Artificial Intelligence and Quantum Computing.pptx

AI

1. Artificial Intelligence and Quantum Computing 25 th July 2020 From Traditional AI to Next-gen AI Systems Himanshu Vaidya Technical Director and Chief Architect Big Data & AI/ML, Globant EMEA https://himanshuvaidya.in https://www.linkedin.com/in/himanshuscisoft

2. What is Artificial Intelligence ? Jarvis ? Algorithms Enabled By Constraints Exposed By Representations That Support Mathematical Models Targeted At Thinking + Perception + Action Loops That Tie All 3 Together Sweeter Representation that will help you to build programs that are intelligent Simple Ideas Are Often The Most Powerful

3. Artificial Intelligence - Definitions Machine intelligence - intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving" https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-Definition_of_AI-1 https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-sciencedirect.com-2 https://en.wikipedia.org/wiki/Artificial_intelligence#cite_note-FOOTNOTERussellNorvig20092-3

4. AI and its associated disciplines Or More?

5. AI - Technology Landscape

6. If AI is Software - What this software includes

7. Techniques for Artificial Intelligence Reference: ISO/IEC JTC 1/SC 42

8. AI Platforms https://www.g2crowd.com/categories/artificial-intelligence

9. Conversational Intelligence Software (Chatbots) https://www.g2crowd.com/categories/artificial-intelligence

10. Deep Learning Software https://www.g2crowd.com/categories/artificial-intelligence

11. Machine Learning Software https://www.g2crowd.com/categories/artificial-intelligence

12. What Humans can Infer - An Example

13. Cognitive Science

14. Artificial Neural Networks ( As Connectionist Systems) Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules Connectionism is an approach in the fields of cognitive science, that hopes to explain mental phenomena using artificial neural networks (ANN) A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layersDeep neural networks are generally interpreted in terms of the universal approximation theorem or probabilistic inference https://en.wikipedia.org/wiki/Artificial_neural_network#cite_note-1 https://en.wikipedia.org/wiki/Artificial_neural_network#cite_note-2 https://en.wikipedia.org/wiki/Connectionism#cite_note-1

15. Artificial Neural Networks - 24 Adjustments

16. Backpropagation Algorithm

17. A mostly complete chart of Artificial Neural Networks

18. What is Machine Learning Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead ML is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task

19. What is Deep Learning Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms Learning can be supervised, semi-supervised or unsupervised Deep learning is a class of machine learning algorithms that: use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

20. AI Videos Iron Man interacting with Jarvis: https://youtu.be/Wx7RCJvoCMc Jarvis vs Ultron: https://youtu.be/LO8SpOT3Thc https://youtu.be/I4iJgghujzU Transformers - Transformium: https://youtu.be/BZxwXVdPFpo Globant’s AI Product: https://youtu.be/_JiGOYmtUhE Albertha

21. Evolutionary Algorithms E volutionary Computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm. An evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection.

22. Swarm Intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. Application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence.

23. What is Quantum Computing Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers. Quantum computers are believed to be able to solve certain computational problems, such as integer factorization (which underlies RSA encryption), substantially faster than classical computers. The study of quantum computing is a subfield of quantum information science. Quantum information science is an area of study about information science related to quantum effects in physics. It includes theoretical issues in computational models as well as more experimental topics in quantum physics including what can and cannot be done with quantum information. The term quantum information theory is also used, but it fails to encompass experimental research in the area and can be confused with a subfield of quantum information science that studies the processing of quantum information.

24. Basics of Quantum Computing Definition: Quantum computing is the study of a non-classical model of computation. Quantum Supremacy Inherently Probabilistic Measurement Process Qubit - the fundamental building block of quantum computers The Bloch sphere - representation of a qubit Quantum Superpositions Exponential Algorithmic Time Complexity reduced to Linear

25. Models of Computation ↔ Applicability

26. Quantum Complexity Theory Quantum complexity theory is the subfield of computational complexity theory that deals with complexity classes defined using quantum computers, a computational model based on quantum mechanics. It studies the hardness of computational problems in relation to these complexity classes, as well as the relationship between quantum complexity classes and classical (i.e., non-quantum) complexity classes. Two important quantum complexity classes are BQP and QMA. In computational complexity theory, bounded-error quantum polynomial time (BQP) is the class of decision problems solvable by a quantum computer in polynomial time, with an error probability of at most 1/3 for all instances. It is the quantum analogue to the complexity class BPP. In computational complexity theory, QMA, which stands for Quantum Merlin Arthur, is the quantum analog of the nonprobabilistic complexity class NP or the probabilistic complexity class MA. It is related to BQP in the same way NP is related to P, or MA is related to BPP.

27. Quantum Algorithms In quantum computing, a quantum algorithm is an algorithm which runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is usually used for those algorithms which seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement. Problems which are undecidable using classical computers remain undecidable using quantum computers. What makes quantum algorithms interesting is that they might be able to solve some problems faster than classical algorithms because the quantum superposition and quantum entanglement that quantum algorithms exploit probably can't be efficiently simulated on classical computers. In quantum computing, quantum supremacy is the goal of demonstrating that a programmable quantum device can solve a problem that no classical computer can solve in any feasible amount of time.

28. Quantum Machine Learning

29. Quantum Computing as a Service IBM - IBM Q Experience, The IBM Qiskit DWaveSystems - LEAP Riggeti - Forest Google - Quantum Playground Microsoft - LIQUiL, Q# Amazon just entering the market Cambridge Software - A company leading several innovations in Quantum Software Frameworks as well as Quantum Hardware Let's Code !

30. Quantum Optimisation is Qiskit

31. Applications and Challenges! Applications: Solving Optimization Problems Developing Highly Available Computing and Storage Systems Developing Evolutionary Systems with Exponential Speed Up Challenges: Quantum Hardware not yet large scale and stable Quantum Software needs lot of work No Software Frameworks exist for Quantum Algorithm development and Quantum Machine Learning (Of course research is progressing with exponential speed up!)

32. Thank You Artificial Intelligence and Quantum Computing 24th July 2020 From Traditional AI to Next-gen AI Systems Himanshu Vaidya Technical Director and Chief Architect Big Data & AI/ML, Globant EMEA