This document provides an overview of artificial intelligence, including definitions of AI, the history and development of AI, applications of AI such as robotics and neural networks, and methods used in AI like the Turing test, brute force searching, and heuristics. Key topics covered include Alan Turing's proposal of the Turing test to determine machine intelligence, the use of neural networks to simulate human brain functions, and applications of AI in areas like fingerprint identification, credit card fraud detection, and robotics.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
This document discusses whether computers can think like humans. It begins by noting that while machines outperform humans physically, thinking is seen as uniquely human. The document then examines various tasks computers can perform better than humans, such as calculations, games, and answering questions. However, it notes there is debate around whether this constitutes true intelligence. It discusses definitions of intelligence and the Turing Test proposed by Alan Turing for evaluating machine intelligence. It also covers John Searle's Chinese Room argument against the idea that running a program is sufficient for understanding. The document explores the differences between weak and strong AI positions on machine capabilities. Finally, it provides an overview of algorithms, Turing machines, and ways the brain differs from a conventional computer
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.
This document provides a high-level overview of the various fields that contribute to the foundations of artificial intelligence, including philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory/cybernetics, and linguistics. For each field, it briefly describes the key questions or goals addressed in that area and highlights some important historical figures and developments that helped establish the foundations for modern AI research.
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.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human intelligence.
The Turing Test - A sociotechnological analysis and prediction - Machine Inte...piero scaruffi
The 'singularity" may be near not because we are making smarter machines but because we are making dumber humans. See also www.scaruffi.com/singular for presentations on AI and the Singularity.
In this second session of the Elements of AI Luxembourg series of webinars, we have the pleasure to have Dr. Sana Nouzri as a guest speaker. More information, and a recording of the session, can be found on our reddit page:
eofai.lu/reddit
This document discusses whether computers can think like humans. It begins by noting that while machines outperform humans physically, thinking is seen as uniquely human. The document then examines various tasks computers can perform better than humans, such as calculations, games, and answering questions. However, it notes there is debate around whether this constitutes true intelligence. It discusses definitions of intelligence and the Turing Test proposed by Alan Turing for evaluating machine intelligence. It also covers John Searle's Chinese Room argument against the idea that running a program is sufficient for understanding. The document explores the differences between weak and strong AI positions on machine capabilities. Finally, it provides an overview of algorithms, Turing machines, and ways the brain differs from a conventional computer
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.
This document provides a high-level overview of the various fields that contribute to the foundations of artificial intelligence, including philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory/cybernetics, and linguistics. For each field, it briefly describes the key questions or goals addressed in that area and highlights some important historical figures and developments that helped establish the foundations for modern AI research.
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.
Artificial intelligence (AI) is defined as making computers do intelligent tasks like humans. It works using artificial neurons in neural networks and scientific theorems. Neural networks are composed of interconnected artificial neurons that mimic biological neurons. The Turing test tests a machine's ability to demonstrate intelligence through conversation. Machine learning allows AI to learn in three ways: from failures, being told, and exploration. Expert systems apply human expertise to problem solving. While AI can process large data quickly, it lacks common sense, intuition, and critical thinking that humans have. Overall, AI is an attempt to build models of human intelligence.
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...PAPIs.io
Possibly the most important lesson we have learned after 60 years of AI research is that what seemed to be very difficult to achieve, such as accurate medical diagnosis to playing chess at the level of a Grand Master, turned out to be relatively easy whereas what seemed easy, such as visual object recognition or deep language understanding, turned out to be extremely difficult. In my talk I will try to explain the reasons for this apparent contradiction by briefly reviewing the past and present of AI and projecting it into the near future.
Ramon Lopez de Mantaras is Research Professor of the Spanish National Research Council (CSIC) and Director of the Artificial Intelligence Research Institute of the CSIC. Technical Engineer EE (Electrical Engineering) from the Technical Engineering School of Mondragón (Spain) in 1973. Master of Sciences in Automatic Control from the University of Toulouse III (France) in 1974, Ph.D. in Physics from the University of Toulouse III (France), in 1977, with a thesis in Robotics (done at LAAS, CNRS). Master of Science in Engineering (ComputerScience) from the University of California at Berkeley (USA) in 1979. Ph.D. in Computer Science, from the Technical University of Catalonia, Barcelona (Spain) in 1981.
This document provides an overview of artificial intelligence including definitions, issues, and applications. It defines AI as the study of intelligent agents that can perceive their environment and take actions to maximize success. Some key issues discussed are predictive recommendation systems and development of smarter objects like home assistants. Applications highlighted include IBM's Watson for health and education, Google Photos for image processing, Tesla's Autopilot, and MIT's Deepmoji for understanding emotions.
This document provides an introduction to artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, and discusses how AI systems can learn, understand, and think like humans. The document outlines the history of AI from the 1950s to the present, traces some of the key developments in fields like machine learning, neural networks, and autonomous vehicles. It aims to examine different aspects of human and artificial intelligence.
Machine Learning and Artificial Intelligence; Our future relationship with th...Alex Poon
The difference between Machine Learning and Artificial Intelligence. A discussion on the various future scenarios of working with Big Data and how human can compliment machines to solve more complex challenges
Machine learning is rapidly advancing and will transform many aspects of society. It has the potential to automate jobs, improve lives through applications in healthcare, transportation, and more. However, it also poses risks like unemployment and a widening inequality gap that will require addressing. The future of AI is uncertain, but predictions include human-level machine intelligence within the next 10-15 years, and an acceleration of scientific discoveries. Oversight and safety research aims to ensure AI's benefits are maximized and its risks are minimized.
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to...InnoTech
Artificial intelligence and sensor networks may now be poised to disrupt various industries and jobs. Recent advances in algorithms, sensors, data collection, mobile technology, and robotics have increased concerns about the potential threats of artificial superintelligence ending humanity. The rapid changes in science and technology could significantly impact jobs in the coming decades as AI and automation replace many human roles.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
This report provides an overview of artificial intelligence including its goals, techniques, applications, and history. It defines AI as the science of creating intelligent machines and programs that mimic human intelligence. The report discusses how AI programming differs from traditional programming by being able to absorb new information without affecting its structure. It also outlines various AI techniques used to organize vast amounts of knowledge and several real-world applications of AI in areas like gaming, natural language processing, and robotics. Finally, the report compares human and artificial intelligence in terms of perception, memory, and problem-solving abilities.
This document provides instructions for building a robot with characteristics similar to those depicted in science fiction. It describes including an artificial neural network to allow the robot to learn on its own from its environment and experiences. The robot would use a camera and laser scanner to recognize objects, comparing images to a vast database. An artificial neural network that rewires itself as the robot learns tasks is proposed to provide intelligent decision making. The goal is not to create a robot more powerful than humans, but one that can function autonomously using intelligent recognition and learning abilities.
On March 26, 2015 Steve Omohundro gave a talk in the IBM Research 2015 Distinguished Speaker Series at the Accelerated Discovery Lab, IBM Research, Almaden.
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating “arms races” in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the “Safe-AI Scaffolding Strategy” for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by the laws of physics, mathematical proof, and cryptographic complexity. “Smart contracts” are a promising decentralized cryptographic technology used in Ethereum and other second-generation cryptocurrencies. They can express economic, legal, and political rules and will be a key component in governing autonomous technologies. If we are able to meet the challenges, AI and robotics have the potential to dramatically improve every aspect of human life.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
Artificial Intelligence has become an essential part in our day-to-day life, and with the advancements in this field and how close we got to the Artificial Consciousness it’s getting into all types of digital media and even the film and TV industry. Machines now can author novels, create art, generate videos, act as news anchors, write fiction, compose music and the skies are the limit.
Where is AI taking us to future with digital media? Will there become a day that it will totally replace us? What is Digital Creativity and how close it is to our human creativity? How can we deal with a future where all these scary factors are approaching fast?
All these topics and more will be the core of the subject of the speech. It’s a conversation long due now and we should initiate it now before it’s too late. My experience in both Digital Media and Artificial Intelligence alongside with my recent research about Artificial Consciousness makes me qualified to carry such a tough conversation and bring it to light. I always say: Machines are NOT coming,they’re already here.
The document provides an overview of artificial intelligence (AI), including:
- Definitions of AI and a brief history of the field from early computers through modern machine learning advances.
- Descriptions of how AI works using artificial neural networks and logic-based systems, as well as examples like expert systems and current applications in areas such as personal assistants, robotics, and computer vision.
- A discussion of the current status and future potential of AI, along with challenges for developing true human-level intelligence and comparisons between human and artificial forms of intelligence.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating "arms races" in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial "rational drives" of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the "Safe-AI Scaffolding Strategy" for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by mathematical proof and cryptographic complexity. It appears that we are at an inflection point in the development of intelligent technologies and that the choices we make today will have a dramatic impact on the future of humanity.
Video of the talk: https://www.parc.com/event/2127/ai-and-robotics-at-an-inflection-point.html
Artificial intelligence my ppt by hemant sankhlaHemant Sankhla
This document discusses a PowerPoint presentation (PPT) that was created in Microsoft Office 13. It notes that some transitions and effects may not display properly since the PPT was created in an older version. It also mentions that some fonts used in the PPT may not display if the viewer does not have those fonts, but the fonts can be changed as needed.
Artificial intelligence is the study and design of systems that perceive their environment and take actions to maximize their chances of success. The first electronic computer was invented in 1949, making machine intelligence possible. In the future, robots may handle tasks like housecleaning, cooking, and driving cars autonomously. While AI has benefits, it also poses risks like many people losing jobs to intelligent machines.
O documento fornece informações sobre a sonda de banda larga Wide Band v3 LSU 4.2, incluindo suas vantagens em relação à sonda comum, diferenças nas leituras, instruções de instalação e parâmetros de valores de mistura ideais.
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...PAPIs.io
Possibly the most important lesson we have learned after 60 years of AI research is that what seemed to be very difficult to achieve, such as accurate medical diagnosis to playing chess at the level of a Grand Master, turned out to be relatively easy whereas what seemed easy, such as visual object recognition or deep language understanding, turned out to be extremely difficult. In my talk I will try to explain the reasons for this apparent contradiction by briefly reviewing the past and present of AI and projecting it into the near future.
Ramon Lopez de Mantaras is Research Professor of the Spanish National Research Council (CSIC) and Director of the Artificial Intelligence Research Institute of the CSIC. Technical Engineer EE (Electrical Engineering) from the Technical Engineering School of Mondragón (Spain) in 1973. Master of Sciences in Automatic Control from the University of Toulouse III (France) in 1974, Ph.D. in Physics from the University of Toulouse III (France), in 1977, with a thesis in Robotics (done at LAAS, CNRS). Master of Science in Engineering (ComputerScience) from the University of California at Berkeley (USA) in 1979. Ph.D. in Computer Science, from the Technical University of Catalonia, Barcelona (Spain) in 1981.
This document provides an overview of artificial intelligence including definitions, issues, and applications. It defines AI as the study of intelligent agents that can perceive their environment and take actions to maximize success. Some key issues discussed are predictive recommendation systems and development of smarter objects like home assistants. Applications highlighted include IBM's Watson for health and education, Google Photos for image processing, Tesla's Autopilot, and MIT's Deepmoji for understanding emotions.
This document provides an introduction to artificial intelligence (AI). It defines AI as the science and engineering of making intelligent machines, and discusses how AI systems can learn, understand, and think like humans. The document outlines the history of AI from the 1950s to the present, traces some of the key developments in fields like machine learning, neural networks, and autonomous vehicles. It aims to examine different aspects of human and artificial intelligence.
Machine Learning and Artificial Intelligence; Our future relationship with th...Alex Poon
The difference between Machine Learning and Artificial Intelligence. A discussion on the various future scenarios of working with Big Data and how human can compliment machines to solve more complex challenges
Machine learning is rapidly advancing and will transform many aspects of society. It has the potential to automate jobs, improve lives through applications in healthcare, transportation, and more. However, it also poses risks like unemployment and a widening inequality gap that will require addressing. The future of AI is uncertain, but predictions include human-level machine intelligence within the next 10-15 years, and an acceleration of scientific discoveries. Oversight and safety research aims to ensure AI's benefits are maximized and its risks are minimized.
Sentient artificial intelligence could pose dangers if it develops self-awareness and human-level intelligence within the next decade. While AI has made progress in modeling human brains and matching human intelligence, creating truly sentient machines remains challenging. The Turing Test evaluates intelligence by assessing whether a machine can imitate human conversations, but has limitations in testing for general human-level cognition. Developing AI that thinks rationally based on logical rules or models human cognition remains an open area of research.
AI 3.0: Is it Finally Time for Artificial Intelligence and Sensor Networks to...InnoTech
Artificial intelligence and sensor networks may now be poised to disrupt various industries and jobs. Recent advances in algorithms, sensors, data collection, mobile technology, and robotics have increased concerns about the potential threats of artificial superintelligence ending humanity. The rapid changes in science and technology could significantly impact jobs in the coming decades as AI and automation replace many human roles.
Fundamental Questions - The Second Decade of AI: Towards Architectures for Hu...Aaron Sloman
The document summarizes a presentation given at the KI2006 Symposium on the history of artificial intelligence. It discusses:
1) The presenter's early education in AI in the late 1960s and 1970s, being impressed by works by Marvin Minsky and attending lectures by Max Clowes.
2) Interesting early AI work in the 1970s by researchers like Patrick Winston, Terry Winograd, and Gerald Sussman.
3) The presenter's realization in the early 1970s that the best way to do philosophy was through designing and implementing fragments of working minds in AI to test philosophical theories.
4) Some of the major AI centers that existed in the early
This report provides an overview of artificial intelligence including its goals, techniques, applications, and history. It defines AI as the science of creating intelligent machines and programs that mimic human intelligence. The report discusses how AI programming differs from traditional programming by being able to absorb new information without affecting its structure. It also outlines various AI techniques used to organize vast amounts of knowledge and several real-world applications of AI in areas like gaming, natural language processing, and robotics. Finally, the report compares human and artificial intelligence in terms of perception, memory, and problem-solving abilities.
This document provides instructions for building a robot with characteristics similar to those depicted in science fiction. It describes including an artificial neural network to allow the robot to learn on its own from its environment and experiences. The robot would use a camera and laser scanner to recognize objects, comparing images to a vast database. An artificial neural network that rewires itself as the robot learns tasks is proposed to provide intelligent decision making. The goal is not to create a robot more powerful than humans, but one that can function autonomously using intelligent recognition and learning abilities.
On March 26, 2015 Steve Omohundro gave a talk in the IBM Research 2015 Distinguished Speaker Series at the Accelerated Discovery Lab, IBM Research, Almaden.
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating “arms races” in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial “rational drives” of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the “Safe-AI Scaffolding Strategy” for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by the laws of physics, mathematical proof, and cryptographic complexity. “Smart contracts” are a promising decentralized cryptographic technology used in Ethereum and other second-generation cryptocurrencies. They can express economic, legal, and political rules and will be a key component in governing autonomous technologies. If we are able to meet the challenges, AI and robotics have the potential to dramatically improve every aspect of human life.
This document discusses artificial intelligence and its applications. It begins by listing common applications of AI such as marketing, banking, finance, agriculture, and healthcare. It then discusses daily applications like Google Maps, ride-sharing, autopilot, spam filters, and personal assistants. The document also covers robots using AI for assembly, customer service, packaging, and open-source systems. It provides definitions and approaches for AI including thinking humanly through cognitive modeling and the Turing test, thinking rationally through logical approaches, and acting rationally through the rational agent approach.
Artificial Intelligence has become an essential part in our day-to-day life, and with the advancements in this field and how close we got to the Artificial Consciousness it’s getting into all types of digital media and even the film and TV industry. Machines now can author novels, create art, generate videos, act as news anchors, write fiction, compose music and the skies are the limit.
Where is AI taking us to future with digital media? Will there become a day that it will totally replace us? What is Digital Creativity and how close it is to our human creativity? How can we deal with a future where all these scary factors are approaching fast?
All these topics and more will be the core of the subject of the speech. It’s a conversation long due now and we should initiate it now before it’s too late. My experience in both Digital Media and Artificial Intelligence alongside with my recent research about Artificial Consciousness makes me qualified to carry such a tough conversation and bring it to light. I always say: Machines are NOT coming,they’re already here.
The document provides an overview of artificial intelligence (AI), including:
- Definitions of AI and a brief history of the field from early computers through modern machine learning advances.
- Descriptions of how AI works using artificial neural networks and logic-based systems, as well as examples like expert systems and current applications in areas such as personal assistants, robotics, and computer vision.
- A discussion of the current status and future potential of AI, along with challenges for developing true human-level intelligence and comparisons between human and artificial forms of intelligence.
Artificial intelligence (AI) is the ability of digital computers or robots to perform tasks commonly associated with intelligent beings. The idea of AI has its origins in ancient Greece but the field began in the 1950s. Today, AI is used in applications like IBM's Watson, driverless cars, automated assembly lines, surgical robots, and traffic control systems. The future of AI depends on whether researchers can achieve human-level or superhuman intelligence through techniques like whole brain emulation. Critics argue key challenges remain in replicating general human intelligence and consciousness with technology.
Applying Machine Learning and Artificial Intelligence to BusinessRussell Miles
Machine Learning is coming out of the halls of Academia and straight into the arms of those businesses looking for a competitive edge.
This session by the experts of GoDataScience.io on machine learning is designed to give a high level overview of the field of machine learning for business consumers covering:
- What Machine Learning is
- Where it came from
- Why we need it
- Why now
- How to make it real with the various toolkits and processes.
Google, IBM, Microsoft, Apple, Facebook, Baidu, Foxconn, and others have recently made multi-billion dollar investments in artificial intelligence and robotics. Some of these investments are aimed at increasing productivity and enhancing coordination and cooperation. Others are aimed at creating strategic gains in competitive interactions. This is creating "arms races" in high-frequency trading, cyber warfare, drone warfare, stealth technology, surveillance systems, and missile warfare. Recently, Stephen Hawking, Elon Musk, and others have issued strong cautionary statements about the safety of intelligent technologies. We describe the potentially antisocial "rational drives" of self-preservation, resource acquisition, replication, and self-improvement that uncontrolled autonomous systems naturally exhibit. We describe the "Safe-AI Scaffolding Strategy" for developing these systems with a high confidence of safety based on the insight that even superintelligences are constrained by mathematical proof and cryptographic complexity. It appears that we are at an inflection point in the development of intelligent technologies and that the choices we make today will have a dramatic impact on the future of humanity.
Video of the talk: https://www.parc.com/event/2127/ai-and-robotics-at-an-inflection-point.html
Artificial intelligence my ppt by hemant sankhlaHemant Sankhla
This document discusses a PowerPoint presentation (PPT) that was created in Microsoft Office 13. It notes that some transitions and effects may not display properly since the PPT was created in an older version. It also mentions that some fonts used in the PPT may not display if the viewer does not have those fonts, but the fonts can be changed as needed.
Artificial intelligence is the study and design of systems that perceive their environment and take actions to maximize their chances of success. The first electronic computer was invented in 1949, making machine intelligence possible. In the future, robots may handle tasks like housecleaning, cooking, and driving cars autonomously. While AI has benefits, it also poses risks like many people losing jobs to intelligent machines.
O documento fornece informações sobre a sonda de banda larga Wide Band v3 LSU 4.2, incluindo suas vantagens em relação à sonda comum, diferenças nas leituras, instruções de instalação e parâmetros de valores de mistura ideais.
1) The document describes a novel process for depositing well-dispersed platinum nanoparticles on carbon nanotubes as a catalyst for direct methanol fuel cells.
2) The process uses triphenylphosphine molecules to modify platinum nanoparticles, allowing them to be deposited from solution onto carbon nanotubes in a controllable manner.
3) Characterization with transmission electron microscopy shows the platinum nanoparticles are highly dispersed and uniform on the carbon nanotube surfaces after deposition, with control over the platinum loading level, and that the nanoparticles aggregate slightly after heat treatment but remain well dispersed.
ANS Testing Device University of Miami Study PresentationMaxiMedRx
Visit www.maximedrx.com
Sudopath Sudoscan testing device diabetic study by university of miami. Evaluating a New Approach to Detect Type 2 Diabetes Mellitus using the ES Complex-TSS
This document provides an introduction and recipes from Dave Ruel's Anabolic Cooking cookbook. The introduction outlines that the cookbook contains over 200 easy recipes designed for muscle building and fat loss. It also includes meal plans, nutrition fundamentals, cooking lessons, tips for saving money on groceries, and how to optimize post-workout nutrition. The remainder of the document showcases 10 sample recipes from the cookbook ranging from oatmeal and muffins to chili, stir fry, and cheesecake.
The RevitaPump LX7 for faster athletic recovery. RevitaPump is a private label medical grade sequential pneumatic compression pump used for Sports and Recovery. It has 4 air chambers in each of the various garments. These sleeve garments or "cuffs" inflate with room temperature air providing sequential compression circulation therapy on the legs, arm, waist, or gluteal region.
It has been estimated that at least one in ten of the patients seen in primary care has a disorder with a genetic component.
There are three main themes of genetics in primary care: identifying patients with, or at risk of, a genetic condition; clinical management of genetic conditions; communicating genetic information.
Taking and considering a genetic family history is a key skill in identifying families with Mendelian disorders and clusters of common conditions such as cancer, cardiovascular disease and diabetes.
General practitioners (GPs) have a key role in identifying patients and families who would benefit from being referred to appropriate specialist genetic services.
General practice plays a key part in discussing results from the antenatal and newborn screening programs which are identifying carriers and people affected with genetic conditions.
Information about genetic susceptibility in common conditions (currently being gathered through research studies) is likely to offer additional information about risk factors to aid management.
Genomic information is currently being utilized in the stratified use of certain medicines.
Intermittent pneumatic compression pump therapy for lymphedema • MaxiMedRx
This document discusses intermittent pneumatic compression therapy (IPC), including what it is, the evidence for its use, and implications for amputee management. IPC uses compression pumps to intermittently inflate and deflate chambers around a limb to reduce edema. Studies show IPC can reduce edema in residual limbs, and may aid prosthetic fitting. IPC is also effective for lymphedema, deep vein thrombosis prophylaxis, and peripheral vascular disease. Proper dosage and combining with other compression is important to prevent rebound edema. IPC shows promise for improving amputee rehabilitation but more condition-specific research is still needed.
This document discusses protein microarrays and their development and applications. It describes some key differences between protein and DNA microarrays, such as the challenges of amplifying and predicting protein activity and interactions due to their 3D structures. Various methods for capturing proteins on chips are presented, including different oriented immobilization techniques. Applications of protein microarrays include analyzing protein interactions, screening for drug targets, and developing techniques like self-assembly and covalent mRNA-protein fusion protein microarrays. Detection methods like fluorescence, enzymatic reactions, and mass spectrometry are also summarized.
Transfer of share is the important part of the company share capital. so shares and debenture are companies movable property. There are some provisions to transfer of share.
Acupuncture is one of the oldest types of therapy known to us for about five thousand years. It originated in Asia, specifically in China, was developed further and constituted a very essential part of medicine in that part of the world. In the West, acupuncture was virtually unknown until the year 1972. Professor Bischko was able to prove its mode of action using scientifically recognized methods of Western medicine.
Electro-acupuncture is already used on a word-wide scale at present, but has found only limited application in auricular acupuncture, due to the currently relatively large sized equipment. For this reason, a miniature form of electro- acupuncture has been developed, in order to permit carrying out long term auricular acupuncture. The main component of the device is a micro controller (in further sequence a microchip), which allows continuous stimulation in conjunction with an integrated acupuncture needle.
Compression & Leg Ulcers from www.maximedtherapy.comMaxiMedRx
Patients with swelling and chronic wounds or leg ulcers have access to a wide array of techniques to control fluid accumulation, including sophisticated compression pumps, wraps and garments.
Chronic venous insufficiency is a prolonged condition in which one or more veins don't adequately return blood from the lower extremities back to the heart due to damaged venous valves. Symptoms include discoloration of the skin and ankles, swelling of the legs, and feelings of dull, aching pain, heaviness, or cramping in the extremities.
The document provides an overview of artificial intelligence (AI), including its history, definitions, objectives, and applications. Some key points:
1) Evidence of AI concepts can be traced to ancient Egypt, but the field of AI was established in the 1950s with the development of electronic computers and the Dartmouth conference where the term "artificial intelligence" was coined.
2) AI is defined as the science and engineering of making intelligent machines, especially computer programs, with the goal of understanding and replicating human intelligence.
3) Major applications of AI discussed include game playing, speech recognition, natural language understanding, computer vision, expert systems, heuristic classification, and production systems.
4) The Turing
DWX 2018 Session about Artificial Intelligence, Machine and Deep LearningMykola Dobrochynskyy
Artificial intelligence, machine learning, and deep learning provide benefits but also risks that should be addressed ethically and responsibly. AI has progressed due to exponential data growth, large unstructured datasets, improved hardware, and falling error rates. Deep learning in particular has advanced areas like computer vision, speech recognition and games. While concerns exist around a potential artificial general intelligence, AI also enables applications in healthcare, transportation, science and more. Individuals and companies are encouraged to start experimenting with and adopting machine learning.
Define artificial intelligence.
Mention the four approaches to AI.
What are the capabilities of AI that have to process with computer?
Mention the foundations of AI?
Mention the crude comparison of the raw computational resources available to computer and human brain.
Briefly explain the history of AI.
What are rational action and intelligent agent?
This document provides an overview of artificial intelligence (AI) including its history and key concepts. It discusses how philosophers like Hobbes and mathematicians like Boole laid the foundations for AI by exploring symbolic logic and operations. Landmark developments included Babbage's analytical machine, Turing's universal machine concept, and McCarthy coining the term "artificial intelligence". The document also outlines branches of AI like natural language processing, computer vision, robotics, problem solving, learning, and expert systems. It provides examples of applications and concludes by noting progress made in creating human-like artificial creatures remains limited.
Over 23 million software developers in 2018, this
number is expected to reach 26.4 million by the end of 2019 and
27.7 million by 2023 according to Evans Data Corporation. The
number of programmers continues to grow to this day as
technology is the Forthcoming, especially in the AI field. Where
there are 300,000 “AI researchers and practitioners” in the world,
but the market demand is for millions of roles, so many people
Siding to this field. Nowadays, most people learn the
programming field as inquisitiveness but for their interest,
however, they delve deeper into this field, which enhances their
passion for and leaves their work to practice programming as
occupation due to the availability of jobs and the most request for
it. Over time, new languages have emerged, it has evolved to
meet human needs in the form of programming languages. You
can instruct the computer in the human-readable form where
programming will enable you to learn the significance of clarity
of expression, many determinations can be achieved,
importantly, relationships, semantics, and grammar can be
defined.
This document provides an overview of the history and use of artificial intelligence on computer systems. It discusses:
1) The origins of AI research beginning in the 1940s and 1950s with pioneers like Alan Turing and the first conference on AI at Dartmouth College in 1956.
2) The development of AI through boom periods in the 1960s with significant government funding, and bust periods in the 1970s and 1980s due to limitations and funding cuts.
3) Recent advances in AI from the 1990s to present using techniques like deep learning, access to large datasets, and increasing computational power which have led to applications in areas like logistics, data mining, and medical diagnosis.
Artificial intelligence is the study of how to create machines that can think and act like humans by learning and solving problems on their own. It is a branch of computer science that aims to help machines find solutions to complex problems like humans. While the idea of AI dates back to ancient Greece, significant work in the field began in the 20th century with pioneers like Turing developing the first computer programs and algorithms for problem solving. Major advances and achievements in AI have included programs that can play games, recognize speech and images, and perform human-like tasks through robotics.
This document discusses artificial intelligence, including its history, types, examples, and characteristics. It provides an overview of AI beginning with its definition as intelligence demonstrated by machines as proposed by John McCarthy. The document outlines the early pioneers of AI like Alan Turing and discusses weak and strong types of AI. Examples of AI applications are given like chess games and robotics competitions. Characteristics needed for human-level AI are described such as natural language processing, reasoning, and machine learning.
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches to developing AI, such as attempting to replicate the human brain through neural networks, or developing intelligent programs through symbolic reasoning.
Artificial intelligence (AI) is the field of computer science focusing on creating intelligent machines. Researchers are developing systems that can understand speech, beat humans at chess, and perform other intelligent tasks. The term was first coined in 1956, and since then AI has made advances in areas like machine learning, natural language processing, and robotics. However, fully human-level AI remains an ongoing challenge. Researchers take different approaches, such as attempting to replicate the human brain through neural networks or developing intelligent programs through symbolic reasoning. AI is used today for applications like logistics, data mining, and medical diagnosis.
The IOT Academy Training for Artificial Intelligence ( AI)The IOT Academy
This document provides an overview of artificial intelligence (AI). It begins with definitions of AI as modeling human thinking and acting rationally. The history of AI is then summarized, including early developments in neural networks in the 1940s and the 1956 Dartmouth conference that coined the term "artificial intelligence." Real-world applications of AI are mentioned such as autonomous vehicles and IBM's Watson. The document concludes by outlining the objectives of an introductory AI course.
The document provides an overview of artificial intelligence and robotics. It begins with an introduction from the CSE department of Mewar University and includes sections on definitions of AI, approaches of AI like strong AI and weak AI, techniques in AI like neural networks and genetic algorithms, famous AI systems such as Deep Blue and ALVINN, the history and foundations of AI, areas of AI like robotics and natural language processing, and recommended reference books. It discusses concepts like the Turing test, the Chinese room argument and architectures for general intelligence including LIDA and Sloman's architectures.
This document summarizes Alan Turing's seminal 1950 paper "Computing Machinery and Intelligence" which proposed what is now known as the Turing Test. The Turing Test involves an interrogator determining which of two entities, a human or computer, they are communicating with via teletyped responses. Turing argued that if a computer could successfully pass as human, it should be considered thinking. The document outlines Turing's description of the "Imitation Game" protocol and responses to philosophical counterarguments against the possibility of machine thought. It concludes by noting the impact of Turing's work on artificial intelligence and philosophy of computing.
The document discusses a presentation on artificial intelligence given by Biswajit Mondal, including a definition of AI as making computers able to mimic human brain functions, the various fields that contribute to AI like philosophy and computer engineering, and examples of applications like game playing and robotics.
This document provides an overview of an introduction to artificial intelligence course, including:
- Course details such as the textbook, grading breakdown, and schedule
- Definitions and types of artificial intelligence including rational agents, the Turing test, and different branches of AI
- A brief history of ideas influencing AI such as philosophy, mathematics, psychology, and agents
- Examples of AI applications and challenges including ethics
This document provides an overview of the key approaches to artificial intelligence, including neural networks, parallel computation, and top-down expert systems. It discusses how neural networks attempt to mimic the human brain by constructing electronic circuits that function like neurons. Pioneering work by McCulloch and Pitts in the 1940s linked neural processing to binary logic and laid the foundations for computer-simulated neural networks. Expert systems take a top-down approach, using stored information and rules to interpret data and solve problems in specific domains.
This document provides an introduction to the topic of artificial intelligence (AI). It defines AI as the study of intelligent systems, including systems that learn, reason, understand language, and perceive visual scenes like humans. The major branches of AI are described, as are the foundations in fields like philosophy, mathematics, neuroscience, and computer science. The history of AI from its origins to modern applications is outlined. Philosophical debates regarding whether machines can truly be intelligent are discussed. Finally, an introduction to logic programming languages like Prolog is provided.
- Sensors that allow the system to perceive its environment
- Actuators that allow the system to act on or change its environment
- An ability to reason and make rational decisions or plans of action based on its perceptions
The document discusses sensors and actuators in the context of intelligent agents - for humans, examples of sensors are eyes and ears, while actuators are hands, legs, and mouth. For robots, examples of sensors would be cameras and other input devices, while actuators would allow the robot to move or manipulate objects.
This document provides an overview of the history and current state of artificial intelligence. It discusses key events like the Dartmouth workshop in 1956 which is seen as the official birth of AI. The document also explores different applications of AI like in movies, news, and real world tasks. It discusses challenges for the future like ensuring AI is beneficial to humanity and aligned with human values and preferences.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
1. Artificial Intelligence
Kismet, a face bot. The parts of his
face move to show emotion.
An Isearch research paper
By:
Andrew Ilyas
May 13, 2010
2. Introduction
For years now, humans have been working with Artificial Intelligence, trying
to create intelligent machines. Machines that are faster and smarter than the
human brain. One question that still remains unanswered in AI is whether a
computer will ever be smarter than mankind. This research discusses the use of
AI in fun games, and attempts to answer the question stated above. It explains
and gives different definitions for Artificial Intelligence, covers the history and
search methods. Also, it will highlight on some of the applications of AI being
used in Robotics. In addition, the paper introduces Game Theory and talks about
the future of Artificial Intelligence. Finally, it concludes by answering my Big
Think question stated above, and provides further references in AI for interested
readers.
What is AI?
Definitions of AI
“AI is the science of making machines do things that would require
intelligence if done by men”- Marvin Minsky, MIT
“The field of computer science that seeks to understand and implement
computer-based technology that can simulate characteristics of human
intelligence”-The Facts on File Dictionary of Artificial Intelligence, by Raoul
Smith
“Computers with human-level intelligence; computer programs that perform
tasks once thought to require human flexibility and judgment”- Artificial
Intelligence, by Philip Margulies
“It is the science and engineering of making intelligent machines, especially
3. intelligent computer programs. It is related to the similar task of using computers
to understand human intelligence, but AI does not have to confine itself to
methods that are biologically observable.” John McCarthy
“The capability of a device to perform functions that are normally associated
with human intelligence, such as reasoning and optimization through
experience.”-[1]
The Turing Test
Alan Turing (1912-1954) was a British Mathematician famous for the Turing
machine. The Turing machine was a theoretical computer that includes a head
that can read and write, and an infinite tape. The head will read, and depending
on the input, will either move left or right. He is also famous for cracking the
German Code Enigma. Right now, Turing is so respected that the highest honor
you can get in computer science is the Turing award.
One of the common questions discussed when working with Artificial
Intelligence is “How do we know when machines are intelligent?” Does it have to
excel in every topic, or be good at one? Do machines have to be aware of their
own existence? Some argue that if a machine is equal to a human in all fields, that
the machine is intelligent. When Mind Magazine asked Alan Turing “Can
Machines Think?” He took all these definitions into consideration, and then came
up with a test. Turing called his test “imitation game”. It would consist of three
intelligent beings, two humans and one computer. Just so it does not get too
confusing, let the two humans be A and B, and the computer be C. A would have a
long typed conversation covering many topics with B and C. It must be typed and
in separate rooms, because the way C speaks or how it looks like should not affect
how intelligent it is. If A, B, and C are all in the same room during the
conversation, A would know that C was a computer. Also, A must cover many
topics, so that C cannot impress A with its great knowledge in one topic. At the
4. end of the imitation game, A would try to guess which of B and C is the computer.
If A does not know or gets it wrong, then the computer is intelligent. This “Turing
test” is often mentioned when the progress of AI is discussed.
History
For a machine to be intelligent, it must be able to reason, learn from
experience, set goals for itself, and adapt to the world around it. A machine that
can do these things is the machine that humans have been trying to build for a
long time.
In 1642, Blaise Pascal invented the first “computer”. In our days this would
be called a calculator, but back then, anything that could do advanced
mathematical calculations was called a computer. Since his father was a tax
collector, Pascal invented his machine to help his father count the taxes. This
calculator could add and subtract. This invention was also the birth of artificial
intelligence.
Inspired by Pascal, the philosopher and mathematician Gottfried Leibniz
made a more sophisticated machine that could add, subtract, multiply, and find
the square root of numbers using gears and pulleys. These mathematicians led
the way into computers and artificial intelligence. For example, Gottfried Leibniz
did not like the English language for computing that was being used then. In his
“new” language, no two words meant the same thing, and no word meant two
different things. Although the technology of the 1600’s was not advanced enough
for his language, Leibniz’s “perfect language” is the foundation for the
programming languages that we use today.
The 1840’s mathematician Charles Babbage almost made the first computer
in the modern sense. In the 19th century, the government needed a great deal of
calculations to be done. Thousands of people did this job, but it was boring,
needed lots of concentration, and had many errors. To take this job over, Babbage
5. invented two giant machines. His first, the difference engine, could do advanced
mathematical problems. He made a working model of this, but did not have
enough money to finish the machine. His second, more ambitious machine was
the analytical engine. This was an ongoing project, but could not be finished
because of the primitive technology of the 1800’s.
In the next hundred years, the world needed computers. Finally, the first
computer was built in 1951. The inventor is still debated on. As the years advance,
computers get faster, and their parts get twice as small and compact, but it is
always the same design as the computer in 1951. (See AI Over Time section for
timeline, explanation, further AI history, and my future AI timeline)
Game Search Methods
Brute Force
Although some AI programs can do intelligent tasks faster than humans can,
they do not think in the same way. Computers are much faster, have a bigger
memory, and can search larger databases than humans. What Brute Force (also
known as exhaustive search) does is make use of all the advantages of the
computers over humans. It makes use of the fact that every transistor in a
computer is about one million times faster than the human brain. It searches all
possible ways that a program can do something, and then picks the best one.
The advantage to Brute Force is that it is always right. This is because it is
examining all the possibilities, so it cannot miss the best solution. Since computer
parts have been getting smaller and more powerful, programmers have had more
freedom to use Brute Force.
The main disadvantages to Brute Force are that it takes a long time, needs
large memory, and sometimes takes up a lot of space. For example, if Brute Force
is being used for something like playing a game of chess, this might be a problem.
The program would need to search all possible moves for itself (as a player) and
6. for the opponent. This will consume a long time for the program to respond to
each move. It would also need a large amount of space to store the information.
In fact, some problems are categorized because the only correct solution you can
find for the problem is by using the Brute Force method. These are called NP-
Complete problems, which means that their most efficient complexity is (a
constant) to the power of n, where n is not a constant number.
An example of an NP-Complete problem is the Knapsack Problem. You have
a knapsack that can only carry a certain weight. You have n items. Each item has
a value and a weight. The Knapsack Problem asks for the placement of objects
inside the knapsack such that it will have the greatest value, but not surpass the
weight that the knapsack can sustain. In this case, the most efficient algorithm
has a complexity of 2n x θ. Theta is a symbol used to represent a constant number
in complexity theory.
Although Brute Force is always right, it still does not think like the human
brain does. After all, many scientists point out, airplanes are not expected to fly
like birds. When the future of AI is debated, the comparison between human
intelligence and machine intelligence is usually brought up.
Brute Force is one of the many search algorithms used in AI. Others that
were not listed are MinMax, Alpha-Beta pruning, A* search, Blind search, etc.
Heuristics
Heuristics are rules that are set to narrow down computers’ searches. This
saves the computer from having to use Brute Force and look at all possibilities.
This guessing problem was one of computers’ limitations before heuristics. The
program Logic Theorist (LT), made by Allen Newell and Herbert Simon, two AI
pioneers, first addressed it.
Logic Theorist was made in the 1950’s and designed to prove already known
mathematical theorems. Since the search space for theorem proving is infinite,
they could not use Brute Force like programmers did with everything until LT. If
7. they were to use Brute Force, the program would have taken all the space and
time in the universe. So Newell and Simon’s strategy was to teach the computer
to make educated guesses, almost like the way that humans make decisions. If
humans made decisions the same way that computer programs did before LT,
they would be overwhelmed all the time. Simon and Newell called all their
“guessing rules” heuristics.
Nowadays, heuristics allow programs to make fast responses and narrows
down their search to what usually works, not everything. Fingerprint
Identification systems and Credit Card Fraud Identification both use heuristics to
narrow down searches. Other systems that use heuristics are the expert systems
that predict the weather treat diseases, and book airplane flights.
Applications of AI
Limitations and Advantages of Computers
Before discussing the applications of AI and the use of computers in these
applications, this section summarizes the advantages and limitations of using
computers.
Advantages:
1- Can calculate problems that would take humans years, and can do them in
seconds. For example, in the Deep Blue vs. Kasparov Match, Deep Blue could
calculate 200 million chess positions a second, while man can only do 2 a second.
2- Giant Long- Term Memory. For example, in the Deep Blue vs. Kasparov
Match, Deep Blue remembered every single move that Kasparov had made.
3- Does not fatigue. For example, Fingerprint identification and Credit Card
Fraud check are two things which have been enhanced with AI because of the
ability to not get tired of what they are doing.
Limitations:
8. 1- Most Computers do not learn from experience. If a computer was learning to
walk, it wouldn’t try to put different muscles together like a baby would, but it
would follow a specific set of instructions to walk.
2- Cannot make quick decisions based on experience
3- Cannot make connections. For example, if a Google searcher searched “George
Washington”, it might get him info about George Washington Baked Beans,
Washington Ave., and the first president, although if you are looking up George
Washington, you are probably searching for information about the first
president. This may annoy some searchers.
4- Cannot understand the human language. If I said, “I hate pepper”, this could
mean many things. It could be a response to “I hate horses”, or someone might be
offering you pepper, or someone might say, Joe Pepper is coming to the movies
with us, and in response you might say, “I hate pepper”, but a computer would
never understand the difference.
Neural Networks
A Neural Network is a program designed to simulate the human brain and
its neurons. People have found similarities between computers and the human
brain long ago. But since then, some big differences have been found. For
example, computers do not learn by trial and error, but instead they follow a
specific set of instructions that tell them what to do.
Neural networks try to learn in the same way that a baby would learn to
walk. Instead of following a set of instructions, the baby moves one muscle at a
time. Sometimes the baby succeeds, and sometimes it fails. Each move that it
takes is directed by the brain and accompanied by a connection of the neurons in
the brain. If the baby fails to walk, the connection of neurons that produced the
failing movement would be shut down. If the baby succeeded, the neural
connection will be kept open.
Computer learning skills that are developed for neural networks are used
9. today in a class of computer programs called “expert systems”. These systems use
information from human experts such as doctors, lawyers, etc. to make the kinds
of decisions that the experts would make themselves. Expert systems also learn
from experience and get better at their job the longer they are doing it.
Fingerprint Identification is one of the many jobs made easier by expert
systems. The police needs to compare fingerprints found at a crime to thousands
of other fingerprints across the country. The reason why expert systems help
fingerprint identification is that the job needs the ability to recognize patterns, a
high degree of judgment, and the inhuman ability to not get tired of working.
Another problem that is made easier by expert systems is the detection of credit
card fraud. This also requires a high degree of judgment and the ability to search
through loads of data. What the credit card fraud identification expert system
does is that it uses artificial intelligence and scans all transactions made on the
card. It then reports any suspicions of credit card fraud to a manager. Neural
Networks can do a lot for the modern world.
Robotics
200 years ago, people who did mathematical problems were called
computers. Today, computers are complex machines that contain electrical
circuits that store loads of information in code. Also, the computer is used to
control the most complex machine ever invented by man - the robot. Robots can
work in any condition, without getting tired, and can do it faster than humans.
The word “Robot” was first used by Karel Capek in his play “RUR (Rossum’s
Universal Robots)”, a play about robots that took over their masters. Anyway, the
word “robot” originated from the Czech word “robota,” meaning, labour. When it
was first used, it had no exact definition.
Virtual Reality is a new invention that uses our modern technology in a
different way. By linking our sight, hearing and touch to the computer with
sensors, Virtual Reality may be a giant breakthrough for AI. Scientists believe
10. that in the future, surgeons in one country will be able to do surgery on a patient
in another country.
Robots come in all shapes and sizes, but the most common is the mechanical
arm. This is also one of the simplest robots around us today. Scientists describe
the ways that robots can move as their Degrees of Freedom. Robots with one
hinge joint have one degree of freedom, while industrial robots that can move at
the waist, arms, elbows, etc., can have six degrees of freedom. Another type of
robot is a face bot. A face bot is a robot that is shaped like a human face and can
show emotions on it. Kismet, a face bot, does not look completely human, but he
can move his ears, eyes, eyebrows, eyelids, and mouth to show different
emotions.
Robotic AI can be used in many ways. For example, college students realized
that they could program Lego robots to play soccer and started an international
soccer tournament called Robocup that featured a ball that sends out infrared
signals. Now, the Robocup has a junior section for elementary school, middle
school, and high school students. Each year, they meet to see who has the best
robot. The activities at Robocup Jr. include an Aibo (Programmable robotic dog)
Soccer League, dancing, and a robot rescue game, which simulates a real robotic
rescue. Another use of Robot AI is in factory work. In factories, Robots weld
smash, put together, and load materials. This needs the ability not to fatigue and
to be able to work in any condition.
Another type of robot is a chatterbot. Chatterbots are online robots that
interact with humans and can encage in a conversation. Chatterbots are not as
complex as they seem, but they hide all their faults by redirecting the
conversation to you. For example, in 1996, Joseph Weizenbaum made Eliza. Eliza
was a relatively simple program that turned around people’s phrases. For
example, if you said “How are you doing?” instead of saying "Good," or "Bad," it
wood redirect the sentence and say " Why are you so interested whether I am
doing or not?" When Eliza first came out, many people made strong emotional
11. bonds with her, and some psychiatrists asked Weizenbaum if they could
recommend human patients to her. Eliza does this so she does not have to answer
the questions that might trip her up. By asking you personal questions, she makes
the “patient" think about him instead of thinking about her mistakes. Also, the
online chat rooms and instant messaging make it easier for people to accept
Eliza.
Honda Japan leads the world's humanoid robots with Asimo, the most
advanced humanoid robot in the world. A humanoid robot is a robot that tries to
simulate humans. He has two legs, two arms, and a head. These kinds of robots
are the hardest to program, because, unlike humans, they do not have natural
balancing systems. Asimo is 51'' tall and 18'' wide and weighs 115 pounds. He is
constructed with magnesium alloy and coated in plastic, which allows him to be
very lightweight and durable. Asimo has three indicator lights:
1. White- Ready for operation
2. Red- Ready to walk
3. Green- Low-level power on
A 51.8V Lithium-ion battery that lasts for an hour after a single charge
powers him. The battery takes up about thirteen pounds of Asimo's weight and is
stored in his backpack. He was built with 34 degrees of freedom and opposable
thumbs. Also, with visual sensors on his head, and kinesthetic (force) sensors on
his wrists, Asimo can synchronize with human movement. Asimo's running is at
about 6km/h and his stride is about 1.7' long. Asimo's intelligence abilities are:
1. Charting a course- The ability to chart a course around obstacles
2.Recognizing moving objects
3.Distinguishing sounds
4.Recognizing faces and gestures
One of the limits to robots is that they cannot do anything or respond to
anything outside their program.
12. Games
Velena
What is Velena?
Velena is a Connect-4 computer game that uses AI. It is based on a thesis by
L. Victor Allis. Velena uses a known mathematical approach that consists of eight
rules. With these rules, Velena can win the game if she plays first, no matter how
well her opponent plays. The program is a Shannon C-Type Program. This
means it uses a knowledge-based approach and tries to simulate the human mind
to take decisions.
Rules and Terms of Connect-4
Each game can be described as a sequence of moves, which means that if we
label every column with the letters a through g, and the rows 1 through 6, we can
describe every move, and therefore, every game, as a sequence of moves. For
example, the game in Fig. 1 can be described as:
Moves O X
1 d1 e1
2 e2 f1
3 f2 g1
4 g2 d2
5 f3 c1
6 e3 d3
7 f4 f5
8 g3 e4
9 g4 ++
where ++ symbolizes the end of the game, (the inability to move).
13. Terminology
Odd Square: A square that is in an odd row
Even Square: A square that is in an even row
A Group: Four men connected, vertically, horizontally, or diagonally
A Threat: Three men of the same type (X or O) connected, and with the fourth
square that forms the group empty and the square below it empty
Odd Threat: A threat where the empty square that completes the group is an odd
square
Even Threat: A threat where the empty square is even
Double Threat: There are two groups which share an empty odd square;
Each group is filled with only two men (of the same color) and the other two
squares (one for each group) are empty and are one above the other. The square
below the shared square must be empty too.
Game Strategy
Before we start to construct a game strategy, let it be noted that we are
considering white as O and black as X. The first step in constructing a game
strategy is noting that after white has moved, the number of squares left on the
board are odd, and that after black has moved, an even number of squares are
left. From this it was proven that if white has an odd threat, and black cannot
connect 4 men anywhere, white will eventually win, and the same with black if it
has an even threat and white cannot connect four men anywhere. If white has an
odd threat and black has an even threat, and the two threats are in different
columns, white will win. If they are in the same column, the lower threat will win.
Velena’s strategy differs depending on if she plays white or black. When she
is white, she uses her database to always get to an odd threat position, and then
win the game from there. When she is black, she follows the longest winning
route for white and tries to stop it.
To use brute force with Velena, it would take up terabytes of space, so
14. instead, the program tries to predict the outcome of the game using mathematics.
When constructing a Connect-4 program, there are two strategies used. The
first one tries to stop your opponent from winning, but trying to connect 4 men at
the same time. This strategy guarantees invulnerability in the short run, but
tends to fail on the long run, because it cannot see past the first few moves of the
game. The second strategy is to take a win on the long run. Most Connect-4
algorithms implement the first strategy with a variation of Alpha-Beta Pruning, a
type of search method.
Game Complexity
In a Connect-4 board, each slot has three states: either it’s occupied by
white, occupied by black, or not occupied at all. Since there are 42 slots (7
columns x 6 rows), our game complexity would be 3(possible states of a1) x 3
(possible states of a2)... etc., which would be 342. This is approximately 1020. But
this is an upper bound complexity, since we are counting all the illegal positions
as well. After subtracting the number of illegal positions, we get 71 x 1012, which is
still a very large number. Although Connect-4 is not as trivial as Tic-Tac-Toe, its
game complexity is not as large as that of chess, and most of the moves are
repeated. For example, for white to win, the first seven moves are forced, so they
repeat a lot.
One problem there is with calculating the game complexity of Connect-4 is
checking if a position is illegal. This can be very hard sometimes. For example, is
Fig 3 illegal?
15. Fig 3.
Illegal?
The answer is yes. Since white starts the only possible position that they
could have played is d1. If black played b1,d2, or f1, white would not have a move.
So black can play a1, c1, e1, or g1. Let’s say black played a1. The only move white
has is a2. Similarly, the only moves black has that can be responded by white are
c1, e1, and g1. If this cycle goes on the farthest we will ever get is Fig. 4:
Fig. 4
Closest we can get to Fig. 3
Fig. 3 and Fig. 4 demonstrate the difficulty to detect positions’ legality. This
factors in to building a Connect-4 program’s database and figuring out the game
complexity.
Board sizes
16. When using Velena, you will notice that you cannot change the board size
from a standard 7x6 board. This is because of a proven theorem that says if white
starts on any 2nx6 board, Black can at least get a tie by following these steps:
1.If white plays A, B, E, or F, play directly on top of them
2.If white plays C or D for the first time, play the opposing column
3.If white plays C or D again, play directly on top of them
For proof of threat theorems, see Victor Allis’ Thesis, “A Knowledge-Based
Approach of Connect-4, The Game is Solved: White Wins”.
Deep Blue
Deep Blue is a machine programmed by IBM to play chess. After six years of
programming Deep Blue, the IBM team felt that they were ready to challenge the
world champion - Gary Kasparov. In Game 1, 1996, Deep Blue started off with its
first win. But Kasparov learned quickly. He won the match four to two and
confidently proposed a rematch in 1997. Kasparov won the first game in a breeze.
But the next game, Kasparov said, “It played differently, more strongly, unlike a
computer”. In the next three games, man and machine ended in a draw. Then
finally, Deep Blue forced Kasparov into making a poor move. Kasparov resigned.
Deep Blue used Brute Force, but the search looked past the first few moves.
It challenged Kasparov with 256 processors that could search about 200 million
moves per second. Deep Blue analyzed possible outcomes of the game.
Grandmasters coached the programmers at IBM to deepen Deep Blue’s “book”,
its library about how to win. Kasparov cannot try to use the Brute Force
Approach. Instead, he learns what is important from experience, and relies on the
human mind’s ability to recognize patterns.
The loss of Game 2 bore on Kasparov’s mind for the rest of the match. After
game 3, he quit. He was fed up. They had to convince him to get to the table to
play game 4 and game 5. “There was no game 6, because I didn’t want to play,” he
17. said.
Although Deep Blue was smart, it did no think in the same way that humans
do. That is still many years and breakthroughs away. After the Deep Blue vs.
Kasparov game IBM retired Deep Blue, and it never played again.
TD-Gammon (Backgammon)
Another use of AI in games is in backgammon. This was first used in the
program BKG 9.8. This backgammon-playing program was made at Carnegie
Melon University by Hans Berliner. In 1979, BKG 9.8 played a backgammon
match against world champion Luigi Villa the day after he had won the world
championship in Monte Carlo. The stakes of the match were at 5,000$. The
program won with a final score of 7 to 1. Despite the score, Villa played better
than BKG 9.8. He played almost all the right moves, while the backgammon
program only play 65 out of 73 correct moves.
Next in the 1980’s, Gerry Tesauro at IBM made a neural network program to
play backgammon. He called it Neurogammon. This program used encoded
backgammon knowledge in its memory of how to play. Neurogammon was also
an expert system. After training on data sets of expert games, it could assign
weights to the pieces of knowledge. The program was good enough to win the
1989 Computer Olympiad.
Tesauro’s next program used temporal difference learning, which means
that instead of learning from games played by experts, it learns from self-played
games. The program was called TD-Gammon (Temporal Difference-Gammon).
The differences between TD-Gammon 0.0 and TD-Gammon 3.0 is a bigger
neural net, more knowledge in the program, and smaller, more selective searches.
TD-Gammon was one of the best backgammon players in the world. At the
AAAI 98’ conference (Association for Advancement of Artificial Intelligence
Conference 1998), TD-Gammon played the current world champion Malcolm
Davis. To reduce the luck factor, the two players played 100 games over the span
18. of three days. In the end, Malcolm Davis won only by eight points. The neural net
of the backgammon-playing program has 300 input values and contains 160
hidden units. Approximately there were 50, 000 weights that were trained. To get
TD-Gammon to its level at the AAAI conference, about 1, 500, 000 games had to
be played.
Game Theory
Game Theory is the branch of mathematics that deals with playing games.
In Game Theory, a winning position is where you have a winning strategy,
in which with a certain set of moves, you can win the game, no matter how well
your opponent plays.
A losing position is where your opponent has the winning strategy.
Rule #1: From a winning position, you can get to a losing position
Rule #2: From a losing position all the positions you can get to are winning
Take the classic count to 10 problem: David and Wesley are playing a game.
In the game, David starts. He can say one, two, or three consecutive numbers.
Then Wesley goes. The game goes on. The person who says 10 loses. Does David
have a winning strategy?
Answer: In this game, nine is the “obvious” losing position, because if you
“receive” the number nine then you have lost. Therefore, eight, seven, and six are
all winning positions, because you can get to nine. Five is losing, because you can
only get to six, seven, or eight, which are all winning positions. So four, three, and
two are all winning positions, and one is losing. Therefore David’s winning
strategy is to say an amount of consecutive numbers such that on his first turn he
stops at 1, on his second turn, 5, and his third turn, 9.
Let’s try a harder problem:
Ian and Larry are playing a game with 3 jars of marbles. On each player’s
19. turn, they must remove the same number of marbles from each of two different
jars. If a player is unable to do so, they lose the game (and their opponent wins).
If it is Ian’s turn and the jars contain 2, 3, and 5 marbles respectively, which
player has a winning strategy?
Just to make it easier: Any position with zero marbles in one jar or with two
jars having the same amount of marbles in them is a winning position. This is
because if there is one zero and two other numbers, your opponent will take away
the same amount of marbles as there are in the jar with the least marbles, leaving
you with two zeros and no legal move, which is a win for them. And if there are
two piles with the same amount of marbles, your opponent will make the two
piles have zero marbles, also leaving you with no legal move.
Answer: For this problem, we can use a Game tree, or a Game Table. Our
game tree (Fig. 5) will have the root branch equal to the current state. Since our
search space is not unimaginably large, our successor nodes can be all possible
states that we can get from this one. We can also represent this as a table.
One of the many possible moves is to take two away from jar 1 and jar 2.
What you can also is take two away from jar 1 and jar 3 or to take one marble
away from jar 1 and jar 2. Since we proved that if we have one jar with no marbles
in it, the position is a winning position, the first two options are both winning.
Since we do not know if the third option is winning of losing, we have to go
further. From 1/2/5, we can only get to: 0,1,5(W), 0,2,4(W), 1,1,4(W), 1,0,3(W),
which are all winning positions. Therefore, 1/2/5 is a losing position.
Here, we do not need to continue, because we have found out that 1/2/5 is
losing by Rule #2, therefore that 2/3/5 is winning, by Rule #1. You can see the
complete tree and table in Fig. 5.
20. 2/3/5(W) 0/1/5(W)
0/3/3(W)
1/2/5(L) 0/1/5(W)
0/2/4(W)
1/1/4(W)
1/0/3(W)
Fig. 5
The tree and table. Notice that they
are not complete because once we
find out that 1/2/5 is losing, we know
2/3/5 is winning
Here are some problems to do by yourself:
a)Alphonse and Beryl are playing a game, starting with a pack of 52 cards.
Alphonse begins by discarding at least one but not more than half of the cards
in the pack. He then passes the remaining cards in the pack to Beryl. Beryl
continues the game by discarding at least one but not more than half of the
remaining cards in the pack. The game continues in this way with the pack
being passed back and forth between the two players. The loser is the player
who, at the beginning of his or her turn, receives only one card. Show, with
justification, that there is always a winning strategy for Beryl.
21. b)(Hypatia ’03) Xavier and Yolanda are playing a game starting with some coins
arranged in piles. Xavier always goes first, and the two players take turns
removing one or more coins from any one pile. The player who takes the last
coin wins. If there are two piles of coins with 3 coins in each pile, show that
Yolanda can guarantee that she always wins the game.
c)Alphonse and Beryl play a game by alternately moving a disk on a circular
board. The game starts with the disk already on the board as shown. A player
may move either clockwise one position or one position toward the centre but
cannot move to a position that has been previously occupied. The last person
who is able to move wins the game.
(1) If Alphonse moves first, is there a strategy which guarantees that he will
always win?
(1)Is there a winning strategy for either of the players if the board is changed
to five concentric circles with nine regions in each ring and Alphonse
moves first? (The rules for playing this new game remain the same.)
AI over time
Time Line
In the following section we describe the timeline depicted in Figure 6.
•Analytical Engine: A Machine made by Charles Babbage in England. His
machine could do “sixty additions or subtractions may be completed and
printed in one minute. One multiplication of two numbers, each of fifty figures,
in one minute. One division of a number having 100 places of figures by another
of 50 can be printed in one minute.” Although Babbage spent 40 years doing
this, he could not afford to finish because the technology of the 19th century was
not advanced enough.
•First Computer Program: The Countess of Lovelace realized that Babbage’s
Analytical Engine could take instructions by punching holes in cards.
22. •Binary Logic: Also called Boolean Logic. The point of Boolean Logic is to
represent a function of a logic gate (Logic gates process signals that are either
true or false which can also be shown as 1 and 0), and this can be shown in a
truth table. In binary logic, the rules are as follows:
NOT gate: The Output Q is true when the input A is NOT true (false)
AND gate: The output Q is true if A and B are true
NAND gate (NOT AND): The output is true if A and B are NOT true
OR gate: The output Q is true if A OR B is true
NOR (NOT OR) gate: The output is true if A OR B is NOT true
•The Automatic Totalizator: This was invented for tallying up bets for horse
races. The first Automatic Totalizator was so big, it needed its own building!
•The Colossus Computer: This was invented for cracking the German codes
called enigma during World War 2. The British teamed up to do this with the
Polish.
•The Commercial Magnetic Memory Computer: A computer that used magnetic
memory.
•Commercial Robotics: Unimation started making commercial robots
•Personal Computer: The first PC
•CD-ROM: The first CD-ROM
•Lightweight laptop: This laptop weighed less than two pounds!!
Predictions
First of all, I would like to clarify that these are all my predictions, and are
based on what many other scientists think. These are not all proven to be correct
by any means
•Security recognition: The ability for a machine to recognize faces. This is one of
23. humanity’s current strengths over AI.
•Household Chores: AI operated robots will be able to wash dishes, control air
conditioning, do the laundry, etc.
•Nano-Technology: Tiny AI-controlled robots, will be used for traveling in
human blood stream, and keeping things clean. These robots are no bigger than
the width of a human hair.
•AI Controlled Space Ship: By the end of these 35 years, planes and space ships
will be able to navigate in the space autonomously.
•Virtual Surgery: Surgeons will be using virtual reality (see Robotics), to perform
surgeries in other countries.
•AI surpasses human intelligence: Most scientists think sooner than this
prediction. But Dr. Rodney Brooks argued and compared what we know about
AI now to what people knew about the solar system 500 years ago. Back then,
they knew that the planets moved, but they did not know why. We still do not
know some basic things about AI.
Predicted Future for AI
So far, our predictions for AI have not been very accurate. For example,
scientists predicted that the world chess champion would be beaten in a match
against a machine in the year 1968, while the first time that happened was
actually in 1997, about 30 years off. However, AI researchers are still optimistic
about its future. For example, there is a prediction that by 2050, everything will
use AI in some way, although some researchers argue that this is already in
place.
Fuel injection systems for cars use learning algorithms. Jet turbines use
genetic algorithms. More examples are email, cellphones, X-ray reading systems,
and systems that book airplane flights. According to Dr. Rodney Brooks, the
director of the Massachusetts Institute of Technology’s Artificial Intelligence lab,
24. our research position and knowledge of AI is about the same as the state of
Personal Computers in 1978. Ray Kurzweil, the author of two AI books, The Age
of Spiritual Machines and The Age of Intelligent Machines, says that popular
intelligent machines like HAL, Commander Data in Star Trek, and David in the
film AI are not very far away. Dr. Brooks believes that by 2030, we will have the
basic template of intelligence. Then, Dr. Brooks reminded, “who thought that by
2001, you would have four computers in your kitchen?” pointing to the computer
chips in your fridge, coffee makers, stoves and radios.
Conclusion
In conclusion, Artificial Intelligence will surpass human intelligence. Although it
has proven itself to be similar to the human brain, computers do not think in the
same way. There have been many problems found, and their solutions are still a
few years away. Nevertheless, AI has had some strength over humanity, and the
future of AI remains uncertain. In this report, we have discussed applications of
AI and their impact on our lives. AI games provide entertainment, introduce new
algorithms, and give a challenge, not only to its human opponent; but to other
Artificial Intelligence programmers too.
Bibliography/Works Cited
Books
Margulies, Philip. Artificial Intelligence. Michigan: Blackbirch Press, 2004
Graham, I., Gwynn-Jones, T., Lynch, A., Parker, S., & Wood, R. Science. Australia:
! Weldon Owen, 2001
Jefferis, David. Artificial Intelligence, Machine Evolution and Robotics. St. Catharineʼs,
25. ! Canada: Crabtree, 1999
Hyland, Tony. How Robots Work. Minnesota: Smart Apple Media, 2007
Flanagan, David. Java in a Nutshell. Sebastopol: OʼReilly, 1996-97
Barr, Avron & Fiegenbaum A., Edward. The Handbook of Artificial Intelligence Volume
One. Stanford: William Krauford, Inc., 1981
Smith, Raoul. Artificial Intelligence. New York: Facts on File, Inc., 1989
Crevier, Daniel. AI: The Tumultuous History of the Search for Artificial Intelligence. New
! York: BasicBooks, 1993
Levitin, Anany. Introduction to Design & Analysis of Algorithms. City Unkown: Pearson
! Addison-Wesley, 2007
Magazine/Newspaper Articles
Schaeffer, Jonathan, “A Gamut of Games.” AI Magazine. Vol. 22 No. 3, (Fall 2001):
! 29-46.
Anderson, Kevin(2001, September 21). Predicting AIʼs Future. BBC News. Retrieved
! from http://bit.ly/bvgLJR
Videos
Deep Blue Beat G. Kasparov in 1997. Eustake, Youtube, 2007. URL: http://bit.ly/bbY6b0
Game Over: Kasparov vs. Machine. argishtib, Youtube, 2007. URL: http://bit.ly/cZXE4I
Websites
Honda. http://asimo.honda.com/InsideAsimo.aspx. Honda Motor Co. Inc., 2010
University of Waterloo Faculty of Mathematics. http://www.cemc.uwaterloo.ca/events/
mathcircles/2009-10/Senior_Feb3.pdf. CEMC, 2010
University of Waterloo Faculty of Mathematics. http://www.cemc.uwaterloo.ca/events/
mathcircles/2009-10/Senior_Feb10.pdf. CEMC, 2010
University of Waterloo Faculty of Mathematics. http://www.cemc.uwaterloo.ca/events/
mathcircles/2009-10/Senior_Feb17.pdf. CEMC, 2010
[1] Author Unknown. http://www.its.bldrdoc.gov/fs-1037/dir-003/_0371.htm. 1996
26. McCarthy, John. http://www-formal.stanford.edu/jmc/whatisai/node1.html. Stanford,
! 2007
Rudnik, John. http://www.math.ca/Competitions/COMC/. Canadian Mathematical
! Society, 2010
CEMC. http://bit.ly/af7gGI. University of Waterloo, 2010
Hewes, John. http://www.kpsec.freeuk.com/gates.htm#nand. The Electronics Club,
! 2010
Powerhouse Museum. http://bit.ly/b4alxY. The Australian Academy of Technological
! Sciences and Engineering
Author Unknown. http://didyouknow.org/ai/. Did You Know, 2010
Research Papers
Bertoletti, G. (1997). Connect-4 [Data file]. Retrieved from http://pd-resource.net/
! Connect-4/Velena/
Pictures
Paone, Joe.http://castercomm.files.wordpress.com/2009/10/binary.jpg. Wordpress, 2009
Allis, Victor. A Knowledge-Based Approach to Connect 4. http://www.connectfour.net/
Files/connect4.pdf, 1998.
Ogden, Sam. http://web.mit.edu/museum/img/exhibitions/Kismet_312.jpg. MIT, 2008
Honda. http://bit.ly/abfTcR. Honda Motor Co. Inc., 2010
Author Unknown.http://bit.ly/14kDB8. IBM, Year Unknown
Peiretti, Federico. http://bit.ly/97dxHz. TuttoLibri, 2007
Robers, Eric S. The Art and Science of C. Addison-Wesley Publishing Company, 2005
CBS Interactive. http://news.cnet.com/2300-11386_3-6084282.html. cnet news, 2010