A Beginner’s Guide to Symbolic Reasoning Symbolic AI & Deep Learning Deeplearning4j: Open-source, Distributed Deep Learning for the JVM

Below is a quick overview of approaches to knowledge representation and automated reasoning. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages. Symbolic AI algorithms are able to solve problems that are too difficult for traditional AI algorithms.

Other non-monotonic logics provided truth maintenance systems that revised beliefs leading to contradictions. The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create plans. Graphplan takes a least-commitment approach to planning, rather than sequentially choosing actions from an initial state, working forwards, or a goal state if working backwards. Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[88] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove.

“This grammar can generate all the questions people ask and also infinitely many other questions,” says Lake. “You could think of it as the space of possible questions that people can ask.” For a given state of the game board, the symbolic AI has to search this enormous space of possible questions to find a good question, which makes it extremely slow. Once trained, the deep nets far outperform the purely symbolic AI at generating questions. First, a neural network learns to break up the video clip into a frame-by-frame representation of the objects. This is fed to another neural network, which learns to analyze the movements of these objects and how they interact with each other and can predict the motion of objects and collisions, if any.

Powered by such a structure, the DSN model is expected to learn like humans, because of its unique characteristics. First, it is universal, using the same structure to store any knowledge. Second, it can learn symbols from the world and construct the deep symbolic networks automatically, by utilizing the fact that real world objects have been naturally separated by singularities. Third, it is symbolic, with the capacity of performing causal deduction and generalization.

Planning is used in a variety of applications, including robotics and automated planning. Using symbolic AI, everything is visible, understandable and explainable, leading to what is called a ‘transparent box’ as opposed to the ‘black box’ created by machine learning. Armed with its knowledge base and propositions, symbolic AI employs an inference engine, which uses rules of logic to answer queries.

This kind of knowledge is taken for granted and not viewed as noteworthy. This page includes some recent, notable research that attempts to combine deep learning with symbolic learning to answer those questions. While these advancements mark significant steps towards replicating human reasoning skills, current iterations of Neuro-symbolic AI systems still fall short of being able to solve more advanced and abstract mathematical problems.

For example, a Neuro-Symbolic AI system could learn to recognize objects in images (a task typically suited to neural networks) and also use symbolic reasoning to make inferences about those objects (a task typically suited to symbolic AI). This could enable more sophisticated AI applications, such as robots that can navigate complex environments or virtual assistants that can understand and respond to natural language queries in a more human-like way. There are now several efforts to combine neural networks and symbolic AI.

This approach involves the fusion of deep learning neural network topologies with symbolic reasoning techniques, thereby elevating the sophistication of AI beyond its traditional counterparts. For example, neural networks have proven effective in identifying an item’s shape or color. 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. It contained 100,000 computer-generated images of simple 3-D shapes (spheres, cubes, cylinders and so on).

During training, they adjust the strength of the connections between layers of nodes. The hybrid uses deep nets, instead of humans, to generate only those portions of the knowledge base that it needs to answer a given question. Henry Kautz,[17] Francesca Rossi,[79] and Bart Selman[80] have also argued for a synthesis. 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.

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. Knowledge representation algorithms are used to store and retrieve information from a knowledge base. Knowledge representation is used in a variety of applications, including expert systems and decision support systems. A hybrid approach, known as neurosymbolic AI, combines features of the two main AI strategies. In symbolic AI (upper left), humans must supply a “knowledge base” that the AI uses to answer questions.

Deep learning and neuro-symbolic AI 2011–now

Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available. Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning. Finally, their operation is largely opaque to humans, rendering them unsuitable for domains in which verifiability is important. In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings.

In ML, knowledge is often represented in a high-dimensional space, which requires a lot of computing power to process and manipulate. In contrast, symbolic AI uses more efficient algorithms and techniques, such as rule-based systems and logic programming, which require less computing power. So, while naysayers may decry the addition of symbolic modules to deep learning as unrepresentative of how our brains work, proponents of neurosymbolic AI see its modularity as a strength when it comes to solving practical problems.

They have created a revolution in computer vision applications such as facial recognition and cancer detection. Symbolic AI algorithms are used in a variety of AI applications, including knowledge representation, planning, and natural language processing. He is worried that the approach may not scale up to handle problems bigger than those being tackled in research projects. These have massive knowledge bases and sophisticated inference engines.

As you can easily imagine, this is a very heavy and time-consuming job as there are many many ways of asking or formulating the same question. And if you take into account that a knowledge base usually holds on average 300 intents, you now see how repetitive maintaining a knowledge base can be when using machine learning. In the CLEVR challenge, artificial intelligences were faced with a world containing geometric objects of various sizes, shapes, colors and materials. The AIs were then given English-language questions (examples shown) about the objects in their world. Maybe in the future, we’ll invent AI technologies that can both reason and learn. But for the moment, symbolic AI is the leading method to deal with problems that require logical thinking and knowledge representation.

Symbolic AI systems are only as good as the knowledge that is fed into them. If the knowledge is incomplete or inaccurate, the results of the AI system will be as well. Generative AI (GAI) has been the talk of the town since ChatGPT exploded late 2022. Symbolic AI is also known as Good Old-Fashioned Artificial Intelligence (GOFAI), as it was influenced by the work of Alan Turing and others in the 1950s and 60s. Most important, if a mistake occurs, it’s easier to see what went wrong. “You can check which module didn’t work properly and needs to be corrected,” says team member Pushmeet Kohli of Google DeepMind in London.

Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists. We began to add to their knowledge, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL more and more knowledge. The words sign and symbol derive from Latin and Greek words, respectively, that mean mark or token, as in “take this rose as a token of my esteem.” Both words mean “to stand for something else” or “to represent something else”.

While symbolic AI used to dominate in the first decades, machine learning has been very trendy lately, so let’s try to understand each of these approaches and their main differences when applied to Natural Language Processing (NLP). The tremendous success of deep learning systems is forcing researchers to examine the theoretical principles that underlie how deep nets learn. Researchers are uncovering the connections between deep nets and principles in physics and mathematics. Take, for example, a neural network tasked with telling apart images of cats from those of dogs.

One solution is to take pictures of your cat from different angles and create new rules for your application to compare each input against all those images. Even if you take a million pictures of your cat, you still won’t account for every possible case. A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail. We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson).

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. As opposed to pure neural network–based models, the hybrid AI can learn new tasks with less data and is explainable.

A more flexible kind of problem-solving occurs when reasoning about what to do next occurs, rather than simply choosing one of the available actions. This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture. Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages.

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. Symbolic AI is a subfield of AI that deals with the manipulation of symbols. Symbolic AI algorithms are designed to deal with the kind of problems that require human-like reasoning, such as planning, natural language processing, and knowledge representation. For other AI programming languages see this list of programming languages for artificial intelligence.

For example, a symbolic AI built to emulate the ducklings would have symbols such as “sphere,” “cylinder” and “cube” to represent the physical objects, and symbols such as “red,” “blue” and “green” for colors and “small” and “large” for size. The knowledge base would also have a general rule that says that two objects are similar if they are of the same size or color or shape. In addition, the AI needs to know about propositions, which are statements that assert something is true or false, to tell the AI that, in some limited world, there’s a big, red cylinder, a big, blue cube and a small, red sphere. All of this is encoded as a symbolic program in a programming language a computer can understand. A Neuro-Symbolic AI system in this context would use a neural network to learn to recognize objects from data (images from the car’s cameras) and a symbolic system to reason about these objects and make decisions according to traffic rules.

How Symbolic AI remains relevant today

Asked if the sphere and cube are similar, it will answer “No” (because they are not of the same size or color). But symbolic AI starts to break when you must deal with the messiness of the world. For instance, consider computer vision, the science of enabling computers to make sense of the content of images and video. Say you have a picture of your cat and want Chat PG to create a program that can detect images that contain your cat. You create a rule-based program that takes new images as inputs, compares the pixels to the original cat image, and responds by saying whether your cat is in those images. Multiple different approaches to represent knowledge and then reason with those representations have been investigated.

Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O. As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings.

However, the future of AI with Neuro-Symbolic AI looks promising as researchers continue to explore and innovate in this space. The potential of Neuro-Symbolic AI in advancing AI capabilities and adaptability is immense, and we can expect to see more breakthroughs in the near future. As we look to the future, it’s clear that Neuro-Symbolic AI has the potential to significantly advance the field of AI.

Again, the deep nets eventually learned to ask the right questions, which were both informative and creative. The second module uses something called a recurrent neural network, another type of deep net designed to uncover patterns in inputs that come sequentially. (Speech is sequential information, for example, and speech recognition programs like Apple’s Siri use a recurrent network.) In this case, the network takes a question and transforms it into a query in the form of a symbolic program. The output of the recurrent network is also used to decide on which convolutional networks are tasked to look over the image and in what order. This entire process is akin to generating a knowledge base on demand, and having an inference engine run the query on the knowledge base to reason and answer the question.

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. In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost.

Reasons Conversational AI is a Must-Have for Businesses This Holiday

Each method executes a series of rule-based instructions that might read and change the properties of the current and other objects. Similar to the problems in handling dynamic domains, common-sense reasoning symbolic ai examples is also difficult to capture in formal reasoning. Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures.

Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. And it’s very hard to communicate and troubleshoot their inner-workings. You can foun additiona information about ai customer service and artificial intelligence and NLP. The advantage of neural networks is that they can deal with messy and unstructured data. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat.

Inbenta works in the initially-symbolic field of Natural Language Processing, but adds a layer of ML to increase the efficiency of this processing. The ML layer processes hundreds of thousands of lexical functions, featured in dictionaries, that allow the system to better ‘understand’ relationships between words. Together, they built the General Problem Solver, which uses formal operators via state-space search using means-ends analysis (the principle which aims to reduce the distance between a project’s current state and its goal state). In a nutshell, symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs.

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. In 2019, Kohli and colleagues at MIT, Harvard and IBM designed a more sophisticated challenge in which the AI has to answer questions based not on images but on videos. The videos feature the types of objects that appeared in the CLEVR dataset, but these objects are moving and even colliding. If you ask it questions for which the knowledge is either missing or erroneous, it fails. In the emulated duckling example, the AI doesn’t know whether a pyramid and cube are similar, because a pyramid doesn’t exist in the knowledge base.

Like Inbenta’s, “our technology is frugal in energy and data, it learns autonomously, and can explain its decisions”, affirms AnotherBrain on its website. And given the startup’s founder, Bruno Maisonnier, previously founded Aldebaran Robotics (creators of the NAO and Pepper robots), AnotherBrain is unlikely to be a flash in the pan. This https://chat.openai.com/ will give a “Semantic Coincidence Score” which allows the query to be matched with a pre-established frequently-asked question and answer, and thereby provide the chatbot user with the answer she was looking for. This impact is further reduced by choosing a cloud provider with data centers in France, as Golem.ai does with Scaleway.

• First, a neural network learns to break up the video clip into a frame-by-frame representation of the objects.
• Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods.
• A more flexible kind of problem-solving occurs when reasoning about what to do next occurs, rather than simply choosing one of the available actions.
• For example, debuggers can inspect the knowledge base or processed question and see what the AI is doing.
• Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.).

The challenge for any AI is to analyze these images and answer questions that require reasoning. To build AI that can do this, some researchers are hybridizing deep nets with what the research community calls “good old-fashioned artificial intelligence,” otherwise known as symbolic AI. The offspring, which they call neurosymbolic AI, are showing duckling-like abilities and then some. “It’s one of the most exciting areas in today’s machine learning,” says Brenden Lake, a computer and cognitive scientist at New York University.

A Beginner’s Guide to Symbolic Reasoning & Deep Learning

To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analog to the human concept learning, given the parsed program, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains.

As carbon intensity (the quantity of CO2 generated by kWh produced) is nearly 12 times lower in France than in the US, for example, the energy needed for AI computing produces considerably less emissions. “Everywhere we try mixing some of these ideas together, we find that we can create hybrids that are … more than the sum of their parts,” says computational neuroscientist David Cox, IBM’s head of the MIT-IBM Watson AI Lab in Cambridge, Massachusetts. To think that we can simply abandon symbol-manipulation is to suspend disbelief. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance.

As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system – though just a prototype – learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game. Symbolic AI, a branch of artificial intelligence, focuses on the manipulation of symbols to emulate human-like reasoning for tasks such as planning, natural language processing, and knowledge representation.

All operations are executed in an input-driven fashion, thus sparsity and dynamic computation per sample are naturally supported, complementing recent popular ideas of dynamic networks and may enable new types of hardware accelerations. We experimentally show on CIFAR-10 that it can perform flexible visual processing, rivaling the performance of ConvNet, but without using any convolution. Furthermore, it can generalize to novel rotations of images that it was not trained for. First of all, every deep neural net trained by supervised learning combines deep learning and symbolic manipulation, at least in a rudimentary sense. Because symbolic reasoning encodes knowledge in symbols and strings of characters. In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model.

The output of a classifier (let’s say we’re dealing with an image recognition algorithm that tells us whether we’re looking at a pedestrian, a stop sign, a traffic lane line or a moving semi-truck), can trigger business logic that reacts to each classification. Symbolic AI, a branch of artificial intelligence, specializes in symbol manipulation to perform tasks such as natural language processing (NLP), knowledge representation, and planning. These algorithms enable machines to parse and understand human language, manage complex data in knowledge bases, and devise strategies to achieve specific goals. 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. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.

Because machine learning algorithms can be retrained on new data, and will revise their parameters based on that new data, they are better at encoding tentative knowledge that can be retracted later if necessary. Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs. Google made a big one, too, which is what provides the information in the top box under your query when you search for something easy like the capital of Germany. 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). Such causal and counterfactual reasoning about things that are changing with time is extremely difficult for today’s deep neural networks, which mainly excel at discovering static patterns in data, Kohli says. The researchers broke the problem into smaller chunks familiar from symbolic AI.

Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axioms for what did not change after an action was performed. Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters. Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships.

Prolog provided a built-in store of facts and clauses that could be queried by a read-eval-print loop. The store could act as a knowledge base and the clauses could act as rules or a restricted form of logic. As a subset of first-order logic Prolog was based on Horn clauses with a closed-world assumption—any facts not known were considered false—and a unique name assumption for primitive terms—e.g., the identifier barack_obama was considered to refer to exactly one object.

• “This grammar can generate all the questions people ask and also infinitely many other questions,” says Lake.
• And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.
• 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).
• “You could think of it as the space of possible questions that people can ask.” For a given state of the game board, the symbolic AI has to search this enormous space of possible questions to find a good question, which makes it extremely slow.
• Like Inbenta’s, “our technology is frugal in energy and data, it learns autonomously, and can explain its decisions”, affirms AnotherBrain on its website.

Cyc has attempted to capture useful common-sense knowledge and has “micro-theories” to handle particular kinds of domain-specific reasoning. Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. 2) The two problems may overlap, and solving one could lead to solving the other, since a concept that helps explain a model will also help it recognize certain patterns in data using fewer examples.

As pressure mounts on GAI companies to explain where their apps’ answers come from, symbolic AI will never have that problem. As a consequence, the Botmaster’s job is completely different when using Symbolic AI technology than with Machine Learning-based technology as he focuses on writing new content for the knowledge base rather than utterances of existing content. He also has full transparency on how to fine-tune the engine when it doesn’t work properly as he’s been able to understand why a specific decision has been made and has the tools to fix it. This will only work as you provide an exact copy of the original image to your program. For instance, if you take a picture of your cat from a somewhat different angle, the program will fail. McCarthy’s approach to fix the frame problem was circumscription, a kind of non-monotonic logic where deductions could be made from actions that need only specify what would change while not having to explicitly specify everything that would not change.

Consequently, learning to drive safely requires enormous amounts of training data, and the AI cannot be trained out in the real world. It’s possible to solve this problem using sophisticated deep neural networks. However, Cox’s colleagues at IBM, along with researchers at Google’s DeepMind and MIT, came up with a distinctly different solution that shows the power of neurosymbolic AI. But the benefits of deep learning and neural networks are not without tradeoffs. Deep learning has several deep challenges and disadvantages in comparison to symbolic AI.

NLP is used in a variety of applications, including machine translation, question answering, and information retrieval. One of their projects involves technology that could be used for self-driving cars. “In order to learn not to do bad stuff, it has to do the bad stuff, experience that the stuff was bad, and then figure out, 30 steps before it did the bad thing, how to prevent putting itself in that position,” says MIT-IBM Watson AI Lab team member Nathan Fulton.

The method involves training these weak learners sequentially, with each one focusing on the errors of the previous ones in an effort to correct them. Using OOP, you can create extensive and complex symbolic AI programs that perform various tasks. 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. When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade.

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. This video shows a more sophisticated challenge, called CLEVRER, in which artificial intelligences had to answer questions about video sequences showing objects in motion.

At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. Symbolic AI is able to deal with more complex problems, and can often find solutions that are more elegant than those found by traditional AI algorithms.

We know how it works out answers to queries, and it doesn’t require energy-intensive training. This aspect also saves time compared with GAI, as without the need for training, models can be up and running in minutes. Some companies have chosen to ‘boost’ symbolic AI by combining it with other kinds of artificial intelligence.

Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity.

The unlikely marriage of two major artificial intelligence approaches has given rise to a new hybrid called neurosymbolic AI. It’s taking baby steps toward reasoning like humans and might one day take the wheel in self-driving cars. For instance, Facebook uses neural networks for its automatic tagging feature. When you upload a photo, the neural network model has been trained on a vast amount of data to recognize and differentiate faces.