AI News, Imitation or something simpler? modeling simple mechanisms for ... artificial intelligence

Recurrent neural network

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence.

The term 'recurrent neural network' is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse.

A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.

Both finite impulse and infinite impulse recurrent networks can have additional stored state, and the storage can be under direct control by the neural network.

Such controlled states are referred to as gated state or gated memory, and are part of long short-term memory networks (LSTMs) and gated recurrent units.

In 1993, a neural history compressor system solved a 'Very Deep Learning' task that required more than 1000 subsequent layers in an RNN unfolded in time.[6]

In 2014, the Chinese search giant Baidu used CTC-trained RNNs to break the Switchboard Hub5'00 speech recognition benchmark without using any traditional speech processing methods.[11]

Basic RNNs are a network of neuron-like nodes organized into successive 'layers', each node in a given layer is connected with a directed (one-way) connection to every other node in the next successive layer.[citation needed]

Nodes are either input nodes (receiving data from outside the network), output nodes (yielding results), or hidden nodes (that modify the data en route from input to output).

For supervised learning in discrete time settings, sequences of real-valued input vectors arrive at the input nodes, one vector at a time.

At any given time step, each non-input unit computes its current activation (result) as a nonlinear function of the weighted sum of the activations of all units that connect to it.

For example, if the input sequence is a speech signal corresponding to a spoken digit, the final target output at the end of the sequence may be a label classifying the digit.

Instead a fitness function or reward function is occasionally used to evaluate the RNN's performance, which influences its input stream through output units connected to actuators that affect the environment.

An Elman network is a three-layer network (arranged horizontally as x, y, and z in the illustration) with the addition of a set of 'context units' (u in the illustration).

The fixed back-connections save a copy of the previous values of the hidden units in the context units (since they propagate over the connections before the learning rule is applied).

Thus the network can maintain a sort of state, allowing it to perform such tasks as sequence-prediction that are beyond the power of a standard multilayer perceptron.

Each neuron in one layer only receives its own past state as context information (instead of full connectivity to all other neurons in this layer) and thus neurons are independent of each other's history.

Given a lot of learnable predictability in the incoming data sequence, the highest level RNN can use supervised learning to easily classify even deep sequences with long intervals between important events.

Once the chunker has learned to predict and compress inputs that are unpredictable by the automatizer, then the automatizer can be forced in the next learning phase to predict or imitate through additional units the hidden units of the more slowly changing chunker.

LSTM works even given long delays between significant events and can handle signals that mix low and high frequency components.

to find an RNN weight matrix that maximizes the probability of the label sequences in a training set, given the corresponding input sequences.

continuous time recurrent neural network (CTRNN) uses a system of ordinary differential equations to model the effects on a neuron of the incoming spike train.

Note that, by the Shannon sampling theorem, discrete time recurrent neural networks can be viewed as continuous-time recurrent neural networks where the differential equations have transformed into equivalent difference equations.

multiple timescales recurrent neural network (MTRNN) is a neural-based computational model that can simulate the functional hierarchy of the brain through self-organization that depends on spatial connection between neurons and on distinct types of neuron activities, each with distinct time properties.[54][55]

With such varied neuronal activities, continuous sequences of any set of behaviors are segmented into reusable primitives, which in turn are flexibly integrated into diverse sequential behaviors.

In neural networks, it can be used to minimize the error term by changing each weight in proportion to the derivative of the error with respect to that weight, provided the non-linear activation functions are differentiable.

In this context, local in space means that a unit's weight vector can be updated using only information stored in the connected units and the unit itself such that update complexity of a single unit is linear in the dimensionality of the weight vector.

Local in time means that the updates take place continually (on-line) and depend only on the most recent time step rather than on multiple time steps within a given time horizon as in BPTT.

For recursively computing the partial derivatives, RTRL has a time-complexity of O(number of hidden x number of weights) per time step for computing the Jacobian matrices, while BPTT only takes O(number of weights) per time step, at the cost of storing all forward activations within the given time horizon.[64]

major problem with gradient descent for standard RNN architectures is that error gradients vanish exponentially quickly with the size of the time lag between important events.[35][68]

This fact improves stability of the algorithm, providing a unifying view on gradient calculation techniques for recurrent networks with local feedback.

A target function can be formed to evaluate the fitness or error of a particular weight vector as follows: First, the weights in the network are set according to the weight vector.

Initially, the genetic algorithm is encoded with the neural network weights in a predefined manner where one gene in the chromosome represents one weight link.The whole network is represented as a single chromosome.

Other global (and/or evolutionary) optimization techniques may be used to seek a good set of weights, such as simulated annealing or particle swarm optimization.

Whereas recursive neural networks operate on any hierarchical structure, combining child representations into parent representations, recurrent neural networks operate on the linear progression of time, combining the previous time step and a hidden representation into the representation for the current time step.

In particular, RNNs can appear as nonlinear versions of finite impulse response and infinite impulse response filters and also as a nonlinear autoregressive exogenous model (NARX).[75]

12 Blind Spots in AI Research

Her book “Lost in Math” explores the cognitive biases found in a group of the most intellectually gifted scientists on the planet.

Despite the surplus of cognitive capability, Hossenfelder argues that theoretical physicists’ bias that is in relentless pursuit of beauty has led to elegant mathematics but also wrong science.

This has lead to the following belief that many scientists have devoted their careers to: These are good rules of thumb for humans to comprehend the world, however, this is precisely the kind of appeal to aesthetics that Hossenfelder has argued against.

Breakthroughs in science will always be based on the gut feelings of scientists, however, these assumptions should never lead to a dogmatic application of these hunches.

The failure of GOFAI can be traced to the belief that human cognition is based primarily on a rational cognitive system despite all evidence to the contrary.

Incomplete Representations There is a long tradition of scientists to seek out the existence or the generation of internal mental representation that mirrors the external world.

Cognitive blindness exists and unlike machines, that are able to capture a photographic representation, we attend only to a visual subset and imagine the rest.

Perception apparently isn’t based on single snapshots of reality, but rather multiple snapshots of attention that are stitched together into a consistent whole.

We have better representations of the world because we have richer interactions with the world and are better integrators of disparate attentive information.

Most people cannot attend to more than five items at a time, furthermore almost all cannot go deeper than three levels of recursion.

The point here is that simple heuristics may be all that drives human thinking and we don’t have to appeal to elegant mathematical approaches.

Kolmogorov complexity is an interesting formulation of a complexity measure, however, this measure is unlikely to be relevant to bounded rational human-complete intelligence.

What this means is that scientists recognize order in the world in the form of invariant laws and these are used as compressed descriptions of reality.

However, descriptive models are not intrinsically part of reality, they are the observed order that emerges from complex generative behavior.

The dynamics of the cloud has no intentionality to generate an Easter bunny, it is our subjective observation of the world that sees order.

What this implies is that descriptive models of the world at best characterize the behavior of reality, but are not the same as a generative model.

Furthermore, the halting problem implies that discovering the hidden generative model through observational descriptive models may be unrealistic.

The problem with current machine learning methodology is that the mathematics always assumes some kind of equilibrium process.

This is because each new emergent capability expands the space of possibilities and thus something becomes known when it was previously unknown.

So when we begin to explore higher cognitive beings like ourselves, we must incorporate in our models the notion of a self (or many selves).

If every neuron has the same complex behavior as eukaryotic cells then the coordinated dynamic models found in Deep Learning may be inadequate to capture basic capabilities such as adaptation and self-repair.

However, to achieve the kind of adaptability found in biology, our cognitive models must be non-stationary, continual, just-in-time and conversational.

This should also imply that advanced cognitive capabilities like language could not have evolved separate from the cultural environment humans find themselves in.

We make the assumption that human cognitive development begins after birth when there’s ample evidence that cognitive development begins pre-birth and therefore a lot of the mother’s interaction and reaction to the world is reflected onto her child in the womb.

Recent experiments have revealed that in 299 age-linked genes in primates that were studied, 40 genes in humans are expressed later in life.

A young person has higher brain plasticity than adults and being young longer affords humans enough runway to develop their socially attained cognitive skills (i.e.

This final fact should tell you that an ossified learning strategy, one that clings to their ‘guns or religion or antipathy’ are in danger of failing to innovate.

The drum beat of constant computer security failures tells us that something drastically needs to be done to incorporate the kind of robustness we find in biology.

But what *is* a Neural Network? | Deep learning, chapter 1

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