ARTIFICIAL BRAIN

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artifical brain

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Slide 1: 

AN A THINKING, REMEMBERING, DECISION-MAKING, BIOLOGICALLY ACCURATE BRAIN BE BUILT FROM A SUPERCOMPUTER? C.SIVANAGIREDDY P.SANTOSH Computer science, 2-2 Computer science,2-2 ACE Engineering College ACE Engineering College siva09.c@gmail.com santosh_parsa@yahoo.co.in voice no :8125104883 voice no:9160396545

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ARTIFICIAL BRAIN

Artificial brain is the research to develop  software  and hardware that has cognitive abilities similar to the animal or  human brain What is artificial brain?

idea plays three important roles in science : 

idea plays three important roles in science An ongoing attempt by the nueroscientist to understand how the human brain works. A thought experiment in the philosphy of artificial intelligence , demonstrating that it is possible, in theory, to create a machine that has all the capabilities of a human being. A serious long term project to create strong AI (a machine as intelligent as a human being),.

Different approaches used for artificial brain : 

Different approaches used for artificial brain artificial neurons on a parallel platform, such as e.g. the CAM Brain Machine Holographic Neural Technology (HNeT) non linear phase coherence/decoherence principles

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In November 2008, IBM received a $4.9 million grant from the Pentagon for research into creating intelligent computers. A network of artificial nerves is growing in a Swiss supercomputer -- meant to simulate a natural brain, cell-for-cell. The researchers at work on "Blue Brain" promise new insights into the sources of human consciousness. RECENT DEVELOPMENT

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to create a computer simulation of these fundamental neurological units, called neocortical columns. The process will involve building a Blue Gene supercomputer with 8,000 processors that can roar along at 23 trillion operations per second. Each processor will be used to simulate one or two neurons. If finished immediately, the machine would be one of the five fastest supercomputers in the world. Blue brain project

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BASIC IDEA FOR PROCESSES IN ARTIFICIAL BRAIN Implementation of artificial brain system and its mechanisms - Auditory part- Vision part- Agent (Service) part

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Each group generates functional modules:- developed independently- integrated using the de-centralized system service (DSS) on the Microsoft .Net framework. - The common language runtime (CLR) property in .Net framework enables each module can be developed in any languages like C#, C++, Java, etc.

Main Functionalities of Human Brain : 

Main Functionalities of Human Brain

Artificial Brain System – Overall Configuration : 

Artificial Brain System – Overall Configuration Auditory Module Vision Module Service Module (Agent) Knowledge-Base Stereo- Camera Stereo- Microphone Speaker Face Recognition Expression Recognition Object Recognition Speech Separation Sound Localization Speech Recognition Speaker Recognition Attention Area Response Sentence Generation Context Analysis Dialog Manager Robot Head Movement TCP/IP Text-to- Speech Robot Control

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Auditory part

Auditory Part – Module Diagram : 

Auditory Part – Module Diagram Active Noise Canceller Auditory Filterbank Masking Sound Localization Voice Activity Detection BSS (ICA) Noises Speech Speech Recognition Speaker Recognition Keyword Recognition Stereo- Microphone

Binaural pathways and sound localization : 

Binaural pathways and sound localization The superior olivary complex (SOC) receives bilateral ascending input from the auditory ventral cochlea nucleus (AVCN) and descending input from the ipsilateral inferior colliculus (IC). The medial superior olive (MSO) cells are sensitive to interaural time difference (ITD) and the lateral superior olive (LSO) cells are sensitive to interaural intensity difference (IID).

Auditory Part – Mechanisms : 

Auditory Part – Mechanisms Cocktail party problem- Human speech perception is robust in the presence of diffusive noise and interfering sounds. - But, machine speech recognition remains problematic in such conditions. Auditory masking? - When a sound is masked, it is eliminated from perception as if the sound never reached the ear. - Sound source can be segregated by identifying the segments of the sources in the time-freq. domain.

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VISION MODULE

Vision Part – Module Diagram : 

Vision Part – Module Diagram Flow diagram of visual perception

Vision Part – Mechanisms : 

Vision Part – Mechanisms Biological visual pathway of bottom-up and top-down processing

PROBLEMS FACEING IN CURRENT VISION SYSTM : 

PROBLEMS FACEING IN CURRENT VISION SYSTM The segmentation problem?- finding different objects in the image.. - But what is the image of a “single object”?- Is a nose an object? Is a head one? Finding salient regions in an image! - Human brain draws attention to the salient object in the image. - The saliency of an image may be determined by the combination of local and global aspects.

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SERVICE PART MODULE

Service Part - Scenarios : 

Service Part - Scenarios Service domains of the OffceMate:- schedule management- patent search - new knowledge acquisition from the internet - object perception in an office A Demo for the schedule management

Keyword Spotting Model with Attention : 

Keyword Spotting Model with Attention FB Confidence Measure OOV Rejection Compare Likelihood & Decision Making Confidence Measure VAD OOV Rejection signal Attention Filter Attention Filter Activation Attention Keyword?

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In memory, our experiences are represented in structure that cluster together with related information.- Little is known about the neural underpinnings of contextual analysis and scene perception.

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BUILDING ARTIFICIALBRAIN USING CAM BRAIN Artificial Brain Building “CAM Brain Project” Aims to build an artificial brain with 32000 evolved net modules, 40 million neurons

Human brain : 

Human brain A network of 1014 neurons Data transfer by electric signals Dendrite cells (neurons Input) Collect signals and pass them to the neuron Neurons “Decide” when to initiate a signal Axon cells (neurons Output) Propagate neuron signals

A process imitating natural evolution : 

A process imitating natural evolution Genetic algorithm Random population Fitness function The fittest New Generation REPRODUCTION

The CAM-Brain Machine (CBM) : 

The CAM-Brain Machine (CBM) A research tool of an artificial brain Consists of 32,768 neural modules Neural modules evolve in hardware using Genetic Algorithms

Human brain vs cam brain : 

Human brain vs cam brain 1014 Neurons 4*107Neurons Speed: 100+ Approx. speed M./sec. Of light Natural Evolution “Designable” Evolution Parallel Computing

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- We have achieved a great development of computer technologies, but the ability of machines is limited to simple tasks which require human beings have to order what to do. - We lack the specific and concrete algorithms to solve practical problems in the real world. - A human brain is the best model in solving practical problems in the real world, and we came up with neural networks based on the human neural information processing NEED FOR ARTIFICIAL BRAIN

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REFERENCE 1)Building an Artificial Brain Using an FPGA Based CAM-Brain Machine", Applied Mathematics and Computation Journal, Special Issue on "Artificial Life and Robotics, Artificial Brain, Brain Computing and Brainware", North Holland. (Invited by Editor, to appear 1999), Hugo de Garis, Michael Korkin, Felix Gers, Eiji Nawa, Michael Hough. 2)A Brief Introduction to Genetic Algorithms, by Moshe Sipper, http://lslsun.epfl.ch/~moshes/ga_main.html 3)Non-uniform cellular automata, by Moshe Sipper, http://lslsun.epfl.ch/~moshes/ga_main.html 4)Holographic Neural Technology (HNeT) 5)http://www.artificialbrains.com 6)Kurzweil AI.net 7)seed magazine 8) VINT reseach Institute of Sogeti, 2009

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