When people think of Geoffrey Hinton, they usually think of backpropagation, Boltzmann Machines, Deep Belief Networks, or the deep learning revolution that transformed artificial intelligence.
But few people look further back to the beginning of his research career.
In 1977, nearly a decade before the famous backpropagation paper, Hinton completed his PhD thesis at the University of Edinburgh titled "Relaxation and its Role in Vision." At first glance, it seems to be a thesis about computer vision and relaxation methods. That was exactly what I expected when I began reading it.
As I worked through the thesis, however, I realized that it was about much more than a vision algorithm. Many of the ideas that would later define Hinton's research were already taking shape. The terminology was different, the math was simpler, and neural networks hadn't yet become the focus of his work. But the same way of thinking was already there.
This review isn't a chapter-by-chapter summary of the thesis. Instead, it focuses on the ideas that stood out to me while reading it and explores how many of them reappeared in Hinton's later work. Some of these ideas became central to modern AI, while others remain surprisingly overlooked despite being discussed nearly fifty years ago.
Looking back, what impressed me most was not that the thesis predicted specific algorithms. It was that it introduced a consistent way of thinking about intelligence, perception, and computation that would continue to shape Hinton's research for decades.
I hope this review encourages more people to read this remarkable thesis, not simply as a historical document, but as the starting point of one of the most influential research journeys in artificial intelligence.
Thesis Overview
In this review, we'll explore Geoffrey Hinton's 1977 PhD thesis, "Relaxation and its Role in Vision", completed at the University of Edinburgh.
We'll begin by looking at the central problem Hinton set out to solve and the ideas that motivated his relaxation approach. From there, we'll explore how the thesis represents uncertainty, reasons about competing hypotheses, and searches for globally consistent interpretations.
Next, we'll examine the puppet program, the relaxation operator, the role of schemas and stored knowledge, the SETTLE system, and Hinton's comparisons with other approaches of the time. We'll also discuss the limitations he identified in his own method and why they mattered.
Finally, we'll look at how many of the ideas introduced in this thesis reappeared throughout Hinton's later work and helped shape the development of modern AI.
If you'd like to follow along, you can also read the original thesis:
Geoffrey Hinton. Relaxation and its Role in Vision. PhD thesis, University of Edinburgh, 1977.
Here is an infographic gives a quick overview of Geoffrey Hinton's 1977 PhD thesis. It summarizes the main ideas, how the relaxation method works, its applications, its limitations, and why many of these ideas still matter today.
Table of Contents:
The Core Challenge: Why Visual Systems Can't Afford to Guess Too Soon
Beyond Pattern Recognition: Why Internal Representations Matter More Than the Final Output
The Importance of Intermediate and Hierarchical Representations
The Core Challenge: Why Visual Systems Can't Afford to Guess Too Soon
Before exploring the ideas in Hinton's thesis, it helps to understand the problem he set out to solve. The opening chapter asks a deceptively simple question: How can a visual system choose the correct interpretation when a single image may support many plausible explanations?
This is the central challenge of visual perception. Real-world scenes are often ambiguous or partially hidden, so a system can't afford to commit to one interpretation too early. A premature decision can introduce errors that spread through the rest of the reasoning process and lead to an incorrect understanding of the entire scene.
The real challenge is to keep multiple plausible interpretations alive until there is enough evidence to determine which one is most consistent.
Hinton argues that the common approaches of the 1970s didn't solve this problem. One approach, known as the principle of least commitment, delayed decisions by leaving information unspecified. According to Hinton, this simply postponed the real issue because it offered no way to compare competing hypotheses or determine how they should become consistent with one another.
Another approach assigned fixed meanings to low-level visual features. But since the meaning of a feature depends on its surrounding context, these rigid definitions often failed when objects were partially hidden or appeared in different situations.
The infographic below summarizes the central challenge Hinton identifies at the beginning of his thesis. Rather than committing to the first plausible interpretation of a visual scene, he argues that a vision system should maintain many competing hypotheses simultaneously and allow them to interact until they converge on a single, globally consistent explanation.
It also highlights two contemporary approaches that Hinton rejects, the principle of least commitment and rigid feature semantics, because, in his view, they avoid the core problem instead of solving it.
This framing establishes the motivation for the relaxation framework developed throughout the rest of the thesis.
The First Appearance of Thinking as Optimization
One of the most interesting ideas in Hinton's thesis is that perception isn't a matter of instantly recognizing an object. Instead, he treats it as a process of finding the best explanation for what the eyes are seeing.
Rather than committing to a single interpretation from the start, the system considers many possible hypotheses at the same time. Some support each other, others compete, and their confidence changes as they interact. Through repeated updates, weak explanations gradually disappear while the strongest and most consistent interpretation emerges.
Although Hinton applies this idea to visual perception, the underlying principle reaches far beyond computer vision. It introduces a way of thinking about intelligence as an optimization problem: many possible explanations compete until the system settles on the one that best fits the available evidence.
Looking back, this idea feels surprisingly familiar. The same general philosophy later appeared in probabilistic inference, energy-based models, Conditional Random Fields (CRFs), Boltzmann Machines, and many other approaches where intelligence emerges by searching for the most consistent solution rather than making a single immediate decision.
Vision Is Inference, Not Pattern Matching
One idea that stands out throughout the thesis is Hinton's view of what it actually means to see. He argues that vision is not simply recognizing patterns or assigning an image to a category. Instead, perception is the process of building an internal explanation of the scene.
A visual system doesn't immediately know what it's looking at. It must decide which objects are present, how they relate to one another, and which interpretation best explains the available evidence. In other words, seeing is a process of inference, not just recognition.
Hinton also rejects the idea that perception works by simply comparing an input with a collection of stored templates. He argues that this view is too limited to explain how we understand complex and unfamiliar scenes.
Instead, perception is presented as a constructive process. The system builds an interpretation by combining evidence, relationships, and prior knowledge until a coherent explanation emerges. It's not retrieving an answer from memory but actively constructing one.
Reading this today is striking because it closely resembles ideas that became popular decades later. Modern generative models and latent variable methods are also built around the idea of explaining observations by inferring the hidden structure that produced them.
These ideas also feel remarkably close to modern representation learning, where the goal isn't to memorize examples but to learn meaningful internal representations that can explain new observations.
Hinton was exploring these ways of thinking in 1977, long before they became a central theme in modern AI.
Why Perception Requires Hypotheses
Hinton argues that perception can't be a purely reactive process. A visual system often receives incomplete, ambiguous, or even misleading information, so it can't simply accept the first interpretation that comes to mind.
Instead, it must begin with several possible explanations. As more evidence is considered, some hypotheses become more convincing while others are weakened or rejected. The final interpretation is reached only after this process of evaluation and refinement.
Although Hinton doesn't describe it using modern Bayesian terminology, the underlying idea is remarkably similar. Rather than making an immediate decision, the system continuously updates its beliefs as evidence accumulates until the most consistent explanation remains.
From Binary Decisions to Degrees of Belief
Another idea that feels remarkably modern is Hinton's decision to avoid treating hypotheses as simply true or false. Instead, every hypothesis is assigned a value between 0 and 1 that reflects how strongly the system currently believes it. As the relaxation process unfolds, these values are updated repeatedly until the most consistent interpretation stands out while the others gradually fade away.
Today, we use different terms for similar concepts, including probabilities, belief values, confidence scores, activations, and logits. The terminology has evolved over the years, but the underlying idea remains the same: intelligence often depends on representing uncertainty instead of making immediate, irreversible decisions.
The infographic below illustrates how Hinton's relaxation process operates after hypotheses have been assigned continuous belief values.
Rather than selecting a single answer immediately, the system repeatedly updates all competing hypotheses in parallel, using both numerical constraints and individual preferences until one coherent interpretation gradually emerges.
By replacing rigid yes-or-no decisions with continuous optimization, the relaxation framework makes it possible to search efficiently for a globally consistent solution.
Distributed Computation Before Neural Networks
One of the most forward-looking ideas in the thesis is that intelligence shouldn't depend on a single central controller making every decision. Instead, Hinton describes a system made up of many local hypotheses that interact with one another at the same time. Each contributes a small part of the final solution, and together they produce a coherent interpretation.
Instead of focusing on individual components, Hinton emphasizes how these connections allow information to flow through the system until a consistent interpretation emerges.
This way of thinking feels surprisingly familiar today. Modern neural networks are also built on the idea that complex behavior can emerge from the combined activity of many simple units rather than from one component directing the entire process.
The terminology is different from modern deep learning, but the emphasis on networks, interactions, and distributed computation is already clearly visible.
Parallelism as the Natural Way to Compute
Another idea that stands out is Hinton's emphasis on parallel computation. At a time when most computers were designed to execute instructions one after another, he argued that perception is better viewed as many processes working simultaneously and influencing one another.
Looking back, this was an unusually forward-looking perspective. Decades before massively parallel hardware became common, Hinton was already describing computation in a way that closely resembles how modern neural networks run today, with many simple operations happening at the same time rather than one step after another.
Constraint Propagation
A recurring idea throughout the thesis is that no hypothesis should be evaluated in isolation. Instead, each one influences the others through a network of constraints. When the confidence of one hypothesis changes, that change spreads across the network, strengthening compatible explanations and weakening conflicting ones.
This idea later became a common theme in several areas of AI. Graphical models, factor graphs, message passing, and belief propagation all rely on the same basic intuition: local interactions can gradually lead to a globally consistent solution.
Although these methods were developed later and use different mathematical frameworks, it's not difficult to see the conceptual connection.
To demonstrate how constraints interact during relaxation, Hinton chose a deliberately simplified vision problem instead of real photographs.
A user first drew several transparent, overlapping rectangles on a graphics terminal. Some rectangles represented genuine parts of a stick-figure puppet, such as the torso, arms, or legs, while others acted as irrelevant distractors.
Every overlap between rectangles became a candidate joint, and the system generated competing hypotheses about which rectangles belonged to the puppet and which overlaps represented real connections. Its goal was to identify the interpretation with the greatest number of mutually consistent instantiated joints, while remaining robust to missing body parts and irrelevant clutter.
By removing the complexity of natural images, Hinton isolated the combinatorial challenge of visual interpretation while keeping the problem mathematically manageable.
The infographic below illustrates how Hinton used this simplified puppet domain to evaluate the relaxation framework. By reducing vision to identifying consistent body parts and joints, the example isolates the core challenge of combining many competing local hypotheses into a single globally consistent interpretation.
Although intentionally simple, the puppet experiment captures the essential reasoning problem of computer vision: many local hypotheses compete simultaneously, constraints propagate between them, and only the globally most consistent interpretation survives.
Hinton presents the domain as a controlled laboratory for studying these interactions before extending the same relaxation principles to more realistic vision problems.
Local Rules Can Produce Global Intelligence
One of the ideas I enjoyed most in this thesis is how a complex solution emerges from simple local interactions. Each hypothesis only needs to communicate with the hypotheses directly connected to it. There's no central component that knows the correct answer or controls the entire process.
As information flows through the network, the system gradually settles on a consistent interpretation. The final result emerges from cooperation rather than command.
This same principle continues to appear throughout AI research. Neural networks, swarm intelligence, graph neural networks, and belief propagation all demonstrate how complex behavior can arise from many simple components following local rules.
The puppet task wasn't just a toy example. It was a complete program with its own processing pipeline. Starting from a drawing of overlapping rectangles, the system generated hypotheses, applied constraints, repeatedly updated their confidence, and finally selected the most consistent interpretation.
The infographic below illustrates how Hinton's entire relaxation framework fits together as a complete computational pipeline. It shows how hypothesis generation, constraint construction, iterative relaxation, and final selection work as successive stages of a single reasoning process.
Rather than relying on a central controller or modern end-to-end training, the system reaches a coherent solution through repeated local interactions among competing hypotheses. This illustrates how simple local rules can produce globally consistent behavior.
Why Local Consistency Is Not Enough
One important point Hinton makes is that solving small local conflicts doesn't necessarily produce the best overall interpretation. A hypothesis may fit well with its immediate neighbors while still contributing to an incorrect explanation of the entire scene.
For that reason, the system must evaluate how all the hypotheses work together rather than judging each one independently.
This shift from local agreement to finding the best overall solution is a key theme throughout the thesis. It also reflects a broader direction that AI would later take, where many problems are formulated as global optimization rather than a collection of isolated local decisions.
Relaxation as a Way of Reasoning
As I read the thesis, I began to see relaxation as more than just an algorithm. It is a way of approaching difficult problems. Instead of trying to reach the correct answer in a single step, the system starts with tentative beliefs, refines them through repeated interactions, and continues until the solution becomes stable.
This idea feels surprisingly familiar today. Although the mathematics is different, many modern methods follow the same pattern. Gradient descent improves parameters step by step, the Expectation-Maximization (EM) algorithm alternates between refinement stages, belief propagation repeatedly exchanges information, and diffusion models generate samples through a sequence of gradual updates.
The methods are different, but the underlying philosophy is remarkably similar: good solutions often emerge through many small improvements rather than one decisive computation.
So how does relaxation actually work? Hinton's answer is surprisingly simple. During each update, the system balances two forces: one keeps the hypotheses consistent with the constraints, while the other gently pushes them toward better explanations. Repeating this process eventually leads to a stable solution.
The infographic below illustrates how Hinton translates this reasoning process into a concrete computational procedure. Rather than making a single decision, the relaxation operator repeatedly updates all hypotheses in parallel, applying the same two-force rule until the entire network settles into a stable, globally consistent state.
The process shows how simple local updates can collectively produce a coherent global interpretation.
The Importance of Equilibrium
Another idea that appears throughout the thesis is the importance of allowing the system to reach a stable state. The final interpretation isn't imposed by a central controller or chosen by a fixed rule. Instead, it emerges naturally as the hypotheses interact until no further changes are needed.
This idea became a recurring theme in Hinton's later work. Hopfield Networks, Boltzmann Machines, and energy-based models all rely on systems evolving toward stable configurations through their own internal dynamics. Although the models are different, the underlying intuition is much the same: a good solution is one the system naturally settles into.
Hinton didn't just describe what worked. He also analyzed the situations where relaxation could break down, discussing both the possibility of converging to ambiguous intermediate solutions and the architectural limitations of the overall framework.
The infographic below illustrates these two limitations. During relaxation, the system may converge to a stable solution that lies between discrete interpretations rather than fully committing to a single one.
It also highlights a broader architectural weakness: hypothesis generation and hypothesis selection remain separate stages, preventing later reasoning from influencing which candidate hypotheses are created in the first place.
Hinton openly presents these limitations, which later AI systems addressed through more integrated and end-to-end learning approaches.
From Symbolic Decisions to Numerical Reasoning
One of the subtle but important shifts in the thesis is the move away from treating knowledge as simply true or false. Instead of relying on rigid symbolic decisions, Hinton represents beliefs with numerical values that can increase or decrease as new evidence is considered.
This may seem like a small design choice, but it reflects a much broader change in how intelligent systems can reason. Rather than forcing early decisions, the system keeps track of uncertainty and adjusts its beliefs over time. Looking back, this is the same direction that much of modern machine learning would eventually follow.
Hinton didn't develop these ideas in isolation. In the thesis, he evaluates his relaxation framework alongside other prominent approaches of the time, highlighting their different ways of representing uncertainty and reasoning about visual scenes.
The infographic below compares three approaches side by side. Using the same line-labeling problem as a common benchmark, it shows how Waltz's filtering algorithm, fuzzy-weight models, and Hinton's relaxation framework represent uncertainty, update hypotheses, and enforce consistency.
Hinton argues that his supposition-value framework provides a more principled way to reason under uncertainty by combining continuous confidence values with explicit numerical constraints, allowing competing interpretations to evolve toward a globally consistent solution.
Why Perception Is a Search Problem
One of the ideas that repeatedly appears throughout the thesis is that perception is fundamentally a search process. A visual system isn't simply recognizing an object from what it sees. Instead, it's searching through many possible explanations to find the one that best fits the available evidence.
This distinction is more important than it might first appear. Recognition suggests that the answer is already obvious and only needs to be retrieved. Search assumes that the correct interpretation must be discovered by exploring alternatives and resolving uncertainty.
Even today, that way of thinking continues to shape many approaches to artificial intelligence.
If perception is a search problem, the next question becomes what the system is actually searching through. Hinton answers this by introducing a geometric view of the search process, where every possible interpretation occupies a position within a structured space of feasible solutions.
The infographic below illustrates this geometric perspective. It represents all valid hypothesis assignments as points inside a feasible search space, where the corners correspond to clean all-or-nothing interpretations and intermediate points represent uncertain or partial beliefs.
Rather than searching directly among discrete solutions, the relaxation process moves continuously through this space, gradually improving the current state until it converges on the highest-scoring feasible interpretation.
This geometric perspective provides an intuitive way to understand how relaxation transforms a difficult combinatorial search into a continuous optimization problem.
Beyond Pattern Recognition: Why Internal Representations Matter More Than the Final Output
One of the most thought-provoking parts of the thesis is Hinton's criticism of viewing perception as nothing more than pattern recognition. He argues that recognizing visual features alone can't explain how we understand a scene. A vision system must also determine how objects are related, how their parts fit together, and how those relationships combine into a coherent interpretation of the world.
This emphasis on relationships is central to Hinton's view of perception. Understanding a scene requires more than identifying individual objects. The system must represent objects, their constituent parts, and the structural relationships that connect them into larger wholes. In other words, perception is fundamentally about building a structured representation of the scene rather than recognizing isolated patterns.
As I read the thesis, one theme kept resurfacing: Hinton is less interested in the final answer than in the internal representation the system builds while interpreting a scene. The goal isn't simply to assign a label to an image, but to construct a structured description that captures the relationships between its components. The final decision is simply the outcome of this richer reasoning process.
Looking back, this perspective was remarkably forward-looking. Many modern AI systems have moved beyond simple classification toward learning internal representations that capture structure, relationships, and context. Although today's models use very different mathematical tools, the underlying intuition is strikingly similar.
Throughout his later career, Hinton consistently emphasized that the quality of an intelligent system depends less on its final output than on the representations it learns along the way.
The Importance of Intermediate and Hierarchical Representations
One idea that caught my attention is Hinton's discussion of intermediate-level hypotheses. Rather than moving directly from visual input to a final interpretation, he argues that perception benefits from intermediate representations that bridge the gap between raw observations and complete understanding.
Understanding a scene happens at multiple levels. Simple elements combine to form larger structures, and those structures become part of an even richer interpretation. Perception is built gradually through a hierarchy rather than all at once.
Looking back, these ideas feel strikingly familiar. Modern deep learning is built on the principle of intermediate-level hypotheses, with each layer learning increasingly abstract representations before reaching a final prediction.
And the idea of hierarchical perception would continue to appear throughout Hinton's later research. Whether in Deep Belief Nets, Capsule Networks, or hierarchical generative models, the same principle remains: meaningful representations are built layer by layer, with each level capturing patterns that the previous one could not.
The terminology has once again changed, but the intuition is much the same: complex understanding is achieved through a hierarchy of intermediate representations, not in a single step.
Schemas and Stored Knowledge
In the later chapters, Hinton introduces the idea of schemas as a way to organize knowledge and connect it to perception. Rather than treating perception and stored knowledge as separate processes, he shows how they can work together to interpret what the system observes.
One of the most interesting ideas in these chapters is Hinton's view of schemas. Instead of storing exact examples, he argues that knowledge should capture the rules, relationships, and constraints that define a category. This allows the system to interpret new situations by reasoning about their underlying structure rather than simply matching what it has already seen.
Reading this today, it's easy to see why the idea remains relevant. Although the terminology has changed, many modern AI systems also rely on learned internal representations that support generalization instead of memorization. In that sense, Hinton's discussion of schemas can be seen as an early step toward concepts that later evolved into latent representations and internal world models.
The infographic below illustrates Hinton's contrast between schema-based reasoning and template matching. Instead of relying on stored examples, schemas represent structural knowledge through roles, relationships, and constraints that guide interpretation. New observations are understood by satisfying these structural relationships rather than by finding an exact match to a memorized template.
This perspective foreshadows later developments in representation learning, where successful generalization depends on learning underlying structure instead of memorizing individual examples.
The SETTLE System
One part of the thesis that deserves far more attention is SETTLE, an experimental reasoning system Hinton developed to combine schemas, inference rules, and relaxation into a single computational framework. It's often overshadowed by the earlier chapters on relaxation, but it reveals how Hinton was already thinking about integrating multiple forms of reasoning rather than treating them as separate processes.
Instead of applying rules independently or storing knowledge in isolation, SETTLE allows schemas, inference rules, relaxation, and dynamic network construction to cooperate while the system gradually builds the most consistent interpretation from uncertain evidence.
Looking back, SETTLE is interesting not because it resembles modern AI systems in detail, but because it reflects Hinton's early effort to integrate knowledge, reasoning, and inference into a unified computational process.
The infographic below illustrates how these components interact within SETTLE. Inference rules generate candidate conclusions while the relaxation process continuously evaluates their consistency. This allows evidence, rules, and competing hypotheses to influence one another until they converge on the most coherent interpretation.
Uncertainty and Ambiguity as the Foundation of Reasoning
One theme that runs throughout the thesis is that uncertainty isn't a problem to avoid but a natural starting point for perception. A visual system doesn't begin with complete knowledge or immediate confidence. Instead, it starts with tentative assumptions that are gradually strengthened, weakened, or discarded as more evidence is taken into account.
Closely related to this is Hinton's treatment of ambiguity. Rather than viewing multiple possible interpretations as a failure of perception, he accepts them as an unavoidable consequence of incomplete information.
A visual scene can support several plausible explanations, and the system should allow those possibilities to coexist until enough evidence is available to distinguish between them. Instead of forcing an early decision, it gradually moves toward the most consistent interpretation.
Although Hinton's thesis predates modern probabilistic graphical models and Bayesian inference methods, its underlying perspective is remarkably close to probabilistic thinking.
The mathematical tools would evolve considerably over the following decades, but the central idea remained the same: intelligent systems should reason under uncertainty rather than expect complete and perfect information from the start. Looking back, it's not difficult to see why this perspective became one of the defining principles of modern AI.
The Whole Picture
The infographic below provides an overview of Hinton's thesis by bringing together its main contributions in a single view. It compares his relaxation framework with other approaches available in the late 1970s, outlines the different application domains explored throughout the thesis (from simplified vision tasks to schema-based reasoning and the SETTLE system) and concludes with the key limitations Hinton openly identifies.
Taken together, these elements show that the thesis is not only a proposal for a relaxation algorithm, but also a broader research program for reasoning under uncertainty that influenced many ideas developed in later AI systems.
A Consistent Philosophy Across Five Decades
After finishing the thesis, what impressed me most was not a single algorithm or experiment. It was the continuity of Hinton's thinking. Many of the ideas introduced in 1977 reappear throughout the rest of his career, even though the mathematical tools and models changed dramatically.
The thesis begins with relaxation, competing hypotheses, distributed constraints, optimization, and stable solutions. Later came Boltzmann Machines, backpropagation, Deep Belief Networks, AlexNet, the Forward-Forward algorithm, and more recently, his ideas on mortal computation.
At first glance, these contributions seem very different. Yet they're connected by a remarkably consistent research philosophy.
Throughout his career, Hinton has viewed intelligence as something that emerges from the interaction of many simple computational elements rather than from a central controller. He has consistently emphasized distributed knowledge over isolated symbols, inference over simple recognition, rich internal representations over final outputs, optimization as the mechanism for intelligent behavior, and uncertainty as something to be represented and refined rather than ignored.
Looking back from today, Hinton's 1977 thesis feels less like an isolated piece of early research and more like the beginning of an intellectual journey that would shape nearly five decades of artificial intelligence research.
This final infographic illustrates these conceptual connections. Rather than presenting Hinton's later work as direct implementations of his thesis, it shows how many of its central ideas, including continuous confidence values, optimization-based perception, structured knowledge representations, and integrated reasoning, continued to reappear in later developments such as Boltzmann Machines, backpropagation, Deep Belief Networks, and energy-based models.
The emphasis isn't on a single line of technical development, but on the remarkable continuity of the research philosophy that connects Hinton's earliest work to many of his later contributions.
Permission to Publish
Before writing this review, I contacted Professor Geoffrey Hinton to request permission to publish an educational review of his thesis. Professor Hinton kindly granted permission for the publication of this review.
The article is written entirely in my own words and reflects my own interpretation of the thesis, with full acknowledgment of the original work.
Further Reading
Geoffrey E. Hinton (1977) – Relaxation and its Role in Vision
Judea Pearl (1988) – Probabilistic Reasoning in Intelligent Systems
Rumelhart, Hinton & Williams (1986) – Learning Representations by Back-Propagating Errors
Hinton, Osindero & Teh (2006) – A Fast Learning Algorithm for Deep Belief Nets
Christopher M. Bishop (2006) – Pattern Recognition and Machine Learning
Ho, Jain & Abbeel (2020) – Denoising Diffusion Probabilistic Models
Geoffrey Hinton (2022) – The Forward-Forward Algorithm: Some Preliminary Investigations
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