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Artificial Brain and "Human-Machine" Integration

Profile image of Chi TranChi Tran

2017

https://doi.org/10.15226/2474-9257/2/3/00118
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7 pages

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Abstract

We present, in this short paper, a model of artificial brain based on the Software-Hardware integration in the "1 + 1 = 1" philosophy framework using machine learning and multiprocessor system on chip, SoC. Its virtual experiences are generated by a deep learning process with random changing of the structure of a net of artificial neural network, NoNN, using Monte Carlo method. It ensures creative property of the human cognitive processing and possibility of the "humanmachine" integration/"Human brain-Artificial Brain" integration, which should be applied in various areas of online control. 2 Keywords

Figures (12)
Figure 1: The "real world-virtual world" connection - the Internet of Things, IoT. HILO LELCCT TICE UF EELENL KO, UL, lids CVUIVEM LPO UI LULIVEL gence of the Internet, wireless technologies and Micro-Electro- Mechanical Systems, MEMS, which contains micro-circuitry on a tiny silicon chip into which some mechanical device such as a sen- sor has been manufactured. Wireless is a term used to describe telecommunications in which electromagnetic waves/acoustic waves carry the signal over part or the entire communication path called machine to machine, M2M, communication. Generally, the physical signals from sensor are sent to an analog signal process- ing device in the form of an amplifier or a low-pass filter. On the basis of the information received, the actuators make the neces- sary decisions about how to respond to the appropriate actions. It creates, however, a new type of data, big data, James [9], Vossen G [19] - a large number of datasets in the streaming and dynamic forms. Thus, we need a new approach that seeks to discover new information, identify and categorize data, focusing on exploring natural phenomena, acquiring new knowledge, and understand- ing real-time laws of nature. IoT is an environment where the real world and virtual world are constantly connected via the wireless sensor network, WSN, in which, internet/cloud is referred to as the virtual world. It enables us to observe and control our sur- roundings anytime, anywhere, which is represented graphically in the following figure 1.
Figure 1: The "real world-virtual world" connection - the Internet of Things, IoT. HILO LELCCT TICE UF EELENL KO, UL, lids CVUIVEM LPO UI LULIVEL gence of the Internet, wireless technologies and Micro-Electro- Mechanical Systems, MEMS, which contains micro-circuitry on a tiny silicon chip into which some mechanical device such as a sen- sor has been manufactured. Wireless is a term used to describe telecommunications in which electromagnetic waves/acoustic waves carry the signal over part or the entire communication path called machine to machine, M2M, communication. Generally, the physical signals from sensor are sent to an analog signal process- ing device in the form of an amplifier or a low-pass filter. On the basis of the information received, the actuators make the neces- sary decisions about how to respond to the appropriate actions. It creates, however, a new type of data, big data, James [9], Vossen G [19] - a large number of datasets in the streaming and dynamic forms. Thus, we need a new approach that seeks to discover new information, identify and categorize data, focusing on exploring natural phenomena, acquiring new knowledge, and understand- ing real-time laws of nature. IoT is an environment where the real world and virtual world are constantly connected via the wireless sensor network, WSN, in which, internet/cloud is referred to as the virtual world. It enables us to observe and control our sur- roundings anytime, anywhere, which is represented graphically in the following figure 1.
Figure 3: TThe "Brain-Artificial Brain" integration. be an Artificial Brain/Informatics Brain (AB/IB). Thus, we have like the Internet of Thing the "B (real world) AB (virtual world)" connection. Collected information about the states of the human brain through brainwaves is transmitted to the artificial brain us- ing brain wearable in order to detect the human thoughts, feel- ings regardless of the human behavior and to monitor controller’s experiences, detect focused thoughts of the human brain [20]. It is not the "B-AB" connection in the common sense but rather the "B-AB" integration in the "1 + 1 =1 philosophy" framework, which is so called "Human-Machine" integration. It is neither Hu- man nor Machine but a third option - high level intelligence which is presented in the following figure 3.
Figure 3: TThe "Brain-Artificial Brain" integration. be an Artificial Brain/Informatics Brain (AB/IB). Thus, we have like the Internet of Thing the "B (real world) AB (virtual world)" connection. Collected information about the states of the human brain through brainwaves is transmitted to the artificial brain us- ing brain wearable in order to detect the human thoughts, feel- ings regardless of the human behavior and to monitor controller’s experiences, detect focused thoughts of the human brain [20]. It is not the "B-AB" connection in the common sense but rather the "B-AB" integration in the "1 + 1 =1 philosophy" framework, which is so called "Human-Machine" integration. It is neither Hu- man nor Machine but a third option - high level intelligence which is presented in the following figure 3.
Figure 4: The artificial brain based on the "SW-HW" integration.
Figure 4: The artificial brain based on the "SW-HW" integration.
Where 0 < 7” <1 < 7* . Once the update-value for each weight is adapted, the weight-update it follows a simple rule:
Where 0 < 7” <1 < 7* . Once the update-value for each weight is adapted, the weight-update it follows a simple rule:
tin Riedmiller et al. [12], can be used in order to eliminate harm- ful effects of the magnitudes of the partial derivatives in order to overcome the inherent disadvantages of pure gradient-descent. Only the sign of the derivative is used to determine the direction of the weight update. Size of the weight change is determined by a separate update value. Authors introduce for each weight its individual update-value, A;;, which solely determines the size of the weight-update. This adaptive update-value evolves during the learning process based on its local sight on the error function E, according to the following learning rule:
tin Riedmiller et al. [12], can be used in order to eliminate harm- ful effects of the magnitudes of the partial derivatives in order to overcome the inherent disadvantages of pure gradient-descent. Only the sign of the derivative is used to determine the direction of the weight update. Size of the weight change is determined by a separate update value. Authors introduce for each weight its individual update-value, A;;, which solely determines the size of the weight-update. This adaptive update-value evolves during the learning process based on its local sight on the error function E, according to the following learning rule:
Figure 5: Map Reduce based on "SW-HW' integration using SoC. lowing figure 5.
Figure 5: Map Reduce based on "SW-HW' integration using SoC. lowing figure 5.
Figure 6b: To make an optimal decision in the framework of the "HW-SW" integration. Figure 6a: Deep learning of NoNN based on the "HW-SW' integration.
Figure 6b: To make an optimal decision in the framework of the "HW-SW" integration. Figure 6a: Deep learning of NoNN based on the "HW-SW' integration.
Figure 7: Machine Learning based on the platform of the System on Chip, SoC. municate with one another. ML functions in the SoC, in which, different NNs are distributed in different cores connected by net on chip, NoC, referred to as interface. In this paper, we want to make a learning program as flexible as possible with respect to the configuration of the SoC architecture, including n = 8 pro- cessors referred as 8 slaves. Determining the optimal architecture of NN through deep learning using NoNN and the integration of Monte Carlo and Finite Element Method, MCSFEM, based on the "SW-HW" integration is shown in the following figure 7.
Figure 7: Machine Learning based on the platform of the System on Chip, SoC. municate with one another. ML functions in the SoC, in which, different NNs are distributed in different cores connected by net on chip, NoC, referred to as interface. In this paper, we want to make a learning program as flexible as possible with respect to the configuration of the SoC architecture, including n = 8 pro- cessors referred as 8 slaves. Determining the optimal architecture of NN through deep learning using NoNN and the integration of Monte Carlo and Finite Element Method, MCSFEM, based on the "SW-HW" integration is shown in the following figure 7.
Figure 9: Human-Machine connectivity in the online control. Human-Machine Integration Application. A new idea of PUMA 560 robot control, through concentration of thoughts, us- ing the combination of wireless EEG devices and the artificial brain containing the virtual experiences after training, is shown in the figure 9.
Figure 9: Human-Machine connectivity in the online control. Human-Machine Integration Application. A new idea of PUMA 560 robot control, through concentration of thoughts, us- ing the combination of wireless EEG devices and the artificial brain containing the virtual experiences after training, is shown in the figure 9.
Figure 8: The conversation between the bridge and car - Online control. The "Bridge-Car" Talking Application. For example, the struc- tures equipped with sensors to its monitor in order to communi- cate the information via the wireless internet to cars. This connec- tion from sensors connected directly to internet/cloud, and then to actuator of the car through the wireless internet, turns critical information to make decision.
Figure 8: The conversation between the bridge and car - Online control. The "Bridge-Car" Talking Application. For example, the struc- tures equipped with sensors to its monitor in order to communi- cate the information via the wireless internet to cars. This connec- tion from sensors connected directly to internet/cloud, and then to actuator of the car through the wireless internet, turns critical information to make decision.

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