Bridging Symbols and Neurons: A Gentle Introduction to Neurosymbolic Reinforcement Learning and Planning

OpenAI as an organization are very good at listening and quickly improving based on feedback. And right now, what seems to be their view is that this reinforcement learning from human feedback will solve most of the problems and will get them to AGI. They have smart researchers and know these arguments, but they think providing these expert comparisons with RLHF will get them to AGI. But at the same time, they cannot scale this infinitely, because this solution requires to expert labelling.

As defined, maybe as a system that can do as many things as, for example, an average person in 2022. When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade. By 2015, his hostility toward all things symbols had fully crystallized. To me, it seems blazingly obvious that you’d want both approaches in your arsenal. In the real world, spell checkers tend to use both; as Ernie Davis observes, “If you type ‘cleopxjqco’ into Google, it corrects it to ‘Cleopatra,’ even though no user would likely have typed it. Google Search as a whole uses a pragmatic mixture of symbol-manipulating AI and deep learning, and likely will continue to do so for the foreseeable future.

The current state of symbolic AI

The combination of big data and increased computational power propelled breakthroughs in NLP, computer vision, robotics, machine learning and deep learning. In 1997, as advances in AI accelerated, IBM’s Deep Blue defeated Russian chess grandmaster Garry Kasparov, becoming the first computer program to beat a world chess champion. Strikingly, when relevant labels are unavailable, symbol-tuned Flan-PaLM-8B outperforms FlanPaLM-62B, and symbol-tuned Flan-PaLM-62B outperforms Flan-PaLM-540B. This performance difference suggests that symbol tuning can allow much smaller models to perform as well as large models on these tasks (effectively saving ∼10X inference compute).

In this paper, we place 20 years of research from the area of neurosymbolic AI, known as neural-symbolic integration, in the context of the recent explosion of interest and excitement about the combination of deep learning and symbolic reasoning. We revisit early theoretical results of fundamental relevance to shaping the latest research, and identify bottlenecks and the most promising technical directions for the sound representation of learning and reasoning in neural and symbolic systems. And there, researchers Hinton, Lecun, Bengio, led the neural network revolution in 2010.

AI Artificial Intelligence Learning and Reading Human Symbols Part 5

In the previous section we have shown how to efficiently compute a distribution over symbolic option models H. Recall that the ultimate purpose of H is to compute the success probabilities of plans (see Section 2.2). Thus, the quality of H is determined by the accuracy of its predicted plan success probabilities, and efficiently learning H corresponds to selecting the sequence of observations which maximizes the expected accuracy of H. However, it is difficult to calculate the expected accuracy of H over all possible plans, so we define a proxy measure to optimize which is intended to represent the amount of uncertainty in H. In this section, we introduce our proxy measure, followed by an algorithm for finding the exploration policy which optimizes it. The algorithm operates in an online manner, building H from the data collected so far, using H to select an option to execute, updating H with the new observation, and so on.

• Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.
• Symbolic AI has been successfully applied in various domains, including natural language processing, expert systems, automated reasoning, planning, and robotics.
• Hallucinations are also reduced with this reinforcement learning from human feedback method.
• The concept of neural networks (as they were called before the deep learning “rebranding”) has actually been around, with various ups and downs, for a few decades already.

However, recent advances in data-driven deep learning approaches have reignited this conversation in recent years. The war of words between proponents of both approaches, Dr. Yann LeCun (DL) and Dr. Gary Marcus (Symbolic AI), has been played out publicly. In the next article, we will then explore how the sought-after relational NSI can actually be implemented with such a dynamic neural modeling approach.

But in December, a pure symbol-manipulation based system crushed the best deep learning entries, by a score of 3 to 1—a stunning upset. A subset of the agent’s repertoire of concepts after the generalization experiment. In condition A, the concepts BLUE (A), BROWN (B), GRAY (C), and YELLOW (D) are always observed as cubes or spheres. The agent is not “distracted” by statistical distributions of the environment and learns combinations of attributes that are relevant to solve the communicative task. Furthermore, as these concepts are expressed using human-interpretable feature channels, the model and resulting repertoire of concepts is completely transparant. Figure 7B shows the lexicon size of the learner agent in both environments.

That’s actually a pretty moderate view, giving credit to both sides. Realistically, deep learning is only part of the larger challenge of building intelligent machines. Systems … use techniques like deep learning as just one element in a very complicated ensemble of techniques, ranging from the statistical technique of Bayesian inference to deductive reasoning. It has now been argued by many that a combination of deep learning with the high-level reasoning capabilities present in the symbolic, logic-based approaches is necessary to progress towards more general AI systems [9,11,12]. Deep neural networks are also very suitable for reinforcement learning, AI models that develop their behavior through numerous trial and error.

The essence of eigenvalues and eigenvectors in Machine Learning

Their arguments are based on a need to address the two kinds of thinking discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. 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. The concept of inanimate objects endowed with intelligence has been around since ancient times.

This approach has many advantages, including the ability to do one-shot learning and a powerful compositional representation of concepts that allows not only to classify concepts but also to generate them. While this model achieves impressive results, learning through pen stroke data offers a limited range of possibilities. Other researchers have tackled the Omniglot challenge, mostly using neural approaches as reported by Lake et al. (2019). Almost all of them have focussed on the one-shot classification task using the image data as input. As a result, the BPL approach remains the SOTA model for all tasks in the Omniglot challenge.

Deep Learning Is Hitting a Wall

It is a system with only one input, situation s, and only one output, action (or behavior) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioral environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioral environment.

Similar to how humans can grasp a concept after only a few exemplars, an autonomous agent should ideally acquire these concepts quickly and with relatively little data. Learned concepts should be general enough to extend to similar yet unseen settings. As the environment of the agent can change or new concepts can be introduced at any time, the learning methodology should also be adaptive and allow for incremental learning. Finally, to truly understand the reasoning processes of an autonomous agent, its learning mechanisms and representations should be fully transparent and interpretable in human-understandable terms. In this case the symbolic approach is Monte Carlo tree search and the neural techniques learn how to evaluate game positions. This “knowledge revolution” led to the development and deployment of expert systems , the first commercially successful form of AI software.

In contrast to Wellens (2012), the attributes are continuous, represented through a normal distribution. This enables the use of such concepts in grounded, embodied scenarios. We combine the obtained segments with the original image to extract a number of continuous-valued attributes. As with the previous environment, we foresee a number of continuous attributes for each symbolic attribute of the CLEVR objects. For colors, we extract both the mean and standard deviation of the color of the region, expressed in the HSV color space and split for each channel.

In many real-life networks, both the scale-free distribution of degree and small-world behavior are important features. There are many random or deterministic models of networks to simulate these features separately. Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them.

Read more about https://www.metadialog.com/ here.