ASSESSMENT ITEM 2 LITERATURE REVIEW Artificial Brain (Intelligence) Name of the Student : GIRISH CHEERLA Student ID : 11534510 Subject Code : ITC571 Subject Name : EMERGING TECHNOLOGIES AND INNOVATION Abstract: We have always been interested in the concept of consciousness, in fact, that is, for us, the fact that a person with a brain cannot think of anything related to his position in the world, here and now. This is not the continuity, or performance, nor depth of thought, but it's something to think about a way knowable and can be set from a corner or mathematical linguistics, and is no self-responses and breaches of a given situation. By analogy with the concept studied extensively by philosophers, psychologists, neurobiologists, we ask the question of artificial consciousness, how can we incorporate the fact "think of something" in the calculable field so that an artificial system based computer in the process would be able to generate facts of consciousness, in a visible way? The system will have intentions, feelings and ideas about things and events related to it like us. The system must have a body that is able to direct and limit the system. It would also be a story, and intentions to act and, above all, to think. We must be aware, in particular, knowledge of the language. Must have feelings, intentions, and, finally, a certain self-awareness. We can call this system by pure semantic analogy, an artificial brain. But we will see that the architecture is very different from living brains. The concern is the transpose movement effects; certainly don’t reproduce the components of neurons and glial cells. We should note especially characteristic of the thinking process takes place in a brain: it is a complex neuronal, biochemical, electric drive movement occurs. This movement is coupled to a corresponding, but otherwise the nervous system placed in the body. This movement generates complex, selective growth and the achievement of a given configuration, which we call a thought about something. This quickly leads to tank actuators or voice activity and decreases successively in the next tank which may be identical or different. This very complex phenomenon that must be implemented in the computable domain. Introduction: A system capable of generating sufficient facts concerning the consciousness will be very difficult to replicate. But if we try to classify the different components or layers that control the brain to reach decisions, we are able to reach the following components. This system may have five components: 1. A memory organization is a great memory of facts, knowledge, rules and events where everything comes from this memory is systematically adapted to the current context. This knowledge base cannot remember, but is a continuous dynamic interpretation of knowledge. 2. A building subsystem in a strictly constructive idea today that is artificial here and now: the current construction of the idea that the activity of the agent of any large organization is well controlled. 3. A subsystem to generate emotions that modify the activity subsystem expressing the current idea is that each particular field to change the focus of this generation, depending on the specificity of emotions. 4. A subsystem input-output system of the connection made to produce artificial consciousness with the body of a robot or a data flow software. 5. A subsystem interface to express the mental map that is representative of the current artificial consciousness that causes of this generation. Korean Brain Neuroinformatics Research Program has entered the third phase from July 2004 for four years, which is considered the final stage of the national research program, which began in November 1998 and took 10 years. This is a joint research effort of various disciplines, including electrical and computer engineering, cognitive science, neuroscience, and today about 35 doctors and 70 students participate in the program. Korean Neuroinformatics Research Program brain has two purposes, namely to understand information processing in biological mechanisms in the brain and the development of intelligent machines with human-like features based on the mechanism. In the third phase, we have developed an integrated hardware and software for brain-as-intelligent systems, which combines all the technologies developed for the functioning of the brain in the second phase platform. With two microphones, two cameras (or chips of the retina), and a speaker, seems artificial brain as a human head, and has the characteristics of vision, hearing, cognition and behaviour. Even with this platform, the development of a test application suggested namely alias office "artificial secretary", which will reduce the working hours of the average human Secretary. Each office should have its own unique personality, which can be influenced by their previous statements and the values of input and output current agent. Personality as internal states, recurrent neural networks implemented are modelled. History of Artificial Intelligence:  The beginning in 1943 -McCulloch and Pitts: Boolean circuit model of brain.  In 1950 -Turing’s "Computing Machinery and Intelligence"  In 1956 the birth of the AI -Dartmouth meeting: "Artificial Intelligence" name adopted.  The first promise in 1950, the first AI programs includes Samuel's checkers program; Newell and Simon Logic Theorist.  In 1955-1965, great enthusiasm -Newell and Simon, GPS, a general problem solver; Gelertner: Geometry Theorem Prover; McCarthy: the invention of LISP.  1966-1973: Reality dawns -Understand that many AI problems are difficult; Limitations of existing methods for identified neural networks; The neural network research almost disappears  1969-1985: The sum of the knowledge domain -Development of knowledge-based systems; The success of the rule-based expert systems; For example DENDRAL, Mycin. But they were crazy and do not scale well in practice  1986- Rebellion machine learning; Neural networks back to the popularity; Major advances in machine learning algorithms and applications  1990- Uncertainty paper; Bayesian networks as a framework for knowledge representation  1995- Artificial Intelligence as Science; Integration of learning, reasoning, knowledge representation; AI methods used in vision, language, data mining, etc. Brief Review: Today is the generation where emotions are a well-studied problem in neurobiology. It's not just for psychological knowledge, where we observe the effects on behaviour, but we do not have knowledge of the inner workings of the brain in relation to the unit of the nervous system to emotional states. Can we trust the results available in neurobiology and transpose them to build such a system, the same principles of the architecture and features, but using the multi-agent paradigm with the notion of the self-renewal. In biology, a sense an important function of the central nervous system that activates the typical states. It is a mental state of physiological neuronal activity and behaviour produced by a physical cause inductive behaviour. A system capable of generating a sufficient number of facts related to consciousness will be very difficult to replicate. But if we try to classify the different components or levels controlled by the brain in reaching decisions, we are able to achieve the following substances. This system may have following components: 1. An organizational memory is a great memory of facts, knowledge, rules and events in which some of this memory is systematically adapted to the current context. This report is not based on knowledge, but is a dynamic interpretation of knowledge that is continued. 2. A subsystem that generates feelings as amended subsystem activity expresses the current idea, which is a specific change in the middle of this generation, depending on the specific field of emotion. 3. A subsystem for system link input-output data produce artificial consciousness with the body of a robot or the entire data flow software. 4. Interface expresses the mental map is the representation of the reality of artificial consciousness to know the causes of this generation subsystem. Schematic representation of the system: The system controls the behaviour of an autonomous robot. General architecture of the system: The system uses an electronic representation of the state of the sensor data of the robot (its environment, its internal state based on its current organizational memory and feelings). From this representation, the behaviour of the robot control system via actuators as shown in above fig. The following fig shows the data transfer and its paths. In the picture above I described circuit as:  System input (sensor data: video, audio etc)  Strengthen the mind map, business co-agent  Analysis, semantic interpretation of the mental map  Decisions, orders sent to the robot. The architecture of a system for the generation of emotion is radically different from an input - output levels which is a step by step calculation according to some predefined steps. We must define the specific internal control devices of system as emotional, which is proactive devices (devices running for themselves), generates internal cycles of activity with specific rhythms, according to the actual process in the brain and correspond to the different types of actions or resource efficient. This architecture is mainly based on the aggregation and breakage of groups of software agents rather than formal systems of neurons. The Artificial Brain in real life: Blue Brain Project: The Blue Brain Project is an attempt to reverse the brain, and explore how it works and as a tool for neuroscientists and medical researchers. This is not an attempt to create a brain and is not an artificial intelligence project. One day we will learn about the fundamental nature of intelligence and awareness of using this tool, the Blue Brain project focuses on creating a physiological simulation for biomedical applications. An artificial neural network is developing right now in a Swiss supercomputer. This bizarre creation is able to simulate a natural brain, cell-to-cell. Swiss researchers, who have created what they call "Blue Brain", they believe that soon will it offer a better understanding of human consciousness. This is not a science fiction movie; there is a "real computer brain that can have the ability to think on their own”. The designers say that "Blue Brain" was intentional and unpredictable from day one. When the first electrical pulse fed, strange patterns began to emerge with lightning and produced by "cells" that scientists recognized in human processes and live animals. The neurons have begun to interact with each other before they shot in rhythm. "It 'happened all by itself," says the biologist Henry Markram, director of the project. The project essentially has its own factory for the production of artificial brains. Their computers can clone the nerve cells in a hurry. The system allows the production of neurons in all different types. In a natural brain there is no two identical cells, the researchers say the artificial cells used for the project is also random and unique. In November 2007, the Blue Brain project has officially announced the end of Phase I of the project, with three specific achievements: 1. A new modelling framework for the automatic construction, the application of neural circuits built from biological data. 2. A new simulation and calibration process automatically and systematically analyses the biological accuracy and consistency of each model revision. 3. The first model of the neocortical column cellular level built entirely of biological data can now be used as a key tool for research based on the simulation. Phase I marks the completion of a principle of simulation test of the research in which the process was based on a model at the cellular level in the column of the neocortex. They have conducted biological model of fidelity same time that serves as the primary tool to assess the relationship and relevance of the neurobiological data, providing a guide for further experimental efforts. These new data will help to refine the model of the neocortical column. The process makes it possible for neuroscientists to come together to examine scientific issues, integrating the experimental data and the evaluation of the hypothesis of network dynamics and nerve functions. In the future, information from the molecular and genetic levels will be added to the algorithms that generate the neurons and their connections, so this level of detail will be reflected in the construction of the circuit. Simulations can be used to explore what happens when this information is modified as molecular or genetic - situations such as genetic variation in specific neurotransmitters, or what happens when the environment is modified by molecular drugs. REFERENCES: 1. The Neuromorphic Engineer: Brain-inspired auditory processor and the Artificial Brain project. (March 2007). http://ine-web.org/fileadmin/templates/_docs/NME3-2_01.pdf. 2. Self-study. Blue brain Project (2010). Retrieved March 2010, from http://en.wikipedia.org/wiki/Blue_Brain_Project 3. The Blue Brain Project EPFL. Retrieved September 16, 2014, from http://bluebrain.epfl.ch/ 4. Brandon Bailey (2014, May 10). Tech giants pour resources into artificial intelligence. The Sydney Morning Herald. Retrieved from http://www.smh.com.au/digital-life/digital-life-news/tech-giants-pour-resources-into-artificial-intelligence-20140510-zr8ya.html. 5. Paul S. Rosenbloom. Towards a Conceptual Framework for the Digital Humanities. Retrieved from http://www.digitalhumanities.org/dhq/vol/6/2/000127/000127.html