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Human Level Artificial Intelligence


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It gives an idea about various approaches to achieve Human Level Artificial Intelligence.

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Human Level Artificial Intelligence

  1. 1. Dhole Patil College of Engineering HUMAN LEVEL ARTIFICIAL INTELLIGENCE Presented by – Rahul Chaurasia T.E Computer Science Div - B R.No – T120604282 (Guide – Prof. Manisha Singh)
  2. 2. Contents 1. Definition of Artificial Intelligence 2. Goals of Artificial Intelligence 3. Today's Artificial Intelligence 4. Future Artificial Intelligence 5. Obstacles 6. HUMAN LEVEL MACHINE INTELLIGENCE (HLMI) 7. Assessment of Intelligence 8. Brain vs Hardware of a system 9. Ways towards Human level Artificial Intelligence
  3. 3. What is Artificial Intelligence(AI)?  John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines.
  4. 4.  Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is basically a study of how to create computers and computer software that are capable of intelligent behavior. It is "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
  5. 5. GOALS  Deduction, reasoning, problem solving  Knowledge representation  Planning  Learning  Natural language processing (communication)  Perception  Long-term goals 1) Social intelligence 2) Creativity 3) General intelligence
  6. 6. Today's AI(Narrow AI) 1. Siri( Speech Interpretation and Recognition Interface) 2. Cortana 3. Self Driving cars 4. IBMs Watson 5. Autonomous Weapons 6. Facial Reorganization 7. Deep Blue - defeated the reigning world chess champion Garry Kasparov in 1997 8. Proverb - solves crossword puzzles better than most humans
  7. 7. Future AI(Human level AI)  What are we looking for? We are looking for a machine that can outperform humans at multiple tasks and ideally at nearly every task.
  8. 8. Ways towards human level AI  Deep learning  Symbolic Reasoning  Brain Inspired Computing  Structured Gel  Quantum Weird stuff
  9. 9. Obstacles  Common Sense  Creativity
  10. 10. We will consider 1) Machine Learning  Architecture of General Reinforcement Learning  Deep reinforcement learning model  Enhancing DRL with Predictive Model 2) Language Translation 3) Googles DeepDream
  11. 11. HUMAN LEVEL MACHINE INTELLIGENCE (HLMI) KEY POINTS Informally  Human level machine intelligence = Machine with a human brain More concretely,  A machine, M, has human level machine intelligence if M has human-like capabilities to Understand Converse Learn Reason Answer questions Remember Organize Recall Summarize
  12. 12. Machine Learning
  13. 13. Architecture of general reinforcement learning
  14. 14. Explanation  Agent performs action which influences the environment.  From the environment we get state update(modifications to the state) and then we get a certain type of reward.  Its task is to learn a policy over an action that will maximize the reward over time. It's a trial and error learning.
  15. 15. Optical Action Component  A component that finds an action that will maximize the reward over time.
  16. 16. Compressive model/Predictive model  It is a predicted model. It predicts basically how the world is going to carry on
  17. 17. Deep reinforcement learning  Example. Deep minds DQN (Google bought it for 400 Million Pounds) Q) What Deep Minds DQN does? Ans) It learns to play video game. Only input the DQN has is the pixels on the screen and the reward(score).  It’s a reinforcement learning agent and it keeps on trying different actions and improves the ability to play the game better and better with time. And it does it by
  18. 18. Predictive Model
  19. 19. Deep reinforcement learning
  20. 20. Explanation  We have the input to the agent which are the real pixels on the screen.  Convolution Neural Network(CNN) – It feeds on the data to other layers of neurons that ultimately feeds the data to the function Q*.  Q* functions maps the action to their expected rewards over time. Basically its gonna learn what the best possible action can be to extract the best possible reward in certain condition.
  21. 21. Conclusion  So basically DQN helps the system to learn the game from scratch and can get to a human level ability at that game.  Lets go back to the limitations on slide number 15.  Lets check the solution to the limitations.
  22. 22. How to build the predictive model?
  23. 23. Explanation  It is basically an augmentation of basic architecture with a predictive model.  Here Q* function doesn’t give the result directly but rather also considers a predictive model which looks ahead in time and predicts a result. Now we have two results one is a basic result and other is the predicted result. The best result is chosen and action is selected.
  24. 24. Explanation  RNN – Recurrent Neural Networks.  They are good at learning sequential data.  Consider we are translating from English to French, large data (English words) will be fed to the encoder RNN. And this data will be paired with data(French words) in Decoder RNN. It also predicts what the future words are going to be based on the current or prior words that’s why the name Thought Vectors.
  25. 25. Exercising the Imagination
  26. 26. Deep Dream  DeepDream is a computer vision program created by Google which uses a convolutional neural network to find and enhance patterns in images via algorithmic approach, thus creating a dreamlike hallucinogenic appearance in the deliberately over- processed images.
  27. 27. Using the Model 1. Look ahead for threats and opportunities 2. Rehearse actions and plans 3. Search a tree of possibilities 4. Explore novel recombination's of behavioral repertoire. 5. Think and Imagine
  28. 28. ASSESSMENT OF INTELLIGENCE  Every day experience in the use of automated consumer service systems  The Turing Test (Turing 1950)  Machine IQ (MIQ) (Zadeh 1995) 7/28/08 37 /109
  29. 29. THE CONCEPT OF MIQ  IQ and MIQ are not comprovable  A machine may have superhuman intelligence in some respects and subhuman intelligence in other respects. Example: Google  MIQ of a machine is relative to MIQ of other machines in the same category, e.g., MIQ of Google should be compared with MIQ of other search engines. 7/28/08 38 /109 human machine IQ MIQ
  30. 30. Can we build hardware as complex as the brain?
  31. 31.  How complicated is our brain?  a neuron, or nerve cell, is the basic information processing unit  estimated to be on the order of 10 12 neurons in a human brain  many more synapses (10 14) connecting these neurons  cycle time: 10 -3 seconds (1 millisecond)  How complex can we make computers?  108 or more transistors per CPU  supercomputer: hundreds of CPUs, 1012 bits of RAM  cycle times: order of 10 - 9 seconds  Conclusion  YES: in the near future we can have computers with as many basic processing elements as our brain, but with  far fewer interconnections (wires or synapses) than the brain  much faster updates than the brain  but building hardware is very different from making a computer behave like a brain!
  32. 32. References    Prof. Murray Shanahan - Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neuro dynamics Group