Advancement in artificial intelligence: Should Humans be Worried?
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This document provides an overview of artificial intelligence (AI) including its history, types, technologies, applications, and concerns about its advancement. It defines AI as making intelligent machines through computer programs. It discusses weak AI which is designed for specific tasks, and strong AI which would have general human-level abilities. Key AI technologies explained include machine learning, deep learning, natural language processing, computer vision, and robotics. The document outlines many applications of AI in fields such as healthcare, transportation, education, business, manufacturing and more. It notes concerns from experts like Hawking, Musk and Gates that achieving super intelligent AI without proper oversight could potentially threaten humanity.
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Advancement in artificial intelligence: Should Humans be Worried?
- 1. Advancement in Artificial Intelligence: Should Humans be Worried? By: Raymond Owusu
- 2. What is A.I.? “The science and engineering of making intelligent machines, especially intelligent computer programs”. (J. McCarthy 1956)
- 3. What is Intelligence? The ability of a system to: • Reason, Perceive relationships and analogies, • Learn from experience, store and retrieve information from memory • Solve problems and Comprehend complex ideas • Use natural language • Classify, generalize, and adapt to new situations.
- 4. Types of Intelligence • Linguistic intelligence • Logical mathematical intelligence • Spatial intelligence • Musical intelligence • Bodily-Kinesthetic intelligence • Interpersonal and Intrapersonal intelligence
- 5. Composition of Intelligence • Reasoning • Learning • Problem Solving • Perception • Linguistic Intelligence
- 6. History of AI • 1943 – McCulloh and Pitts, Boolean circuit model of brain • 1950 – Turing’s computing machine and intelligence. • 1950s – Early AI programs including Samuel’s checker program, Newell and Simons logic etc. • 1956 – John McCarthy coins the term Artificial Intelligence at Dartmouth conference. • 1958 – John McCarthy develops LISP programming language.
- 7. History of AI • 1965 – Robinson’s complete algorithm for logical reasoning • 1966-79 – Early development in Knowledge based systems • 1980-88 – Development of Expert systems • 1989-present – Machine Learning
- 8. Main Goals of AI • To Create Expert System • To Implement Human Intelligence in Machines
- 9. Types of AI • Weak AI – AI system designed and trained for a particular task. (Virtual personal assistants such as Apple Siri, Google home) • Strong AI – General intelligence AI system with generalized human cognitive abilities, capable of finding solutions to unfamiliar problems and tasks.
- 10. STRONG AI vs. WEAK AI The machine can actually think and perform tasks on its own just like a human being. Cannot follow these tasks on their own but are made to look intelligent. Algorithm is stored in Strong AI to help them act in different situations All the actions are entered by a human being. There are no proper examples for Strong AI since it is still in the initial stage There are several examples for Weak AI since it has been performed several times. In Strong AI the machine actually has a mind of its own and can take decisions The machine can just simulate the human behavior. There is more focus on Strong Artificial Intelligence by researchers The focus on Weak Artificial Intelligence is from engineers who want them to perform different activities.
- 11. IDEAL STRONG AI • Machines intellectual capability is functionally equal to a human • Have sensory perception as human • Go through the same education and learning processes as a human child
- 12. AI Technologies • Machine learning • Natural language processing (NLP) • Robotic Process Automation • Machine vision • Robotics
- 13. Machine Learning Machine learning is a computer program that can learn from past experience to improve itself without being explicitly programmed.
- 14. Machine Learning • Supervised Learning: Learning with a labeled training set. Example email spam detector with training set of already labeled emails • Unsupervised Learning: Discovering patterns in unlabeled data. Example cluster similar documents based on the text content. • Reinforcement Learning: learning based on feedback or reward. Example: learn to play chess by winning or losing.
- 15. Deep Blue (Chess Computer) vs Garry Deep Blue won chess game against world champion Garry Kasparov in Feb. 1996.
- 16. Deep Learning • Part of the machine learning field of learning representations of data. Exceptionally effective at learning patterns. • Utilizes learning algorithms that derive meaning out of data by using a hierarchy of multiple layers that mimic the neural networks of our brain. • If you provide the system tons of information, it begins to understand it and respond in useful ways.
- 17. Brain Neurons
- 18. Google AlphaGo
- 19. Natural language processing (NLP) AI NLP enables computers and humans to communicate using natural language such as English rather than computer language.
- 20. Robotic Process Automation It is the use of AI and machine learning capabilities to handle high-volume, repeatable tasks that previously required a human to perform.
- 21. Machine Vision It is the ability of a computer to see; it employs a number of video cameras, analog- to-digital conversion and digital signal processing technologies.
- 22. Robotics A Robot is a electro- mechanical device that can be programmed to perform manual tasks such as moving materials, parts, tools or specialized devices.
- 24. Healthcare – Surgical System
- 27. IBM Machine Vision Spots Diabetic Eye Disease
- 31. Drones with AI Technology
- 34. AI in Business • Robotic process automation is being applied to highly repetitive tasks normally performed by humans. • Machine learning algorithms are being integrated into CRM platforms to uncover information on how to better server customrs.
- 35. AI in Education AI tutors can assess students and adapt to their needs, help them to work at their own pace and also provide support to ensure they stay on track.
- 36. AI in Finance • AI is being applied in personal finance applications to collect personal data and provide financial advice. IBM Watson have been applied to the process of buying a home. • Most of the trading on Wall Street is done by AI software.
- 37. AI in Law AI systems being developed to sift through large amounts law documents which can overwhelm humans.
- 40. AI in Air traffic Control
- 41. Should humans be worried?
- 42. Should Humans be Worried About AI?
- 43. Stephen Hawking “I think the development of full artificial intelligence could spell the end of the human race.”
- 44. Elon Musk, Ceo of Tesla, "I think we should be very careful about artificial intelligence. If I were to guess like what our biggest existential threat is, it’s probably that. So we need to be very careful with the artificial intelligence.
- 45. Bill Gates "I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned.“
- 46. CONCLUSION A race by countries to achieve greater AI technology may prove disastrous if precautionary measures are not taking.
- 47. Thank You!
Editor's Notes
- Developing machines or computer programs that think intelligently, in a similar manner as humans.
- The ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations.
- A machine or a system is artificially intelligent if it is equipped with at least one or more type of intelligence.
- Reasoning − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. Learning − It is the activity of gaining knowledge or skill by studying, practicing, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study. Problem Solving − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Perception − It is the process of acquiring, interpreting, selecting, and organizing sensory information. Linguistic Intelligence − It is one’s ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication.
- expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than through conventional procedural code.
- Weak AI: Machine intelligence that equals or exceeds human intelligence or efficiency at a specific task. Strong AI: A machine with the ability to apply intelligent to any problem, rather than just one specific problem (human-level intelligence) Superintelligence AI: An intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.
- Strong AI's goal is to develop artificial intelligence to the point where the machine's intellectual capability is functionally equal to a human's. The ideal Strong AI machine, however, would be built in the form of a man, have the same sensory perception as a human, and go through the same education and learning processes as a human child. (Copeland) Essentially, the machine would be "born" as a child and eventually develop to an adult in a way analogous to human development. Instead of trying to give the computer adult-like knowledge from the outset, the computer would only have to be given the ability to interact with the environment and the ability to learn from those interactions. As time passed it would gain common sense and language on its own. Strong AI's ultimate goal is to make an intelligent computer that can think and understand, but those terms remain ambiguous and undefinable; hence, there is no general measure of "success" in the field of Strong AI.
- Strong AI's goal is to develop artificial intelligence to the point where the machine's intellectual capability is functionally equal to a human's. The ideal Strong AI machine, however, would be built in the form of a man, have the same sensory perception as a human, and go through the same education and learning processes as a human child. (Copeland) Essentially, the machine would be "born" as a child and eventually develop to an adult in a way analogous to human development. Instead of trying to give the computer adult-like knowledge from the outset, the computer would only have to be given the ability to interact with the environment and the ability to learn from those interactions. As time passed it would gain common sense and language on its own. Strong AI's ultimate goal is to make an intelligent computer that can think and understand, but those terms remain ambiguous and undefinable; hence, there is no general measure of "success" in the field of Strong AI.
- Receives input data and use statistical analysis to predict an output. Supervised algorithms require humans to provide input. Unsupervised algorithms do not need to be trained with desired outcome data. They use deep learning approach to review data.
- Receives input data and use statistical analysis to predict an output. Supervised algorithms require humans to provide input. Unsupervised algorithms do not need to be trained with desired outcome data. They use deep learning approach to review data.
- Receives input data and use statistical analysis to predict an output. Supervised algorithms require humans to provide input. Unsupervised algorithms do not need to be trained with desired outcome data. They use deep learning approach to review data.
- NLP systems enables computers to perform useful tasks with natural language. Input of NLP system can be Speech or Written Text. (speech and voice recognition)
- RPA differs from IT automation, since it is able to adapt and change according to situations.
- Machine vision is used in: Electronic component analysis Signature identification Optical character recognition Pattern recognition Medical image analysis
- An intelligent robot is equipped with many sensory devices that enable it to respond to changes in its environment. They are used in assembly lines for car production.
- AI Technology can perform delicate operations more precisely and efficiently. Virtual health assistants that help to schedule follow-up appointments, answer questions and aid patients through billing process. Surgical System; surgical robotic system that can perform delicate brain surgery and enabling physicians to manipulate tools at microscopic levels.
- AI being used to make better and faster diagnoses than humans. Example is IBM Watson which understands natural language and capable of responding to questions.
- The user controls the arm through existing nerves and it is sensitive enough to pic up even a piece of paper. Click image to watch video
- The user controls the arm through existing nerves and it is sensitive enough to pic up even a piece of paper. Click image to watch video
- The Mars Lander or Curiosity is able to navigate on Mars, dig in the Martian Soil, perform chemical analysis and send results to earth. Robotic arm used in the ISS to lift heavy objects
- The Mars Lander or Curiosity is able to navigate on Mars, dig in the Martian Soil, perform chemical analysis and send results to earth. Robotic arm used in the ISS to lift heavy objects
- AI enabled military hardware that can move autonomously, detect enemy unites and take action.
- Most repetitive manufacturing tasks are carried out by robots in modern factories. Especially in the automotive industry.