A presentation about Infinite Chess and the difference between man and machines. From works by C.D.A. Evans and J.D. Hamkins. Presented during the International Interdisciplinary Seminar of London, January 2018.
Two player games involve two players alternating turns trying to maximize or minimize the outcome of a game. The MiniMax algorithm is commonly used to choose the best move for a player in two player games by recursively evaluating all possible future moves. Alpha-beta pruning improves upon MiniMax by pruning branches that cannot influence the final outcome, allowing deeper search of the game tree within the same time limit.
This document discusses game theory concepts like game trees, minimax search, and alpha-beta pruning. It explains that game trees model all possible moves in a game but quickly become intractable due to their exponential size. Minimax search evaluates game states using heuristics and searches the tree to find the optimal move, but is inefficient. Alpha-beta pruning improves on minimax by pruning branches that cannot affect the choice of move, allowing deeper search and exponential time savings. Tic-Tac-Toe is used as a running example to illustrate these concepts.
This document discusses adversarial search techniques for game playing. It introduces minimax search and alpha-beta pruning to find optimal moves. Minimax search finds the best possible outcome against an optimal opponent by choosing the move with the highest minimax value. Alpha-beta pruning improves search efficiency by pruning branches that cannot affect the result. While minimax search is optimal, its complexity makes it infeasible for games like chess; heuristics and resource limits must be used to approximate search.
Game playing problems can be modeled as game trees to apply search techniques like minimax and alpha-beta pruning. Minimax searches a game tree to determine the move that maximizes the minimum payoff against an optimal opponent. It recursively evaluates nodes based on the minimax rule. Alpha-beta pruning improves performance by avoiding evaluating subtrees that cannot impact the result. Chance nodes in games like Backgammon use expected values to model uncertainty from dice rolls.
It is an artificial document, please. regarding Ai topicschougulesup79
Game playing can be studied to understand problems involving adversarial agents. Games define large search spaces that require minimal initial structure to study. Typical perfect games have two players who alternate moves in a zero-sum game with complete information and no chance elements. To play such a game, one considers all legal moves and evaluates the resulting positions to determine the best move. Minimax search evaluates positions and uses the minimax rule to select the move that maximizes the guaranteed payoff. Alpha-beta pruning improves minimax search by avoiding evaluating unpromising moves.
This document discusses algorithms and techniques for game playing by computers, including minimax, alpha-beta pruning, and heuristic evaluation functions. It provides examples of computers that have achieved world-champion level performance at various games, such as Deep Blue in chess, Chinook in checkers, and AlphaGo in Go. While early programs relied mainly on brute force search, modern approaches also incorporate huge databases, pattern recognition, and machine learning. Games have become an important domain for advancing artificial intelligence research.
This document discusses domination numbers in chessboard puzzles. It defines domination as a set of pieces covering every square or attacking every square. Formulas are given for the domination numbers of rooks, bishops, and kings on an nxn board. For queens and knights, only specific values and bounds have been determined due to greater complexity. The movement rules for each piece are also outlined.
Nash equilibrium and Pareto optimality are discussed and compared using examples from game theory. In the Battle of Sexes game, the Nash equilibria coincide with the Pareto efficient outcomes. However, in other games like the Prisoner's Dilemma and the Tragedy of the Commons, the Nash equilibria are not Pareto optimal and there is a possibility of Pareto improvement by playing different strategies. Braess's Paradox is also discussed where adding more resources to a network can paradoxically decrease overall performance at equilibrium due to drivers taking suboptimal routes.
Two player games involve two players alternating turns trying to maximize or minimize the outcome of a game. The MiniMax algorithm is commonly used to choose the best move for a player in two player games by recursively evaluating all possible future moves. Alpha-beta pruning improves upon MiniMax by pruning branches that cannot influence the final outcome, allowing deeper search of the game tree within the same time limit.
This document discusses game theory concepts like game trees, minimax search, and alpha-beta pruning. It explains that game trees model all possible moves in a game but quickly become intractable due to their exponential size. Minimax search evaluates game states using heuristics and searches the tree to find the optimal move, but is inefficient. Alpha-beta pruning improves on minimax by pruning branches that cannot affect the choice of move, allowing deeper search and exponential time savings. Tic-Tac-Toe is used as a running example to illustrate these concepts.
This document discusses adversarial search techniques for game playing. It introduces minimax search and alpha-beta pruning to find optimal moves. Minimax search finds the best possible outcome against an optimal opponent by choosing the move with the highest minimax value. Alpha-beta pruning improves search efficiency by pruning branches that cannot affect the result. While minimax search is optimal, its complexity makes it infeasible for games like chess; heuristics and resource limits must be used to approximate search.
Game playing problems can be modeled as game trees to apply search techniques like minimax and alpha-beta pruning. Minimax searches a game tree to determine the move that maximizes the minimum payoff against an optimal opponent. It recursively evaluates nodes based on the minimax rule. Alpha-beta pruning improves performance by avoiding evaluating subtrees that cannot impact the result. Chance nodes in games like Backgammon use expected values to model uncertainty from dice rolls.
It is an artificial document, please. regarding Ai topicschougulesup79
Game playing can be studied to understand problems involving adversarial agents. Games define large search spaces that require minimal initial structure to study. Typical perfect games have two players who alternate moves in a zero-sum game with complete information and no chance elements. To play such a game, one considers all legal moves and evaluates the resulting positions to determine the best move. Minimax search evaluates positions and uses the minimax rule to select the move that maximizes the guaranteed payoff. Alpha-beta pruning improves minimax search by avoiding evaluating unpromising moves.
This document discusses algorithms and techniques for game playing by computers, including minimax, alpha-beta pruning, and heuristic evaluation functions. It provides examples of computers that have achieved world-champion level performance at various games, such as Deep Blue in chess, Chinook in checkers, and AlphaGo in Go. While early programs relied mainly on brute force search, modern approaches also incorporate huge databases, pattern recognition, and machine learning. Games have become an important domain for advancing artificial intelligence research.
This document discusses domination numbers in chessboard puzzles. It defines domination as a set of pieces covering every square or attacking every square. Formulas are given for the domination numbers of rooks, bishops, and kings on an nxn board. For queens and knights, only specific values and bounds have been determined due to greater complexity. The movement rules for each piece are also outlined.
Nash equilibrium and Pareto optimality are discussed and compared using examples from game theory. In the Battle of Sexes game, the Nash equilibria coincide with the Pareto efficient outcomes. However, in other games like the Prisoner's Dilemma and the Tragedy of the Commons, the Nash equilibria are not Pareto optimal and there is a possibility of Pareto improvement by playing different strategies. Braess's Paradox is also discussed where adding more resources to a network can paradoxically decrease overall performance at equilibrium due to drivers taking suboptimal routes.
1. Game playing is an important domain for artificial intelligence research as games provide formal reasoning problems that allow direct comparison between computer programs and humans.
2. Alpha-beta pruning can speed up minimax search in game trees by pruning branches that cannot alter the outcome. It works by maintaining lower and upper bounds on the score.
3. Evaluating leaf nodes is challenging. For chess, linear evaluation functions combining weighted features like material and position are commonly used, and reinforcement learning can help tune the weights.
Adversarial search is a technique used in artificial intelligence and game theory to model competitive interactions between agents. It involves building a search tree to evaluate all possible moves in a game and selecting the optimal choice. The minimax algorithm and alpha-beta pruning are common approaches used in adversarial search to maximize an agent's chances of winning by minimizing an opponent's best possible outcome. Minimax works by having agents alternately assume the role of minimizing or maximizing player at each turn, while alpha-beta pruning improves the efficiency of minimax by pruning branches that cannot affect the outcome.
This document discusses the Knight's Tour problem in chess and two algorithms for solving it: a neural network approach and Warnsdorff's algorithm. It explains that the Knight's Tour problem involves finding a path for a knight to visit every square on a chessboard exactly once. It then summarizes that Warnsdorff's algorithm, which selects the next square with the fewest available future moves, is simpler and faster than the neural network approach and always produces a closed tour, making it a better solution to the Knight's Tour problem.
Analysis & Design of Algorithms
Backtracking
N-Queens Problem
Hamiltonian circuit
Graph coloring
A presentation on unit Backtracking from the ADA subject of Engineering.
The document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on game rules and circumstances. The key techniques covered include MinMax for decision making, finite state machines to model agent behavior, and evaluating board positions in games like chess. It also notes important considerations for AI in games like responding in real-time, allowing players to defeat the AI in fun ways, and adjusting the difficulty through the "amount and type of AI."
This document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on their programming. It notes that while human-level general intelligence is difficult to achieve, AI can perform well in narrow contexts like chess. For games, the AI must be intentionally flawed to ensure a fun challenge and cannot have obvious weaknesses. It must also be able to perform calculations and make decisions in real-time to interact with the game. The document then outlines some common AI techniques used in games, including MinMax search trees and finite state machines to control agent behavior. It provides pseudocode examples of the MinMax algorithm and discusses enhancements like Alpha-Beta pruning
This document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on their perceptions and the game rules. The key techniques covered include MinMax and alpha-beta pruning for decision making, finite state machines to define agent behavior, and evaluating game states. It also outlines some of the additional considerations for AI in games compared to other applications, such as ensuring agents are intentionally flawed to provide a fun challenge and can perform actions in real-time to match the pace of gameplay.
The Mini-Max algorithm is a recursive algorithm used in game theory and decision making to find the optimal move for a player assuming the opponent also plays optimally. It works by having two players, MAX and MIN, where MAX aims to maximize their score and MIN aims to minimize MAX's score. The algorithm performs a depth-first search of the game tree, evaluating each terminal node and backing up values until it reaches the initial state node to determine the optimal first move.
AlphaZero: A General Reinforcement Learning Algorithm that Masters Chess, Sho...Joonhyung Lee
An introduction to DeepMind's newest board-game playing AI, AlphaZero.
I have improved significantly on my previous presentation in https://www.slideshare.net/ssuserc416e2/alphago-zero-mastering-the-game-of-go-without-human-knowledge, which had several errors (some rather glaring, such as the temperature equation for simulated annealing). Also, DeepMind released far more details in their new Science paper for AlphaZero.
One comment I would like to add is that the AlphaGo Zero used for comparison in this paper is a very weak version, not the final version. Thus, AlphaGo Zero is still SOTA for Go.
Minimax is an algorithm that is commonly used for game playing. It works by constructing a game tree that represents all possible future game states up to a certain depth, and uses an evaluation function to assign a value to each state. The minimax algorithm traverses the tree and "backs up" values from the leaves to the root by maximizing values at MAX nodes and minimizing values at MIN nodes. Alpha-beta pruning improves upon minimax by pruning branches that cannot affect the final output value.
This document provides an overview of chess algorithms and techniques used in computer chess programs. It discusses the complexity of chess, the history of computer chess programs, common search algorithms like minimax and negamax, and pruning techniques like alpha-beta pruning. It also covers challenges like node explosion, transposition tables, endgame tablebases, and evaluations functions. The goal is to explain how modern chess programs are able to search deeply despite the enormous game tree size.
Backtracking and branch and bound are algorithms used to solve problems with large search spaces. Backtracking uses depth-first search and prunes subtrees that don't lead to viable solutions. Branch and bound uses breadth-first search and pruning, maintaining partial solutions in a priority queue. Both techniques systematically eliminate possibilities to find optimal solutions faster than exhaustive search. Examples where they can be applied include maze pathfinding, the eight queens problem, sudoku, and the traveling salesman problem.
This document discusses game theory concepts like winning strategies through examples of two games:
1) A game where two players alternately remove 1-3 tiles from a board until none remain. The winning strategy is to always leave a number of tiles that is a multiple of 4.
2) A game where two players alternate coloring squares on a 19x94 lattice. The board can be split into symmetric halves, allowing one player to force a win using a pairing strategy.
Backtracking and branch and bound are algorithms for solving problems systematically by trying options in an orderly manner. Backtracking uses depth-first search and prunes subtrees that don't lead to solutions. Branch and bound uses breadth-first search and pruning, maintaining upper and lower bounds to eliminate options. Both aim to avoid exhaustive search by eliminating non-promising options early. Examples that can use these techniques include maze navigation, the eight queens problem, and Sudoku puzzles.
The document discusses game playing strategies and algorithms, including tic-tac-toe, minimax algorithm, alpha-beta pruning, and AND/OR graphs. It describes how the minimax algorithm works to generate a game tree and evaluate moves to maximize winning potential. Alpha-beta pruning improves on minimax by pruning unwanted portions of the search tree. AND/OR graphs represent problems that can be decomposed into subproblems, some of which must be solved, and are explored using the AO* algorithm.
Game playing has been an important area of AI research as games allow experiments with adversarial situations in a constrained environment. Minimax search and alpha-beta pruning are commonly used techniques for two-player zero-sum games, evaluating the game tree to varying depths depending on the time available and using a static board evaluator to estimate leaf node values. While early AI programs solved simple games like tic-tac-toe perfectly, scaling search to capture the complexity of chess required innovations like progressive deepening, quiescence search, and evaluation function learning from self-play. Modern AI programs now surpass top human players in many games including checkers, Scrabble, Othello, and backgammon.
This document provides an overview of game theory concepts including:
- 2-player zero-sum games and minimax optimal strategies
- The minimax theorem which states that every 2-player zero-sum game has a value and optimal strategies for both players
- General-sum games and the concept of a Nash equilibrium as a stable pair of strategies where neither player benefits from deviating
- The proof of existence of Nash equilibria in general-sum games using Brouwer's fixed-point theorem
This document provides an overview of the course "Design and Analysis of Approximation Algorithms". It discusses the textbook, schedule, homework rules, and covers topics like greedy strategies, restrictions, partitions, and relaxation. It also introduces computational complexity, deterministic and nondeterministic Turing machines, and provides early approximation results for problems like vertex cover, traveling salesman, and knapsack.
This document provides an overview of the course "Design and Analysis of Approximation Algorithms". It discusses the textbook, schedule, homework rules, and covers topics like greedy strategies, restrictions, partitions, and relaxation. It also introduces computational complexity, deterministic and nondeterministic Turing machines, and provides early approximation results for problems like vertex cover, traveling salesman, and knapsack.
What makes us humans different from animals? Culture? The ability to make tools? The language? Morality? Art? This presentation will show us that these criteria alone are not enough to explain what makes us different from animals.
This document discusses the Axiom of Choice (AC), a foundational principle in set theory that states that for any set of nonempty sets, there exists a function that chooses one element from each set. The document provides examples to illustrate AC with finite and infinite sets. It explains that while AC seems intuitive for finite sets, it leads to counterintuitive conclusions for infinite sets. The document also discusses the relationship between AC and Zermelo-Fraenkel set theory (ZF), noting that ZF does not prove or disprove AC, so its inclusion in ZF is a matter of mathematical preference.
Can christian schools continue to teach only about traditional marriage2idseminar
An approach to the big general issue whether faith schools should be allowed to teach based on their fundamental beliefs and discriminate amongst teachers and students because of their faith.
1. Game playing is an important domain for artificial intelligence research as games provide formal reasoning problems that allow direct comparison between computer programs and humans.
2. Alpha-beta pruning can speed up minimax search in game trees by pruning branches that cannot alter the outcome. It works by maintaining lower and upper bounds on the score.
3. Evaluating leaf nodes is challenging. For chess, linear evaluation functions combining weighted features like material and position are commonly used, and reinforcement learning can help tune the weights.
Adversarial search is a technique used in artificial intelligence and game theory to model competitive interactions between agents. It involves building a search tree to evaluate all possible moves in a game and selecting the optimal choice. The minimax algorithm and alpha-beta pruning are common approaches used in adversarial search to maximize an agent's chances of winning by minimizing an opponent's best possible outcome. Minimax works by having agents alternately assume the role of minimizing or maximizing player at each turn, while alpha-beta pruning improves the efficiency of minimax by pruning branches that cannot affect the outcome.
This document discusses the Knight's Tour problem in chess and two algorithms for solving it: a neural network approach and Warnsdorff's algorithm. It explains that the Knight's Tour problem involves finding a path for a knight to visit every square on a chessboard exactly once. It then summarizes that Warnsdorff's algorithm, which selects the next square with the fewest available future moves, is simpler and faster than the neural network approach and always produces a closed tour, making it a better solution to the Knight's Tour problem.
Analysis & Design of Algorithms
Backtracking
N-Queens Problem
Hamiltonian circuit
Graph coloring
A presentation on unit Backtracking from the ADA subject of Engineering.
The document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on game rules and circumstances. The key techniques covered include MinMax for decision making, finite state machines to model agent behavior, and evaluating board positions in games like chess. It also notes important considerations for AI in games like responding in real-time, allowing players to defeat the AI in fun ways, and adjusting the difficulty through the "amount and type of AI."
This document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on their programming. It notes that while human-level general intelligence is difficult to achieve, AI can perform well in narrow contexts like chess. For games, the AI must be intentionally flawed to ensure a fun challenge and cannot have obvious weaknesses. It must also be able to perform calculations and make decisions in real-time to interact with the game. The document then outlines some common AI techniques used in games, including MinMax search trees and finite state machines to control agent behavior. It provides pseudocode examples of the MinMax algorithm and discusses enhancements like Alpha-Beta pruning
This document provides an introduction to using artificial intelligence for games. It discusses how AI can be used to create challenging opponents or helpful allies that act autonomously based on their perceptions and the game rules. The key techniques covered include MinMax and alpha-beta pruning for decision making, finite state machines to define agent behavior, and evaluating game states. It also outlines some of the additional considerations for AI in games compared to other applications, such as ensuring agents are intentionally flawed to provide a fun challenge and can perform actions in real-time to match the pace of gameplay.
The Mini-Max algorithm is a recursive algorithm used in game theory and decision making to find the optimal move for a player assuming the opponent also plays optimally. It works by having two players, MAX and MIN, where MAX aims to maximize their score and MIN aims to minimize MAX's score. The algorithm performs a depth-first search of the game tree, evaluating each terminal node and backing up values until it reaches the initial state node to determine the optimal first move.
AlphaZero: A General Reinforcement Learning Algorithm that Masters Chess, Sho...Joonhyung Lee
An introduction to DeepMind's newest board-game playing AI, AlphaZero.
I have improved significantly on my previous presentation in https://www.slideshare.net/ssuserc416e2/alphago-zero-mastering-the-game-of-go-without-human-knowledge, which had several errors (some rather glaring, such as the temperature equation for simulated annealing). Also, DeepMind released far more details in their new Science paper for AlphaZero.
One comment I would like to add is that the AlphaGo Zero used for comparison in this paper is a very weak version, not the final version. Thus, AlphaGo Zero is still SOTA for Go.
Minimax is an algorithm that is commonly used for game playing. It works by constructing a game tree that represents all possible future game states up to a certain depth, and uses an evaluation function to assign a value to each state. The minimax algorithm traverses the tree and "backs up" values from the leaves to the root by maximizing values at MAX nodes and minimizing values at MIN nodes. Alpha-beta pruning improves upon minimax by pruning branches that cannot affect the final output value.
This document provides an overview of chess algorithms and techniques used in computer chess programs. It discusses the complexity of chess, the history of computer chess programs, common search algorithms like minimax and negamax, and pruning techniques like alpha-beta pruning. It also covers challenges like node explosion, transposition tables, endgame tablebases, and evaluations functions. The goal is to explain how modern chess programs are able to search deeply despite the enormous game tree size.
Backtracking and branch and bound are algorithms used to solve problems with large search spaces. Backtracking uses depth-first search and prunes subtrees that don't lead to viable solutions. Branch and bound uses breadth-first search and pruning, maintaining partial solutions in a priority queue. Both techniques systematically eliminate possibilities to find optimal solutions faster than exhaustive search. Examples where they can be applied include maze pathfinding, the eight queens problem, sudoku, and the traveling salesman problem.
This document discusses game theory concepts like winning strategies through examples of two games:
1) A game where two players alternately remove 1-3 tiles from a board until none remain. The winning strategy is to always leave a number of tiles that is a multiple of 4.
2) A game where two players alternate coloring squares on a 19x94 lattice. The board can be split into symmetric halves, allowing one player to force a win using a pairing strategy.
Backtracking and branch and bound are algorithms for solving problems systematically by trying options in an orderly manner. Backtracking uses depth-first search and prunes subtrees that don't lead to solutions. Branch and bound uses breadth-first search and pruning, maintaining upper and lower bounds to eliminate options. Both aim to avoid exhaustive search by eliminating non-promising options early. Examples that can use these techniques include maze navigation, the eight queens problem, and Sudoku puzzles.
The document discusses game playing strategies and algorithms, including tic-tac-toe, minimax algorithm, alpha-beta pruning, and AND/OR graphs. It describes how the minimax algorithm works to generate a game tree and evaluate moves to maximize winning potential. Alpha-beta pruning improves on minimax by pruning unwanted portions of the search tree. AND/OR graphs represent problems that can be decomposed into subproblems, some of which must be solved, and are explored using the AO* algorithm.
Game playing has been an important area of AI research as games allow experiments with adversarial situations in a constrained environment. Minimax search and alpha-beta pruning are commonly used techniques for two-player zero-sum games, evaluating the game tree to varying depths depending on the time available and using a static board evaluator to estimate leaf node values. While early AI programs solved simple games like tic-tac-toe perfectly, scaling search to capture the complexity of chess required innovations like progressive deepening, quiescence search, and evaluation function learning from self-play. Modern AI programs now surpass top human players in many games including checkers, Scrabble, Othello, and backgammon.
This document provides an overview of game theory concepts including:
- 2-player zero-sum games and minimax optimal strategies
- The minimax theorem which states that every 2-player zero-sum game has a value and optimal strategies for both players
- General-sum games and the concept of a Nash equilibrium as a stable pair of strategies where neither player benefits from deviating
- The proof of existence of Nash equilibria in general-sum games using Brouwer's fixed-point theorem
This document provides an overview of the course "Design and Analysis of Approximation Algorithms". It discusses the textbook, schedule, homework rules, and covers topics like greedy strategies, restrictions, partitions, and relaxation. It also introduces computational complexity, deterministic and nondeterministic Turing machines, and provides early approximation results for problems like vertex cover, traveling salesman, and knapsack.
This document provides an overview of the course "Design and Analysis of Approximation Algorithms". It discusses the textbook, schedule, homework rules, and covers topics like greedy strategies, restrictions, partitions, and relaxation. It also introduces computational complexity, deterministic and nondeterministic Turing machines, and provides early approximation results for problems like vertex cover, traveling salesman, and knapsack.
What makes us humans different from animals? Culture? The ability to make tools? The language? Morality? Art? This presentation will show us that these criteria alone are not enough to explain what makes us different from animals.
This document discusses the Axiom of Choice (AC), a foundational principle in set theory that states that for any set of nonempty sets, there exists a function that chooses one element from each set. The document provides examples to illustrate AC with finite and infinite sets. It explains that while AC seems intuitive for finite sets, it leads to counterintuitive conclusions for infinite sets. The document also discusses the relationship between AC and Zermelo-Fraenkel set theory (ZF), noting that ZF does not prove or disprove AC, so its inclusion in ZF is a matter of mathematical preference.
Can christian schools continue to teach only about traditional marriage2idseminar
An approach to the big general issue whether faith schools should be allowed to teach based on their fundamental beliefs and discriminate amongst teachers and students because of their faith.
1. The document discusses three potential traits that define humanity: understanding and language, freedom and self-responsibility, and unlimited desire.
2. It argues that unlimited desire, which drives humans to constantly search for meaning, happiness, and fulfillment through questions, choices, love and communion, may be the most fundamental human trait.
3. This trait of unlimited desire can justify fundamental personal rights like the right to life, freedom, and due process, which in turn other rights are derived from, in order to ensure humans can fulfill their desires. However, recognizing another's humanity ultimately requires choice and acknowledgement of their desires too.
Causality from outside Time
Alfred Driessen
Talk presented at the 21th International Interdisciplinary Seminar, Science and Society: Defining what is human
Netherhall House, London, 5-1-2019
Content
Introduction
Time in Relativity
Time in Quantum Mechanics
Conclusions
Conclusions from this study:
There are causes beyond the realm of science,
- they are not observable by physical or scientific means
- the effects of these causes, however, are observable by physical and scientific means.
Physics is not complete.
The presentation has two parts. In the first one, we review a series of studies that compare the efficacy of learning with "digital teachers" as opposed to learning with normal teachers. In the second part, we make several considerations from different points of view that may be helpful to answer the question.
1) The document discusses future contingencies and the multiverse perspective from Antoine Suarez combining two philosophical views.
2) It describes Ernst Specker's work on quantum contextuality and includes photos of Specker with his son and colleague Simon Kochen.
3) Suarez identifies Hugh Everett's many worlds theory with thoughts in the "mind of God", finding a way to incorporate a deity into the picture according to a quote in Nature.
The CRISPR/CAS9 genome editing system and humans2idseminar
This is a brief introduction to the CRISPR/Cas9 genome editing technique and a quick review of two articles that have to do with potential applications in humans. There is a draft for an ethical reflexion.
Achilles, the Tortoise and Quantum Mechanics2idseminar
Achilles, the Tortoise and Quantum Mechanics
Alfred Driessen
prof. emer. University of Twente
In several places of his Physica Aristotle analyzes the famous antimony of Zeno about the competition between Achilles and the Tortoise. He emphasizes that any movement, or more general any change, is actually a continuum, i.e. an unity. It depends on the specific movement or change whether this continuum is potentially divisible in parts. In fact, there could be certain minima of the division. In line with this approach, Quantum Mechanics states that there are minima or quanta of movement (or change), with other words, there are no gradual changes in the world of micro- and nano-structures. This behavior is completely unexpected when starting with the mechanistic approach of classical physics.
Taking another finding of Aristotle, the four aspects of causality including final cause, one gets another ingredient of Quantum Mechanics. Movements and changes are not only influenced by the initial state -describing the present situation- but also by the final state which takes account of the future situation. As an example one may mention Fermi’s golden rule, where the initial and final state symmetrically determine the transition probability.
Bringing these two philosophical concepts of Aristotle together namely quanta of movement and final cause, a new light is shed on fundamental issues in Quantum Mechanics. One may mention the experimental evidence for contextuality, which is considered one of the weird phenomena in Quantum Mechanics. As illustration, some of the examples of experiments with optical microresonators are given.
This talk has been presented at the 20th International Interdisciplinary Seminar "Can Science and Technology Shape a New Humanity", Netherhall House, London, 5-1-2018
This document discusses transhumanism and postgenderism. It summarizes views from gender studies that gender is more a social construct than biological essentialism. It explores how technologies may enable a postgender society without the gender binary, including artificial wombs, drugs controlling sexual behaviors and bonding, genetic engineering to change sex, and robots for sexual needs. However, the document concludes that pursuing postgenderism through technologies may dehumanize humanity and lose the richness of gender complementarity.
This presentation lists some brain-computer interface technologies that exist today and that could be attainable in future. At the end, philosophical comments about this kind of technology and transhumanism are purposed, in order to reveal the key difference between a humain brain and artificial intelligence.
The document discusses transhumanism and compares human and artificial intelligence. It covers current technologies like mini antennas and potential future technologies like telepathy and neuroreality. It then compares how humans and artificial systems learn, with humans able to reach an understanding of essence while artificial intelligence relies on computational power. The document concludes that hard transhumanism, with robots becoming the dominant lifeform, clashes with philosophical views, while soft transhumanism involves more integration between humans and robots but maintains humans as the primary agents.
The document discusses experimental attempts to realize quantum computers. It begins with an introduction to quantum technologies and quantum bits or qubits. It then describes the current state of the art in quantum computing technologies, including ion traps, superconducting circuits, and linear optic quantum computing. The document provides examples of single qubit gates and two-qubit gates needed to build a universal quantum computer. It also summarizes different physical systems used to implement qubits and discusses challenges in scaling up quantum computers.
1. The document discusses the differences between sequential processing in nature compared to scientific computation. Protein folding occurs sequentially and efficiently in nature, while computation relies on approximation and linearization.
2. Artificial neural networks provide some similarities to natural processes by using weighted connections between nodes, but training requires vast resources compared to natural systems.
3. Quantum computing may provide solutions in a way analogous to how nature appears to "know" the right protein folding solution, but this ability is not well understood.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
5. Winning Strategy
• A position in an assignment to each location on the board indicating
whether or not there is a piece there and if so, which piece. A position p is
said to be computable if there exists Turing machine (i.e. a deterministic
procedure) able to computable it.
• A strategy for one of the players from a given position p is a function that
tells the player, given the prior sequence of moves from p, what he is to
play next. A strategy τ is computable, if this function is a computable
function in the sense of Turing computability.
• If τ and σ are strategies, then τ ∗ σ indicates the play resulting from having
the first player play strategy τ and the second player play strategy σ.
• The strategy τ defeats σ, if this play results in a win for the first player. A
strategy τ is a winning strategy, if it defeats all opposing strategies.
6. Game Value
(a.k.a. the Doomsday Clock)
• A position is said to be mate-in-n for the first
player, if there is a strategy leading to
checkmate in at most n moves, regardless of
how the opponent plays.
• In this case, we say that the Game Value is
equal to n.
10. Infinite Chess Features
• Chess Board: infinite, edgeless (~ℤ×ℤ)
– No pawn promotion, castling nor en passant
• Pieces: familiar chess pieces, which can be
finite or infinite in number (max. 1 king)
• There is no standard starting configuration
• Infinite play is a draw
• No three-fold repetition rule
13. Releasing the Hordes
The pawn columns
continue infinitely up and
down, dividing the plane
into two halves.
White aims to open the
portcullis — the
emphasized pawn below
the central bishop —
thereby releasing the queen
horde into the right half-
plane, which can proceed to
deliver checkmate.
15. Ordinal Numbers
• In set theory, an ordinal number, is
one generalization of the concept of
a natural number that is used to
describe a way to arrange a
collection of objects in order, one
after another.
• Any finite collection of objects can
be put in order just by the process of
counting: labelling the objects with
distinct whole numbers. Ordinal
numbers are thus the "labels"
needed to arrange collections of
objects in order.
• We do not need to count after
infinity, just to order after infinity.
16. Ordinal Numbers
• In mathematics, specifically set theory, an ordinal α is
said to be recursive if there is a Turing machine able
to compute it (at least in principle).
• It is easy to prove that ω is recursive.
• The supremum of all recursive ordinal, the Church-
Kleene ordinal 𝜔1
𝐶𝐾
, is non recursive.
• As a consequence, an ordinal number α is recursive if
and only if 𝛼 < 𝜔1
𝐶𝐾
17. The “Omega One of Chess”
Definition:
• The ordinal ω1
𝐶ℎ
is the supremum of the game
values of the winning finite positions for white
in infinite chess
• The ordinal ω1
𝐶ℎ
is the supremum of the game
values of the winning positions for white in
infinite chess, including positions with
infinitely many pieces
18. The “Omega One of Chess”
• Theorem: ω1
𝐶ℎ
< ω1
𝐶ℎ
< ω1
𝐶𝐾
• Proof: To explain, let us review how the
ordinal game values arise.
– Suppose without loss of generality that white is the
designated player.
– Consider the game tree T of all possible finite
plays, that is, finite sequences of positions, each
obtained by a legal move from the previous
position.
19. The “Omega One of Chess”
• Theorem: ω1
𝐶ℎ
< ω1
𝐶ℎ
< ω1
𝐶𝐾
• Proof:
– Specifically, the positions with value 0 are
precisely the positions in which the game has just
been completed with a win for white.
– If a position p is black-to-play, but all legal black
plays result in a position having a previously
assigned ordinal value, then the value of position p
is decreed to be the supremum of these ordinals.
20. The “Omega One of Chess”
• Theorem: ω1
𝐶ℎ
< ω1
𝐶ℎ
< ω1
𝐶𝐾
• Proof:
– If a position p is white-to-play, and there is a white
move to a position with value β, with β minimal,
then p has value β+1.
– In this way, for any position p with value, we may
recursively associate to p the value-reducing
strategy p → v(p).
21. The “Omega One of Chess”
• Theorem: ω1
𝐶ℎ
< ω1
𝐶ℎ
< ω1
𝐶𝐾
• Conclusion:
– If a position p is a winning position for white,
there is a Turing machine able to compute the
value-reducing strategy p → v(p), and therefore to
win.
22. An interesting position
• Theorem: there is a position in infinite chess
such that:
– The position is computable,
– In the category of computable play, the position is
a win for white (i.e. white has a computable
strategy defeating every computable strategy for
black),
– In the category of arbitrary play, the position is a
draw.
23. An interesting position
• Proof:
– Let T be any computable infinite tree having no
computable infinite branch. (Such trees are known
to exist by classical results).
– Since the tree is infinite, it is an infinite finitely-
branching tree, and hence it has an infinite branch,
even if it has no infinite computable branch.
24. An interesting position
• The position consists of a
series of channels, branching
points and dead-ends, arranged
in the form of a tree, whose
tree structure coincides with T,
• Every branch is finite, so it is
computable,
• Anyway, the whole tree is not
computable (hint: Axiom of
Choice).
25. An interesting position
• Because the tree T has an
infinite branch, then it is clear
that black has a drawing
strategy: simply climb the tree
along an infinite branch.
• If black is restricted to play a
computable strategy, since the
tree has no computable infinite
branch, black will inevitably
find himself in one of the dead-
end zones.
26. An interesting position
• If black is not restricted to
play in according to a
computable strategy, he
can simply continue to
move infinitely through
the tree, therefore forcing
a draw.
27. Conclusion
We have found a position in infinite chess such that:
• if both players are required to implement computable
strategies (i.e. if they are computer programs), white
can force a win;
• if white implements a computable strategy, while
black is not required to do so, black can force a draw.
28. Conclusion
The draw that black can
force in this position is
therefore an example of
something that a man
could in principle do,
which is not allowed to
a machine.
29. References
• C.D.A. Evans and Joel David Hamkins. “Transfinite
game values in infinite chess”. Integers, 14: Paper
No. G2, 36, 2014.
• C.D.A. Evans, J. D. Hamkins, and N.L. Perlmutter,
“A position in infinite chess with game value ω4,”
Integers, vol. 17, p. Paper No. G4, 22, 2017.