HUMAN LEVEL ARTIFICIAL INTELLIGENCE

Dhole Patil College of Engineering

HUMAN LEVEL ARTIFICIAL INTELLIGENCE

Presented by Rahul ChaurasiaT.E Computer Science Div - BR.No T120604282(Guide Prof. Manisha Singh)

ContentsDefinition of Artificial IntelligenceGoals of Artificial IntelligenceToday's Artificial IntelligenceFuture Artificial IntelligenceObstaclesHUMAN LEVEL MACHINE INTELLIGENCE (HLMI)Assessment of IntelligenceBrain vs Hardware of a systemWays towards Human level Artificial Intelligence

What is Artificial Intelligence(AI)?John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines.

Artificial intelligence(AI) is theintelligenceexhibited by machines or software. It is basically a study of how to create computers and computersoftwarethat are capable of intelligent behavior. It is "the study and design of intelligent agents", in which an intelligent agentis a system that perceives its environment and takes actions that maximize its chances of success.

GOALSDeduction, reasoning, problem solvingKnowledge representationPlanningLearningNatural language processing (communication)PerceptionLong-term goalsSocial intelligenceCreativityGeneral intelligence

Today's AI(Narrow AI)Siri(SpeechInterpretation andRecognitionInterface) CortanaSelf Driving carsIBMs WatsonAutonomous WeaponsFacial ReorganizationDeep Blue - defeated the reigning world chess champion Garry Kasparov in 1997Proverb - solves crossword puzzles better than most humans

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.

Ways towards human level AIDeep learningSymbolic ReasoningBrain Inspired ComputingStructured GelQuantum Weird stuff

ObstaclesCommon SenseCreativity

We will consider1) Machine LearningArchitecture of General Reinforcement LearningDeep reinforcement learning modelEnhancing DRL with Predictive Model2) Language Translation3) Googles DeepDream

HUMAN LEVEL MACHINE INTELLIGENCE (HLMI)

KEY POINTSInformallyHuman level machine intelligence = Machine with a human brainMore concretely, A machine, M, has human level machine intelligence if M has human-like capabilities toUnderstandConverseLearn ReasonAnswer questionsRememberOrganize Recall Summarize

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Machine Learning

Architecture of general reinforcement learning

ExplanationAgent 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.

Optical Action ComponentA component that finds an action that will maximize the reward over time.

Compressive model/Predictive modelIt is a predicted model. It predicts basically how the world is going to carry on

Deep reinforcement learningExample. 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). Its 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

Predictive Model

Deep reinforcement learning

ExplanationWe 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.

ConclusionSo 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.

How to build the predictive model?

ExplanationIt is basically an augmentation of basic architecture with a predictive model.Here Q* function doesnt 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.

ExplanationRNN 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 thats why the name Thought Vectors.

Exercising the Imagination

Deep DreamDeepDreamis acomputer visionprogram created byGooglewhich uses aconvolutional neural networkto find and enhance patterns in images viaalgorithmicapproach, thus creating a dreamlike hallucinogenic appearance in the deliberately over-processed images.

Using the ModelLook ahead for threats and opportunitiesRehearse actions and plansSearch a tree of possibilitiesExplore novel recombination's of behavioral repertoire.Think and Imagine

ASSESSMENT OF INTELLIGENCEEvery day experience in the use of automated consumer service systems

The Turing Test (Turing 1950)

Machine IQ (MIQ) (Zadeh 1995)7/28/0837 /109

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THE CONCEPT OF MIQIQ and MIQ are not comprovableA machine may have superhuman intelligence in some respects and subhuman intelligence in other respects. Example: GoogleMIQ 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. 7/28/0838 /109humanmachineIQMIQ

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Can we build hardware as complex as the brain?

How complicated is our brain?a neuron, or nerve cell, is the basic information processing unitestimated to be on the order of 10 12 neurons in a human brainmany more synapses (10 14) connecting these neuronscycle 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

ConclusionYES: in the near future we can have computers with as many basic processing elements as our brain, but withfar fewer interconnections (wires or synapses) than the brainmuch faster updates than the brainbut building hardware is very different from making a computer behave like a brain!

Referenceswww.wikipedia.orgwww.youtube.comProf. Murray Shanahan - Professor of Cognitive Robotics in the Dept. of Computing at Imperial College London, where he heads the Neuro dynamics Group