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Artificial intelligence- The science of intelligent programs

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.

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Artificial intelligence- The science of intelligent programs
Intelligence is…
• Capacity for learning, reasoning, understanding and similar forms of
mental activity; aptitude in grasping truths, relationships, facts,
meanings, etc.
We are able to…
• Interact with the real world.
• Searching the best solution.
• Reasoning and planning.
• Learning and adaption.
Artificial intelligence- The science of intelligent programs
Artificial Intelligence: The science of intelligent
programs
• AI is the science and engineering of making intelligent machines,
especially intelligent computer programs.
• Complicated activities involve thousands of data sets and non-linear
relationships between variables.
• Machine Learning: offloading optimization.
• Field of study that gives computers the ability to learn without being
explicitly programmed.
• Machine learning algorithms learn through training.
• 15 approaches to machine learning. One approach —‘Deep Learning’
Artificial Intelligence
• Deep Learning: offloading feature specification.
• Deep Learning is achieved not by processing exhaustive rules but
through practice and feedback.
• Deep learning work using an artificial ‘neural network’ — a collection
of ‘neurons’ (software-based calculators) connected together.
• An artificial neuron has one or more inputs. It performs a
mathematical calculation based on these to deliver an output.
• A neural network is created when neurons are connected to one
another; the output of one neuron becomes an input for another.
Artificial Intelligence: The Beginning
• We believe AI is an evolution in computing as, or more, important
than the shifts to mobile or cloud computing.
• The term artificial intelligence was first coined by John McCarthy in
1956.
• Five years later Alan Turing wrote a paper on the notion of machines
being able to simulate human beings and the ability to do intelligent
things, such as play Chess.
• There is an argument is that since computers would always be
applying rote fact lookup they could never ‘understand’ a subject.

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Artificial intelligence- The science of intelligent programs

  • 2. Intelligence is… • Capacity for learning, reasoning, understanding and similar forms of mental activity; aptitude in grasping truths, relationships, facts, meanings, etc. We are able to… • Interact with the real world. • Searching the best solution. • Reasoning and planning. • Learning and adaption.
  • 4. Artificial Intelligence: The science of intelligent programs • AI is the science and engineering of making intelligent machines, especially intelligent computer programs. • Complicated activities involve thousands of data sets and non-linear relationships between variables. • Machine Learning: offloading optimization. • Field of study that gives computers the ability to learn without being explicitly programmed. • Machine learning algorithms learn through training. • 15 approaches to machine learning. One approach —‘Deep Learning’
  • 5. Artificial Intelligence • Deep Learning: offloading feature specification. • Deep Learning is achieved not by processing exhaustive rules but through practice and feedback. • Deep learning work using an artificial ‘neural network’ — a collection of ‘neurons’ (software-based calculators) connected together. • An artificial neuron has one or more inputs. It performs a mathematical calculation based on these to deliver an output. • A neural network is created when neurons are connected to one another; the output of one neuron becomes an input for another.
  • 6. Artificial Intelligence: The Beginning • We believe AI is an evolution in computing as, or more, important than the shifts to mobile or cloud computing. • The term artificial intelligence was first coined by John McCarthy in 1956. • Five years later Alan Turing wrote a paper on the notion of machines being able to simulate human beings and the ability to do intelligent things, such as play Chess. • There is an argument is that since computers would always be applying rote fact lookup they could never ‘understand’ a subject.
  • 7. Artificial Intelligence: The Beginning Themes of AI • The main advances over the past sixty years have been advances in search algorithms, machine learning algorithms, and integrating statistical analysis. • ‘AI Effect’ contributed to the downfall of US-based AI research in the 80s. • After decades of research, no computer has come close to passing the Turing Test (a model for measuring ‘intelligence’). • While they’ve built software that can beat humans at some games .
  • 8. The Turing Test • “Computing Machinery and Intelligence”- by Alan Turing • It put forward a question “Can machines think?” • Imitation Game – also called The Turing Test • The Turing test takes a simple pragmatic approach, assuming that a computer that is indistinguishable from an intelligent human actually has shown that machines can think. • The fact that the Turing test is still discussed and researchers attempt to produce software capable of passing it are indications that Alan Turing and the proposed test provided a strong and useful vision to the field of AI.
  • 9. Expert Systems: As subset of AI • First emerged in the early 1950s. • According to K. S. Metaxiotis et al, expert systems can be characterized by: • Using symbolic logic rather than only numerical calculations; • The processing is data-driven; • A knowledge database containing explicit contents of certain area of knowledge; and • The ability to interpret its conclusions in the way that is understandable to the user. • LISP: Programming language in AI and expert systems by McCarthy.
  • 10. Expert Systems: As subset of AI • Expert systems were increasingly used in industrial applications • DENDRAL (a chemical structure analyser), XCON (a computer hardware configuration system), MYCIN (a medical diagnosis system), and ACE (AT&T's cable maintenance system). • PROLOG: An alternative to LISP in logic programming – 1972. • The success of these systems stimulated a near-magical fascination with smart applications. • More companies taken part in pursuing expert system technology and developing practical applications. • Nowadays, expert systems has expanded into many sectors of our society.
  • 11. Technological Issues and Performance Limitations • Software Standards and Interoperability • American Association of Artificial Intelligence (AAAI), the IEEE Computer Society, DARPA, and the US government. • Knowledge Acquisition and Analysis • The problem-solving skills in humans oftentimes are far more complicated and complex than what knowledge collection can achieve. • For example, humans learn how to walk at an early age through practice and sometimes painful experience. • Handling Uncertain Situation • The ability of expert system to derive correct output is often compromised by the lack of precision in rules and inputs.
  • 12. Technological Issues and Performance Limitations • System Integration • Expert system tools are often LISP-based, which lacks the ability to integrate with other applications written in traditional languages. • System integration issues can contribute to higher costs and risks. • Validation • The quality of expert systems is often measured by comparing the results to those derived from human experts. • However, there are no clear specifications in validation or verification techniques.
  • 14. Why AI is Important • It tackles profoundly difficult problems. • AI research has focused on five fields of enquiry: • Reasoning: the ability to solve problems through logical deduction. • Knowledge: the ability to represent knowledge about the world. • Planning: the ability to set and achieve goals. • Communication: the ability to understand written and spoken language. • Perception: the ability to deduce things about the world from visual images, sounds and other sensory inputs. • Example applications of AI include the following; there are many more.
  • 15. Why AI is Important • Reasoning: Legal assessment; financial asset management; financial application processing; games; autonomous weapons systems. • Knowledge: Medical diagnosis; drug creation; media recommendation; purchase prediction; financial market trading; fraud prevention. • Planning: Logistics; scheduling; navigation; physical and digital network optimization; predictive maintenance; demand forecasting. • Communication: Voice control; intelligent agents, assistants and customer support; real-time translation of written and spoken languages. • Perception: Autonomous vehicles; medical diagnosis; surveillance
  • 17. Say Hello to WAYMO • WAYMOBILITY WAYMOVEMENTS WAYMOMENTS • Fully self driving technology. No steering wheel, No stress, Navigating every day traffic. • It will improve road safety and mobility to everyone.
  • 18. Conclusion • Is "Thinking" Machine Ever Possible? • Are they “dangerous”? • “The development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded.” — Stephen Hawking • What you think...? • Are you wish AI-Agents in your life...?