Patent Publication Number: US-10325223-B1

Title: Recurrent machine learning system for lifelong learning

Description:
TECHNICAL FIELD 
     This disclosure relates to the field of artificial intelligence and machine learning, and in particular to a machine learning system that has memory and is capable of lifelong learning. 
     BACKGROUND 
     Contemporary machine learning paradigms, such as most deep learning architectures and their governing supervised learning algorithms, consist of a single system that includes a feature representation component with a classifier or regression layers that map features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters, pooling and non-linearities in its lower layers, on top of which a multi-layer perceptron is commonly appended, mapping the top layer features extracted by the convolutional layers to decisions (e.g. classification outputs). Training is achieved in a supervised learning manner, which involves feeding a training dataset consisting of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the outputs and the label values), and using techniques such as deep gradient descent and backpropagation to tune the weights of the network across all its layers and nodes such that the error is minimized. In many applications, repeating this process across the many labeled inputs in the training dataset yields a network that can produce correct output when presented with inputs that are different than the ones present in the training dataset. In high-dimensional settings, such as large images, this generalization is achieved only if a sufficiently large and diverse training dataset is made available. In the absence of sufficient number of labeled examples in the training dataset, or lack of diverse examples, the network accuracy often degrades below a desired level of performance. 
     There are several key limitations to supervised learning, particularly when applied to large parametric models (such as contemporary deep learning architectures). The first is commonly referred to as catastrophic forgetting—a problem that affects artificial neural networks as well as many other supervised learning systems. When trained with novel patterns, neural networks tend to quickly unlearn (or forget) prior representations and their mapping to outputs. Traditionally, neural network training is structured in a way that helps mitigate this effect. When learning in stationary settings, training data is generally shuffled and presented in a random order. The data presentation is stationary in that samples are drawn in an independent and identically distributed manner. If the data is presented in a manner that is non-stationary, the network may not be able to capture and retain representations that are presented at different time intervals. Traditionally, dynamic environments (environments in which labels change and/or new labels are added) have been recognized as tasks that are challenging for neural networks. If the task or environment changes (e.g., new labels are added), a neural network tends to catastrophically forget the previously learned task or environment setting (how to classify inputs using the original labels), as it learns how to classify inputs pertaining to the new labels. In other words, the network weights (parameters) of a machine learning system tune to accommodate the recent statistics of the input data, thereby “forgetting” prior representations. 
     Catastrophic forgetting renders lifelong learning unachievable for conventional machine learning architectures. Accordingly, to date machine learning systems have been unable to progressively learn new representations while not unlearning prior ones in a principled scalable manner. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the present invention, which, however, should not be taken to limit the present invention to the specific embodiments, but are for explanation and understanding only. 
         FIG. 1A  is a block diagram illustrating an architecture for a machine learning system, in accordance with embodiments of the present disclosure. 
         FIG. 1B  is a block diagram illustrating an architecture for a machine learning system, in accordance with embodiments of the present disclosure. 
         FIG. 2  is a block diagram illustrating a node update paradigm for a machine learning architecture, in accordance with embodiments of the present disclosure. 
         FIGS. 3A-B  are graphs illustrating a centroid update process for multiple nodes, in accordance with embodiments of the present disclosure. 
         FIGS. 3C-D  are graphs illustrating a process to add new centroids to a node, in accordance with embodiments of the present disclosure. 
         FIG. 4A  is a graph illustrating an input signal received by a node of a machine learning architecture, in accordance with embodiments of the present disclosure. 
         FIG. 4B  is a graph illustrating error associated with an output signal of a second node of the machine learning architecture after the input signal of  FIG. 4A  has been processed, in accordance with embodiments of the present disclosure. 
         FIG. 5  is a graph illustrating centroid allocation for a node of a machine learning architecture, in accordance with embodiments of the present disclosure. 
         FIGS. 6A-E  are flow diagrams illustrating a method for performing lifelong learning in a machine learning system, in accordance with embodiments of the present disclosure. 
         FIG. 7  is a block diagram illustrating an exemplary computer system, in accordance with embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure relate to a new machine learning architecture capable of achieving one or more of lifelong learning, sample-efficient learning, semi-supervised learning and localized learning for spatial information, temporal information and spatial-temporal information. 
     Lifelong learning refers to the ability of a machine learning system to continue to learn new input-output mappings, and construct new internal representations, without forgetting previously learned representations and their corresponding outputs. One type of traditional machine learning architecture is a recurrent neural network (RNN). An RNN is a type of neural network that includes a memory to enable the neural network to capture temporal dependencies. An RNN is able to learn input-output mappings that depend on both a current input and past inputs.]RNNs] may be trained using a training dataset to generate a fixed number of outputs (e.g., to classify time varying data such as video data as belonging to a fixed number of classes). However, in order to train the RNN to generate a new output (e.g., to classify a new type of activity), the training process generally needs to re-introduce samples from the previously learned classes using the entire original training dataset plus new data items associated with the new output. Failure to restart the training process for traditional RNNs generally results in catastrophic forgetting, in which the accuracy associated with previously trained outputs drops precipitously. Embodiments describe a machine learning system with a memory component that is capable of achieving lifelong learning (learning progressively) for data sets exhibiting temporal dependency, thereby mitigating the effects of catastrophic forgetting. As a result, machine learning systems described herein are capable of learning to produce new temporal-dependent outputs with high accuracy without forgetting how to produce previously learned outputs and without degrading the accuracy associated with such previously learned outputs. 
     In an example of catastrophic forgetting associated with traditional machine learning architectures, an RNN may be trained to classify a sequence of images as containing a walking person or a running person. After training the RNN using a training dataset that contains sequences of labeled images of people running and walking, the RNN is able to classify new sequences of images as containing a walking person or a running person with high accuracy. If a new labeled dataset of a skipping person and a dancing person is introduced and used to further train the RNN, that RNN will learn to classify skipping people and dancing people, but will forget how to classify running and walking people, thus resulting in catastrophic forgetting. In particular, the weights or properties of each node in the RNN are adjusted during the initial training to enable the RNN to identify running and walking. However, those weights are all changed during the further training to teach the neural network to identify skipping and dancing. The changed weights essentially erase the earlier training for identifying running and walking. Embodiments described herein mitigate the problem of catastrophic forgetting by using techniques such as localized learning, the ability to add new representational resources (e.g., centroids) within nodes to capture new patterns in input data items, the use of a two part machine learning system that includes a perception subsystem and an application subsystem, and the use of both novelty criteria and relevancy criteria for determining when to update centroids in nodes. 
     Localized learning refers to the ability of one or more nodes in a machine learning system to independently determine whether they should be updated and in what manner. In traditional supervised machine learning architectures, training is achieved by computing an output by the machine learning system for an input data item and then comparing the output to a known target associated with the input data item. The difference between the known target and the determined output is then used to determine an error, and the error is used to obtain a global loss function, which in turn is used to establish the gradient of said loss function with respect to the parameters in the network in order to update all of the nodes in the machine learning system. The process of propagating said global loss function gradient with respect to the network parameters, across multiple layers of the network is commonly referred to as backpropagation. Accordingly, traditional machine learning models (e.g., neural networks such as RNNs) apply globalized learning when utilizing gradient descent to determine how the network weights are to be updated. In contrast, embodiments use one or more of sparse vectors and distinct learning rules associated with each node to achieve localized learning. In embodiments an error or other relevancy rating, which may be a scalar or a vector, is broadcast to multiple nodes, and each node makes its own determination as to whether to update. Localized learning facilitates lifelong learning and sample-efficient learning in embodiments. 
     The globalized learning techniques used by existing machine learning architectures such as neural networks (e.g., back propagation and deep gradient descent) cause training of the machine learning architectures to be a slow process. With each new input in a training dataset, only very small updates are made to the weights of the machine learning architecture. As the parametric models that are used in neural networks become large, the update process becomes even slower. In contrast, embodiments of the present invention apply a machine learning architecture that can be trained notably faster, and require far fewer input presentations, than a conventional neural network. 
     Traditional machine learning architectures commonly learn through a process called supervised learning. In supervised learning, every data item in the training dataset has an associated target value. The target value for each data item is then used to compute an error, and the error is used to train the machine learning model. Semi-supervised learning refers to a learning process for machine learning models in which only a portion (e.g., a small portion such as 5-10%) of the data items used to train the machine learning model include targets. Embodiments of the present invention achieve semi-supervised learning by using techniques such as localized learning of nodes, the ability to add new resources (e.g., centroids) within nodes to identify new patterns in input data items, the use of a two part machine learning system that includes a perception subsystem and an application subsystem, and the use of both novelty criteria and relevancy criteria for determining when to update weights of centroids in nodes. 
     In one embodiment, a processing device executing a first node of a machine learning system receives a data item at the first node. The first node comprises a plurality of representational resources, such as a plurality of centroids. The first node additionally receives a first feature vector that was generated at a first time, the first feature vector comprising a first plurality of feature element values. In one embodiment, the first feature vector comprises a plurality of feature elements, where each of the feature elements is associated with one of the plurality of centroids in the first node and comprises one of the plurality of feature element values. Each feature element value may be a similarity value that represents a similarity of a combined input comprising at least a portion of a previous data item and at least a portion of a previous feature vector to one of the plurality of centroids. 
     The first node processes information from the combined input comprising at least a portion of the data item and at least a portion of the first feature vector at a second time to generate a second feature vector. The second feature vector comprises a second plurality of feature element values. In one embodiment, the second feature vector comprises a plurality of feature elements, where each of the feature elements is associated with one of the plurality of centroids in the first node and comprises one of the second plurality of feature element values. Each feature element value may be a similarity value that represents a similarity of at least a portion of the data item and at least a portion of a first feature vector to one of the plurality of centroids. 
     The first node generates a sparse feature vector from the second feature vector. The sparse feature vector may be generated by replacing one or more feature element values with values of zero. Accordingly, a majority of feature element values in the sparse feature vector have a value of zero. In one embodiment, the sparse feature vector has only a single non-zero feature element value. The first node sends the sparse feature vector to a second node of the machine learning system, which may be executed by the processing device or by a second processing device. The second node processes the sparse feature vector to determine an output. 
     The machine learning system may additionally determine a relevancy rating for the data item, which may be used to determine whether to update the first node and/or the second node. The relevancy rating may be based on an error and/or a confidence measure associated with an output produced by the second node. Additionally, the first node may determine a novelty rating associated with the data item, and may determine whether to update one or more centroids of the first node based on the relevancy rating and the novelty rating. The novelty rating may be based on feature element values (e.g., similarity values) of the feature elements in the first feature vector. 
     Referring now to the figures,  FIG. 1A  is a block diagram illustrating an architecture for a machine learning system  105 , in accordance with embodiments of the present disclosure. The machine learning system  105  comprises two complementing subsystems: a perception subsystem  110  that contains a perception node  114  and an application subsystem  115  that contains an application node  130 . The role of the perception node  114  in the perception subsystem  110  is to receive streams of multi-dimensional sensory inputs (e.g., data items  112 ), which may originate from one or more modalities (e.g., audio inputs, 2D image inputs, 3D image inputs, text inputs, and so on), and form representations (e.g., feature vectors) that capture the salient attributes of said inputs such that the application node  130  can receive said representations as inputs and map those to desired outputs. The streams of multi-dimensional inputs may include temporal dependencies and/or temporal patterns (e.g., regularities over time). The representations (e.g., feature vectors) generated by the perception node  114  may capture spatial information (e.g., regularities within a single input) as well as temporal information (e.g., regularities spanning multiple inputs) and/or spatial-temporal information (e.g., regularities within inputs as well as regularities between inputs). Many types of inputs exhibit temporal patterns as well as spatial patterns. Some examples include audio data (e.g., audio data containing speech (e.g., a sequence of utterances or phonemes) or music, video data, stock market value data, temperature data, and so on. Without loss of generality, the application node output can be in the form of classification decisions, predicted values (i.e. regression output) or actions to be applied to an environment with which the machine learning system  105  interacts. 
     In one embodiment, the perception subsystem  110  receives an input data item d t    112  at a time t. The perception system  110  additionally receives a previously generated feature vector b t−1    124  at time t. The previously generated feature vector b t−1    124  may have been generated at time t−1 and sent to a delay (or feedback) module  150 . The previously generated feature vector b t−1    124  may then be fed back into the perception node  114  with a one unit time delay (e.g., at time t). The data item d r    112  and previously generated feature vector b t−1    124  are input into a perception node  114  of the perception subsystem  110 . The data item d t    112  and previously generated feature vector b t−1    124  may together form an input for the perception node  114  at time t. 
     A clustering layer  120  of the perception node  114  processes the data item d t    112  and the previously generated feature vector b t−1    124  (or portions thereof) to generate a feature vector b t    122  at time t, which may be viewed as a representation of the combined input comprising at least a portion of the data item d t    112  and at least a portion of the previously generated feature vector b t−1    124 . In an example, if the input data item d t  has 10 dimensions and there are 6 centroids (causing the feature vector b t−1  to be a 6 dimensional feature vector that has 6 feature elements), then the combined input may be a 16 dimension input. The clustering layer  120  outputs the feature vector b t    122  to the delay module  150  so that the feature vector b t    122  can be input back into the perception node  144  along with a new data item d t+1  at the next time interval t+1. 
     By using both a current input data item d t    112  and a previously generated feature vector b t−1    124  (generated from a previous data item d t−1 ) as a combined input, spatio-temporal and temporal regularities can be captured as well as spatial regularities. Accordingly, patterns that repeat over time (possibly with variability) such as words, speech, gestures, videos, temperature, weather, stock values, and so on may be captured. Patterns across a sequence of input data items are captured in a single feature vector in embodiments. For example, a current feature vector b t    122  is based on a current data item d t  and a previous feature vector b t−1 , where the previous feature vector b t−1  was based on a previous data item d t−1  and a previous feature vector b t−2 , where that previous feature vector b t−2  was based on a previous data item d t−2  and a previous feature vector b t−3 , and so on. In some embodiments, a current feature vector b t    122  includes information for previous data items that were received up to 8-10 time steps into the past. 
     In embodiments, each centroid of the perception node  114  may represent a particular state of the dynamical system and may act as an attractor of the dynamical system represented. In embodiments, the previous feature vector b t−1    124  that was delayed by one or more time units represents a previous state of the dynamical system and the input data item d t    112  acts as an excitation. The input data item d t    112  may cause a buildup of excitation that may cause a state transition. The state transition may occur when the combined input of the data item d t    112  and the previous feature vector b t−1    124  causes a generated feature vector b t    122  to be most similar to a different centroid than the previous feature vector b t−1    124 . 
     A sparsity layer  125  of the perception node  114  then processes the feature vector b t    122  to generate a sparse feature vector s t    128 . In one embodiment, the sparsity layer  125  selects one or more feature element (also referred to as an index) of the feature vector  122  having a highest feature element value, and replaces the values of all unselected feature elements (indexes) with zeros to generate the sparse feature vector  128 . In one embodiment, the value of the selected feature element is compared to a threshold. If the value of the selected feature element (or feature elements) is lower than the threshold, then the value of the selected feature element(s) may also be replaced with zeros. In such an instance, the sparse feature vector  128  contains only zeros and is referred to herein as a null inference. In an alternative embodiment, the sparse feature vector s t    128  is not generated, and the feature vector b t    122  is instead passed to the application subsystem  115 . 
     The sparse feature vector  128  (or feature vector  122 ) is provided to the application subsystem  115  and is input into an application node  130  of the application subsystem  115 . The application node  130  includes a mapping component  135  that maps the sparse feature vector  128  into an output space of possible outputs to generate an output o t    145  associated with the input data item d t  that was received at time t. In embodiments the output space is not fixed, and at any time the output space of possible outputs may expand to include new possible outputs. In one embodiment, the output  145  comprises a score or probability for each possible output in the output space. The possible output with the highest score or probability may be selected. 
     The application subsystem  115  may be trained to perform numerous different types of activities (e.g., to produce numerous different types of outputs). Some example application subsystems  115  include a classifier, a predictor, and a controller. A classifier learns to classify input data items as belonging to a particular class from among a group of possible classes. For example, an image classifier might classify an input data item as a dog, a cat, a horse, a person, a car, and so on. Notably, in embodiments the number of classes that a classifier can distinguish between is not fixed, and the classifier can continually learn to classify new types of inputs as belonging to new classes without forgetting previous classifications. 
     A predictor attempts to predict a next value (e.g., a next data item) in a data sequence based on an input data item and possibly previous input data items. A predictor may learn to predict a particular outcome based on a given set of observations. Examples of predictors are time series predictors that predict next words in a sentence based on previous words, predictors that predict next frames in a video based on previous frames, predictors that predict next stock values based on previous stock values and other data, and so on. Notably, in embodiments the number of different predictions that may be output can grow over time. 
     A controller may learn to issue actions that will impact an environment based on input data items. In an example, a controller may be used to determine when an automated (self-driving) car should speed up, slow down, turn, and so on. 
     In some instances the data item d t    112  may include a target that identifies a correct output associated with the data item  112 . If the data item  112  included a target, then the output  145  may be compared to the target at error calculator  140  to generate an error e t    142 , as is discussed in greater detail below. The error e t    142  may be sent to the application node  130  to update the application node (e.g., to update weights in the application node associated with one or more feature elements or indexes) and to the perception node  114  to update one or more representations or states (e.g., centroids) of the perception node  114 . The update mechanisms for the perception node  114  and the application node  130  may differ, as is set forth in greater detail below. In one embodiment, the error  142  is broadcast to all nodes  114 ,  130 , and each node makes its own determination as to whether that node should be updated and/or how that node should be updated. 
     An embodiment is shown in  FIG. 1A  that includes only a single perception node  114  and a single application node  130 . However, it should be understood that in alternative embodiments the perception subsystem  110  and/or the application subsystem  115  may contain multiple nodes. These nodes may be arranged in a single layer or may be arranged hierarchically (e.g., in which one layer of perception nodes generates feature vectors which are provided to a next layer of perception nodes). 
       FIG. 1B  is a block diagram illustrating an architecture for a machine learning system  108  that includes multiple perception nodes, in accordance with embodiments of the present disclosure. Machine learning system  108  is similar to machine learning system  105 , where machine learning system  108  includes a perception subsystem  111 , an application subsystem  116  and an error calculator  141 . However, in contrast to perception subsystem  110 , perception subsystem  111  includes multiple perception nodes including perception nodes  114 A,  114 B,  114 C through  114 N. The perception nodes  114 A-N may be arranged in a single layer or hierarchically in two or more layers. 
     In embodiments, a data item d t    112  is input at time t, and each of the perception nodes  114 A-N processes information for at least a portion of the data item  112 . For example, the data item  112  may be divided into multiple parts, and each of the perception nodes  114 A-N may process one of the multiple parts of the data item  112 . Additionally, each perception node  114 A-N may have generated a previous feature vector b t−1    129  based on a previously received data item d t−1  and a previous feature vector b t−2 . Each perception node  114 A-N may receive the feature vector  129  that was previously generated by that perception node  114 A-N. Each perception node  114 A-N may then generate a feature vector b t    127  based on at least a portion of the data item d t    112  and the feature vector b t−1    129  previously generated by that perception node. For example, the data item  112  may be divided into multiple parts, and each of the perception nodes  114 A-N may process one of the multiple parts of the data item  112  and combine it with a previously generated feature vector  129  to generate a feature vector  127 . Each perception node  114 A-N may then generate a sparse feature vector  128 A,  128 B,  128 B through  128 N. These sparse feature vectors  128 A-N may then be sent from perception subsystem  111  to application subsystem  116 . 
     In an example of images, an input image may be partitioned into multiple image patches (e.g., 4 pixel by 4 pixel image patches), and each perception node  114 A-N may receive one of the image patches and produce a sparse feature vector  128 A-N based on the contents of a received image patch. 
     In embodiments, two or more of the perception nodes  114 A-N share a common code book (e.g., have the same set of centroids). For example, groups of perception nodes  114 A-N may share common code books. Groups may be set based on proximity. Proximity may be based, for example, on Euclidean distance. For example, data items may be two-dimensional (2D) or three-dimensional (3D) images, and these images may be divided into 2D or 3D image patches. Each perception node  114 A-N may be responsible for a different image patch. Perception nodes that are responsible for image patches that are near to each other may be grouped together and share a common code book. Another example for audio data may divide an input audio data item into different frequency ranges, and each perception node  114 A-N may be responsible for a particular frequency range. A shared codebook may be used for perception nodes  114 A-N that handle similar or nearby frequency ranges. 
     Application node  131  of application subsystem  116  includes a mapping component  136  that maps the multiple sparse feature vectors  128 A-N into an output space to determine an output o t    146 . Application node  131  and mapping component  136  may function as described with reference to the similarly named application node  130  and mapping component  135  of  FIG. 1A , except that application node  131  receives multiple sparse feature vectors  128 A-N as input rather than a single sparse feature vector. In some embodiments (not shown), application subsystem  116  includes multiple application nodes  131 , which may be arranged in a single layer or may be arranged hierarchically. Error calculator  141  may generate an error e t    143  based on a function of a target associated with data item  112  and output  146 , and may provide the error to the application subsystem  116  and perception subsystem  111 . In one embodiment, error calculator component  141  broadcasts the error  143  to each of the perception nodes  114 A-N, and each perception node  114 A-N may determine whether it should be updated based at least in part on the error  143 . 
     One objective achieved in some embodiments of this invention is to facilitate a system which offers continuous, or lifelong, learning. Such lifelong learning may be achieved to recognize static spatial patterns (e.g., in input data that does not have a time dependency), temporal patterns, and/or spatio-temporal patterns (e.g., for input data that exhibits both spatial dependencies as well as temporal dependencies). In one embodiment, we define lifelong learning as the ability of a system to acquire new representations and mappings of said representations to desired outputs, in an online and gradual manner, without the requirement of having all training information be made available at any one time. Moreover, a change in the statistics of the input data should be detected by the system without the need for external indication of said change. In non-stationary learning settings, whereby the inputs presented to the system are characterized by statistics that change over time, a key challenge is mitigating forgetting of old representations learned as new ones get acquired. This is something that animals, including humans, exhibit as a core skill and has thus far posed a formidable challenge when attempting to achieve at scale using neural networks and related techniques. 
     In most supervised learning frameworks, a neural network can be viewed as a large parametric model, where the parameters of the network are its weights. Training said networks involves an iterative process of repeatedly presenting input/target pairs, and adjusting the weights of the network with each such presentation so as to minimize the error associated with the network outputs. However, a key assumption in supervised learning is that the training regime is stationary, which basically means that the statistics of the training set are fixed and do not change over time. While that&#39;s a reasonable assumption to make in many settings in which a large training corpus has been collected, embodiments of the present disclosure pertain to many real-world scenarios in which not all training data is available a priori and thus the system learns progressively. 
     The approach conveyed in embodiments of the present disclosure comprises of three main assertions. The first is that in order to support lifelong learning, nodes of the perception subsystem dynamically allocate representational resources (e.g., centroid) in a data-driven manner and upon need. This means that one condition for allocating new representational resources is detection of novelty in the patterns observed, relative to previously observed patterns. However, all practical systems are resource-limited and, consequently, novelty alone may be an inefficient condition for resource allocation. The second assertion is that the complementing condition triggering resource allocation is an indication from the application node  130 ,  131  that resource allocations are merited (referred to herein as relevancy). In one embodiment, when the application node  130 ,  131  indicates that its current mapping (transformation) of the feature elements produced by the perception node(s)  114  yield relatively high error, this high error condition forms an indication signal to the perception node that it should consider allocating additional representational resources so as to improve the performance of the system as a whole (given that the output of the application node  130 ,  131  is the system&#39;s output). 
     The third assertion guiding embodiments described herein is that the encoding of representations from the perception node  114 ( s ) to the application node  130 ,  131  are sparse (e.g., are sparse feature vectors). A sparse feature vector is a feature vector in which the values for most feature elements are zero. In one embodiment, all feature element values except for one feature element value are zero in a sparse feature vector. Specifically, at any given time, only one of the feature elements is nonzero in some embodiments. An underlying principle behind the sparse encoding is to enforce different regions of the sensory input space to correspond to different feature elements in the representations passed to the application node  130 ,  131 . If that is guaranteed, by gradually increasing the number of possible feature elements (e.g. centroids) that can appear in the representations passed to the application, a controlled and progressive characterization of the input space takes place, which results in continual learning of representations while mitigating forgetting of prior representations. 
     As an example illustrating this principle, let us assume that the perception node  114  receives as input various patterns corresponding to the letters “A” and “B”. Let us assume that when presented with patterns corresponding to the letter “A”, feature elements 1, 2 and 3 get activated (i.e. take on nonzero values). Let us further assume that when presented with patterns corresponding to the letter “B”, feature elements 4, 5 and 6 get activated. Correspondingly, the application node  130 , which may be trained as a classifier charged with distinguishing between “A” and “B” patterns, learns to map feature vectors with elements 1, 2 and 3 active to class “A” output, and feature vectors with elements 4, 5 and 6 active to class “B” output. Next, let us assume that the perception node  114  is provided with patterns of the letter “C”, which it had not received as input prior to that point. The guiding principles followed by some embodiments of this invention would facilitate the allocation of novel feature elements, say 7, 8 and 9, when presented with patterns corresponding to the letter “C”. This, in turn, will mean that the classifier (e.g., application subsystem  115 ,  116 ) would learn the mapping of feature elements 7, 8 and 9 to class “C”. However, in doing so, it will fully retain the mapping of feature elements 1 through 6 to classes “A” and “B”, as indicated above, since feature elements with value of zero do not incur changes to weights that they correspond to. In other words, by utilizing sparsity and relying on different underlying patterns yielding mutually exclusive (or strongly non-overlapping) set of active feature elements, the system as a whole mitigates forgetting effects that would otherwise take place, as they do in conventional supervised learning settings. 
     The perception node(s)  114  is charged with receiving sensory inputs (e.g., data items  112 ), that may originate from multiple modalities, creating long-term memory that captures regularities in said inputs and exporting to the application node  130 ,  131  a sparse feature vector(s)  128  extracted from the memory it generates, which facilitates mapping by the application node  130 ,  131  to a desired output space (e.g., to produce an output  145 ,  146 ). Learning at the perception node(s)  114  may occur in a mostly unsupervised (i.e. weakly-supervised) manner such that the machine learning system  105 ,  108  can continue to learn about the underlying structure of observations even for inputs (data items  112 ) that do not have corresponding targets or labels. Such semi-supervised learning can be important in many real-world applications, whereby labeled examples are either hard to obtain or costly to collect. 
     The process governing the learning in the perception node(s)  114  in one embodiment is an online dynamic clustering algorithm, in which a set of centroids, represented as points in the input space, are maintained providing compact (possibly lossy) representation of the sensory inputs observed. Clustering is inherently an unsupervised learning process in that it aims to capture dense regions of samples observed. Embodiments expand on that basic notion to facilitate a semi-supervised learning framework that attempts to both capture regularities in the observations as well as adhere to the demands of the application node  130 ,  131 , which it ultimately serves. 
     The online aspect of the learning suggests that the allocation of and adjustments made to said centroids are conducted in an incremental manner, with each new input (data item  112 ) received, and may not require extensive storing of past inputs and processing of any past inputs. In other words, with each new data item  112  that is input, the online clustering process determines whether any updates to the centroids values should be made. 
     The dynamic aspect of the clustering process refers to the incremental growing of the centroids set, which may begin with a single centroid and grow to an arbitrary size, in accordance with the nature of the input data that is being processed. The framework additionally allows for pruning (or removal) of centroids, assuming such pruning is deemed necessary or beneficial. 
     At an initialization stage, a single centroid (or an initial set of centroids) is established in the perception node(s)  114 . The mean (location) of this first centroid can be arbitrarily set or set according to the first input received by the node. Subsequent new inputs may be observed, and under the right conditions said inputs will impact one or more of the allocated centroids. In one embodiment, a winner-take-all framework is used for updating centroids and/or for generation of a sparse feature vector  128 , in which for every incoming input there is at most one centroid that is the “winning centroid” and is thus selected for inference and potential updating. 
     In one embodiment, perception node(s)  114  includes a clustering layer  120  and a sparsity layer  125 . Alternatively, the sparsity layer  125  may be omitted in some embodiments. Note that the clustering layer  120  and sparsity layer  125  are not shown in perception nodes  114 A-N. The input data item  112  may be viewed as a point in a multi-dimensional space. In one embodiment, the clustering layer  120  applies the aforementioned clustering process (e.g., the online dynamic clustering process) to determine values for each feature element or resource based on a distance measure between a) a centroid associated with the feature element or resource and b) a combined input comprising the input data item  112  and the previously generated feature vector  124 ,  129 . The clustering layer  120  may generate a feature vector that includes feature element values for each of the feature elements and provide the feature vector  122  to the sparsity layer  125 . The sparsity layer  125  may then generate the sparse feature vector  128  from the feature vector  122  by selecting one or more feature elements having highest values and then replacing the values of all unselected feature elements with zeros. In one embodiment, a winner-takes-all approach is used, in which only the feature element with the highest value is selected. In another embodiment, n feature elements having highest values are selected, where n is an integer value greater than 1. 
     In one embodiment, a goal of the perception node(s)  114  is to process each incoming input (combination of the data item  112  and previously generated feature vector  124 ,  129 ) by associating it with one (or more) of the centroids. Such association can lead to one or more centroids being updated as well as directly impact the content of the representation (e.g., sparse feature vector(s)  128 ) forwarded to the application node  130 ,  131 . Growing the number of centroids in the perception node(s)  114  is defined by a combination of two considerations. The first is the degree of novelty that the current input and/or recent past inputs exhibits and the second is the potential relevancy a newly allocated centroid (or centroids) would have on the accuracy of the application node  130 ,  131 , as reflected by the error signals or other relevancy signals or scores it produces and has produced in the recent past. In other words, in one embodiment two events take place in order for a new centroid to be allocated in a perception node  114 : the first is that the current input pattern and/or recent past input patterns are dissimilar enough to the existing set of centroids and the second is that a relevancy measure (e.g., a relevancy rating), which may be derived from one or a sequence of errors and/or confidence values broadcasted by the application node  130 ,  131 , is sufficiently high. In another embodiment, the relevancy measure may be used to define a relevancy-weighted metric of novelty. In view of this framework, three disjoint operating regimes can be defined, as shown in  FIG. 2 . 
     One possible way to define novelty is to measure the similarity between a given input and each of the existing (previously allocated) centroids and determine whether said similarity is sufficiently low (indicating high novelty of the pattern). Potential similarity measures are the cosine similarity and the inverse Euclidean distance, however any distance or similarity measure may be used. In one embodiment, the distances or similarities are compared to one or more similarity threshold (or distance threshold) to determine whether the new input has sufficiently high or low novelty to satisfy one or more novelty criteria. The threshold (or thresholds) may be an adaptive threshold or a fixed threshold. For adaptive thresholds, the threshold may be determined dynamically based on one or more input parameters. In one embodiment, the threshold for novelty can be determined as the mean distance to the centroid+N standard deviations of the distance, wherein N is an integer value of 1 or greater. The mean and standard deviation can be approximated using moving average estimates, which adapt over time as data comes in. 
     One possible way to define relevancy is to measure the error associated with the output  145 ,  146  to determine a relevancy rating. An error calculator  140 ,  141  may perform an error calculation to produce an error  142 ,  143 , and the error may be provided to the perception node(s)  114  and/or to the application node  130 ,  131  as a relevancy rating. In one embodiment, the error is linearly or non-linearly proportional to relevancy. Accordingly, a high error means a high relevancy and a low error means a low relevancy. In one embodiment, the error is the relevancy rating. In one embodiment, the error is vector-valued, and different nodes calculate different relevancy ratings based on different weightings of the error vector. 
     Each of the perception node(s)  114  and application node  130 ,  131  may then perform distinct updates based on the error. The provided error  142 ,  143  may be a simple error based on a difference between output  145 ,  146  and a target associated with data item  112 . Alternatively, the error  142 ,  143  may be an accumulated error that takes into account a current error as well as errors associated with previous inputs (e.g., data items  112  and/or previously generated feature vectors  124 ,  129 ). Accordingly, the level of relevancy (relevancy rating) may be determined by accumulating instantaneous application error values. For example, we can define an accumulated error measure B, and define it as B=B+err(t), where err(t) is the current error for an input data item at time t. Error calculator  140 ,  141  can check to see if this accumulated error has crossed some threshold to determine whether relevancy is high or low. This is a way to base the decision on relevancy not necessarily on instantaneous errors but rather on sufficient accumulation of errors. In one embodiment, a leaky bucket model is used that accumulates errors associated with multiple inputs, but progressively reduces the accumulated error using a reduction factor. For example, we can define the error measure as B=max[0, B*alpha+err(t)], where alpha is a constant between 0 and 1, which would accumulate error but also continuously decrease the accumulated error, which means that now the errors that accumulate should arrive within a certain period of time. This is often referred to as a leaky-bucket integrator. One example leaky bucket integrator variation for determining a relevancy rating defines a relative accumulated application error at time n, A(n), as:
 
 A ( n )=max{0, e   app ( n )− e′   app ( n )}
 
where e′(n) denotes a moving average estimate of the mean of the application error at time n.
 
     This particular formulation of accumulated error takes on high values when a sequence of instantaneous application error values are observed while the prior mean application error was relatively low. 
     In some embodiments, multiple different error thresholds are used to determine whether novelty criteria are satisfied. For example, novelty criteria may include an upper error threshold and a lower error threshold. Under some circumstances the novelty criteria may be satisfied when the error exceeds the upper error threshold. Under other circumstances, the novelty criteria may be satisfied when the error exceeds the lower error threshold but does not necessarily exceed the upper error threshold. This may mitigate or prevent excessive new centroid allocation. 
     Referring to  FIG. 5 , a graph illustrating centroid allocation for a node of a machine learning architecture is shown, in accordance with embodiments of the present disclosure. Relevancy criteria may include an upper error threshold  505  and a lower error threshold  510 . One or more of the perception nodes, the error calculator  140 ,  141 , or another module may keep track of when a determined error is below the lower error threshold  510  (referred to as a first condition) as well as when the determined error exceeds the upper error threshold  505  (referred to as a second condition). When a new error is determined, a perception node, the error calculator  140 ,  141  or another module may determine whether the first condition occurred more recently or the second condition occurred more recently. If the first condition occurred more recently, then the error may satisfy relevancy criteria for allocation of a new centroid if the error is above the upper error threshold  505 . If the second condition occurred more recently, then the error may satisfy relevancy criteria for allocation of a new centroid if the error is above the lower error threshold  510  (whether or not the error is above the upper error threshold  505 ). As shown, at time t 1  a calculated error (which may be an instant error, an accumulated error, or some other error value) is below the upper threshold  505  but above the lower threshold  510 . Accordingly, a new centroid may be allocated at time t 1 . Alternatively, a centroid may not be allocated at time t 1  since the upper threshold  505  has not yet been exceeded. In either case, at time t 2  a new centroid is allocated since the error exceeds the upper threshold  505 . Two more new centroids may also be allocated at times t 3  and t 4 . At time t 5  the error is below the lower error threshold. Accordingly, no centroid is allocated at time t 5 . No new centroid is allocated at time t 6  either since the error is again below the lower threshold  510 . At time t 7  the error is above the lower threshold  510 . However, since the first condition (error lower than the lower threshold  510 ) occurred more recently than the second condition (error higher than the upper threshold  505 ), no centroid is allocated. Similarly, no centroids are allocated at times t 8  through t 29 . At times t 30  and t 31  the error exceeds the upper threshold  505 , which causes new centroids to be allocated. At time t 32  the error is below the upper threshold  510  but above the lower threshold  510 . Since the second condition occurred more recently than the first condition, a new centroid is allocated at time t 32 . Similarly, new centroids are also allocated at times t 33  to t 36 . At time t 37  no centroid is allocated. 
     Another possible way to define relevancy is based on confidence. Referring back to  FIGS. 1A-B , in some embodiments, application node  130 ,  131  generates a confidence value in addition to output  145 ,  146  or as a part of the output  145 ,  146 . The confidence may indicate an estimate as to a likelihood that the output  145 ,  146  is a correct output. An input  112  for which the output  145 ,  146  has low confidence may suggest that the error would be high. The confidence may therefore be used to generate a relevancy rating for the output  145 ,  146 . A confidence may be determined whether or not an input data item  112  is associated with a target. Accordingly, in cases where a target is not provided, the confidence estimate can be used as a relevancy rating. In one embodiment, confidence is approximately inversely proportional to relevancy. Accordingly, a high confidence may indicate a low relevancy while a low confidence may indicate a high relevancy. 
     The provided confidence (or novelty rating based on confidence) may be a simple confidence determined for a current data item  112 . Alternatively, the confidence may be an accumulated confidence measure that takes into account a current confidence value as well as confidence values associated with previous data items  112 . In one embodiment, a leaky bucket integrator model is used that accumulates confidences associated with multiple data items, but progressively reduces the accumulated confidence value using a reduction factor. 
     In one embodiment, the distances or similarities computed for centroids of a given perception node  114  are compared to one or more similarity threshold (or distance threshold) to determine whether the new input has sufficiently high or low novelty to satisfy one or more novelty criteria for the given perception node. The threshold (or thresholds) may be an adaptive threshold or a fixed threshold. For adaptive thresholds, the threshold may be determined dynamically based on one or more input parameters. In one embodiment, the threshold for novelty can be determined as the mean distance to the centroid+N standard deviations of the distance, wherein N is a real value. The mean and standard deviation can be approximated using moving average estimates, which adapt over time as data comes in. 
     In one embodiment, a first similarity threshold is used to determine whether the new input has high or low novelty, and a second similarity threshold is used to determine whether a generated feature vector should be a null feature vector (a feature vector in which all values are zero) rather than a sparse feature vector. If the similarity values for all centroids of a node are below the second similarity threshold, this indicates that the new input is dissimilar from all of the centroids. In such instances, a null feature vector may be generated. The second similarity threshold may have a value, for example, of 0.1, 0.05, 0.01, or some other low value. 
       FIG. 2  is a block diagram illustrating a node update paradigm  200  for a machine learning architecture, in accordance with embodiments of the present disclosure. The node update paradigm  200  includes the dual criteria of novelty and relevancy. Each of the novelty criterion (or criteria) and the relevancy criterion (or criteria) may be associated with its own threshold or set of thresholds, which may be static thresholds or dynamic (variable) thresholds. A high novelty may be determined if the novelty rating associated with a data item exceeds a novelty threshold, and a low novelty may be determined if the novelty associated with the data item is below the novelty threshold. Similarly, a high relevancy may be determined if the relevancy rating associated with the data item exceeds a relevancy threshold, while a low relevancy may be determined for the data item if the relevancy rating for that data item is below the relevancy threshold. 
     Different operations associated with updating a perception node are determined based on a combination of novelty and relevancy, where novelty is determined by a perception node and relevancy is dictated based on an output of an application node. Each perception node of a perception subsystem may apply the node update paradigm  200  to determine whether that perception node should be updated. In one embodiment, two or more perception nodes share a common code book, where the code book is a set of centroids. In such an embodiment, updates may be made to the shared code book rather than to the individual perception node that uses that code book. Accordingly, updates made by one perception node may be later applied by a second perception node. Additionally, if two nodes use a common code book, then each of those nodes may contribute separate updates to that code book in parallel or in series. 
     The node update paradigm  200  includes a first regime of pure inference  220  (where centroids are not updated), a second regime of refinement  215 , and a third regime of allocation  210 . Each perception node in the perception subsystem may make its own individual determination as to which of the three regimes of pure inference  220 , allocation  210  and refinement  215  applies for a given input and/or output. The term inference as used herein means the passing of a feature vector from a perception node to an application node. 
     The first regime (pure inference  220 ) pertains to the case of high novelty and low relevancy. This would suggest that the processed input pattern deviates enough from the existing set of centroids (which have presumably captured dense regions of the input space based on prior inputs received). It would also suggest that the relevancy of this novel pattern is low. As a result, no updating of any of the existing centroids should take place. Accordingly, in the pure inference regime  220 , a sparse feature vector (inference) is produced (block  240 ) and no centroids of a perception node are updated (block  230 ). In some embodiments, the input (data item plus previous feature vector) is stored in association with the winning centroid. 
     In one embodiment, a similarity threshold is used to determine whether the sparse feature vector should be a null feature vector. If every value in the sparse feature vector is below the similarity threshold, then the sparse feature vector may be replaced with a null feature vector. In such an instance, the inference produced at block  240  may be a null feature vector. 
     The second regime is the refinement regime  215  in which low novelty and low relevancy are observed, suggesting that the current input is close enough to one of the centroids and as such should shape that centroid so as to pull it closer to the current input. Accordingly, the winning centroid (centroid that is closest in the multi-dimensional space to the input that includes the current data item and the previous feature vector) is updated for the perception node. Additionally, other centroids that were recently winning centroids (e.g., e.g., centroids that were closest in the multi-dimensional space to previous inputs that included a previous data item and a previous feature vector) may also be updated, as is discussed in greater detail below. Additionally, in one embodiment a sparse feature vector (also referred to as an inference vector) is produced at block  240 . The sparse feature vector exported to the application node may contain all zeros other than in the index (feature element) corresponding to the winning centroid, for which the similarity to said winning centroid is injected. 
     The third regime corresponds to the allocation scenario  210 , in which the relevancy is high and the novelty is high as well. This suggests that the current input is novel and given that the application node carries a high error for the current representation corresponding to this input, allocation of a new centroid should be carried out. Accordingly, in one embodiment a new centroid is allocated (block  235 ), and the mean of the new centroid takes the value of the current input. Additionally, further new centroids may also be allocated based on recent previous inputs, as is discussed in greater detail below. A corresponding sparse feature vector may be produced (block  240 ), which takes into account a new feature element or index associated with the new centroid as well as the preexisting feature elements. In one embodiment, the sparse feature vector has a value of 1 (or an otherwise maximal value) for the new feature element and values of 0 for the other feature elements. 
     In embodiments, the ability to allocate new centroids to represent new observations in input data items ensures that representations of new types of data/new observations can be generated without distorting representations for previous observations about other data items. 
     The pure inference regime  220  also applies when high relevancy is coupled with low novelty results. As mentioned, in the pure inference regime  220  the node essentially passes the inference with the winning centroid using sparse encoding (e.g., by using a sparse feature vector). In this operational regime no updating of the centroids takes place given that the assumption is that the high error stems from an outlier sample, which is essentially noise rather than a signal carrying sample. Such noise need not impact the centroids. Accordingly, the sparse feature vector is produced at block  240  and no centroids are updated (block  230 ). In some embodiments, the input (data item plus previous feature vector) is stored in association with the winning centroid. 
     As discussed above, under some operational conditions (e.g., those conditions that trigger the refinement regime  215 ), the winning centroid of a perception node is to be updated relative to the current input. In the most basic form, updating the centroids can be achieved using the following general formulation:
 
 M   i   =M   i +η( X−M   i )
 
where X denotes the current input as a point in a multi-dimensional space, i is an index for the particular centroid, M i  is a position of the particular centroid in the multi-dimensional space and η is a step size. This formulation of the centroid update rule effectively brings the winning centroid slightly closer to the current input.
 
     Updates to the centroid may be modulated by multiple factors, including the error associated with the output (so as to emphasize some samples vs. others). This would result in an update rule of the following form:
 
 M   i   =M   i +η( X−M   i ) E ( n )
 
where E(n) is a function of the error (also referred to as application error or output error) at time step n.
 
     In some embodiments, the update to the centroid is modulated by an update multiplier (also referred to herein as an update eligibility factor) u i . Each centroid may maintain its own update multiplier. With each time step (each input), a winning centroid may be determined after the feature vector is computed. The feature vector may include a plurality of feature elements, each having a feature element (e.g., similarity) value. The winning centroid may be the centroid that is associated with the feature element having the highest feature element value. The update multiplier associated with the winning centroid may be reset to a predetermined value. The predetermined value may be a maximum update multiplier. In one embodiment, the maximum update multiplier is 1. A decay factor may then be applied to the update multipliers associated with each of the non-winning centroids. In one embodiment, the decay factor is 0.95. Other example decay factors include 0.99, 0.90, 0.85, 0.80, and so on. 
     In embodiments, the update multiplier u i  for a centroid is updated with each input according to the formula u i =u i ·beta, where beta is a constant between 0 and 1. In embodiments, each node may have its own set of centroids, and each centroid may have its own update multiplier. In one embodiment, an update multiplier threshold is used. An update multiplier of a centroid may be compared to the update multiplier threshold (e.g., 10 −5 ), and if the update multiplier is equal to or less than the update multiplier threshold, then the associated centroid may not be updated. 
     Referring back to  FIG. 1A , the perception node  114  may include one or more memory modules  152 , and each centroid may use the memory module(s)  152  to remember the combined input (e.g., data item and previous feature vector) that last resulted in a feature vector for which that centroid was the winning centroid. In some embodiments, when update criteria are satisfied, every centroid is permitted to perform an update operation. The update operation for a centroid may be based on the current error, the input stored by a centroid, the step size, and/or other parameters. One example update rule that uses the update multiplier has the following form:
 
 M   i   =M   i +η( X−M   i ) E ( n ) u   i  
 
where u i  is the current update multiplier for a given centroid i. In some embodiments, an update multiplier threshold is applied. If the update multiplier is below the update multiplier threshold, then no update may be performed on a given centroid.
 
     The above modulations can further be generalized to include any function of the sequence of past errors, such as with an accumulated error measure. For example, E(n) in the above update rules may be substituted with an accumulated error measure B, where B is defined as B=B+E(n). Another variation could be a leaky accumulation, where E(n) in the above update rule may be substituted with an accumulated error measure B, where B is defined as B=B*alpha+E(n), where alpha is a constant between 0 and 1, which would accumulate but leak as well. 
     In one embodiment, each centroid of a perception node is associated with a plasticity factor. The plasticity factor for a centroid controls how much that centroid is permitted to change based on a given input or set of inputs. In one embodiment, the plasticity factor is initialized with a high value (e.g., a value of 1), and then gradually decays with each update to that centroid. This enables the centroid to be adjusted relatively quickly when the centroid is first allocated, but slowly reduces the ability of that centroid to change. Eventually, a centroid may become essentially fixed, after which the centroid will no longer be updated. This process promotes stabilization of representations pertaining to regularities in the observations. 
     Three example update rules that employ a plasticity factor have the forms:
 
 M   i   =M   i +η( X−M   i ) p   i  
 
 M   i   =M   i +η( X−M   i ) E ( n ) p   i  
 
 M   i   =M   i +η( X−M   i ) E ( n ) u   i   p   i  
 
where p i  is a plasticity factor for the particular centroid, the plasticity factor having a value that is based on a number of times that the particular centroid has been updated.
 
     In one embodiment, the plasticity factor p i  for a centroid is updated with each update according to the formula p i =p i ·beta, where beta is a constant between 0 and 1. In embodiments, each node may have its own set of centroids, and each centroid may have its own plasticity factor. In one embodiment, a plasticity value threshold is used. A plasticity value of a centroid may be compared to the plasticity value threshold (e.g., 10 −5 ), and if the plasticity value is equal to or less than the plasticity value threshold, then the associated centroid may not be updated any longer. 
     In some instances, centroids may be allocated which are not later used, or which are only rarely used. For example, new centroids may be allocated as a result of noisy conditions or otherwise transient conditions that did not persist. Such centroids may not be useful. Accordingly, in some embodiments centroids may be removed if one or more centroid removal criteria are satisfied and/or if one or more criteria for retaining centroids (centroid retention criteria) are not satisfied. The ability to remove centroids yields a simpler (in terms of memory and compute footprint) implementation that improves efficiency and reduces use of system resources. Additionally, in some embodiments each node is limited to a finite number of centroids. The ability to remove centroids ensures that the finite number of centroids that are allocated for a node are all useful centroids. 
     In one embodiment, newly allocated centroids have a probation period during which those centroids will be removed if they fail to satisfy one or more criteria. In one embodiment, should a newly allocated centroid not be selected as the winning centroid more than N times within a period of time T (probation period) following that centroid&#39;s allocation for a node, that newly allocated centroid is removed from the node. The values of “N” and “T” define how conservative the system is in removing allocated centroids. 
     In one embodiment, a centroid retention criterion is based on the plasticity factor associated with that centroid. As discussed above, in some embodiments centroids are associated with plasticity factors, which are reduced after each update. If the plasticity factor associated with a centroid is above a threshold value after the probation period is ended, then that centroid fails to satisfy the centroid retention criterion and is removed from the node. 
       FIGS. 3A-B  are graphs illustrating a centroid update process for multiple nodes, in accordance with embodiments of the present disclosure. The centroid update process of  FIGS. 3A-B  is shown for a degenerate two-dimensional (2D) space including dimension 1 (D 1 ) and dimension 2 (D 2 ). However, in most instances the multi-dimensional space will contain many more dimensions (e.g., tens of dimensions, hundreds of dimensions, thousands of dimensions, etc.). As shown, an example perception node contains three centroids  305 ,  310  and  315 . For illustration, multiple different past data items (represented as points in a 2D space)  320  that have affected the positions of the centroids  305 ,  310 ,  315  are also shown. 
     With relation to  FIG. 3A , a new data point  325  (e.g., a new input based on a current data item received at time t 1  and a previously generated feature vector generated at time t 0 ) is input into the perception node at a time t 1 , and similarity values are computed for each of the centroids  315 ,  320 ,  325 . The resulting similarity values are S 1,1  for the similarity to a first centroid  305 , S 2,1  for a similarity to a second centroid  310  and S 3,1  for a similarity to a third centroid. The similarity S 1,1  is greatest between the data point  325  and the first centroid  305 . However, either the similarity S 1,1  does not satisfy a low novelty criterion (e.g., the similarity is below a similarity threshold or conversely a distance is above a distance threshold) or a relevancy rating does not satisfy a relevancy criterion for updating centroids. Accordingly, the data point  325  is stored with relation to the winning first centroid  305  at time t 1 . 
     With relation to  FIG. 3B , another new data point  328  (e.g., a new input based on a current data item received at time t 2  and a previously generated feature vector generated at time t 1 ) is input into the perception node at a time t 2 , and similarity values are computed for each of the centroids  315 ,  320 ,  325 . The resulting similarity values are S 1,2  for the similarity to a first centroid  305 , S 2,2  for a similarity to a second centroid  310  and S 3,2  for a similarity to a third centroid. The similarity S 3,2  is greatest between the data point  325  and the third centroid  315 . Additionally, the similarity S 3,2  satisfies a low novelty criterion (e.g., the similarity is above a similarity threshold or conversely a distance is below a distance threshold) and a determined relevancy rating satisfies a low relevancy criterion. Accordingly, the third centroid  315  is updated by moving the third  315  centroid in the direction of the data point  328 . This may be performed by computing a vector between the third centroid  315  and the data point  328 , and then moving the third centroid  315  along the vector. In one embodiment, the third centroid  315  is adjusted according to one of the update rules set forth above. 
     Additionally, the first centroid  305  may be updated based on the previous input that was stored for the first centroid  305  when the first centroid was the winning centroid (as set forth with reference to  FIG. 3A ). The first centroid  305  may be updated by moving the first  305  centroid in the direction of the data point  325 . This may be performed by computing a vector between the first centroid  305  and the data point  325 , and then moving the first centroid  305  along the vector. In one embodiment, the third centroid  315  is adjusted according to one of the update rules set forth above. For example, the magnitude of the update to the first centroid  305  may be modulated based on an update multiplier in some embodiments. 
     By updating a current winning centroid as well as one or more other centroids based on previous inputs that were received that resulted in those centroids being the winning centroids, a trace of updates may be made. 
       FIGS. 3C-D  are graphs illustrating a process to add new centroids to a node, in accordance with embodiments of the present disclosure. The centroid allocation process of  FIGS. 3C-D  is shown for a simplified two-dimensional (2D) space including dimension 1 (D 1 ) and dimension 2 (D 2 ). However, in most instances the multi-dimensional space will contain many more dimensions. As shown, an example perception node contains three centroids  305 ,  310  and  315 . For illustration, multiple different past data items (represented as points in a 2D space)  320  that have affected the positions of the centroids  305 ,  310 ,  315  are also shown. 
     With relation to  FIG. 3C , a new data point  350  is input into the perception node at a time t 1 , and similarity values are computed for each of the centroids  305 ,  310 ,  315 . The resulting similarity values are S 1,1 ′ for the similarity to a first centroid  305 , S 2,1 ′ for a similarity to a second centroid  310  and S 3,1 ′ for a similarity to a third centroid  315 . Each of the similarity values is below a similarity threshold, which causes a novelty rating associated with the data point  350  to satisfy a high novelty criterion. However, a relevancy rating may not satisfy update criteria for allocation of a new centroid. Accordingly, the data point  350  is stored with relation to the winning first centroid  305  at time t 1 . 
     With relation to  FIG. 3D , a new data point  355  is input into the perception node at a time t 2 , and similarity values are computed for each of the centroids  305 ,  310 ,  315 . The resulting similarity values are S 1,12 ′ for the similarity to a first centroid  305 , S 2,2 ′ for a similarity to a second centroid  310  and S 3,2 ′ for a similarity to a third centroid  315 . Each of the similarity values is below a similarity threshold, which causes a novelty rating associated with the data point  350  to satisfy a high novelty criterion. Additionally, a relevancy rating satisfies an update criterion for allocation of a new centroid. Accordingly, a new centroid is allocated at the data point  355 . In some embodiments, the new centroid is allocated by or with reference to the closest existing centroid, which in this example is the third centroid  315 . Additional new centroids may also be allocated by or with reference to other existing centroids. For example, first centroid  305  may cause another new centroid to be allocated at data point  350  using the stored information for data point  350 . In some embodiments, each centroid determines a value of an update multiplier associated with that centroid when update criteria for centroid allocation are satisfied. These update multiplier values may be compared to an update multiplier threshold. Any centroids that have an associated update multiplier that is above the update multiplier threshold may allocate a new centroid. Those centroids having associated update multiplier values that are below the update multiplier threshold may not allocate new centroids. 
     In some embodiments, a single update multiplier is used for determining a magnitude of an update to make to a centroid as well as for determining whether to allocate a new centroid by an existing centroid. Alternatively, separate update multipliers may be used for determining the magnitude of an update to make to a centroid and for determining whether to allocate a new centroid by an existing centroid. When different update multipliers are used, those update multipliers may have different values, different reset values and/or different decay factors. 
     When subsequent data points are input into the perception node, those data points will be compared to the previous centroids  315 ,  320 ,  325  as well as to the new centroid at data point  355  (and possibly to the a new centroid at data point  350 ). 
     In one embodiment, a perception node sets a refractory period when a new centroid (or set of new centroids) is allocated for that perception node. The refractory period is a period during which the perception node is not permitted to allocate new centroids. The refractory period enables the application node to adjust weights associated with the new centroid to improve its use of the new centroid for generating accurate outputs. The refractory period additionally prevents the perception node from allocating too many new centroids in a short time span. In one embodiment, a refractory value of a perception node is set to 1 when a new centroid is allocated, and the refractory value is decayed geometrically over time until the refractory value is below a threshold. Once the refractory value is below the threshold (e.g., 0.01), the refractory period is over and the perception node may again allocate a new centroid. In one embodiment, the refractory value is decayed based on amount of time passed (e.g., based on processor cycles). In one embodiment, the refractory value is decayed based on number of data items processed, such that the refractory value is updated with each new processed data item. The refractory value may be decayed, for example, by multiplying the refractory value by some fraction (e.g., 0.9999) each cycle. 
       FIG. 4A  is a graph illustrating an input signal received by a node of a machine learning architecture, in accordance with embodiments of the present disclosure.  FIG. 4B  is a graph illustrating error associated with an output signal of a second node of the machine learning architecture after the input signal of  FIG. 4A  has been processed, in accordance with embodiments of the present disclosure. As shown in  FIG. 4A , the input signal is noise, followed by a sine wave, followed by more noise. The sine wave is divided into a sequence of input signals  405  at time t 1 ,  410  at time t 2 ,  420  at time t 3 ,  425  at time t 4  and  430  at time t 5 . For input signals  405 - 425 , the perception node identifies a high novelty, thus satisfying novelty criteria for node allocation. However, as shown in  FIG. 4B , the error output in association with input signals  405 - 425  at times t 1 -t 4  is 0. Accordingly, no new centroids are allocated at times t 1 -t 4 . However, for each of these input signals  405 - 425  a centroid that most closely matches the given input signal  405 - 425  records that input signal. In one embodiment, a centroid stores a single input signal. Accordingly, if the same centroid is the winning centroid for multiple input signals, then the most recent input signal may overwrite a previous input signal for that centroid. 
     The input signal  430  at time t 5  (corresponding to the end of the sine wave) has a high novelty and is also associated with a high error (an error of 1). Accordingly, centroid allocation is triggered and a winning centroid that was closest to input signal  430  may allocate a new centroid having the values of the input signal  430 . In some embodiments, other centroids may have been the winning centroids for previous input signals  405 - 425 . One or more of these centroids may have stored the input signals  405 - 425  for which they were the winning centroid. These centroids may also allocate new centroids based on their respective stored input signals  405 - 425 . Accordingly, multiple centroids may be allocated together to quickly develop centroids that will be usable to identify the sine wave over time. An associated application node may then be trained as a sequence detector that interprets the output of a perception node that includes these centroids. The machine learning system could then later be exposed to triangle waveforms, for example, and use the existing centroids and/or allocate new centroids to identify triangle waveforms. 
     Referring back to  FIGS. 1A-1B , the application node  130 ,  131  is charged with mapping the sparse feature vector(s)  128 ,  128 A-N (or dense feature vectors) received from the perception node  114  (or perception nodes  114 A-N) to the desired output space (e.g., using mapping component  135 ,  136 ). The output space may be a classification decision, a prediction signal (or signals), or an action to control or take place in some environment. To that end, the application node  130 ,  131  remains general and is not confined to a particular problem domain structure. 
     In one embodiment, the application node  130 ,  131  comprises a linear function of the feature elements from the sparse feature vector(s)  128 ,  128 A-N (or dense feature vectors). Alternatively, a non-linear function may be used. Assuming N inputs and M outputs to the application system, the following linear mapping exists between the inputs and the outputs: 
               y   i     =         ∑     j   =   1     N     ⁢       X   j     ⁢     w   ij         +     b   i             
where index i denotes the output signal (output element) index, index j denotes the input (feature element) signal index and w ij  denotes a dedicated weight from input j to output i, X j  denotes a value for a feature element j from a received sparse vector, y i  denotes the value for a particular output element i, and b i  denotes a constant associated with i.
 
     This formulation implies that the mapping created by the application node  130 ,  131  is piecewise linear with respect to the function that is approximated. Moreover, assuming a sparse encoding of the input signals, only one signal from a given sparse feature vector is nonzero, which when denoted by j yields the simplified expression:
 
 y   i   =X   j   w   ij   +b   i  
 
     Let d(i) denote the target value for output i given a specific input to the application node. Furthermore, let&#39;s define the error for node i as the difference between the target and the output in one embodiment, such that:
 
 e   i   =d   i   −y   i  
 
     In one embodiment, we derive the weight update rule for the weight associated with a nonzero feature value and the output for which the target exists using gradient descent. To do so, we minimize the negative gradient (partial derivative) of the squared error with respect to weight w ij , such that: 
     
       
         
           
             
               w 
               ij 
             
             = 
             
               
                 
                   w 
                   ij 
                 
                 - 
                 
                   η 
                   ⁢ 
                   
                     
                       ∂ 
                       
                         e 
                         i 
                         2 
                       
                     
                     
                       ∂ 
                       
                         w 
                         ij 
                       
                     
                   
                 
               
               = 
               
                 
                   w 
                   ij 
                 
                 + 
                 
                   2 
                   ⁢ 
                   η 
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     e 
                     i 
                   
                   ⁢ 
                   
                     X 
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     The above is a localized learning rule that correlates the error and the input with the weight change. Alternatively, if dense feature vectors are used, then a threshold may be applied, and updates to any weights that are below the threshold may be discarded to achieve a similar result. 
     The gradient descent used for updating the application node  130 ,  131  in embodiments does not involve deep (i.e. multiple-layer) propagation of gradients, and thus does not rely on a backpropagation like scheme, in contrast to traditional artificial neural networks training methodologies. Artificial neural networks typically use deep gradient descent and backpropagation to update every node in the artificial neural network for a given output error. This results in a heavily entangled state where every feature element of every node contributes to each output decision. In contrast, in embodiments an error (or other novelty rating) is broadcast directly to each node, and each node may determine whether or not to make any updates to its internal representations based on the error or other novelty rating. 
     In one embodiment, a single-layer linear transformation employed by the application node  130 ,  131  offers mitigation of forgetting effects and hence supports lifelong learning. If two different underlying patterns received by the perception node(s)  114 ,  114 A-N yield disjoint (i.e. non-overlapping) representations exported to the application node  130 ,  131 , forgetting is completely avoided given that updates to one class do not impact the other. This is because sparse feature vectors  128 ,  128 A-N have one or a few non-zero values. Updates to weights in the application node  130 ,  131  are performed by multiplying values of feature elements by some value associated with the error in embodiments. However, if the value of a feature element is zero, then anything multiplied by that value is also zero, which results in no change to the weight associated with that feature element. Accordingly, only those feature elements with non-zero values are updated. 
     In one embodiment, a plasticity factor is applied to the weight update mechanism, such that weights that have undergone numerous changes will undergo very small changes in subsequent steps. This is done in order to stabilize the mapping learned so that future inputs don&#39;t distort previously learned inputs to output mappings. A possible interpretation of this plasticity modulation takes the form: 
               w   ij     =         w   ij     -     η   ⁢       ∂     e   i   2         ∂     w   ij         ⁢     p   ij         =       w   ij     +     2   ⁢   η   ⁢           ⁢     e   i     ⁢     X   j     ⁢     p   ij                 
where p ij  is the plasticity term associated with weight w i,j .
 
     One possible interpretation of the plasticity term will be to have each p ij  be initiated to the value of 1. With each update to weight w ij  said plasticity term will be decayed proportionally to the weight update magnitude. This will result in gradual decrease in the plasticity term thus decreasing the weight change magnitude. 
     As set forth above, the perception node(s)  114 ,  114 A-N use a first update model and application node  130 ,  131  uses a second update model. The perception nodes  114 ,  114 A-N of the perception subsystem  110 ,  111  and the application node  130 ,  131  of the application subsystem  115 ,  116  are co-trained using the above described methodologies. The co-training enables the perception subsystem  110 ,  111  to learn how best to generate representations that are usable by the application subsystem  115 ,  116  to generate accurate and meaningful outputs. 
       FIGS. 6A-E  are flow diagrams illustrating a method  600  for performing lifelong learning in a machine learning system, in accordance with embodiments of the present disclosure. The method  600  may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processor), firmware, or a combination thereof. The method  600  may be performed, for example, by a computing device such as computing device  700  executing a machine learning system  780  of  FIG. 7 . 
     For simplicity of explanation, the method  600  is depicted and described as a series of acts. However, acts in accordance with this disclosure can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts may be required to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states via a state diagram or events. 
     At block  602  of method  600 , a machine learning system receives a data item at a time t. The machine learning system includes a perception subsystem comprising one or more perception nodes and an application subsystem comprising one or more application nodes, as described herein above. The data item may be any type of data, including an image, an audio file, a text document, software code, a stock quote, weather data, trajectory data, and so on. A nature of the data item may depend on what the machine learning system is being trained to do and/or what the machine learning system has been trained to do. For example, if the machine learning system is trained to classify images, then the data item may be an image. If the machine learning system has been trained to interpret speech, then the data item may consist of a sequence of acoustic feature vectors extracted from an audio file or audio stream. The data item may be considered a data point in a multi-dimensional space. The data item may be received by a first node of the machine learning system (e.g., a perception node). 
     In some embodiments, the previous data item was stored, and the received data item is compared to some criteria to determine whether to process that data item (e.g., whether to increment a clock cycle for the machine learning system). In one embodiment, the received data item is compared to a previous data item, and a difference measure or similarity measure is determined based on a result of the comparison. If the distance is below a distance threshold (or the similarity is above a similarity threshold), then the current data item is very similar to the previous data item or data items, and may not generate a different result. Accordingly, it may not be worthwhile to process the current data item. In one embodiment, a moving average of distances between current input and previous input is maintained. If the moving average is below the distance threshold, then the most recent data item may not be processed. 
     At block  604 , the first node of the machine learning system receives a first feature vector generated by the first node from a previous data item that was received at a previous time t−1. 
     At block  606 , the first node of the perception subsystem processes a combined input that includes the data item (or a portion of the data item or representation of a portion of the data item) and the first feature vector (or a portion of the first feature vector). The first node may include a set of centroids, and may compare the combined input to each of the centroids. The first node may generate a second feature vector with a different feature element value associated with a similarity of the combined input to or distance of the combined input from each of the centroids. For example, if the first node contains three centroids, then a generated feature vector may have a first feature element value associated with a distance from the first centroid, a second feature element value associated with a distance from the second centroid and a third feature element value associated with a distance from a third centroid. 
     In some embodiments, the algorithm used to compute the distance measure may weight different dimensions depending on whether those dimensions originate from a data item or from a previous feature vector. Such weighting may be used to either emphasize or deemphasize elements of the spatial patterns and/or temporal patterns. For example, an image may include many dimensions (e.g., on the order of tens, hundreds, thousands, etc.), and the first feature vector may have far fewer dimensions (e.g., 32, 64, etc.). The number of dimensions of the first feature vector may be based on the number of centroids in the first node. By increasing the weighting for the dimensions from the first feature vector higher than dimensions from the data item, the temporal element represented in the feature vector may be emphasized. 
     In some embodiments, to determine a distance to a centroid that centroid is partitioned into a first set of dimensions that are based on data items and a second set of dimensions based on feature vectors. A first distance measure (or a first similarity sub-value) is computed between the first set of dimensions and the data item portion of the combined input and second distance measure (or a second similarity sub-value) is computed between the second set of dimensions and the first feature vector portion of the combined input. The first distance and the second distance (or first and second similarity sub-values) may then be combined to arrive at the distance measure (or similarity measure). 
     At block  608 , the first node may retain the second feature vector. This enables the second feature vector to be delayed by one or more time unit, and then fed back into the first node together with a new data item (e.g., at time t+1). By combining the data item with a feature vector based on previous data items into a combined input, temporal and spatial-temporal regularities may be captured by the first node as well as spatial patterns. 
     At block  610 , the first node selects a subset of feature elements in the feature vector having highest similarity values (or smallest distance values). In one embodiment, a winner-takes-all approach is used, and a single feature element with a highest feature element value is selected. Alternatively, two or more feature elements with highest values are selected. 
     At block  612 , the first node generates a sparse feature vector based on the selected subset of feature elements by replacing the non-selected values with zeros. In an example, the second feature vector may have the values (0.01, 0.12, 0.87). The feature element with the feature element value of 0.87 may be selected, and a sparse feature vector having the values (0,0,0.87) may be generated. 
     At block  614 , the first node sends the sparse feature vector to a second node of the application subsystem. At block  616 , the second node processes the sparse feature vector to determine an output. As discussed herein above, many different types of outputs are possible, depending on what the machine learning system has been trained to do. Some example outputs include a classification of the data item, a prediction based on the data item, or an operation that affects some environment. Examples of operations may include movement commands (e.g., to be made by a robotic system or automated vehicle), words or sentences (e.g., to be output by an internet bot), and so on. 
     The second node may apply the sparse feature vector to a linear or non-linear function, as set forth above. The function may be used to determine a score or value for each output element, and may select an output element with a highest score as the output. Alternatively, an output may include a separate value for each output element. In some embodiments, the second node further receives additional sparse feature vectors from one or more other nodes of the perception subsystem, and the multiple sparse feature vectors are all used as inputs into the linear or non-linear function to determine the output. 
     In embodiments, the machine learning system does not have distinct training modes and inference modes. Instead, the machine learning system may continually learn while in a production environment using both inputs that include targets (e.g., labeled inputs) and inputs that lack targets (e.g., unlabeled inputs). The perception subsystem and the application subsystem may co-train in a lifelong learning paradigm, where the application subsystem continually learns to improve the accuracy of its outputs and the perception subsystem continually learns to produce representations (feature vectors) that enable the application system to accurately determine outputs. 
     Continuing at  FIG. 6B , at block  622 , processing logic determines whether the input data item includes a target output (e.g., a label). If the data item includes a target, then an error may be calculated and used to update one or more nodes. If the data item does not include a target, then no error may be calculated. However, a confidence value may be determined even if there is no target, and the confidence value may be used to determine a relevancy. If the data item includes a target, the method continues to block  624 . If the data item does not include a target, the method proceeds to block  635 . 
     At block  624 , processing logic determines an error for the output generated by the second node of the application subsystem. In one embodiment, the error is computed based on a difference between the target output and the generated output. In a further embodiment, the error is computed as an accumulation of errors across multiple data items. The accumulation may be an accumulated error within a moving window, for example. In a further embodiment, the error is computed as an accumulation of errors that is adjusted by a decay factor. The decay factor may reduce a current accumulation of errors at every interval (e.g., with every time increment or every new data item), for example. A few examples of error calculations are provided above with reference to  FIGS. 1A-B . 
     At block  626 , processing logic updates weights in the second node associated with a feature element or feature elements that contributed to the output. The weights associated with feature elements that contributed to the output are updated without updating the weights associated with additional feature elements that did not contribute to the output. Those feature elements that did not contribute to the update have zero values. Accordingly, if a simple gradient descent update mechanism is used to update the weights of the second node, update values for each of the feature elements with zero values are multiplied by zero, resulting in no changes being made to the associated weights. Accordingly, the use of sparse feature vectors enables updates to weights of feature elements that are relevant to a particular output without updating weights of feature elements that are not relevant to that particular output. This prevents the second node from forgetting how to accurately produce other outputs while training the second node to accurately produce a first output. Some examples of update mechanisms for the second node have been discussed above with reference to  FIGS. 1A-B . 
     At block  630 , processing logic sends the error to the first node of the perception subsystem. In the case where there are multiple nodes in the perception subsystem, processing logic may broadcast the error to each of the perception nodes. Each perception node may then make its own determination as to whether any centroids of that node should be updated. Note that the error is not back propagated through any intermediate layers of nodes to get to the perception nodes in embodiments. Instead, the error may be sent directly to each perception node in some embodiments. This may reduce or prevent entanglement between nodes with regards to their contribution to particular outputs. 
     At block  635 , processing logic determines a relevancy rating as well as a novelty rating for the data item with regards to the first node. The novelty and relevancy rating may also be determined with regards to other perception nodes if other perception nodes are present. Accordingly, the operations of block  635  may be performed independently for each perception node. Processing logic then determines whether the relevancy rating satisfies one or more relevancy criteria and determines whether the novelty rating satisfies one or more novelty criteria separately for each perception node. The relevancy criteria may include a first relevancy criterion for high relevancy and a second relevancy criterion for low relevancy. Similarly, the novelty criteria may include a first novelty criterion for high novelty and a second novelty criterion for low novelty. In one embodiment, the relevancy rating is determined globally for a data item such that the same relevancy rating is used for each perception node. In one embodiment, the novelty rating is computed separately for each perception node. 
     In one embodiment, the relevancy rating is based on a computed error. For example, the relevancy rating may be based on direct error, on accumulated error, or on some other function of error. In some embodiments, a relevancy rating may be computed even in the absence of an error value (e.g., in the absence of a target associated with the data item). In one embodiment, the output determined at block  430  includes or is accompanied by a determined confidence score. The second node may be trained to generate a confidence score for each output that it generates. The confidence score may indicate a confidence that the output is a correct output. If the data item did not include a target, and thus no error was computed, then the confidence score may be used as the relevancy rating or to determine the relevancy rating. For example, a high error may be correlated with a low confidence, and a low error may be correlated with a high confidence. Accordingly, a high error and/or a low confidence may result in a high relevancy, and a low error and/or a high confidence may result in a low relevancy. 
     In one embodiment, one or more relevancy thresholds are used to determine whether the relevancy rating is representative of a high relevancy or a low relevancy. The relevancy threshold (or thresholds) may be static thresholds or dynamic thresholds, as discussed in greater detail above. If the relevancy rating meets or exceeds a relevancy threshold, then a high relevancy may be determined. If the relevancy rating is below a relevancy threshold, then a low relevancy may be determined. 
     As mentioned, a relevancy rating may be determined for an input and associated output, and the same relevancy rating may be used by multiple different perception nodes (in the use case that more than one perception node is present in the perception subsystem). In contrast, each perception node may determine its own novelty rating for the input and/or output. In one embodiment, the novelty rating for a perception node is determined based on comparing the combined input (data item plus first feature vector) to each centroid in the perception node and computing distances between the centroids and the combined input in a multi-dimensional space. Novelty may be determined based on one or more shortest distances (or highest similarities) that are computed between the combined input and a centroid. The novelty criteria may include a distance threshold (or similarity threshold). The one or more shortest distances may be compared to the distance threshold (or similarity threshold), and the combined input may be determined to have high novelty if the one or more shortest distances exceed or meet the distance threshold (or are equal to or less than the similarity threshold), and may be determined to have low novelty if the one or more distances are below the distance threshold (or are above the similarity threshold). The distance threshold (or similarity threshold) may be a static threshold or a dynamic threshold. Additionally, the distance threshold may be different for each node and/or for each centroid. In one embodiment, the distance threshold associated with a centroid is based on a standard deviation associated with that centroid (e.g., based on the data items that have been used to update that centroid). 
     If at block  635  it is determined that a data item has low novelty and low relevancy, the method continues to block  642  shown in  FIG. 6C . If it is determined that the data item has low novelty and high relevancy, the method continues to block  662  at  FIG. 6D . If it is determined that the data item has high novelty and low relevancy, the method continues to block  662 . If it is determined that the data item has high novelty and high relevancy, the method continues to block  682  at  FIG. 6E . 
     Referring to  FIG. 6C , at block  642  processing logic determines a feature element in the second feature vector having a highest feature element value. Each feature element may correspond to a different centroid, and the feature element values may represent similarity between an input and a given centroid. The feature element with the highest feature element value is the feature element that is most similar to the input, and represents the “winning” centroid. Accordingly, at block  644  processing logic determines a centroid of the first node that is associated with the determined feature element. 
     At block  645 , processing logic resets an update multiplier (update eligibility factor) for the determined centroid to a preset value. In embodiments, the preset value is the highest value for the update multiplier. In one embodiment, the preset value is 1. The update multiplier may be used to adjust how much to update a centroid based on how recently that centroid was the winning centroid. By resetting the update multiplier for the currently winning centroid, that centroid will be updated by the highest possible amount. 
     At block  646 , processing logic updates the winning centroid of the first node. The winning centroid may be updated by moving that centroid towards the combined input in the multi-dimensional space. The updated centroid may be a centroid associated with a feature element in the feature vector having a highest feature element value (e.g., highest similarity value). In one embodiment, the centroid is updated using an online clustering process, as described above. 
     At block  648 , processing logic applies a decay factor to the update multipliers of all other centroids of the first node (e.g., those centroids that are not the winning centroid). The update multiplier may be decayed over time with each new input. Accordingly, a centroid that was a winning centroid 30 steps in the past may have been decayed to a value that is close to or approximately 0. However, a centroid that was recently the winning centroid (e.g., for an input that was received 2-8 steps in the past) may be updated. However, the magnitude of the update may be reduced. 
     At block  650 , processing logic may determine a subset of other centroids (those that are not presently the winning centroid) having an update multiplier that is above a threshold. The threshold may be, for example, 0.1, 0.05, 0.01, 0.15, or some other value. At block  652 , processing logic updates the other centroids of the first node. In one embodiment, all centroids of the first node that have update multipliers that are over the threshold are candidates for update. Alternatively, the operation of block  650  may be omitted, and all centroids may be eligible for update. However, centroids having an update multiplier that is very small will undergo at most a marginal update. The update for each centroid may be based on the input that is stored by that centroid modulated by one or more of a plasticity factor, an update multiplier, an error, and so on. By updating some or all centroids, and modulating the updates based on the update multiplier, a trace of updates may be made. Centroids that were recently winners may be more relevant to the current output and so may be updated more than centroids that were less recently winners. 
     In one embodiment, the updated centroids are associated with plasticity factors that affect the magnitude of changes to those centroids. Each centroid may be associated with its own plasticity factor. At block  654 , the plasticity factors for the centroids that were updated are decreased. In one embodiment, each plasticity factor is compared to a plasticity factor threshold prior to updating the associated centroid at block  646  and/or  652 . If a plasticity factor is below the plasticity factor threshold, then no update is made to the centroid having that plasticity factor in one embodiment. 
     Referring to  FIG. 6D , at block  662  processing logic determines a feature element in the feature vector having a highest feature element value. At block  664 , processing logic determines a centroid of the first node associated with the determined feature element (the winning centroid). At block  666 , processing logic stores the combined input comprising the first feature vector and the data item for the determined centroid. At block  667 , processing logic resets the update multiplier for the determined centroid to the preset value. At block  668 , processing logic applies a decay factor to the update multipliers associated with each of the other centroids of the first node (the non-winning centroids). At block  670 , no centroids of the first node are updated since update criteria were not satisfied. 
     Referring to  FIG. 6E , at block  682  processing logic determines a feature element in the second feature vector having a highest feature element value. At bock  684 , processing logic determines a centroid of the first node associated with the determined feature element (the winning centroid). At block  686 , processing logic allocates a first new centroid in the first node for the first centroid. The new centroid may have a position in a multi-dimensional space that is equal to the combined input (e.g., a combination of the data item and the first feature vector) in the multi-dimensional space. 
     At block  688 , processing logic may apply a decay factor to the update multipliers of other centroids (non-winning centroids) of the first node. At block  690 , processing logic may determine a subset of other centroids having an update multiplier that is above a threshold. The threshold may be an allocation threshold. Some example allocation threshold values include 0.5, 0.8, 0.4, 0.6, or other value. Relevancy for each centroid may be proportional to that centroid&#39;s update multiplier (eligibility value). Those centroids with too low an update multiplier may not be relevant to the current output, and so may not warrant allocation of new centroids. At block  692 , for each centroid in the subset, processing logic allocates an additional new centroid. Each additional new centroid that is generated for an existing centroid may be based on a previous input (e.g., combination of a previous data item and previous feature vector) stored by that existing centroid. 
     In one embodiment, at block  484  processing logic sets a refractory period for the first node. No new centroids may be added to the first node during the refractory period. 
     As set forth above in method  400 , different techniques are used for updating the second (application) node and the first (perception node) and different criteria are used to determine when perception nodes are to be updated and when application nodes are to be updated. By applying these different techniques and criteria, the perception subsystem and application subsystem may be co-trained in a manner that results in lifelong learning, localized learning and high sample efficiency. Additionally, the machine learning system is trained continuously with both data items that include targets and data items that do not include targets. Accordingly, the machine learning system achieves semi-supervised learning. 
       FIG. 7  illustrates a diagrammatic representation of a machine in the exemplary form of a computing device  700  within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. The computing device  700  may be in the form of a computing device within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server machine in client-server network environment. The machine may be a personal computer (PC), a set-top box (STB), a server computing device, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
     The computing device  700  includes a processing device (processor)  702 , a main memory  704  (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory  706  (e.g., flash memory, static random access memory (SRAM)), and a data storage device  718 , which communicate with each other via a bus  730 . 
     Processing device  702  represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device  702  may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device  702  may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. 
     The computing device  700  may further include a network interface device  708 . The computing device  700  also may include a video display unit  710  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device  712  (e.g., a keyboard), a cursor control device  714  (e.g., a mouse), and a signal generation device  716  (e.g., a speaker). 
     The data storage device  718  may include a computer-readable medium  728  on which is stored one or more sets of instructions  722  (e.g., instructions of machine learning system  780 ) embodying any one or more of the methodologies or functions described herein. Machine learning system  780  may correspond to machine learning system  105  of  FIG. 1A  and/or machine learning system  106  of  FIG. 1B  in embodiments. The instructions  722  may also reside, completely or at least partially, within the main memory  704  and/or the processing device  702  during execution thereof by the computer system  700 , the main memory  704  and the processing device  702  also constituting computer-readable media. 
     While the computer-readable storage medium  728  is shown in an exemplary embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any non-transitory medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies described herein. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media. 
     The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a good understanding of several embodiments of the present invention. It will be apparent to one skilled in the art, however, that at least some embodiments of the present invention may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring embodiments of the present invention. Thus, the specific details set forth are merely exemplary. Particular implementations may vary from these exemplary details and still be contemplated to be within the scope of the present invention. 
     In the above description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that embodiments of the invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the description. 
     Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining”, “processing”, “receiving”, “generating” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     Embodiments of the invention also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions. 
     It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.