploration)
Obstacle Tower Challenge
(population based training)
ForTheWin: Capture the Flag
(2 speed rnns, population based
learning, population based training)
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combining EC and Deep Reinforcement Learning
•some (limited) successes in weight optimization with EC
•population-based learning:
- GANs, multiple agent architectures
- resembles co-evolutionary approaches, same problems (how to control)
•population-based based training:
- generating test cases as needed
- procedural content generation (PCG), mainly done via EC methods
•not really exploited yet: multi-modal/multi-objective approaches
•maybe more things to transfer?
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what I did in Game AI
●procedural generation of strategy game maps
● organizing StarCraft I competitions, survey
● some works on balancing (also using EC):
○there are usually many solutions in balancing
○the problem is very multi-modal
●recently: facet orchestration (how to combine
several generators, e.g. visuals, audio)
●very recently: transfer AlphaAnything to new
problems (not from the games domain)
●understand how it works on small games
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Obstacle Tower Challenge
current DRL challenge
for artificial general
intelligence (AGI)
• levels increase in
difficulty
• levels procedurally
generated
• decorations change
• includes minigames
(Sokoban)
runs in 2 phases:
- qualification (gone)
- finals (up to July
15th, 2019)
https://www.aicrowd.com/challenges/unity-
obstacle-tower-challenge
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bla
Procedural Content Generation
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search-based vs. generate-and-test
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Evolutionary Algorithms
• initial solutions needed: existing or randomly generated solutions
• fitness function shall measure the targeted properties (designer input)
• matching variation operators need to be established
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why using EAs in PCG?
pros:
- flexible, only needs good utility function
- not very sensitive concerning noise/mistakes
- anytime method: we can obtain a result whenever needed
cons:
- optimality cannot be guaranteed (except for very few special cases)
- stagnation phases of unpredictable length (break?)
- depends on matching representation and operators
- slow on simple problems
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back in 2009: galactic arms race
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how are the new weapons produced?
• very small population, 3 solutions (candidates, individuals)
• interactive selection: player removes existing solution when new one is generated
• weapons are created via a parametrized particle system by complex neural
network
• cgNEAT: neural network topology optimized by means of evolutionary algorithm
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StarCraft I map evolution (2009-2012)
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RTS map design with Sentient Sketchbook
Preuss, Liapis, Togelius. Searching for Good and Diverse Game Levels. IEEE CIG 2014.
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gamesbyangelina and the procjam
• Michael Cook’s AI that is designed to intelligently design video games:
http://www.gamesbyangelina.org/
• PROCJAM: yearly game jam with strong emphasis on PCG
http://itch.io/jam/procjam
• coming soon: facet orchestration
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Game AI hot topics
• procedural content generation (PCG)
- large game worlds are expensive to make
- represent specific (designer) styles
• believability of non-player character (NPC) behavior
- more humanlike behavior (also evoke and show emotions)
- better cooperation of units (team AI)
- human/AI interaction
• personalization
- preference modeling (what do they like?), player type analysis
- dynamic adaptation of game content and mechanics
balancing is
everywhere
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balancing project with BlueByte
• prototype from BlueByte
• ZombieVillage game, related to tower defense
• several parameters: can we balance automatically?
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balancing project with BlueByte
• number of (most important) parameters reduced to 5
• interchange between manual and automated (integrated) balancing
• core result: both steps needed, many different solutions exist
Marlene Beyer, Aleksandr Agureikin, Alexander Anokhin, Christoph Laenger, Felix Nolte, Jonas Winterberg, Marcel Renka,
Martin Rieger, Nicolas Pflanzl, Mike Preuss, Vanessa Volz: An integrated process for game balancing. CIG 2016
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tackling a complex RTS
Mike Preuss, Thomas Pfeiffer, Nicolas Pflanzl, Vanessa Volz: Integrated Balancing of an RTS Game:
Case Study and Toolbox Refinement. CIG 2018
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bla
nd
general game AI/competitions
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can we also have general (game) AI?
• instead of this:
• we rather want this:
game 1
game 2
game 3AI 3
AI 2
AI 1
develop for
develop for
develop for
game 1
game 2
game 3
AI 1
learn to play
learn to play
learn to play
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GVGAI ENVIRONMENT
• general video-game playing (GVGP) succeeds general game-playing
• targeted at more complex, non-deterministic games
• language first shaped during Dagstuhl seminar CI and AI in games 2012
• we want to obtain controllers that can play “any game”
• important publications:
John Levine, Clare B. Congdon, Michal Bída, Marc Ebner, Graham Kendall, Simon Lucas, Risto Miikkulainen,
Tom Schaul, and Tommy Thompson. General Video Game Playing. Dagstuhl Follow-up, 6:1–7, 2013.
Tom Schaul. A Video Game Description Language for Model-based or Interactive Learning. In Proceedings of the
IEEE Conference on Computational Intelligence in Games, Niagara Falls, 2013. IEEE Press
http://www.gvgai.net
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GVGAI competition
• build general AI controllers
• learn to play many games, not one
• many different challenges
• many successful controllers
use Monte-Carlo Tree search
• but Rolling Horizon EA also an option
• but how can this work?
• “traditional track”: 1-player planning
• employs forward model (action simulation, may be non-deterministic)
• new: 2-player planning, learning (no forward model)
• now also: level generation, rule generation
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some example games
Infection:
Missile Command:
Pacman
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VGDL – game and level definitions
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Rolling Horizon EA for search based planning
• basic idea: evolve whole sequence of moves at once, apply only first action
• hybrids with MCTS like playouts are also possible
• simple extension with shift buffer (only remove latest action) performs better
Raluca Gaina, Simon Lucas, Diego Perez. Rolling Horizon Evolution Enhancements in General Video
Game Playing. IEEE CIG 2017
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current competitions held at COG 2019
• 4th Angry Birds Level Generation Competition
• Bot Bowl I
• Fighting Game AI Competition
• First TextWorld Problems: A Reinforcement and Language Learning Challenge
• Geometry Friends Game AI Competition
• General Video Game AI Competitions (Learning Track)
• Hanabi Competition
• Hearthstone AI competition
• MicroRTS AI Competition
• Short Video Competition
• StarCraft AI Competition
• Strategy Card Game AI Competition
lots of demo and explanation videos included:
http://ieee-cog.org/competitions_conference/
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conclusions
•EC is part of AI and has always been
•Procedural Content Generation getting very important for games
industry but also for Deep Learning
•evolutionary approaches have great potential for improving other
algorithms, e.g. RHEA (rolling horizon EA) instead of MCTS
•3 important applications of EC algorithms in game AI:
PCG, balancing, search-based planning
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Don’t
panic!
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