Symbolic Deep Learning Explained
Beyond the symbolic vs non-symbolic AI debate by JC Baillie
New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector components is opaque. What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016.
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Next, in section 3, we have discussed related work and results that are pertinent to this article. Then, in section 4, we have presented the architecture of a system that could achieve the desired functionality shown in Figure 1 and shown how it can be trained and used at testing time. Of particular importance is the notion of the Hyperdimensional Inference Layer, which can effectively fuse symbolic representations in the hyperdimensional space. In section 5, we have outlined an experiment to test how well such an architecture would work in practice.
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Asked if the sphere and cube are similar, it will answer “No” (because they are not of the same size or color). A new approach to artificial intelligence combines the strengths of two leading methods, lessening the need for people to train the systems. So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient symbolic machine learning themselves in the world? Enter Tim Rocktäschel, a Research Scientist at Facebook AI Research London and a Lecturer in the Department of Computer Science at University College London. Much of Tim’s work has been focused on ways to make RL agents learn with relatively little data, using strategies known as sample efficient learning, in the hopes of improving their ability to solve more general problems.
As the authors state, their approaches are only valid for a turbulent regime due to the fact that a transition from a laminar to a turbulent regime is not efficiently described by the Colebrook equation. They supplement, in their previous works, approaches were made by genetic algorithms and neural networks to model this transition unefficiently. In a subsequent study [157], simpler equations were discovered that unify laminar and turbulent hydraulic regimes and therefore diminishing the need to account for changes in flow patterns at separate laminar or turbulent flow models [157].
Hinge-loss markov random fields and probabilistic soft logic
The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the amount of data that deep neural networks require in order to learn. This, in turn, enables AI to be trained using multiple techniques, including semantic inferencing and both supervised and unsupervised learning, which will ultimately create AI systems that can reason, learn, and engage in natural language question-and-answer interactions with humans. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research. Symbolic AI’s adherents say it more closely follows the logic of biological intelligence because it analyzes symbols, not just data, to arrive at more intuitive, knowledge-based conclusions.
On the solely symbolic representation and reasoning side, there exists relevant work on using cellular automata based hyperdimensional computing (Yilmaz, 2015). Some formulations based on real-valued vectors can also exhibit similar properties to long binary vectors so far as compositionality and decompositionality is concerned (Summers-Stay et al., 2018). There has been a wealth of ML algorithms being established throughout the years, from simple linear models to laborious deep learning architectures. Some of the most widely adopted are Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Decision Trees (DT) based. The idea behind ANN is that it reacts the same way as the neural networks of human brain, with its abilities spanning to applications such as classification, regression, learning and generalization [64].
“If the agent doesn’t need to encounter a bunch of bad states, then it needs less data,” says Fulton. While the project still isn’t ready for use outside the lab, Cox envisions a future in which cars with neurosymbolic AI could learn out in the real world, with the symbolic component acting as a bulwark against bad driving. Ducklings exposed to two similar objects at birth will later prefer other similar pairs. If exposed to two dissimilar objects instead, the ducklings later prefer pairs that differ. Ducklings easily learn the concepts of “same” and “different” — something that artificial intelligence struggles to do. Two separate experiments were performed to evaluate how well a structure like the one shown in Figure 1 would work in practice.
- Some questions are simple (“Are there fewer cubes than red things?”), but others are much more complicated (“There is a large brown block in front of the tiny rubber cylinder that is behind the cyan block; are there any big cyan metallic cubes that are to the left of it?”).
- Finally, in section 7, we have discussed our results and outlined the pros/cons of using hyperdimensional vectors to fuse learning systems together at a symbolic level as well as what future work is necessary.
- Supplementary applications, deal with models construction towards hydrate formation temperature estimation [181], estimation of equilibrium water dewpoint temperature [182] and the prediction of the gas compressibility factor [178].
- We began to add to their knowledge, inventing knowledge of engineering as we went along.
- Of outmost importance is the fact that SR can identify ambiguous relations in datasets and therefore provide a more profound solution [80].
- Neural networks are good at dealing with complex and unstructured data, such as images and speech.
They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.). Such transformed binary high-dimensional vectors are stored in a computational memory unit, comprising a crossbar array of memristive devices. A single nanoscale memristive device is used to represent each component of the high-dimensional vector that leads to a very high-density memory. The similarity search on these wide vectors can be efficiently computed by exploiting physical laws such as Ohm’s law and Kirchhoff’s current summation law.
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Data fabric developers like Stardog are working to combine both logical and statistical AI to analyze categorical data; that is, data that has been categorized in order of importance to the enterprise. Symbolic AI plays the crucial role of interpreting the rules governing this data and making a reasoned determination of its accuracy. Ultimately this will allow organizations to apply multiple forms of AI to solve virtually any and all situations it faces in the digital realm – essentially using one AI to overcome the deficiencies of another. The early pioneers of AI believed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Therefore, symbolic AI took center stage and became the focus of research projects. This approach was experimentally verified for a few-shot image classification task involving a dataset of 100 classes of images with just five training examples per class.
Bayesian statistics [112] in contrast to conventional statistics (also known as frequentist statistics) do not consider a fixed parameter, but they rather identify it as a random variable which can be described with a probability distribution. BNN functions as a typical Neural Network with the exception that the parameters are distributions, instead of a fixed value, and the training occurs via Bayesian inference. This is an important feature of BNN as it provides the ability to quantify uncertainties, meaning that the algorithm incorporates confidence intervals instead of a single point. Moreover, Bayesian inference considers every plausible scenario that could happen, and it marginalizes the parameters over the most possible outcome.
The video previews the sorts of questions that could be asked, and later parts of the video show how one AI converted the questions into machine-understandable form. Take, for example, a neural network tasked with telling apart images of cats from those of dogs. The image — or, more precisely, the values of each pixel in the image — are fed to the first layer of nodes, and the final layer of nodes produces as an output the label “cat” or “dog.” The network has to be trained using pre-labeled images of cats and dogs.
Kahneman describes human thinking as having two components, System 1 and System 2. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. In general, these networks use features provided by another system and compute hashes based on features extracted from the images into compact codes for image retrieval and classification. Finally, AlexNet (Krizhevsky et al., 2012) features pre-trained on ImageNet (Deng et al., 2009) are used in the DeepHash pipeline and are available for download from the GitHub repository.
Given multiple ML models, the HIL of each can be fused together by repeating the same training procedure. Thus, given an image, each hashing network converts it to a different binary vector, which is projected into hyperdimensional lengths. These are bound with symbolic vectors identifying each individual hashing network and aggregated via consensus sum.
When fused into a HIL, each contributes toward the overall classification result, allowing the best matching classification across all models simultaneously. One advantage of the hyperdimensional architecture for inference is how it can be easily manipulated. Of particular interest is when there are multiple models that can produce features in the form of hyperdimensional vectors for an input. We can fuse their output together to form a consensus system that will consider each network’s feature output before classification. We simply repeat the same method as we did for our classes but with symbolic identifiers for which model aggregated which data.
However, an inherent disadvantage of such approaches is bound to the fact that ML methods are prone to overfitting. Overfitting occurs when an algorithm is “finely” trained on a specific dataset, which in turn results on high statistical errors when applied to a different dataset. In such case, the proposed algorithm becomes unsuitable for further use, or in other words, ungeneralizable. To overcome this issue, various techniques have been proposed, such as hold-out, k-fold cross-validation, and regularization [16]. This creates a crucial turning point for the enterprise, says Analytics Week’s Jelani Harper.
Google AI introduces Symbol Tuning: A Simple Fine-Tuning Method that can improve in-Context Learning by Emphasizing Input–Label Mappings – MarkTechPost
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Overall, LNNs is an important component of neuro-symbolic AI, as they provide a way to integrate the strengths of both neural networks and symbolic reasoning in a single, hybrid architecture. When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence. Researchers tried to simulate symbols into robots to make them operate similarly to humans.
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