Keynote presentation at European Conference on the Impact of Artificial Intelligence and Robotics
November 1, 2019
EM-Normandie Business School, Oxford, UK
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/autonomous-driving-ai-workloads-technology-trends-and-optimization-strategies-a-presentation-from-qualcomm/
Ahmed Sadek, Senior Director of Engineering at Qualcomm, presents the “Autonomous Driving AI Workloads: Technology Trends and Optimization Strategies” tutorial at the May 2022 Embedded Vision Summit.
Enabling safe, comfortable and affordable autonomous driving requires solving some of the most demanding and challenging technological problems. From centimeter-level localization to multimodal sensor perception, sensor fusion, behavior prediction, maneuver planning and trajectory planning and control, each one of these functions introduces its own unique challenges that must be solved, verified, tested and deployed on the road.
In this talk, Sadek reviews recent trends in AI workloads for autonomous driving as well as promising future directions. He covers AI workloads in camera, radar and lidar perception, AI workloads in environmental modeling, behavior prediction and drive policy. To enable optimized network performance at the edge, quantization and neural architecture optimization are typically performed either during training or post-training. Sadek also covers the importance of hardware-aware quantization and network architecture optimization, and introduces the innovation done by Qualcomm in these areas.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
This document provides an overview of artificial intelligence including:
1) It discusses what AI is, its history, and some of the key subfields like games playing, expert systems, natural language processing, and neural networks.
2) It outlines several applications of AI including in computer science, finance, medicine, heavy industry, transportation, telecommunications, toys/games, music, aviation, and news/publishing.
3) It provides a brief history of AI from the 15th century to modern day, highlighting milestones like the first mechanical calculator and Deep Blue's victory over Kasparov in chess.
Artificial intelligence (AI) is the study and creation of intelligent machines and software. The document discusses the history and goals of AI, including how it was founded in the 1950s and experienced periods of increased and decreased funding. It also covers what intelligence is, definitions of artificial intelligence, tools and applications of AI in various industries, as well as the pros and cons of AI technology.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
The document provides an introduction to artificial intelligence (AI), including definitions of AI, descriptions of the eras of AI development, types of AI approaches, and applications of AI. It discusses factors that have influenced recent advancement in AI and identifies areas of AI research focus. The summary is:
The document introduces artificial intelligence (AI), defining it as human-made thinking power. It describes the history and eras of AI development, different types and approaches of AI including weak AI, strong AI, and super AI. Furthermore, it discusses applications of AI and factors influencing recent advancement, and identifies areas of ongoing AI research focus.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/autonomous-driving-ai-workloads-technology-trends-and-optimization-strategies-a-presentation-from-qualcomm/
Ahmed Sadek, Senior Director of Engineering at Qualcomm, presents the “Autonomous Driving AI Workloads: Technology Trends and Optimization Strategies” tutorial at the May 2022 Embedded Vision Summit.
Enabling safe, comfortable and affordable autonomous driving requires solving some of the most demanding and challenging technological problems. From centimeter-level localization to multimodal sensor perception, sensor fusion, behavior prediction, maneuver planning and trajectory planning and control, each one of these functions introduces its own unique challenges that must be solved, verified, tested and deployed on the road.
In this talk, Sadek reviews recent trends in AI workloads for autonomous driving as well as promising future directions. He covers AI workloads in camera, radar and lidar perception, AI workloads in environmental modeling, behavior prediction and drive policy. To enable optimized network performance at the edge, quantization and neural architecture optimization are typically performed either during training or post-training. Sadek also covers the importance of hardware-aware quantization and network architecture optimization, and introduces the innovation done by Qualcomm in these areas.
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
This document provides an overview of artificial intelligence including:
1) It discusses what AI is, its history, and some of the key subfields like games playing, expert systems, natural language processing, and neural networks.
2) It outlines several applications of AI including in computer science, finance, medicine, heavy industry, transportation, telecommunications, toys/games, music, aviation, and news/publishing.
3) It provides a brief history of AI from the 15th century to modern day, highlighting milestones like the first mechanical calculator and Deep Blue's victory over Kasparov in chess.
Artificial intelligence (AI) is the study and creation of intelligent machines and software. The document discusses the history and goals of AI, including how it was founded in the 1950s and experienced periods of increased and decreased funding. It also covers what intelligence is, definitions of artificial intelligence, tools and applications of AI in various industries, as well as the pros and cons of AI technology.
Artificial Intelligence and mobile robotics are transforming businesses and the economy: this deck explores possible futures for companies and workers.
The document provides an introduction to artificial intelligence (AI), including definitions of AI, descriptions of the eras of AI development, types of AI approaches, and applications of AI. It discusses factors that have influenced recent advancement in AI and identifies areas of AI research focus. The summary is:
The document introduces artificial intelligence (AI), defining it as human-made thinking power. It describes the history and eras of AI development, different types and approaches of AI including weak AI, strong AI, and super AI. Furthermore, it discusses applications of AI and factors influencing recent advancement, and identifies areas of ongoing AI research focus.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
1. Introduction
2. How AI originated
3. Interesting facts about AI
4. Real-life application of AI
5. AI tools
6. Something special
7. Limitations of AI
8. Conclusion
10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good Bernard Marr
Artificial intelligence is being used in many positive ways to help address societal problems. Some examples discussed in the document include using AI in cancer screening and healthcare decision-making, saving bee populations by analyzing sensor data, creating apps to help people with disabilities, addressing climate change through climate modeling, aiding wildlife conservation efforts, combating world hunger through crop analysis, reducing inequality by correcting algorithmic bias, identifying "fake news", improving medical imaging analysis, and prioritizing infrastructure upgrades.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceived inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
Understanding the 4 Types of Artificial intelligenceBernard Marr
How do different kinds of artificial intelligence emulate and replicate human functioning? That’s the question that determines how we categorize these four primary types of AI.
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
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
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
Make And Designed by Muhammad Muttaiyab Ahmad & Muhammad Nasir Yousaf
The Best Presentation in Slides Share on Artificial Intelligence.
Professors give them 100% out of 100%
This is the quality of presentation that can revel all parts of Artificial Intelligence from Each and every example that should be added, that is already added in which.
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
Artificial general intelligence (AGI) or Strong A.I.Pratap Dangeti
The document discusses the characteristics and architecture needed for an artificial general intelligence (AGI) system. An AGI should be able to structure and manage its own memory, correct beliefs based on new information, and retrieve and apply knowledge to tasks. The proposed architecture includes a central knowledge graph containing knowledge cells that are linked with logical constraints. The system would be trained incrementally, starting with basic tasks and progressing to more complex abstraction. It should be able to understand queries, solve problems by considering constraints, and explain its solutions.
This document discusses cognitive computing. It begins with an introduction that defines cognition and cognitive computing. Cognitive computing aims to develop systems that can think and react like the human mind through a combination of neuroscience, supercomputing, and nanotechnology. The need for cognitive computing is that today's information is challenging to manage and current search engines are limited. An example provided is IBM's Watson, the first cognitive computer, which was able to answer questions in natural language and defeat human champions on Jeopardy. The document concludes by stating that cognitive systems will help make sense of complex information and create new industries through collaboration with human reasoning.
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.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think like humans. AI involves machines that can learn and solve problems like humans. The document discusses the definition of AI, its history, need, applications in areas like medicine, telecommunications and gaming. It also outlines advantages of AI such as reducing human error and disadvantages including potential lack of creativity. The future scope of AI is discussed as biological intelligence is limited while non-biological computation through AI is growing exponentially.
The talk will be focused on AGI (Artificial General Intelligence) and Peter will give his thoughts and impressions what are the next steps in this field and direction where should we go further.
Peter is an entrepreneur, AI Community Leader & Author of various Reports on AI.
AI ppt introduction , advandtage pros and cons.pptxdeepakkrlkr2002
The document discusses artificial intelligence (AI), including definitions of intelligence and AI, comparisons of human and artificial intelligence, applications of AI in various fields like medicine, music, gaming, banking, and telecommunications. It also covers the future growth of AI, advantages and disadvantages of AI, limitations of current AI, and concludes that AI development will significantly change the world if developed responsibly by engineers.
Addis abeb university ..Artificial intelligence .pptxethiouniverse
The document defines artificial intelligence as the science and engineering of making intelligent machines, especially intelligent computer programs. It discusses that AI is the creation of computer programs that can learn to think and function on their own. The document then provides examples of technologies that use AI, such as machine learning, robotics, and neural networks. It describes the different types of AI as artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. The document also outlines the history of AI and discusses its applications in various domains like agriculture, healthcare, business, and education.
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
The document discusses artificial intelligence and how it works. It defines intelligence and AI, explaining that AI aims to make computers as intelligent as humans. It describes how AI uses artificial neurons and networks to function similarly to the human brain. Examples of AI applications are given, like expert systems used in various domains. The document also compares human and artificial intelligence, noting their differing strengths and weaknesses.
1. Introduction
2. How AI originated
3. Interesting facts about AI
4. Real-life application of AI
5. AI tools
6. Something special
7. Limitations of AI
8. Conclusion
10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good Bernard Marr
Artificial intelligence is being used in many positive ways to help address societal problems. Some examples discussed in the document include using AI in cancer screening and healthcare decision-making, saving bee populations by analyzing sensor data, creating apps to help people with disabilities, addressing climate change through climate modeling, aiding wildlife conservation efforts, combating world hunger through crop analysis, reducing inequality by correcting algorithmic bias, identifying "fake news", improving medical imaging analysis, and prioritizing infrastructure upgrades.
Introduction To Artificial IntelligenceNeHal VeRma
Artificial intelligence (AI) aims to create intelligent machines that can perceive and act like humans. Alan Turing first proposed testing machine intelligence with the Turing Test in 1956. An intelligent agent perceives its environment through sensors and acts through effectors. Rational agents are expected to select actions that maximize their performance given perceived inputs and built-in knowledge. Different types of AI agents include simple reflex agents that react to current inputs, model-based reflex agents that track the world state, goal-based agents that consider goals, and utility-based agents that evaluate action outcomes.
Understanding the 4 Types of Artificial intelligenceBernard Marr
How do different kinds of artificial intelligence emulate and replicate human functioning? That’s the question that determines how we categorize these four primary types of AI.
[Video available at https://sites.google.com/view/ResponsibleAITutorial]
Artificial Intelligence is increasingly being used in decisions and processes that are critical for individuals, businesses, and society, especially in areas such as hiring, lending, criminal justice, healthcare, and education. Recent ethical challenges and undesirable outcomes associated with AI systems have highlighted the need for regulations, best practices, and practical tools to help data scientists and ML developers build AI systems that are secure, privacy-preserving, transparent, explainable, fair, and accountable – to avoid unintended and potentially harmful consequences and compliance challenges.
In this tutorial, we will present an overview of responsible AI, highlighting model explainability, fairness, and privacy in AI, key regulations/laws, and techniques/tools for providing understanding around AI/ML systems. Then, we will focus on the application of explainability, fairness assessment/unfairness mitigation, and privacy techniques in industry, wherein we present practical challenges/guidelines for using such techniques effectively and lessons learned from deploying models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning many industries and application domains. Finally, based on our experiences in industry, we will identify open problems and research directions for the AI community.
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
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
The document discusses generative AI and how it has evolved from earlier forms of AI like artificial intelligence, machine learning, and deep learning. It explains key concepts like generative adversarial networks, large language models, transformers, and techniques like reinforcement learning from human feedback and prompt engineering that are used to develop generative AI models. It also provides examples of using generative AI for image generation using diffusion models and how Stable Diffusion differs from earlier diffusion models by incorporating a text encoder and variational autoencoder.
Make And Designed by Muhammad Muttaiyab Ahmad & Muhammad Nasir Yousaf
The Best Presentation in Slides Share on Artificial Intelligence.
Professors give them 100% out of 100%
This is the quality of presentation that can revel all parts of Artificial Intelligence from Each and every example that should be added, that is already added in which.
Artificial intelligence- The science of intelligent programsDerak Davis
Artificial intelligence (AI) involves creating intelligent computer programs and machines that can interact with the real world similarly to humans. AI uses techniques like machine learning, deep learning, and neural networks to allow programs to learn from data and experience without being explicitly programmed. While AI has potential benefits, some experts warn that advanced AI could pose risks if not developed carefully due to concerns it could become difficult for humans to control once a certain level of intelligence is achieved.
Artificial general intelligence (AGI) or Strong A.I.Pratap Dangeti
The document discusses the characteristics and architecture needed for an artificial general intelligence (AGI) system. An AGI should be able to structure and manage its own memory, correct beliefs based on new information, and retrieve and apply knowledge to tasks. The proposed architecture includes a central knowledge graph containing knowledge cells that are linked with logical constraints. The system would be trained incrementally, starting with basic tasks and progressing to more complex abstraction. It should be able to understand queries, solve problems by considering constraints, and explain its solutions.
This document discusses cognitive computing. It begins with an introduction that defines cognition and cognitive computing. Cognitive computing aims to develop systems that can think and react like the human mind through a combination of neuroscience, supercomputing, and nanotechnology. The need for cognitive computing is that today's information is challenging to manage and current search engines are limited. An example provided is IBM's Watson, the first cognitive computer, which was able to answer questions in natural language and defeat human champions on Jeopardy. The document concludes by stating that cognitive systems will help make sense of complex information and create new industries through collaboration with human reasoning.
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.
Contains a detailed Slides on Artificial Intelligence.
What is artificial intelligence?
What are its uses?
advantages?
disadvantages?
Charasteristics?
examples?
functions
and other criterias.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines programmed to think like humans. AI involves machines that can learn and solve problems like humans. The document discusses the definition of AI, its history, need, applications in areas like medicine, telecommunications and gaming. It also outlines advantages of AI such as reducing human error and disadvantages including potential lack of creativity. The future scope of AI is discussed as biological intelligence is limited while non-biological computation through AI is growing exponentially.
The talk will be focused on AGI (Artificial General Intelligence) and Peter will give his thoughts and impressions what are the next steps in this field and direction where should we go further.
Peter is an entrepreneur, AI Community Leader & Author of various Reports on AI.
AI ppt introduction , advandtage pros and cons.pptxdeepakkrlkr2002
The document discusses artificial intelligence (AI), including definitions of intelligence and AI, comparisons of human and artificial intelligence, applications of AI in various fields like medicine, music, gaming, banking, and telecommunications. It also covers the future growth of AI, advantages and disadvantages of AI, limitations of current AI, and concludes that AI development will significantly change the world if developed responsibly by engineers.
Addis abeb university ..Artificial intelligence .pptxethiouniverse
The document defines artificial intelligence as the science and engineering of making intelligent machines, especially intelligent computer programs. It discusses that AI is the creation of computer programs that can learn to think and function on their own. The document then provides examples of technologies that use AI, such as machine learning, robotics, and neural networks. It describes the different types of AI as artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. The document also outlines the history of AI and discusses its applications in various domains like agriculture, healthcare, business, and education.
IBM Watson & Cognitive Computing - Tech In Asia 2016Nugroho Gito
1. The document provides an overview of cognitive computing, including a brief history of artificial intelligence and significant events that have shaped the evolution of cognitive computing.
2. It discusses what cognitive computing is, how it differs from traditional analytics by addressing ambiguous problems and interacting with humans in a natural way.
3. The document outlines how cognitive computing adoption has increased, providing examples of IBM Watson's applications in various industries and technologies like the Watson Developer Cloud that allow developers to access cognitive capabilities through APIs and tools.
Artificial intelligence dr bhanu ppt 13 09-2020BhanuSagar3
The document discusses a webinar on using artificial intelligence to advance pharmacy and healthcare in India. It will take place on September 13, 2020 from 2-3 pm, hosted by Prof. Bhanu P. S. Sagar. The webinar will cover the history of medical innovations using AI, how AI is applied in various fields like natural language processing and machine learning. It will also discuss the advantages of AI, such as reducing errors and facilitating difficult tasks. The types and applications of AI technology in the pharmaceutical industry will also be presented.
Presentación sobre inteligencia artificial. Los neofito dueños del sitio quieren hacerse millonarios a costa del esfuerzo de los usuarios. Lo malo es que no le dan ni un solo peso al usuario que sube sus trabajos. Sin embargo ellos les cobra a los demás U$s 100 por mes.
Artificial intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception and decision-making. The history of AI began in 1956 when the term was coined and the first conference was held. Notable developments include the first mobile robot in 1969, a chess-playing computer defeating a champion in 1997, and today's applications in areas like speech recognition, robotics, healthcare, and more. AI can be categorized into narrow, general, and super AI based on its capabilities. It provides advantages like more powerful computers and new problem-solving techniques but also faces challenges such as high costs and an inability to duplicate human creativity.
Chapter Three, four, five and six.ppt ITEtxgadisaAdamu
Artificial intelligence (AI) can be summarized as follows:
(1) AI refers to creating intelligent machines that can think and act like humans. It involves machines acquiring knowledge and applying it through experience to solve complex problems.
(2) The key components of human intelligence - learning, reasoning, problem-solving, perception, and linguistic ability - form the basis for developing AI systems.
(3) As data and computing power have increased, AI has advanced from narrow applications to more general problem-solving abilities through techniques like machine learning, neural networks, and deep learning.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
This document discusses artificial intelligence and its applications. It begins with an introduction that defines AI and its core principles such as reasoning, knowledge, planning, learning, communication, perception and object manipulation. Recent updates are provided on how companies like Microsoft and Google are using AI in healthcare to tackle diseases. The differences between AI and natural intelligence are explored. Applications of AI discussed include finance, medicine, social media, robotics, heavy industries, and education. The future potential of AI is discussed along with how it may impact the world as biological intelligence is limited compared to the growing capabilities of AI. In conclusion, the goal of AI development is to solve major problems and achieve tasks humans cannot, and it will change the world, so responsible development
What is Artificial Intelligence? : Everything You Need to Know about AIDashTechnologiesInc
Artificial Intelligence may be a buzzword now, but it’s not a new term. It was coined in 1956 by Minsky and McCarthy. Even though their effort to bring AI into the world’s attention failed, scientists and innovators started researching and developing machines that would mimic humans. In a nutshell;
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
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 is a branch of computer science that aims to create intelligent machines that can think and act like humans. It uses techniques like neural networks and machine learning to solve complex problems. AI has many applications including healthcare, gaming, data security, social media, transportation, robotics, education and more. While it offers benefits like accuracy, speed and reliability, it also faces limitations such as high costs, limited abilities and lack of original creativity.
This document discusses artificial intelligence and its applications. It defines strong AI as attempting to create human-level intelligence in machines, while weak AI focuses on narrow applications using machine learning. Some advantages of AI include reducing errors, exploring dangerous environments, and assisting with repetitive tasks. Challenges include the high cost of development and an inability to match human creativity or emotions. The document outlines several applications of AI in fields like transportation, the military, art, business, education, and hotels.
This document provides an overview of artificial intelligence (AI), including definitions, a history of AI, current applications and challenges. It begins with definitions of AI and discusses early milestones like the first AI conference in 1956 and Deep Blue defeating a chess champion in 1997. It outlines current progress in deep learning, natural language processing, robotics and autonomous vehicles. Key challenges are biases in data, lack of transparency in models, issues with data availability and security/privacy. The future of AI is discussed in terms of advancements in deep learning, increased healthcare applications, and focus on ethics.
AI EXPLAINED Non-Technical Guide for PolicymakersBranka Panic
This guide is meant to help policymakers and citizens understand the basics of Artificial Intelligence (AI) and how it affects our society. It offers explanations and additional resources to help policymakers prepare for the current
and future AI developments.
Artificial intelligence (AI) involves computer systems that attempt to model human intelligence and simulate human behavior. Modern AI applications include intelligent digital assistants like Siri, Cortana, and Google Now that can answer questions, make calls, set reminders and more using voice commands. AI is rising through mobile phones, video games, robotics, and deep learning algorithms used by Google. Challenges to AI include computing power, tolerance, intuitive thinking and judgment. The future of AI is promising in areas like self-driving cars, improved healthcare, and new discoveries through exploration. In conclusion, AI aims to create intelligent machines and applications using cutting-edge machine learning and analytics to solve problems by learning from examples and experience.
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.
Similar to Artificial General Intelligence, Consciousness, and the Future of AI (20)
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
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:
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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.
Pushing the limits of ePRTC: 100ns holdover for 100 days
Artificial General Intelligence, Consciousness, and the Future of AI
1. Dr. Mitt Nowshade Kabir
Dr. Mitt Nowshade Kabir
European
Conference on the
Impact of Artificial
Intelligence and
Robotics
2019
EM-Normandie
Business School,
Oxford, UK
Artificial General Intelligence (AGI),
Consciousness and the Future of AI
3. Somewhere about 20 thousand years ago …
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
4. The Ishango Bone
~18 000 BC
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
5. ▪ Ability to scan environment
and extract signals
▪ Learning capability
▪ Ability to grasp and interpret
▪ Ability to reason
▪ Problem-solving ability
Key Characteristics of Intelligence:
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
6. Rise of the
Artificial Intelligence
and a New Era
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
7. Some Key Advanced Technologies
Quantum
Computing
Blockchain
technology
3D printing
technologies
Internet of
Things (IoT)
Genetic
Engineering
Nanotechnology
Cognitive
Computing
Big Data
Analytics
Machine
Learning
Robotics Bioscience Genetics
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
8. Why AI is
Considered as a
Foundational
Technology
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
9. The
Business
Case of AI
RELX survey of 1000 executives shows AI adoption has
increased to 72 percent in 2019 (RELX Group, 2019)
Globally AI will help producing extra 15.7 trillion dollars
worth of goods and services comprising 14 percent of
the world GDP by 1930 (PriceWaterhouse, 2017)
38% of present jobs will be eliminated by 2030
(Berriman, 2017)
45% of present job functions can be automated using
present deep learning and other AI technologies (Chui et
al., 2015)
Global value of AI enterprises has reached 1.2 trillion
dollars (Gartner, 2018)
AI-based insights driven companies will grow from 27 to
40 percent per year (Forrester, 2017)
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
10. Impacts of AI on Society are More Crucial
AI as a foundational technology changing rapidly the way we
live, work, play and communicate
It is redefining economic relationships
It is already having huge impact on societies
It will eliminate within next decade over 30 percent of present
jobs
All areas of our personal life will be impacted
The AGI will completely change the world as we know it
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
11. Use of AI in Some Key Sectors
Retail
Communication:
chatbots, VA
Personalization:
recommendation
systems
Optimization:
forecasting, pricing
Marketing: precise
targeting
Finance
Asset
management
Robo trading
Fraud detection
Personalized
financial products
Chatbots
Automation
Personal Assistant
Media
Security
Autonomous
driving
Industry 4.0
Healthcare
Diagnostic from
medical images
Patient care
Early warning
R&D
Document
management
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
12. ▪ Regression Analysis: Revenue projection, business process optimization, risk
analysis
▪ Naive Bayes: Diagnosis of disease conditions, SPAM filtering
▪ Decision Trees: Chess, credit card application acceptance
▪ Random Forest: Identifying protein interaction in bioscience, identifying genes
associated with genes in genome, computer vision
▪ Genetic Algorithms: Artificial creativity, code breaking, financial modelling
▪ Nearest Neighbor: Recommendation system, hand movement of a robot
▪ Support Vector Machines: Genes classification, cancer diagnosis
▪ K Means: Customer analytics, fraud detection
▪ Markov Decision Process: Water control, fault identification
▪ Deep Learning: Computer vision, image colouring, machine translation, game playing,
gesture recognition, virtual assistant, autonomous driving, facial recognition, search
optimization, personalization, traffic control
Real World Applications of Machine Learning Techniques
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
13. Types of Machine Learning Algorithms
Neural networks Evolutionary models Probabilistic and
statistical models
Symbolic learning
and rule induction
Analytical and
fuzzy learning
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
14. Defining AI
Artificial narrow intelligence
Artificial general intelligence
(AI equals to humans in all aspects)
Artificial super intelligence
(AI surpasses humans in all aspects)
SINGULARITY EVENT
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
15. DIFFICULT QUESTIONS
WITH NOT SO EASY ANSWERS
- So, can we build AGI?
- If yes, when?
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
16. AGI Development
Two main schools
2. Development of AI agents:
covers all human cognitive and
somatic skills
1. Brain emulation:
computational
neuroscience
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
17. Brain Emulation
Brain emulation
through reverse
engineering and
mapping
Virtual organisms
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
18. Human Brain Emulation
Problems in brain emulation
▪ Individual neurons are complex
elements with numerous
connections.
▪ We are still finding new neurons
in the brain such as the rose hip
neuron.
▪ Functional neuronal networks
are difficult to simulate
▪ Neurotransmitters and their
activities are complex
▪ The Glial cells and their
connections with the neurons
STEPS:
• Lists of all brain parts
• 3D scanning of the entire brain
• Networks of neurons emulation, functional brain
emulation
• Generic human brain emulation
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
19. Virtual Organisms
• Open worm project:
C elegans nematoid – 302 neurons
and 95 muscle cells.
Goal: to recreate all 959 cells in the
first ever digital life form.
• Fruit fly brain emulation:
Fruit fly brain observatory (Ukani et
al., 2019)
• Mice:
Mapping of the primary visual cortex
of mouse’s brain and cortical neurons
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
20. Development of AI
Agents that
Covers all Human
Cognitive and
Somatic Skills
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
21. Towards AGI: Hurdles to Solve
• Common sense,
reasoning,
abstraction and
generalization
• Learning how to
learn
• Communication
with Natural
language
• Long-term planning
• Integration issues
Main differences between
machine and human
intelligence
1. Sense data:
Human - analog
Machine - digital
2. Environmental scanning:
Human - excruciatingly slow
Machine - instantaneous
3. Memory
Human - abstract, often fuzzy, not always reliable
Machine - large, detailed and exact replica
4. Creativity
Human brain - creative, spontaneously resourceful, heavily
heuristic
Machine - computation dependents. Showing signs of creativity
5. Emotions
Human - emotional even at cognitive level
Machine - still at rudimentary level
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
22. OpenAI Five
Dota 2 game
OPEN AI Five
Dota 2 is world’s most
popular strategic game
which requires
extensive teamwork,
collaboration and long
term planning
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
23. The Use of
OpenAI Five
Technology
Many of the
algorithms
developed for
OpenAI Five are
already getting used
in real world
situations such as
movement
dexterity.
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
24. Adaptive
AI Agents
The agent uses recent
experience to fine-tune the prior
model into an adapted one, which
the planner then uses to perform
its action selection (Nagabandi and
Clavera, 2019).
Dynamic legged millirobot, with model-based meta-reinforcement
learning algorithm to enable online adaptation to disturbances
and new settings such as traversing a slippery slope,
accommodating payloads, accounting for pose miscalibration
errors, and adjusting to a missing leg.
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
25. Curiosity-
based Learning
Learning requires presence of
following elements in an agent:
• Curiosity
• Motivation
• Absorptive capacity
• Prior knowledge
• Access to new knowledge
(Burda et al., 2018)
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
26. Boston Dynamics Robots
Two types:
• Atlas
• Spot
Both show
amazing agility
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
27. • OpenAI’s GPT-2 language model
achieved near human level content
generation capability.
• Machine can now not only
translate what you are saying in
real time but also do it using your
voice.
• 75 percent success rate in
reconstructing speech from human
brain’s neural activities
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
Improvement in Natural Language Processing
28. Towards AGI: hardware problems and solutions
Problems:
• Computational power
• Data delivery
• Data storage
Solutions:
• Quantum computing
• 5G technologies ,
quantum network
• DNA-based storage
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M. N. Kabir
30. DNA-Based Storage
One cubic centimeter holds 10 trillion DNA molecules:
• 10 terabytes of data
• 10 trillion simultaneous computations
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
32. WHAT ABOUT
CONSCIOUSNESS?
- Are we going to have sentient
machines any time soon?
Artificial General Intelligence (AGI), Consciousness and the Future of AI
Dr. M.N.Kabir
33. Neuroscience
considers that
various parts of
the brain working
together creates
consciousness
Some Theories of Consciousness
• Mind body problem (Descartes)
• The easy and hard problem (Chalmers, 1995)
• Consciousness is an illusion (Dennett, 1991)
• Consciousness is controlled hallucination
(Seth, 2010)
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
34. Integrated Information Theory of Consciousness
Consciousness according to this theory appears from the integration of the
information when the sum of parts of information becomes something more
than the sum of its parts (Tononi, 2004)
.
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
35. Global Workplace
Consciousness
Theory
Information that has more attention triggers the ignition and
dominate the larger span of brain network and reach the global
workplace creating the consciousness about the particular
information (Baars, 2002).
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
36. Neural Correlates of Consciousness
There exists a
minimal set of
neural activities
that correspond
to a conscious
precept (Koch,
2004).
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
37. Consciousness –
Neurobiological Approach
• Consciousness appears from neuronal activities
• Biological entities require consciousness to reduce the
use of computational and biological resources
• In the process of receiving, integrating and processing
information the brain requires the ability to abstract and
predict continuously
• Consciousness is related to intelligence - the more
intelligent an animal is the more conscious it is
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
38. Gene Mutation and Intelligence
Two networks of gene networks, M1 and M3, appear to influence cognitive
function – which includes memory, attention, processing speed and
reasoning. They lie behind the human gift for lateral thinking, mental
arithmetic, pub quizzes, strategic planning.
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
39. When the AI Outsmarted its Creators
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
40. When will a
machine with
human-level
general
intelligence
emerge?
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
41. Why we are Conscious and not Just
Computational zombies
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
42. Technological
Determinism
• Acceleration of technological
growth
• Simultaneous advancement of
technologies
• The unexpected emergence of
new technologies
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
43. Technological
Determinism
• Technology penetration can be quite pervasive.
Think The Internet or AI.
• It creates new adjacent areas of
technology development. Examples: Cloud
computing, Recommender systems.
• Technologies constantly converge. Example:
Virtual assistants like Siri
• One of the adjacent field creates a paradigm
shift and new technology emerges. Such as IoT.
• Innovation is the key factor in the
technology shift.
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir
44. Shall We Worry About AGI
AGI is still far from becoming a reality
• Transhumanism
• Safely mechanism
• Metalevel module with human
values and ethical codes
• Imminent problems from AI:
• Bias in AI
• Job loss from AI
• Military use of AI
Artificial General Intelligence (AGI), Consciousness and the Future of AI Dr. M.N.Kabir