The field of Artificial Intelligence (AI) has progressed rapidly in the past few years. AI systems are having a growing impact on society and concerns have been raised whether AI system can be trusted. A way to address these concerns is to employ ethically aligned design principles to the development of AI software. Yet these principles are still far away from practical application. This talk provides state-of-the-art empirical insight into what should researchers and professionals do today when the client wants ethics to be added to their system.
The document discusses artificial intelligence (AI), including its definition, history, applications, and future. It defines AI as the study of intelligent behavior in machines and the goal of AI research is to create technology that allows computers and machines to function intelligently. Some current applications of AI discussed are robotics, medical diagnosis, video games, and computer vision. The future of AI could include personal robots or a scenario where robots turn against humans.
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. While very powerful, ChatGPT and other large language models raise legal and ethical concerns regarding copyright, privacy, bias and how training data was obtained. The future potential includes using conversational AI to streamline operations in areas like data entry, scheduling and customer service.
There are two main types of chatbots: decision tree and cognitive. Decision tree chatbots use flowcharts of possible questions and answers while cognitive chatbots use artificial intelligence to understand language. Examples of chatbots include DoNotPay which helps with legal issues, KiK and KLM for customer service, and bots that can order food or provide government information. A video is presented showing what might happen if two chatbots had a conversation with each other.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
Artificial intelligence (AI) is the science of training systems to emulate human tasks through learning and automation. It has evolved from neural networks in the 1950s-1970s to today's machine learning, deep learning, and cognitive systems. AI can be both a threat and opportunity - it can help businesses gain competitive advantages through increased efficiency, customer insights, and reduced costs, but may also replace some human jobs. The document then provides examples of how companies in various industries like telecom, manufacturing, financial services, and conservation are using AI capabilities like machine learning, natural language processing, forecasting and optimization to solve problems. In general, humans are good at common sense, creativity and empathy while machines are good at processing large datasets,
Artificial Intelligence Can Now Copy Your Voice: What Does That Mean For Humans?Bernard Marr
The document discusses recent advances in artificial voice generation technology that allow voices to be cloned from only a few seconds of audio. It notes that this capability could be misused to fabricate the truth if voices are used to mislead people into thinking a fake voice is a real person. While voice cloning technology is improving, awareness of its capabilities and critical assessment of information will be important to avoid being fooled by artificially generated voices in the future.
Artificial Intelligence
ChatGPT
knowledge
data
analytics
knowledge
things to know
modern tool
human-like
conversation
versatile
features
limitations
new concept
education
CS
technology
research
important
AI
Record breaking
over 100 million users
The field of Artificial Intelligence (AI) has progressed rapidly in the past few years. AI systems are having a growing impact on society and concerns have been raised whether AI system can be trusted. A way to address these concerns is to employ ethically aligned design principles to the development of AI software. Yet these principles are still far away from practical application. This talk provides state-of-the-art empirical insight into what should researchers and professionals do today when the client wants ethics to be added to their system.
The document discusses artificial intelligence (AI), including its definition, history, applications, and future. It defines AI as the study of intelligent behavior in machines and the goal of AI research is to create technology that allows computers and machines to function intelligently. Some current applications of AI discussed are robotics, medical diagnosis, video games, and computer vision. The future of AI could include personal robots or a scenario where robots turn against humans.
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. While very powerful, ChatGPT and other large language models raise legal and ethical concerns regarding copyright, privacy, bias and how training data was obtained. The future potential includes using conversational AI to streamline operations in areas like data entry, scheduling and customer service.
There are two main types of chatbots: decision tree and cognitive. Decision tree chatbots use flowcharts of possible questions and answers while cognitive chatbots use artificial intelligence to understand language. Examples of chatbots include DoNotPay which helps with legal issues, KiK and KLM for customer service, and bots that can order food or provide government information. A video is presented showing what might happen if two chatbots had a conversation with each other.
This document outlines a project to design and develop a Sugar CRM bot using Artificial Intelligence Markup Language (AIML). The objective is to create a bot that can answer questions about Sugar CRM. It will be implemented as both a desktop and web application using programming languages like AIML, Python, and Adobe Flex. An automatic AIML generation tool will also be developed to ease the creation of AIML files. The source code for the project is available online for checkout and demonstration.
Artificial intelligence (AI) is the science of training systems to emulate human tasks through learning and automation. It has evolved from neural networks in the 1950s-1970s to today's machine learning, deep learning, and cognitive systems. AI can be both a threat and opportunity - it can help businesses gain competitive advantages through increased efficiency, customer insights, and reduced costs, but may also replace some human jobs. The document then provides examples of how companies in various industries like telecom, manufacturing, financial services, and conservation are using AI capabilities like machine learning, natural language processing, forecasting and optimization to solve problems. In general, humans are good at common sense, creativity and empathy while machines are good at processing large datasets,
Artificial Intelligence Can Now Copy Your Voice: What Does That Mean For Humans?Bernard Marr
The document discusses recent advances in artificial voice generation technology that allow voices to be cloned from only a few seconds of audio. It notes that this capability could be misused to fabricate the truth if voices are used to mislead people into thinking a fake voice is a real person. While voice cloning technology is improving, awareness of its capabilities and critical assessment of information will be important to avoid being fooled by artificially generated voices in the future.
Artificial Intelligence
ChatGPT
knowledge
data
analytics
knowledge
things to know
modern tool
human-like
conversation
versatile
features
limitations
new concept
education
CS
technology
research
important
AI
Record breaking
over 100 million users
Mittelstand trifft künstliche Intelligenz - Point of ViewWeissmanGruppe
Künstliche Intelligenz stellt neben Blockchain, Augmented Reality, Big Data und zahlreichen anderen Themenfelder ein aktuelles Trendmedium dar. Durch die kontinuierliche, technische Weiterentwicklung und zahlreiche Innovationen in diesem Feld wird der Einsatz von künstlicher Intelligenz auch für den deutschen Mittelstand immer interessanter. Die grundlegende Funktionsweise wird, ebenso wie mögliche Anwendungen in diesem Point of View zusammenfassend dargestellt.
The document discusses AI chatbots and their uses in various industries like travel, food/beverage, banking, healthcare, and retail. It covers how chatbots have evolved to leverage techniques like natural language processing. Examples are provided of chatbots assisting with tasks like booking travel, making reservations, checking bank accounts, and answering healthcare questions. The document also discusses emotion AI and sentiment analysis, and provides examples of chatbots like Replika and Woebot that analyze emotion. Demonstrations are included of emotion AI chatbots and integrating chatbots with e-commerce platforms and big data analytics.
The document discusses machine learning, including an introduction defining it as algorithms and data that allow computers to learn without human intervention. It lists common machine learning algorithms like neural networks and decision trees. The three main types of machine learning are supervised, unsupervised, and reinforcement learning. Examples of machine learning uses include traffic prediction, virtual assistants, and bioinformatics. Popular programming languages for machine learning are Python, Java, C/C++, R, and JavaScript. The key difference between machine learning and artificial intelligence is that machine learning allows machines to learn from data without being programmed. Advantages include speed, accuracy, automation, and security, while disadvantages include difficulty identifying errors and requiring large amounts of data and space.
I created this presentation for my college project and its consist everything you need to know about AI.This Presentation contains a HD video who describes application of AI. This presentation is ideal for college students, school students and for beginners.
This presentation provides an introduction to artificial intelligence (AI), its applications, and risks. It defines AI as the ability of machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. Applications of AI discussed include machine learning, improving efficiency in industries, and using AI in healthcare. Risks covered are potential for biased outcomes from training data, malicious use of AI, and advanced AI surpassing human intelligence. The presentation concludes it is important to consider both the benefits and risks/unintended consequences of AI development and deployment.
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
A chatterbot (also known as a talkbot, chatbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.
To find more about it, checkout these slides. For more info, visit our website, www.appgalleryinc.com
This document discusses applications of artificial intelligence and provides examples of widely used AI technologies. It describes machine learning techniques including supervised, unsupervised, and reinforcement learning. It also discusses natural language processing, speech recognition, virtual agents, predictive technology, and deep learning. Examples are provided for how various companies utilize predictive analytics. In conclusion, the document notes that while AI allows for increased productivity, its integration requires addressing legal, ethical, and social implications.
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
The 7 Biggest Artificial Intelligence (AI) Trends In 2022Bernard Marr
The document discusses 8 major artificial intelligence trends for 2022:
1. The augmented workforce, where AI tools will help boost workers' abilities and make jobs more efficient.
2. Bigger and better language models that can generate more human-like text.
3. Increased use of AI in cybersecurity to detect network threats.
4. Role of AI in developing virtual worlds known as the "metaverse."
5. Growth of low-code and no-code tools that make AI development simpler.
6. Advancements in autonomous vehicles like cars and ships.
7. AI that can generate more complex creative works like art and music.
8. Continued pushing of boundaries in what AI systems
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
A keynote presentation given at the ISBA Digital Strategy and Cybersecurity Conference at the BMA in London on Wednesday 27th March 2019.
The presentation looks at the following areas:
1. What is AI?
2. The Ethics of Ai.
3. AI, Education and the #FutureSchool
4. The threat of AI to Fee-paying Education
The Rise Of Conversational AI with David LowDatabricks
In this fast-paced world, customers demand ease and efficiency when they talk to a company. Here comes “Chatbot”, an automated conversational agent which conducts conversations via text or voice.
For this talk, I will be starting with the current state of Conversational Intelligence and some common Python libraries used in building chatbots. Various approaches of building conversation engine such as pattern matching, word embedding and long short-term memory (LSTM) models will be discussed. At the same time, I will present the next generation of Conversational AI that focuses on Question Answering and perform a live-demo of such a system.
After the demo, the inner workings will be explained and relevant resources (including a Python framework for conversational AI research and datasets) will be introduced to the participants. Lastly, I will also share my experience launching commercial chatbots with Fortune 500 clients and a few pitfalls one should be aware of before concluding the talk. Key takeaways:
• Get to know the current state of Conversational AI
• Approaches from pattern matching to generative model; AIML / regex, Word-embeddings, Bi-directional LSTM
• The next generation of conversational AI: ParlAI, SQuAD, bAbi Tasks, MCTest etc. Example architecture: BiDAF, Dynamic Memory Network
• What we learned after launching 3 commercial chatbots with a bank and two insurance companies.
• Challenges: Compliance / General Intelligence / Answer Generation
• A Good Conversational UX is the combination of Art and Science.
This research paper introduces a novel application for predicting plant diseases in cotton and potato plants using Convolutional Neural Networks (CNNs).
Separate CNN models were trained on labeled datasets of cotton and potato leaves, each associated with their respective diseases. The primary goal is to employ a fusion of two standard CNN systems to detect various diseases in cotton and potato plants.
Given India's heavy reliance on agriculture, this innovation is crucial to address challenges faced by the sector, including technological limitations, limited access to credit and markets, and the impact of climate change.
Cotton and potatoes are significant crops; this research paper are susceptible to various diseases that can impede their growth and result in substantial yield losses.
The conventional disease detection methods involve manual inspection and disease prognosis, which are time consuming and less accurate. The research showcases the effectiveness of the automated plant disease detection system, with two best models achieving impressive accuracies of 97.10% and 96.94% for cotton and potato plants, respectively.
These results offer promising insights for potential applications in crop management, benefiting the agricultural sector and contributing to increased productivity and profitability.
This document profiles Kharim Mchatta, the CEO and founder of HACK IT Consultancy, a top cybersecurity company in Tanzania. Mchatta has over 5 years of experience in cybersecurity, digital forensics, training, and consulting. He provides these services across Africa and in countries in Europe and North America. The document also summarizes Mchatta's presentation on artificial intelligence in cybersecurity, covering how AI can benefit and pose risks to security, how cybercriminals use AI for malicious activities, and how cybersecurity experts can leverage AI to counter new cyber threats.
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
This presentation includes - History, Functions, Working, Advancement, Applications, Advantages, Disadvantages, Limitations & Contests Held - of Chatbot Technology.
Leben 4.0: Künstliche Intelligenz und der Weg in die Datengesellschaft. Vortr...Andreas Wagener
Daten und Algorithmen bestimmen bereits heute schon unseren Alltag, oft ohne dass wir dies bemerken. Dieser Trend dürfte in der nahen Zukunft noch deutlich zunehmen. Die Digitalisierung des Menschen einerseits und der Einsatz Künstlicher Intelligenz andererseits werden unser Leben maßgeblich bestimmen und verändern. Werden Maschinen, Roboter und Computer die Oberhand gewinnen oder leiten sich daraus neue, nie dagewesene Chancen für den Menschen ab?
Mittelstand trifft künstliche Intelligenz - Point of ViewWeissmanGruppe
Künstliche Intelligenz stellt neben Blockchain, Augmented Reality, Big Data und zahlreichen anderen Themenfelder ein aktuelles Trendmedium dar. Durch die kontinuierliche, technische Weiterentwicklung und zahlreiche Innovationen in diesem Feld wird der Einsatz von künstlicher Intelligenz auch für den deutschen Mittelstand immer interessanter. Die grundlegende Funktionsweise wird, ebenso wie mögliche Anwendungen in diesem Point of View zusammenfassend dargestellt.
The document discusses AI chatbots and their uses in various industries like travel, food/beverage, banking, healthcare, and retail. It covers how chatbots have evolved to leverage techniques like natural language processing. Examples are provided of chatbots assisting with tasks like booking travel, making reservations, checking bank accounts, and answering healthcare questions. The document also discusses emotion AI and sentiment analysis, and provides examples of chatbots like Replika and Woebot that analyze emotion. Demonstrations are included of emotion AI chatbots and integrating chatbots with e-commerce platforms and big data analytics.
The document discusses machine learning, including an introduction defining it as algorithms and data that allow computers to learn without human intervention. It lists common machine learning algorithms like neural networks and decision trees. The three main types of machine learning are supervised, unsupervised, and reinforcement learning. Examples of machine learning uses include traffic prediction, virtual assistants, and bioinformatics. Popular programming languages for machine learning are Python, Java, C/C++, R, and JavaScript. The key difference between machine learning and artificial intelligence is that machine learning allows machines to learn from data without being programmed. Advantages include speed, accuracy, automation, and security, while disadvantages include difficulty identifying errors and requiring large amounts of data and space.
I created this presentation for my college project and its consist everything you need to know about AI.This Presentation contains a HD video who describes application of AI. This presentation is ideal for college students, school students and for beginners.
This presentation provides an introduction to artificial intelligence (AI), its applications, and risks. It defines AI as the ability of machines to perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. Applications of AI discussed include machine learning, improving efficiency in industries, and using AI in healthcare. Risks covered are potential for biased outcomes from training data, malicious use of AI, and advanced AI surpassing human intelligence. The presentation concludes it is important to consider both the benefits and risks/unintended consequences of AI development and deployment.
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
A chatterbot (also known as a talkbot, chatbot, Bot, chatterbox, Artificial Conversational Entity) is a computer program which conducts a conversation via auditory or textual methods.
To find more about it, checkout these slides. For more info, visit our website, www.appgalleryinc.com
This document discusses applications of artificial intelligence and provides examples of widely used AI technologies. It describes machine learning techniques including supervised, unsupervised, and reinforcement learning. It also discusses natural language processing, speech recognition, virtual agents, predictive technology, and deep learning. Examples are provided for how various companies utilize predictive analytics. In conclusion, the document notes that while AI allows for increased productivity, its integration requires addressing legal, ethical, and social implications.
[DSC DACH 23] ChatGPT and Beyond: How generative AI is Changing the way peopl...DataScienceConferenc1
In recent years, generative AI has made significant advancements in language understanding and generation, leading to the development of chatbots like ChatGPT. These models have the potential to change the way people interact with technology. In this session, we will explore the advancements in generative AI. I will show how these models have evolved, their strengths and limitations, and their potential for improving various applications. Additionally, I will show some of the ethical considerations that arise from the use of these models and their impact on society.
The 7 Biggest Artificial Intelligence (AI) Trends In 2022Bernard Marr
The document discusses 8 major artificial intelligence trends for 2022:
1. The augmented workforce, where AI tools will help boost workers' abilities and make jobs more efficient.
2. Bigger and better language models that can generate more human-like text.
3. Increased use of AI in cybersecurity to detect network threats.
4. Role of AI in developing virtual worlds known as the "metaverse."
5. Growth of low-code and no-code tools that make AI development simpler.
6. Advancements in autonomous vehicles like cars and ships.
7. AI that can generate more complex creative works like art and music.
8. Continued pushing of boundaries in what AI systems
A brief Introduction to AI and its applications in Gaming. Talk was at "Advances & Research Challenges in the Applications of AI in Gaming, Medical Imaging and Bio-Informatics"
A keynote presentation given at the ISBA Digital Strategy and Cybersecurity Conference at the BMA in London on Wednesday 27th March 2019.
The presentation looks at the following areas:
1. What is AI?
2. The Ethics of Ai.
3. AI, Education and the #FutureSchool
4. The threat of AI to Fee-paying Education
The Rise Of Conversational AI with David LowDatabricks
In this fast-paced world, customers demand ease and efficiency when they talk to a company. Here comes “Chatbot”, an automated conversational agent which conducts conversations via text or voice.
For this talk, I will be starting with the current state of Conversational Intelligence and some common Python libraries used in building chatbots. Various approaches of building conversation engine such as pattern matching, word embedding and long short-term memory (LSTM) models will be discussed. At the same time, I will present the next generation of Conversational AI that focuses on Question Answering and perform a live-demo of such a system.
After the demo, the inner workings will be explained and relevant resources (including a Python framework for conversational AI research and datasets) will be introduced to the participants. Lastly, I will also share my experience launching commercial chatbots with Fortune 500 clients and a few pitfalls one should be aware of before concluding the talk. Key takeaways:
• Get to know the current state of Conversational AI
• Approaches from pattern matching to generative model; AIML / regex, Word-embeddings, Bi-directional LSTM
• The next generation of conversational AI: ParlAI, SQuAD, bAbi Tasks, MCTest etc. Example architecture: BiDAF, Dynamic Memory Network
• What we learned after launching 3 commercial chatbots with a bank and two insurance companies.
• Challenges: Compliance / General Intelligence / Answer Generation
• A Good Conversational UX is the combination of Art and Science.
This research paper introduces a novel application for predicting plant diseases in cotton and potato plants using Convolutional Neural Networks (CNNs).
Separate CNN models were trained on labeled datasets of cotton and potato leaves, each associated with their respective diseases. The primary goal is to employ a fusion of two standard CNN systems to detect various diseases in cotton and potato plants.
Given India's heavy reliance on agriculture, this innovation is crucial to address challenges faced by the sector, including technological limitations, limited access to credit and markets, and the impact of climate change.
Cotton and potatoes are significant crops; this research paper are susceptible to various diseases that can impede their growth and result in substantial yield losses.
The conventional disease detection methods involve manual inspection and disease prognosis, which are time consuming and less accurate. The research showcases the effectiveness of the automated plant disease detection system, with two best models achieving impressive accuracies of 97.10% and 96.94% for cotton and potato plants, respectively.
These results offer promising insights for potential applications in crop management, benefiting the agricultural sector and contributing to increased productivity and profitability.
This document profiles Kharim Mchatta, the CEO and founder of HACK IT Consultancy, a top cybersecurity company in Tanzania. Mchatta has over 5 years of experience in cybersecurity, digital forensics, training, and consulting. He provides these services across Africa and in countries in Europe and North America. The document also summarizes Mchatta's presentation on artificial intelligence in cybersecurity, covering how AI can benefit and pose risks to security, how cybercriminals use AI for malicious activities, and how cybersecurity experts can leverage AI to counter new cyber threats.
This document summarizes a lecture on the relationships between artificial intelligence and philosophy. It discusses how AI both relates to and improves upon philosophical inquiry. Specifically, it notes that AI can help clarify philosophical concepts and provide new examples to investigate philosophical questions. At the same time, philosophy helps clarify AI's goals and concepts. The document provides examples of how AI extends the philosophy of mind by allowing the design of varied mind types and clarifying the relationship between mind and body.
This presentation includes - History, Functions, Working, Advancement, Applications, Advantages, Disadvantages, Limitations & Contests Held - of Chatbot Technology.
Leben 4.0: Künstliche Intelligenz und der Weg in die Datengesellschaft. Vortr...Andreas Wagener
Daten und Algorithmen bestimmen bereits heute schon unseren Alltag, oft ohne dass wir dies bemerken. Dieser Trend dürfte in der nahen Zukunft noch deutlich zunehmen. Die Digitalisierung des Menschen einerseits und der Einsatz Künstlicher Intelligenz andererseits werden unser Leben maßgeblich bestimmen und verändern. Werden Maschinen, Roboter und Computer die Oberhand gewinnen oder leiten sich daraus neue, nie dagewesene Chancen für den Menschen ab?
Schema - damit Googles künstliche Intelligenz deine Website besser verstehtnetlive IT AG
Referat Internet-Briefing 06.02.2017
Paris Hilton. Ist damit das Hotel in Paris gemeint oder die schillernde Urenkelin des Hotelgründers? Mit Schema.org sagen Sie Google & Co. um was es auf Ihrer Website geht und nicht nur was darauf steht. Damit ermöglichen Sie Suchmaschinen Ihre Informationen besser zu verstehen und Sie positionieren Ihre Website für eine bessere Platzierung. In diesem Referat gibt Ihnen Daniel Niklaus eine konkrete Starthilfe, damit Google Ihre Website noch besser versteht.
Wovon wir morgen leben werden, wenn intelligente Maschinen und Algorithmen unsere Arbeit machen
Seit jeher haben Innovationen und technologische Umbrüche gravierende Auswirkungen darauf gehabt, wie, wo und mit welchem Selbstverständnis der Mensch seiner Arbeit nachgeht.
Werfen wir einen Blick auf zukünftige Entwicklungen im Bereich der Künstlichen Intelligenz und auf die Bedrohungen und Chancen, die sich daraus ergeben. Wovon werden wir eigentlich morgen leben, wenn intelligente Maschinen und Algorithmen unsere Arbeit machen?
Machine learning fro computer vision - a whirlwind of key concepts for the un...potaters
This document provides an overview of machine learning concepts for computer vision. It discusses why machine learning is useful, especially for visual tasks that are difficult to define algorithmically. It covers supervised and unsupervised learning, common machine learning tasks in computer vision like classification and detection, and example algorithms like decision trees and random forests. It also addresses important concepts like overfitting and techniques to avoid it, such as separating training and test data and using ensemble methods.
Das Erwachen der Roboter im Content Marketing.Michael Schmitt
Einsatzpotentiale und Funktionsweisen von künstlicher Intelligenz (engl. artificial intelligence) im Content Marketing werden anhand aktueller Entwicklungen und den Beispielen einer Sentiment Analyse und dem automatischen Anpassen und Personalisieren von Web-Inhalten veranschaulicht.
The document provides an overview of artificial intelligence, including its definition, history, approaches, tools for evaluation, applications, and predictions for the future. It discusses topics such as the traits of an intelligent system, methods like cybernetics and symbolic/statistical approaches, tools including search algorithms and neural networks, and applications in fields like medicine, robotics, and web search engines.
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
Artificial intelligence is the study and design of intelligent agents, with no single goal. It aims to put the human mind into computers by developing machines that can achieve goals through computation. The origins of AI began in the 1940s with the development of electronic computers. Significant early developments included the first stored program computer in the 1950s, the Dartmouth Conference which coined the term "artificial intelligence" in the 1950s, and the development of the LISP programming language. In the following decades, AI research expanded and led to applications in fields like expert systems, games, and military systems. While progress has been made, the full extent of intelligence and the future of AI remains unknown.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
This document provides an introduction to artificial intelligence (AI). It defines AI as a branch of computer science dealing with symbolic and non-algorithmic problem solving. The document discusses the evolution of AI from early programs in the 1950s to current applications in areas like expert systems, natural language processing, computer vision, robotics, and automatic programming. It also notes both potential positive futures where intelligent robots assist humans as well as potential negative outcomes if robots are used for anti-social purposes. The conclusion is that AI has increased understanding of intelligence while also revealing its complexity.
This document provides an overview of artificial intelligence (AI), including definitions, a brief history, methods, applications, achievements, and the future of AI. It defines AI as the science and engineering of making intelligent machines, especially intelligent computer programs. The document outlines different methods of AI such as symbolic AI, neural networks, and computational intelligence. It also discusses a wide range of applications of AI such as finance, medicine, gaming, robotics, and more. Finally, it discusses some achievements of AI and envisions continued growth and importance of AI in the future.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Artificial Neural Networks, Q-Learning, Monte Carlo Tree Search
SpoookyJS - A JavaScript Multiagent Board Game Framework Based On Monte Carlo Methods
http://www.spoookyjs.de
https://github.com/janwieners/SpoookyJS
2. Definition
Maschinelles Lernen ist der elementare Bereich
künstlicher Intelligenz
Es ist die Konstruktion von Systemen die aus
Daten selbstständig lernen können
“Field of study that gives computers the ability to
learn without being explicitly programmed”
~ Arthur Samuel, 1959
3. Anwendungsbeispiele
Solche Lernsysteme werden benutzt für:
Spam-filter für Emails (zB yahoo-mail)
Texterkennung (zB Handyhandschrifterkennung)
Spracherkennung (zB Telefonroboter)
Diagnoseverfahren (zB Krebs-wahrscheinlichkeit)
Google (zB die Nachrichten-kategorisierung auf News)
Viele weitere Bereiche […]
4. Typen der Lernalgorithmen
Überwachtes Lernen (supervised learning)
Lernt aus gegeben „richtigen“ Ein & Ausgaben
zB „Bei einer Person mit dem Alter 95 (input) war
Krebs vorhanden (richtiger output)“
Unüberwachtes Lernen (unsupervised learning)
Findet Strukturen in Daten. Es werden keine
„richtigen“ Antworten für die Inputs gegeben
zB „Es gibt eine krebskranke Person mit einem Alter von
95 Jahren und einem Haustier“ (Finde Zusammenhänge!)
Bestärkendes Lernen (reinforcement learning)
Lernen durch Belohnung/Bestrafung
zB „Gegen eine Wand fahren ist schlecht,
schnell fahren gut“
5. Typen der Ausgabe
Regression: „durchgehende“ Ausgabe
Für jeden Input liefert das Modell einen durchgehenden Wert
zB für das Voraussagen eines Wohnungspreises nach Wohnungsgröße
(Bei überwachtem Lernen)
6. Classification: bestimmte Ausgabe
Für jeden Input liefert das Modell einen von speziellen Werten
zB für das Erkennen ob eine Email Spam ist (1) oder nicht (0)
8. Das Training Set
Besteht (bei überwachtem Lernen) aus den Daten
durch die gelernt werden soll
zB eine Excel Tabelle mit 250 Spalten mit je einer Zeile
pro Wohnung (Trainingsbeispiel)
und 3 Spalten, wobei die 1. Spalte die
Wohnungsgrößen (x1) beinhaltet, die 2. die Anzahl von
Badezimmern (x2) und die 3. den Wohnungspreis (y -
die „richtige“ Antwort)
Das Training Set sollte so angepasst sein dass alle
Trainingsbeispiele sich in ähnlichem Zahlenbereich
befinden. Dazu verwendet man „Feature Scaling“ und
„Mean Normalization“.
9. Der Lernalgorithmus
Der Lernalgorithmus erstellt die bestmögliche
Hypothese anhand des gegebenen Training Sets
Mit der „Cost-function“ (J) findet man heraus wie „gut“ eine
Hypothese ist (anhand des Training Sets).
Hierbei wird der Abstand der Voraussagen zu den
tatsächlichen „richtigen Antworten“ gemessen.
Mit dem Lernalgorithmus probiert man diese zu optimieren.
Hierbei verändert man die Hypothese und je
niedriger das Ergebnis der Cost-function desto besser
die Hypothese
Ein vielbenutzter Lernalgorithmus ist
Gradient Descent
10. Die Hypothese
Die Hypothese ist das „fertige, befragbare“ Modell.
Man liefert Daten (beispielsweise in Form einer Excel
Tabelle) und bekommt die gewünschte Antwort (zB
einen voraussichtlichen Wohnungspreis)
Form einer Beispielhypothese: hΘ(x) = Θ0 + Θ1 * x
Hierbei ist x die gelieferten Daten
Die Θ („theta“) sind die „Knöpfe“ mit denen die Hypothese
angepasst wird. Diese werden mittels Gradient Descent
verändert um die beste Hypothese zu liefern.
11. Gradient Descent
Hiermit passt man die (thetas der) Hypothese
schrittweise an.
Erst initialisiert man die thetas mit einem Wert (zB 0)
Dann legt man die Schrittgröße (α) fest (zB 0.1)
Dann passt man die thetas schrittweise an bis die
Cost-function den besten (kleinsten) Wert liefert.
12.
13.
14. Octave ist eine gute, freie Software um dies praktisch
umzusetzen
Ein guter Einstieg zu Maschinellem Lernen ist der
kostenlose Kurs auf
https://www.coursera.org/course/ml
(auch Quelle für diese Präsentation)