19 Jul PDF Reasoning System Symbolic , Statistical Artificial Intelligence Click for Website Shivani Sharma

DataSpace: Neurosymbolic Machine Learning for Reasoning

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. It’s most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes. Like in so many other respects, deep learning has had a major impact on neuro-symbolic AI in recent years. This appears to manifest, on the one hand, in an almost exclusive emphasis on deep learning approaches as the neural substrate, while previous neuro-symbolic AI research often deviated from standard artificial neural network architectures [2]. However, we may also be seeing indications or a realization that pure deep-learning-based methods are likely going to be insufficient for certain types of problems that are now being investigated from a neuro-symbolic perspective.

Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life. That is, a symbol offers a level of abstraction above the concrete and granular details of our sensory experience, an abstraction that allows us to transfer what we’ve learned in one place to a problem we may encounter somewhere else. In a certain sense, every abstract category, like chair, asserts an analogy between all the disparate objects called chairs, and we transfer our knowledge about one chair to another with the help of the symbol. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions.

The Secret of Neuro-Symbolic AI, Unsupervised Learning, and Natural Language Technologies

Researchers investigated a more data-driven strategy to address these problems, which gave rise to neural networks’ appeal. While symbolic AI requires constant information input, neural networks could train on their own given a large enough dataset. Although everything was functioning perfectly, as was already noted, a better system is required due to the difficulty in interpreting the model and the amount of data required to continue learning. A key factor in evolution of AI will be dependent on a common programming framework that allows simple integration of both deep learning and symbolic logic. Symbolic AI’s strength lies in its knowledge representation and reasoning through logic, making it more akin to Kahneman’s “System 2” mode of thinking, which is slow, takes work and demands attention.

Using symbolic knowledge bases and expressive metadata to improve deep learning systems. Metadata that augments network input is increasingly being used to improve deep learning system performances, e.g. for conversational agents. Metadata are a form of formally represented for example a knowledge base, a knowledge graph or other structured background knowledge, that adds further information or context to the data or system.

Composing Neural Learning and Symbolic Reasoning with an Application to Visual Discrimination

These are not merely buzz words — they’re techniques that have literally triggered a renaissance of artificial intelligence leading to phenomenal advances in self-driving cars, facial recognition, or real-time speech translations. Minerva, the latest, greatest AI system as of this writing, with billions of “tokens” in its training, still struggles with multiplying 4-digit numbers. When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence.

For example, a computer system with an average 1 GHz CPU can process around 200 million logical operations per second (assuming a CPU with a RISC-V instruction set). This processing power enabled Symbolic AI systems to take over manually exhaustive and mundane tasks quickly. The conversion of the facts of a case and relevant legislation into Prolog is an interesting problem, because it involves an interpretation of real world events which have to be included into Prolog. Logical Neural Networks (LNNs) are neural networks that incorporate symbolic reasoning in their architecture.

Neuro-Symbolic AI: A Reunion of Symbol and Neuron

Therefore, implicit knowledge tends to be more ambiguous to explain or formalize. Examples of implicit human knowledge include learning to ride a bike or to swim. Note that implicit knowledge can eventually be formalized and structured to become explicit knowledge.

But not everyone is convinced that this is the fastest road to achieving general artificial intelligence. This is why we need a middle ground — a broad AI that can multi-task and cover multiple domains, but which also can read data from a variety of sources (text, video, audio, etc), whether the data is structured or unstructured. But although computers are generally much faster and more precise than the human brain at sequential tasks, such as adding numbers or calculating chess moves, such programs are very limited in their scope. Finally, this chapter also covered how one might exploit a set of defined logical propositions to evaluate other expressions and generate conclusions. This chapter also briefly introduced the topic of Boolean logic and how it relates to Symbolic AI. Comparing both paradigms head to head, one can appreciate sub-symbolic systems’ power and flexibility.

Machine learning alone—particularly when only manifest as supervised learning—isn’t enough to handle sophisticated question answering and natural language technology applications at enterprise scale, speed, and affordability. Those who unduly rely on this approach are utilizing only half of AI’s potential to solve business problems with innovative methods. Since the program has logical rules, we can easily trace the conclusion to the root node, precisely understanding the AI’s path.

• These systems are essentially piles of nested if-then statements drawing conclusions about entities (human-readable concepts) and their relations (expressed in well understood semantics like X is-a man or X lives-in Acapulco).
• For organizations looking forward to the day they can interact with AI just like a person, symbolic AI is how it will happen, says tech journalist Surya Maddula.
• In a nutshell, Symbolic AI has been highly performant in situations where the problem is already known and clearly defined (i.e., explicit knowledge).
• We have laid out some of the most important currently investigated research directions, and provided literature pointers suitable as entry points to an in-depth study of the current state of the art.

We use symbols to standardize or, better yet, formalize an abstract form. At face value, symbolic representations provide no value, especially to a computer system. However, we understand these symbols and hold this information in our minds. In our minds, we possess the necessary knowledge to understand the syntactic structure of the individual symbols and their semantics (i.e., how the different symbols combine and interact with each other). It is through this conceptualization that we can interpret symbolic representations. Moving away from logic programming, other researchers are using machine learning approaches in law.

Conversational AI with no need for data training

There are now several efforts to combine neural networks and symbolic AI. One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab. NSCL uses both rule-based programs and neural networks to solve visual question-answering problems.

What is the probability that a child is nearby, perhaps chasing after the ball? This prediction task requires knowledge of the scene that is out of scope for traditional computer vision techniques. More specifically, it requires an understanding of the semantic relations between the various aspects of a scene – e.g., that the ball is a preferred toy of children, and that children often live and play in residential neighborhoods.

Reasoning:

Meanwhile, the human brain can recognize and label objects effortlessly and with minimal training — basically we only need one picture. If you show a child a picture of an elephant — the very first time they’ve ever seen one — that child will instantly recognize that a) that is an animal and b) that this is an elephant next time they’ll come across that animal, either in real life or in a picture. For decades, engineers have been programming machines to perform all sorts of tasks — from software that runs on your personal computer and smartphone to guidance control for space missions.

Research in neuro-symbolic AI has a very long tradition, and we refer the interested reader to overview works such as Refs [1,3] that were written before the most recent developments. Indeed, neuro-symbolic AI has seen a significant increase in activity and research output in recent years, together with an apparent shift in emphasis, as discussed in Ref. [2]. Below, we identify what we believe are the main general research directions the field is currently pursuing. It is of course impossible to give credit to all nuances or all important recent contributions in such a brief overview, but we believe that our literature pointers provide excellent starting points for a deeper engagement with neuro-symbolic AI topics. “As impressive as things like transformers are on our path to natural language understanding, they are not sufficient,” Cox said.

Nevertheless, Neuro-Symbolic AI takes it a step further, leveraging symbolic reasoning to unveil more intriguing facets of the item, such as its area, volume, and other pertinent attributes. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge. This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. This makes it exceptionally adept at understanding context and not just raw data.

Symbolic artificial intelligence is very convenient for settings where the rules are very clear cut,  and you can easily obtain input and transform it into symbols. In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications. Symbolic AI algorithms are used in a variety of applications, including natural language processing, knowledge representation, and planning. Neuro-symbolic AI is a synergistic integration of knowledge representation (KR) and machine learning (ML) leading to improvements in scalability, efficiency, and explainability. The topic has garnered much interest over the last several years, including at Bosch where researchers across the globe are focusing on these methods.

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