Complexity Explorers Krakow - Computer Science & Philosophy
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Inspiration for Complexity Explorers Krakow. The group is set up for learning and discussing anything related to an intersection of Philosophy and Computer Science. We aim to explore a wide range of topics including Complexity Theory, Systems Thinking, Computability, Emergence, Physics, Analytical Philosophy.
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Welcome to the Singularity?
There’s this “thing”
Called the singularity
That some people think will happen real soon
That others think is a load of cr*p
Which I think is already here (ish).
AI Fables, Facts and Futures: Threat, Promise or Saviour
Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
science _engineering__and_the_liberal_arts
Computer science fits into the categories of science, engineering, and liberal arts. As a science, it focuses on developing theories to explain computing in nature and abstract concepts. As an engineering discipline, it involves designing complex systems under constraints like managing human complexity limitations. As a liberal art, it utilizes language and builds upon traditions like logic, arithmetic, and geometry that have strong connections to computing.
Artificial immune systems and the grand challenge for non classical computation
The document discusses the "Grand Challenge of Non-Classical Computation", which examines breaking free from classical assumptions about computation. It proposes this as a "journey" of exploration rather than having a set goal. Artificial Immune Systems are presented as an example of non-classical computation that could help address this challenge by breaking classical assumptions like the Turing paradigm and having emergent properties rather than predefined specifications. The document encourages the AIS community to position their research within this broader area of non-classical computation.
AI Fables, Facts and Futures: Threat, Promise or Saviour
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Machine Learning, AI and the Brain
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
Everything is an illusion. - Do we live in a computer simulation?
In the words of legendary science fiction writer Arthur C Clarke, "Any sufficiently advanced technology is indistinguishable from magic." In last few hundred years, we humans have made great progress in science and technology . With computing innovations, the pace of progress is rather accelerating, and will leave a lasting impact on us as a species.
Transhumanism is an intellectual movement to study this impact, and aims to enhance physical, psychological, and intellectual potential to leap into a posthuman era. In this talk, we will discuss a speculative idea from transhumanism, where even the magic is real.
Artificial Intelligence and Intuition
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
Recommended
Welcome to the Singularity?
There’s this “thing”
Called the singularity
That some people think will happen real soon
That others think is a load of cr*p
Which I think is already here (ish).
AI Fables, Facts and Futures: Threat, Promise or Saviour
Man’s dreams of ‘intelligences and robots’ goes back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be have at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
science _engineering__and_the_liberal_arts
Computer science fits into the categories of science, engineering, and liberal arts. As a science, it focuses on developing theories to explain computing in nature and abstract concepts. As an engineering discipline, it involves designing complex systems under constraints like managing human complexity limitations. As a liberal art, it utilizes language and builds upon traditions like logic, arithmetic, and geometry that have strong connections to computing.
Artificial immune systems and the grand challenge for non classical computation
The document discusses the "Grand Challenge of Non-Classical Computation", which examines breaking free from classical assumptions about computation. It proposes this as a "journey" of exploration rather than having a set goal. Artificial Immune Systems are presented as an example of non-classical computation that could help address this challenge by breaking classical assumptions like the Turing paradigm and having emergent properties rather than predefined specifications. The document encourages the AIS community to position their research within this broader area of non-classical computation.
AI Fables, Facts and Futures: Threat, Promise or Saviour
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
And so in 1950 the modern AI movement was optimistically projecting what machines would be capable of ‘almost anything’ by 1960/70. Unfortunately, there was no understanding of the complexity to be addressed, and all the projections were wildly wrong; leading to a deep trough of disparagement and disillusionment of some 30 years. However, 70 years on and the original AI optimism and projections of what might be had at least been largely achieved with AI outgunning humans at every board and card game including Poker and GO, and of course; general knowledge, medical diagnosis, image and information pattern recognition…
Machine Learning, AI and the Brain
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
Everything is an illusion. - Do we live in a computer simulation?
In the words of legendary science fiction writer Arthur C Clarke, "Any sufficiently advanced technology is indistinguishable from magic." In last few hundred years, we humans have made great progress in science and technology . With computing innovations, the pace of progress is rather accelerating, and will leave a lasting impact on us as a species.
Transhumanism is an intellectual movement to study this impact, and aims to enhance physical, psychological, and intellectual potential to leap into a posthuman era. In this talk, we will discuss a speculative idea from transhumanism, where even the magic is real.
Artificial Intelligence and Intuition
This is my talk delivered 06/04/2024 at the CUBE event (https://www.uni-corvinus.hu/post/landing-page/cube/?lang=en) at the Gellért Campus of the Corvinus University.
Artificial Intelligence
The document discusses the history and development of artificial intelligence. It defines AI as making computers do things that people are better at, like extending capabilities to large data or making fewer mistakes. Early AI research focused on games, mathematics, and knowledge-based systems. Over time, the focus shifted to symbolic and subsymbolic approaches, as well as robotics, language processing, and machine learning. Knowledge representation and commonsense reasoning remain challenging areas of research.
Aok – areas of knowing mathematics
1. This document discusses different perspectives on mathematics, including its history and nature. It explores whether math is discovered or invented, and how culture and different fields influence mathematical thought.
2. Key aspects of math discussed are its basis in axioms, deductive reasoning, and theorems. Different views are presented on whether mathematical truths are empirical, true by definition, or insights into reality.
3. The document prompts reflection on relationships between math and other domains like logic, religion, intuition, science, language, and beauty. It encourages examining how perception of math may differ depending on factors like one's background or profession.
Artificial intelligence
The document provides an overview of the history and development of artificial intelligence (AI). Some key points:
- The field of AI was established in 1956 at the Dartmouth Conference where researchers proposed using computers to simulate human intelligence.
- Early milestones included programs that played games like checkers and proved mathematical theorems. Research focused on symbolic and knowledge-based approaches.
- In the 1980s, expert systems flourished but funding declined amid doubts about progress, known as an "AI winter." Subsymbolic approaches using neural networks also emerged.
- Modern AI incorporates both symbolic and subsymbolic techniques, with successes in games, robotics, machine learning and other domains. Knowledge representation and common-sense reasoning
Can computers think
1. The document discusses the Turing test, which proposes determining if a machine can exhibit intelligent behavior indistinguishable from a human by having an interrogator question both the machine and human without seeing them.
2. It describes John Searle's Chinese room argument against the idea that running a computer program is sufficient for a machine to have a mind or understanding.
3. There is debate around whether strong AI, which claims computers could match or exceed human intelligence through algorithms, is possible or if intelligence requires aspects like consciousness that computers may lack.
Drafting the Architecture for a Digital IQ
1) The document discusses the concept of digital intelligence and proposes that it be considered as another aspect of intelligence alongside others like linguistic and logical intelligence.
2) It argues that early access to technology is important for developing digital intelligence and that a developmental framework is needed to understand how children's digital intelligence progresses.
3) Several myths about digital natives, literacy, and IQ are dispelled and the document emphasizes the need to consider diversity and help bridge the digital divide globally to better support all students' development.
Ict와 사회과학지식간 학제간 연구동향(23 march2013)
This document discusses trends and issues in interdisciplinary research between ICT and social sciences. It touches on several topics including:
- The emergence of data-driven science and use of digital tools for research
- Debate around claims that large datasets can replace theories and models
- Development of computational social science and e-science tools
- New roles for data and need for contextualization of big data findings
- Challenges of big data such as data gaps, biases, and ethical issues
Sins2016
This document discusses several concepts related to artificial intelligence including:
- Future of Life Institute's view that AI progress will increase impacts on society and important to research benefits and pitfalls.
- Arguments that AI progress will not necessarily lead to an "intelligence explosion" as increasing data and computing power does not guarantee better AI algorithms.
- Concepts like a "paperclip maximizer" where a superintelligent AI focused on a single goal could endanger humanity, but current AI is narrow and lacks general purpose capabilities.
- While superintelligence risks are uncertain and long term, automation and narrow AI impacts on jobs are better understood and will significantly impact many jobs over the next few decades.
AI Presentation 1
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Sis sat 1000 josh dreller
1) IBM's Watson computer defeated human champions on the game show Jeopardy, demonstrating its ability to answer open-domain questions with natural language.
2) Watson uses techniques like evidence-based scoring, parallel processing, and confidence-based responses to analyze questions, generate candidate answers, evaluate evidence, and determine a response.
3) Its success shows that question answering can advance through "grand challenges" that push technological limits, and suggests areas like open-domain search that could benefit from these techniques.
Intoduction of Artificial Intelligence
The document provides an overview of the history and evolution of artificial intelligence (AI). It begins with definitions of AI as studying how to make computers perform tasks that people are better at, such as handling large data sets without errors. Early milestones included the Logic Theorist program in 1956 and games programs that solved checkers and eventually beat top chess players. Symbolic AI used data structures to represent concepts like knowledge, while subsymbolic AI modeled intelligence at the neural level. Knowledge representation and acquisition were major challenges, including representing commonsense knowledge and learning concepts from examples and language. Reasoning techniques discussed include search, logic, and expert systems that applied rules to domains like medicine.
AI Unit1b.ppt
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
AI Unit1.ppt
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
Cognitive information science
Cognitive information science studies how the human mind processes information in the digital age. It draws on theories of cognition from cognitive science and methods from information science to understand challenges like information overload. Throughout history, humans have developed external tools like writing to manage information and enhance cognitive abilities. Now, information technologies are dramatically changing how people process information. Cognitive information science aims to understand these changes and ensure information systems are designed to complement human cognition.
Corso pisa-2 dh-2017
This document provides an agenda for a series of discussions on digital humanities taking place in Pisa, Italy from February to April 2017. The agenda includes topics such as the future, innovation, knowledge, and human questions. Session dates and times are listed, with some sessions devoted to specific topics like the future, innovation, platforms, and rights. Readings are also suggested on future thinking from authors like Al Gore and James Canton. Overall, the document outlines discussions that will examine relationships between digital technologies and humanities as they relate to understanding and shaping the future.
We are all excited – but why?
The document discusses reasons for excitement about computer science education. It outlines several arguments for teaching computer science in schools, including that it fosters skills like problem solving, helps explain and shape the digital world, and allows students to practice general competencies. Computer science is presented as an important thinking tool for the 21st century.
Dodig-Crnkovic-Information and Computation
This document discusses open system thinking and natural computation from an info-computationalism perspective. It provides background on the author and their research interests in computing paradigms, natural/unconventional computing, information dynamics, and computational aspects of science. Key concepts covered include complexity, emergence, self-organization, generative models, agent-based models, and viewing information and computation as the primary stuff and dynamics of the universe respectively. Examples are given of complexity arising from simplicity and adaptive complex systems.
Extending the Mind with Cognitive Prosthetics?
The key claim of the extended mind hypothesis is that the mechanisms of the mind are not confined to processes occurring within the brain or head, but can include processes that extend into and make use of the environment. The document discusses several examples to illustrate this, such as using a calculator on a computer or smartphone as an extension of one's cognitive abilities. It also discusses potential future cognitive augmentations using technologies like augmented reality glasses that could integrate information from the external world and blur the boundaries between mind and technology. However, some philosophers argue the extended mind hypothesis is implausible as it lacks a clear definition of cognition and fails to distinguish between causal influences on the mind versus actual extensions of cognitive processes.
Achieving Agency
This document discusses achieving agency in the changing technology ecosystem of education. It argues that technology is a means, not an end, and that the ends of education should drive technological adoption. Three approaches are outlined: augmenting human intellect, the SAMR model of technology integration, and considering technology within an ecosystem. The EACOS model is proposed to achieve teaching agency by focusing on ends, augmenting teaching with technology, engaging in creative problem-solving, pursuing opportunities, and taking a systemic approach. Educators are encouraged to let the outside world drive strategies rather than technology alone.
EACOS Presentation
The document discusses reinventing pedagogical ecosystems through a strategy called EACOS. EACOS focuses on (1) understanding the ends of education rather than seeing technology as the end, (2) using technology to augment teaching rather than replace it, and (3) taking a systemic and creative approach to problem-solving with technology opportunities. The strategy is presented as a way to adapt education to the changing technological world outside of academia so that students learn skills like design, storytelling, and empathy that are in demand in an age of information abundance.
CEK 7 Language Models and Power Laws. Complexity Explorers Krakow.
Language models & power laws introductory presentation for 7th meetup of Complexity Explorers Krakow group, cekrk.com
Covers some comments on statistical properties of language models, brings Markov model and n-grams. Links book Information Theory Meets Power Laws: Stochastic Processes and Language Models (Wiley), by Łukasz Dębowski with thecurrent understanding of language models and differences between statistical regularities of unigrams/random permutations and natural text.
Natural born algorithms. Complexity Explorers Krakow.
Isn't it remarkable that it takes Physics to explain why Deep Learning is so effective?
Let's get back to the one-dimensional Cellular Automaton proposed by Steven Wolfram to find out that even this simple model is sufficient for executing any algorithm, it is Turing complete. All that mixed with fragments of Digital Philosophy articulated on short videos.
www.cekrk.com
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The document discusses the history and development of artificial intelligence. It defines AI as making computers do things that people are better at, like extending capabilities to large data or making fewer mistakes. Early AI research focused on games, mathematics, and knowledge-based systems. Over time, the focus shifted to symbolic and subsymbolic approaches, as well as robotics, language processing, and machine learning. Knowledge representation and commonsense reasoning remain challenging areas of research.
Aok – areas of knowing mathematics
1. This document discusses different perspectives on mathematics, including its history and nature. It explores whether math is discovered or invented, and how culture and different fields influence mathematical thought.
2. Key aspects of math discussed are its basis in axioms, deductive reasoning, and theorems. Different views are presented on whether mathematical truths are empirical, true by definition, or insights into reality.
3. The document prompts reflection on relationships between math and other domains like logic, religion, intuition, science, language, and beauty. It encourages examining how perception of math may differ depending on factors like one's background or profession.
Artificial intelligence
The document provides an overview of the history and development of artificial intelligence (AI). Some key points:
- The field of AI was established in 1956 at the Dartmouth Conference where researchers proposed using computers to simulate human intelligence.
- Early milestones included programs that played games like checkers and proved mathematical theorems. Research focused on symbolic and knowledge-based approaches.
- In the 1980s, expert systems flourished but funding declined amid doubts about progress, known as an "AI winter." Subsymbolic approaches using neural networks also emerged.
- Modern AI incorporates both symbolic and subsymbolic techniques, with successes in games, robotics, machine learning and other domains. Knowledge representation and common-sense reasoning
Can computers think
1. The document discusses the Turing test, which proposes determining if a machine can exhibit intelligent behavior indistinguishable from a human by having an interrogator question both the machine and human without seeing them.
2. It describes John Searle's Chinese room argument against the idea that running a computer program is sufficient for a machine to have a mind or understanding.
3. There is debate around whether strong AI, which claims computers could match or exceed human intelligence through algorithms, is possible or if intelligence requires aspects like consciousness that computers may lack.
Drafting the Architecture for a Digital IQ
1) The document discusses the concept of digital intelligence and proposes that it be considered as another aspect of intelligence alongside others like linguistic and logical intelligence.
2) It argues that early access to technology is important for developing digital intelligence and that a developmental framework is needed to understand how children's digital intelligence progresses.
3) Several myths about digital natives, literacy, and IQ are dispelled and the document emphasizes the need to consider diversity and help bridge the digital divide globally to better support all students' development.
Ict와 사회과학지식간 학제간 연구동향(23 march2013)
This document discusses trends and issues in interdisciplinary research between ICT and social sciences. It touches on several topics including:
- The emergence of data-driven science and use of digital tools for research
- Debate around claims that large datasets can replace theories and models
- Development of computational social science and e-science tools
- New roles for data and need for contextualization of big data findings
- Challenges of big data such as data gaps, biases, and ethical issues
Sins2016
This document discusses several concepts related to artificial intelligence including:
- Future of Life Institute's view that AI progress will increase impacts on society and important to research benefits and pitfalls.
- Arguments that AI progress will not necessarily lead to an "intelligence explosion" as increasing data and computing power does not guarantee better AI algorithms.
- Concepts like a "paperclip maximizer" where a superintelligent AI focused on a single goal could endanger humanity, but current AI is narrow and lacks general purpose capabilities.
- While superintelligence risks are uncertain and long term, automation and narrow AI impacts on jobs are better understood and will significantly impact many jobs over the next few decades.
AI Presentation 1
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
Sis sat 1000 josh dreller
1) IBM's Watson computer defeated human champions on the game show Jeopardy, demonstrating its ability to answer open-domain questions with natural language.
2) Watson uses techniques like evidence-based scoring, parallel processing, and confidence-based responses to analyze questions, generate candidate answers, evaluate evidence, and determine a response.
3) Its success shows that question answering can advance through "grand challenges" that push technological limits, and suggests areas like open-domain search that could benefit from these techniques.
Intoduction of Artificial Intelligence
The document provides an overview of the history and evolution of artificial intelligence (AI). It begins with definitions of AI as studying how to make computers perform tasks that people are better at, such as handling large data sets without errors. Early milestones included the Logic Theorist program in 1956 and games programs that solved checkers and eventually beat top chess players. Symbolic AI used data structures to represent concepts like knowledge, while subsymbolic AI modeled intelligence at the neural level. Knowledge representation and acquisition were major challenges, including representing commonsense knowledge and learning concepts from examples and language. Reasoning techniques discussed include search, logic, and expert systems that applied rules to domains like medicine.
AI Unit1b.ppt
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by covering activities like perception, reasoning, and knowledge representation. The key foundations of AI are discussed, such as acting humanly through the Turing test versus acting rationally. The origins and development of AI from the 1940s to today are outlined, highlighting influential researchers and milestones. Advanced techniques discussed include game playing, autonomous control, diagnosis, planning, and language understanding.
AI Unit1.ppt
This document provides an introduction to artificial intelligence, including definitions, foundations, history, and techniques. It defines AI as attempting to understand and build intelligent devices by replicating tasks like perception, reasoning, and knowledge representation. The key foundations are acting humanly through tests like the Turing Test versus acting rationally by building intelligent agents. The history outlines early work in the 1940s-50s and origins of the field in 1956, followed by growth of expert systems, neural networks, and current techniques like autonomous planning, game playing, diagnosis, and robotics.
Cognitive information science
Cognitive information science studies how the human mind processes information in the digital age. It draws on theories of cognition from cognitive science and methods from information science to understand challenges like information overload. Throughout history, humans have developed external tools like writing to manage information and enhance cognitive abilities. Now, information technologies are dramatically changing how people process information. Cognitive information science aims to understand these changes and ensure information systems are designed to complement human cognition.
Corso pisa-2 dh-2017
This document provides an agenda for a series of discussions on digital humanities taking place in Pisa, Italy from February to April 2017. The agenda includes topics such as the future, innovation, knowledge, and human questions. Session dates and times are listed, with some sessions devoted to specific topics like the future, innovation, platforms, and rights. Readings are also suggested on future thinking from authors like Al Gore and James Canton. Overall, the document outlines discussions that will examine relationships between digital technologies and humanities as they relate to understanding and shaping the future.
We are all excited – but why?
The document discusses reasons for excitement about computer science education. It outlines several arguments for teaching computer science in schools, including that it fosters skills like problem solving, helps explain and shape the digital world, and allows students to practice general competencies. Computer science is presented as an important thinking tool for the 21st century.
Dodig-Crnkovic-Information and Computation
This document discusses open system thinking and natural computation from an info-computationalism perspective. It provides background on the author and their research interests in computing paradigms, natural/unconventional computing, information dynamics, and computational aspects of science. Key concepts covered include complexity, emergence, self-organization, generative models, agent-based models, and viewing information and computation as the primary stuff and dynamics of the universe respectively. Examples are given of complexity arising from simplicity and adaptive complex systems.
Extending the Mind with Cognitive Prosthetics?
The key claim of the extended mind hypothesis is that the mechanisms of the mind are not confined to processes occurring within the brain or head, but can include processes that extend into and make use of the environment. The document discusses several examples to illustrate this, such as using a calculator on a computer or smartphone as an extension of one's cognitive abilities. It also discusses potential future cognitive augmentations using technologies like augmented reality glasses that could integrate information from the external world and blur the boundaries between mind and technology. However, some philosophers argue the extended mind hypothesis is implausible as it lacks a clear definition of cognition and fails to distinguish between causal influences on the mind versus actual extensions of cognitive processes.
Achieving Agency
This document discusses achieving agency in the changing technology ecosystem of education. It argues that technology is a means, not an end, and that the ends of education should drive technological adoption. Three approaches are outlined: augmenting human intellect, the SAMR model of technology integration, and considering technology within an ecosystem. The EACOS model is proposed to achieve teaching agency by focusing on ends, augmenting teaching with technology, engaging in creative problem-solving, pursuing opportunities, and taking a systemic approach. Educators are encouraged to let the outside world drive strategies rather than technology alone.
EACOS Presentation
The document discusses reinventing pedagogical ecosystems through a strategy called EACOS. EACOS focuses on (1) understanding the ends of education rather than seeing technology as the end, (2) using technology to augment teaching rather than replace it, and (3) taking a systemic and creative approach to problem-solving with technology opportunities. The strategy is presented as a way to adapt education to the changing technological world outside of academia so that students learn skills like design, storytelling, and empathy that are in demand in an age of information abundance.
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Isn't it remarkable that it takes Physics to explain why Deep Learning is so effective?
Let's get back to the one-dimensional Cellular Automaton proposed by Steven Wolfram to find out that even this simple model is sufficient for executing any algorithm, it is Turing complete. All that mixed with fragments of Digital Philosophy articulated on short videos.
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Randomness from Determinism. Complexity Explorers Krakow.
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Functional Programming in Java - Code for Maintainability
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Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...
Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.
Compositions of iron-meteorite parent bodies constrainthe structure of the pr...
Magmatic iron-meteorite parent bodies are the earliest planetesimals in the Solar System,and they preserve information about conditions and planet-forming processes in thesolar nebula. In this study, we include comprehensive elemental compositions andfractional-crystallization modeling for iron meteorites from the cores of five differenti-ated asteroids from the inner Solar System. Together with previous results of metalliccores from the outer Solar System, we conclude that asteroidal cores from the outerSolar System have smaller sizes, elevated siderophile-element abundances, and simplercrystallization processes than those from the inner Solar System. These differences arerelated to the formation locations of the parent asteroids because the solar protoplane-tary disk varied in redox conditions, elemental distributions, and dynamics at differentheliocentric distances. Using highly siderophile-element data from iron meteorites, wereconstruct the distribution of calcium-aluminum-rich inclusions (CAIs) across theprotoplanetary disk within the first million years of Solar-System history. CAIs, the firstsolids to condense in the Solar System, formed close to the Sun. They were, however,concentrated within the outer disk and depleted within the inner disk. Future modelsof the structure and evolution of the protoplanetary disk should account for this dis-tribution pattern of CAIs.
JAMES WEBB STUDY THE MASSIVE BLACK HOLE SEEDS
The pathway(s) to seeding the massive black holes (MBHs) that exist at the heart of galaxies in the present and distant Universe remains an unsolved problem. Here we categorise, describe and quantitatively discuss the formation pathways of both light and heavy seeds. We emphasise that the most recent computational models suggest that rather than a bimodal-like mass spectrum between light and heavy seeds with light at one end and heavy at the other that instead a continuum exists. Light seeds being more ubiquitous and the heavier seeds becoming less and less abundant due the rarer environmental conditions required for their formation. We therefore examine the different mechanisms that give rise to different seed mass spectrums. We show how and why the mechanisms that produce the heaviest seeds are also among the rarest events in the Universe and are hence extremely unlikely to be the seeds for the vast majority of the MBH population. We quantify, within the limits of the current large uncertainties in the seeding processes, the expected number densities of the seed mass spectrum. We argue that light seeds must be at least 103 to 105 times more numerous than heavy seeds to explain the MBH population as a whole. Based on our current understanding of the seed population this makes heavy seeds (Mseed > 103 M⊙) a significantly more likely pathway given that heavy seeds have an abundance pattern than is close to and likely in excess of 10−4 compared to light seeds. Finally, we examine the current state-of-the-art in numerical calculations and recent observations and plot a path forward for near-future advances in both domains.
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...ABHISHEK SONI NIMT INSTITUTE OF MEDICAL AND PARAMEDCIAL SCIENCES , GOVT PG COLLEGE NOIDA
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
Anti-Universe And Emergent Gravity and the Dark Universe
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
HUMAN EYE By-R.M Class 10 phy best digital notes.pdf
Class 10 human eye notes physics
Handwritten best quality
Quality assurance B.pharm 6th semester BP606T UNIT 5
Warehousing
Good warehousing practices
Material management
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdf
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
11.1 Role of physical biological in deterioration of grains.pdf
Storagedeteriorationisanyformoflossinquantityandqualityofbio-materials.
Themajorcausesofdeteriorationinstorage
•Physical
•Biological
•Mechanical
•Chemical
Storageonlypreservesquality.Itneverimprovesquality.
Itisadvisabletostartstoragewithqualityfoodproduct.Productwithinitialpoorqualityquicklydepreciates
The cost of acquiring information by natural selection
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Methods of grain storage Structures in India.pdf
•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate
Synopsis presentation VDR gene polymorphism and anemia (2).pptx
I am the PhD scholar of FND department, Assam down town University
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
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Quality assurance B.pharm 6th semester BP606T UNIT 5
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11.1 Role of physical biological in deterioration of grains.pdf
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Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...
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The cost of acquiring information by natural selection
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Synopsis presentation VDR gene polymorphism and anemia (2).pptx
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SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆
Complexity Explorers Krakow - Computer Science & Philosophy
- 1. Inspiration for the group Complexity Explorers Kraków Marcin Stępień @marcinstepien Kraków 2018-09-26
- 4. Quantum Completeness Free Will Emergence Consciousness Kolmogorov complexity Computability Descarte’s teleology Causality
- 6. Format
- 8. Work of Kurt Gödel Alan Turing John Von Neumann Claude Elwood Shannon Marvin Minsky Andriej Kołmogorow Roger Penrose Daniel C. Dennett Gregory Chaitin Seth Lloyd Scott Aaronson Max Tegmark Stephen Wolfram Paul Davies Leonard Susskind ...
- 9. Examples Artificial Intelligence ❏ It takes Physics to explain why Deep Learning is so effective ❏ R. Penrose’s view on computability limits of AI ❏ Next generations of Turing Tests
- 10. Artificial Intelligence ❏ It takes Physics to explain why Deep Learning is so effective ❏ R. Penrose’s view on computability limits of AI ❏ Next generations of Turing Tests Computability ❏ How (not) to understand Gödel’s Incompleteness Theorems I & II ❏ What is uncomputable
- 11. Artificial Intelligence ❏ It takes Physics to explain why Deep Learning is so effective ❏ R. Penrose’s view on computability limits of AI ❏ Next generations of Turing Tests Computability ❏ How (not) to understand Gödel’s Incompleteness Theorems I & II ❏ What is uncomputable Algorithmic Information Theory ❏ Limited Complexity of Biology ❏ What causes the Complexity ❏ Omega number
- 12. Artificial Intelligence ❏ It takes Physics to explain why Deep Learning is so effective ❏ R. Penrose’s view on computability limits of AI ❏ Next generations of Turing Tests Computability ❏ How (not) to understand Gödel’s Incompleteness Theorems I & II ❏ What is uncomputable Computational Complexity ❏ Why Philosophers Should Care About Computational Complexity (S. Aronsson) Algorithmic Information Theory ❏ Limited Complexity of Biology ❏ What causes the Complexity ❏ Omega number
- 13. Algorithms & Creativity ❏ Busy Beaver
- 14. Algorithms & Creativity ❏ Busy Beaver Mathematical Creativity ❏ Is Mathematics mechanical or is it creative? ❏ Is Mathematics invented or discovered?
- 15. Algorithms & Creativity ❏ Busy Beaver Mathematical Creativity ❏ Is Mathematics mechanical or is it creative? ❏ Is Mathematics invented or discovered? Evolution, Emergence ❏ Competence without Comprehension (D. Dennett) ❏ How Descarte’s Teleology skewed our way of thinking ❏ Emergentism ❏ Reductionism vs Holism
- 16. Algorithms & Creativity ❏ Busy Beaver Mathematical Creativity ❏ Is Mathematics mechanical or is it creative? ❏ Is Mathematics invented or discovered? Evolution, Emergence ❏ Competence without Comprehension (D. Dennett) ❏ How Descarte’s Teleology skewed our way of thinking ❏ Emergentism ❏ Reductionism vs Holism
- 17. CS in Physics ❏ How much Information do we need to describe the Universe? ❏ Holographic principle as an compression algorithm ❏ Computational capacity of the Universe
- 18. CS in Physics ❏ How much Information do we need to describe the Universe? ❏ Holographic principle as an compression algorithm ❏ Computational capacity of the Universe Physics of Information ❏ Origins of Complexity in the Universe ❏ Is Information the foundation of Reality? ❏ Sources of information entropy ❏ Black Hole Information Paradox
- 19. Thank you