Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1999-07-02
2002-03-12
Chaki, Kakali (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S015000, C706S028000, C706S030000
Reexamination Certificate
active
06356884
ABSTRACT:
The present invention relates to a system and process for simulating the internal imagery and additional mechanisms which together emulate creativity in the human mind. The system allows for the totally autonomous generation of new concepts, designs, music, processes, discovery, and problem solving using recent developments in the area of artificial neural network (ANN) technology. Several examples of the type of useful information that can be obtained using the present technology are set forth and described herein. The present system can be used to tailor machine responses thereby making computers less rigid in communicating with and interpreting the way a human responds to various stimuli. In a more generalized sense, the subject system supplies the equivalence of free-will and a continuous stream of consciousness through which the system may formulate novel concepts or plans of action or other useful information.
Prior to this invention, artificial neural network (ANN) emulations of biological systems were used for non-creative tasks such as pattern recognition, neural control, and the generalization of experimental data. The present system represents a new approach and a new application of ANN's in which the system synthesizes novel plans of action and original designs or creations. These systems, which we refer to as autonomous systems or “creativity machines” may perform imaginative feats that extend beyond technological invention into the realms of aesthetics and emotions.
The present preferred embodiment of the system employs two essential components, namely, (1) a neural network containing training in some problem domain, which neural network is subjected to perturbations and, as a result of the perturbations, continuously outputs a stream of concepts, and (2) a monitoring portion, such as, in one particular preferred form, a second or patrolling neural network, which portion constantly monitors the outputs of the first network for various reasons, such as to identify and isolate useful outputs. This tandem arrangement may be thought of as constituting a model of creativity, and perhaps attentional consciousness, and this internal imagery is spontaneously generated within the perturbed network, while the monitoring portion is constantly alert to the occurrence of certain outputs, such as specific images possessing either utility or other useful characteristics including aesthetic appeal. The perturbations used may be achieved by any number of different means including by the introduction of noise, relaxation or degradation of the network and so forth. The two components discussed above will be described in more detail hereinafter.
It is important to emphasize that the present systems need not necessarily accept external information. Instead, the system may be allowed to operate such that information emerges spontaneously as a result of any number of stochastic and/or systematic processes applied to the characterizing parameters of the networks involved. With this tandem arrangement of the free-running neural network and the associated monitoring or policing portion, it is possible to generate a notion that is superior in quality to anything generated by a known system, device or machine similarly exposed or perturbed.
DISCUSSION OF THE PRIOR ART
The inventor has demonstrated that the application of certain types of noise to the inputs or weights of an ANN may produce novel outputs if the vector completion process fails to activate an output vector encountered during the network's training. Such outputs generally take the form of a combination of known training outputs and generally emulate the environment in which it was trained. Therefore, a neural network trained to generate the surface profiles of some device or object such as a known mountain range would tend to produce very plausible but unfamiliar mountain ranges if the inputs are subjected to random stimulations. Similarly, a neural network trained to only produce classical music would tend to produce potential classical themes when exposed to random inputs. The inventor has shown that static networks have produced some very novel outputs which have been detected within mathematical studies. In all known cases, however, they have been isolated by a human operator for their novelty. In contrast, the present system autonomously monitors the output of such a network and can operate to identify correspondences with or differences from predetermined criteria associated with the monitoring portion for various purposes, such as, in a preferred embodiment, to select emergent concepts, which may include courses of action, and which are representative of activation patterns of neurons, on the basis of some predetermined criteria established within a policing or patrolling neural network which, in such embodiment, is the monitoring portion of the system. Such concepts may include producing music or musical themes for some purpose, or for designing some device such as a coffee mug, or producing a process planning operation, or solving a problem, such as to seek a target figure of merit in a target seeking application of the system, and for many other applications, some of which will be described more in detail hereinafter.
Known ANNs have obtained a relatively high degree of precision in some areas such as in input-output mapping. The present invention teaches the use of deliberate degradation of an ANN and therefore a corruption of such precise mapping to produce useful information. Thus a network trained to duplicate some knowledge domain may generate fairly representative examples of known devices at low levels of network degradation. For example, in the case of automobile design the known networks may generate fairly representative examples of existing cars at low levels of network degradation owing to the constraints existing within the network. In other words sensible designs are produced. At progressively higher levels of network degradation, such network constraints further relax to produce novel and more unusual hybrid automobile designs, some of which may fill a useful application niche or market. The key to making the transition from the ordinary to the novel is achieved by the control over the network degradation and the ability to relax or perturb certain network parameters from their trained-in values. Thus the present system provides a way to design around the ordinary or the near ordinary and to create new designs in much the same manner as a creative designer would do, unlimited by certain constraints. As a result of the introduction of various forms of perturbations to the inputs, internal activations, weights and biases, such known systems may control a process or create an object or design. The information thus produced with the present system may be stored for later use to control a process or the like and/or used in its own autonomous decisions to modify the output or outputs that have been produced in some desired fashion. Thus the present system provides another tool, and a very broad based tool, for doing design or creative work, including as part of target seeking applications, through utilization of the two elements discussed above. It is contemplated, however, to fine-tune or toggle the subject system to autonomously change its mode of operation from performing one task to performing a different task or different purpose.
Being able to internally modify the network in a myriad of ways allows for vast numerical superiority in the number of viable concepts that may be produced. The present tandem arrangement of system elements allows for complete autonomy in this task.
OBJECTS OF THE INVENTION
It is a principal object of the invention to teach the construction and operation of novel means for simulating creativity.
Another object is to perturb artificial neural networks, previously trained, in order to produce useful and imaginative output information.
Another object is to monitor output information from a perturbed neural network in order to select desired outputs and reject others.
An
Chaki Kakali
Haverstock, Garrett & Roberts LLP
Starks Wilbert
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