2304 13626 The Roles of Symbols in Neural-based AI: They are Not What You Think!

Hinton and many others have tried hard to banish symbols altogether. The deep learning hope—seemingly grounded not so much in science, but in a sort of historical grudge—is that intelligent behavior will emerge purely from the confluence of massive data and deep learning. Because neural networks have achieved so much so fast, in speech recognition, photo tagging, and so forth, many deep-learning proponents have written symbols off. In order to be able to communicate and reason about their environment, autonomous agents must be able from low-level, sensori-motor data streams. They therefore require an abstraction layer that links sensori-motor experiences to high-level symbolic concepts that are meaningful in the environment and task at hand. A repertoire of meaningful concepts provides the necessary building blocks for achieving success in the agent’s higher-level cognitive tasks, such as reasoning or action planning.

They do not necessarily need to cooperate to solve new challenges, but they do need to exploit each other’s expertise. For further reading on the topic of symbolic vs. connectionist approaches, you can refer to [17]. This could potentially address the fundamental challenges of reasoning and transferable learning. The rigidity of the symbolic approach has been criticized as has been the inability to reason in deep learning. Symbolic systems suffer from an inability to deal with heuristic and fuzzy relationships, while deep learning excels at this.

These problems include abstract reasoning and language, which are, after all, the domains for which the tools of formal logic and symbolic reasoning were invented. To anyone who has seriously engaged in trying to understand, say, commonsense reasoning, this seems obvious. Nowadays, the words “artificial intelligence” seem to be on practically everyone’s lips, from Elon Musk to Henry Kissinger. At least a dozen countries have mounted major AI initiatives, and companies like Google and Facebook are locked in a massive battle for talent. Since 2012, virtually all the attention has been on one technique in particular, known as deep learning, a statistical technique that uses sets of of simplified “neurons” to approximate the dynamics inherent in large, complex collections of data.

Towards Deep Relational Learning

But people like Hinton have pushed back against any role for symbols whatsoever, again and again. I suspect that the answer begins with the fact that the dungeon is generated anew every game—which means that you can’t simply memorize (or approximate) the game board. To win, you need a reasonably deep understanding of the entities in the game, and their abstract relationships to one another. Ultimately, players need to reason about what they can and cannot do in a complex world. Specific sequences of moves (“go left, then forward, then right”) are too superficial to be helpful, because every action inherently depends on freshly-generated context.

Additionally, symbol-tuned models achieve similar or better than average performance as pre-training–only models. SUX was the language used in this writing system, and cuneiform letters were used to inscribe texts on clay tablets. Cuneiform letters consist of signs and shapes resembling pointed nails and grooves that are carved into the clay with a stick or pen intended for writing, so the process of interpreting cuneiform symbols is a difficult task and requires expertise. Therefore, this article aims to build an intelligent system that has the ability to distinguish the cuneiform symbols of different civilizations. Experiments were conducted on the CLI dataset to classify it into seven categories, but this dataset had a category imbalance. Researchers say that symbol tuning doesn’t require many steps of finetuning for any model with small datasets.

Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases

• So much of the world’s knowledge, from recipes to history to technology is currently available mainly or only in symbolic form. Trying to build AGI without that knowledge, instead relearning absolutely everything from scratch, as pure deep learning aims to do, seems like an excessive and foolhardy burden. Sure, Elon Musk recently said that the new humanoid robot he was hoping to build, Optimus, would someday be bigger than the vehicle industry, but as of Tesla’s AI Demo Day 2021, in which the robot was announced, Optimus was nothing more than a human in a costume. Google’s latest contribution to language is a system (Lamda) that is so flighty that one of its own authors recently acknowledged it is prone to producing “bullshit.”5  Turning the tide, and getting to AI we can really trust, ain’t going to be easy.

On the other hand, neural networks can statistically find the patterns. Learning strategies and knowledge representation languages they employ. However, all of

these algorithms learn by searching through a space of possible concepts to find an accept-

able generalization. In Section 10, we outline a framework for symbol-based machine

learning that emphasizes the common assumptions behind all of this work.

Development of machine learning model for diagnostic disease prediction based on laboratory tests

Rather than modeling the unexplored state space, instead, if an unobserved transition is encountered during an MCTS update, it immediately terminates with a large bonus to the score, a similar approach to that of the R-max algorithm [2]. The form of the bonus is -zg, where g is the depth that the update terminated and z is a constant. The bonus reflects the opportunity cost of not experiencing something novel as quickly as possible, and in practice it tends to dominate (as it should). A symbolic option model h ~ H can be sampled by drawing parameters for each of the Bernoulli and categorical distributions from the corresponding Beta and sparse Dirichlet distributions, and drawing outcomes for each qo. It is also possible to consider distributions over other parts of the model such as the symbolic state space and/or a more complicated one for the option partitionings, which we leave for future work.

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). Each approach—symbolic, connectionist, and behavior-based—has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels. In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in incorporating learning and knowledge.

The inevitable failure of DL has been predicted before, but it didn’t pay to bet against it. The tool selected for the project has to

match the capability and sophistication of the projected ES, in particular, the need to

integrate it with other subsystems such as databases and other components of a larger

information system. The facts of the given case are entered into the working

memory, which acts as a blackboard, accumulating the knowledge about the case at

hand.

Application areas include classification, diagnosis,

monitoring, process control, design, scheduling and planning, and generation of options. There were also studies of language, and people started to build these statistical models of representing words as these vectors, as this array of floating point numbers. So now we burn through a gajillion, it’s like trillions of floating point operations with all these multiplications and we still get hallucinations and we still get quite poor reasoning capabilities. And there are approaches reducing these judgement deficiencies, but something still seems to be missing.

In 2019, Paetzold and Zampieri [10] applied machine learning techniques to determine the language of cuneiform texts. The authors use a dataset of cuneiform texts written in various languages, including Sumerian (SUX), Akkadian, and Hittite in the CLI dataset. They extract features from texts, such as n-grams of one–five characters, and use these features to train the SVM machine learning algorithm. Their method achieved a 73.8% F1 in identifying the language of cuneiform texts. The article shows that machine learning techniques can be effective in identifying the language of cuneiform texts and that character-based features are particularly useful for this task.

It is important to note that the concept of LEFT refers to “left in the image” and not “left of another object.” With this definition of left, the x-coordinate is an important attribute for this concept. If we consider the images of the CLEVR dataset, the x-coordinate of an object can be anywhere between 0 and 480. In this setting, we consider an object to be LEFT when the x-coordinate is smaller than 240. The bulk of objects that can be considered LEFT will not be close to 0, nor close to 240, but somewhere in between, e.g., around x-coordinate 170.

Unfortunately, it is difficult to model Im(o, Z) for arbitrary options, so we focus on restricted types of options. 10 years after the first GPU was created by Nvidia, the group of. Andrew NG’s group at Stanford began to promote the use of specialized GPUs for Deep Learning. This would allow them to train neuronal networks faster and more efficiently. Geforce 256 was the first GPU in history, created in 1999 by Nvidia.

The grandfather of AI, Thomas Hobbes said — Thinking is manipulation of symbols and Reasoning is computation. But it can take vast amounts of data and then make and bring, this is what I love about data is, I mean, this is what I love about AI is the potential that it can take mass amounts of information and create unity. You can’t fundamentally put the AI on that basis for interpreting the data of a symbol subjectively because the objective nature of what is actually occurring. And so that makes me wonder, is the information that’s being transmitted something that is of an objective nature, something that is truly truthful and beneficial? Because when the subjective aspect comes into it, that’s when things become difficult.

• Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features.
• Additionally, since the concepts are learned through unsupervised exploration, the proposed model is adaptive to the environment.
• The program improved as it played more and more games and ultimately defeated its own creator.
• It was the first computer built specifically to create neural networks.
• However, real-world data such as images, video, and sensory data has not yielded attempts to algorithmically define specific features.

Not long ago, for example, a Tesla in so-called “Full Self Driving Mode” encountered a person holding up a stop sign in the middle of a road. The car failed to recognize the person (partly obscured by the stop sign) and the stop sign (out of its usual context on the side of a road); the human driver had to take over. The scene was far enough outside of the training database that the system had no idea what to do. Finally, we consider the repertoire of concepts and find, similar to the first experiment, that the agent has found discriminative sets of attributes that are intuitively related to the concept they describe. The concept METAL is shown in Figure 15, both for the simulated and noisy environment. Interestingly, we note from this Figure that the agent has learned to identify the material of an object through the “value” dimension of the HSV color space.

• This makes the concept learning task easier and allows us to validate the proposed learning mechanisms before moving to an environment with more realistic perceptual processing.
• For this experiment, we use the CLEVR CoGenT dataset, which consists of two conditions.
• The popularity of ChatGPT has led to the development of new tools such as LangChain [18], which allow us to incorporate disparate sources of knowledge to determine the ideal action given a particular state.
• Using these measures as features, two types of feature architectures were established, one only included hubs and the other contained both hubs and non hubs.

Thus, it is required to determine the mapping function from the received symbol to the transmitted symbol. 1(a), where the red-colored box shows one of the transmitted symbols d(i) and the nearby green point represents the noisy symbol ˆd(i), for a given SNR. The receiver then tries for the best approximation to the transmitted symbol d(i) utilizing the statistical properties of the corrupting AWGN. On increasing the SNR, the discrete received noisy symbols tend to concentrate on the respective transmitted symbol, as shown in Fig. OSHA’s requirements regarding machine guarding Risk assessment in machine guarding Robot safeguards Lockout/tagout systems General precautions Taking corrective action. ● Humans can generalize a wide range of universals to arbitrary novel instances.

The main innovation of this network is its memory system that will help various RNR models in the modern era of Deep Learning. It was the first computer built specifically to create neural networks. Thanks to it, it was able to execute 40,000 instructions per second. Why include all that much innateness, and then draw the line precisely at symbol manipulation? If a baby ibex can clamber down the side of a mountain shortly after birth, why shouldn’t a fresh-grown neural network be able to incorporate a little symbol manipulation out of the box? In the end, it’s puzzling why LeCun and Browning bother to argue against the innateness of symbol manipulation at all.

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What is symbolic artificial intelligence?

Additionally, the numerical values will be subject to more noise due to variations in the images such as overlapping objects, lighting conditions or shade effects. The first method starts from the symbolic scene these into continuous-valued attributes based on simple rules and procedures. Each symbolic attribute is mapped to one or more continuous attributes with a possible range of values. For example, color is mapped to three attributes, one for each channel of the RGB color space, and size is mapped to a single attribute, namely area. These attributes were already present in the CLEVR dataset and are simply adopted. Scallop [20] is a framework that attempts to bridge the gap between logical/symbolic reasoning and deep learning.

The trained model is then compared with the existing MLH decoder for its performance. The results show the comparable performance of both the decoding schemes, however, the proposed model is reconfigurable since it utilizes the ML algorithms. Another advantage of the proposed model is its lower complexity and faster operation due to the following reasons.

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

Deep-learning systems are outstanding at interpolating between specific examples they have seen before, but frequently stumble when confronted with novelty. In order to ensure that the learned concepts are human-interpretable, the methodology starts from a predefined set of human-interpretable features that are extracted from the raw images. While we argue that this is necessary to achieve true interpretability, it can also be seen as a limitation inherent to the methodology. However, this limitation cannot be lifted without losing interpretability that the method brings.

• The term AI, coined in the 1950s, refers to the simulation of human intelligence by machines.
• 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.
• This led to the emergence of machine learning, a subfield of AI that focuses on developing algorithms that can learn from data and improve their performance over time.

You can create instances of these classes (called objects) and manipulate their properties. Class instances can also perform actions, also known as functions, methods, or procedures. Each method executes a series of rule-based instructions that might read and change the properties of the current and other objects.

1. Language Game

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.

This is a multistep process, a chain of though, where the model can review what it inferred in a bit of algorithmic computational model way to get better results. Today I would like to tell you what is increasingly becoming popular in large language models. What I think will be a future of this field that could potentially provide some things I think are missing for us perhaps to get to the artificial general intelligence.

3. Incremental Learning

This kind of knowledge is taken for granted and not viewed as noteworthy. AI and machine learning are at the top of the buzzword list security vendors use to market their products, so buyers should approach with caution. Still, AI techniques are being successfully applied to multiple aspects of cybersecurity, including anomaly detection, solving the false-positive problem and conducting behavioral threat analytics. Organizations use machine learning in security information and event management (SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations. AI, machine learning and deep learning are common terms in enterprise IT and sometimes used interchangeably, especially by companies in their marketing materials.

In the following sections, we introduce the frameworks that we use to represent the agent’s high-level skills, and symbolic models for those skills. A look into HyperMask’s use of adaptive hypernetworks for efficient continual learning in neural networks. One of the greatest exponents of Deep Learning, Yann LeCun used convolutional neural networks and backpropagation to teach a machine how to read handwritten digits. John Hopefield creates the first recurrent neural network, which he calls Hopefield network.

Decisive Analysis of Fixed Power Allocation Coefficients in a PD-NOMA Network

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• Few fields have been more filled with hype than artificial intelligence.
• Reinforcement learning from human feedback, that’s a very interesting approach not the same as use of expert before the second AI winter.
• For more detail see the section on the origins of Prolog in the PLANNER article.
• One particular experiment by Wellens (2012) has heavily inspired this work.

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Active Exploration for Learning Symbolic Representations PMC

AlphaZero, where you have this chess playing engine, which gets superhuman. You use neural guided search, where the network serves as an intuition. And it can sort of think about, okay, now I’m in this position, and I think I could do this. And then it can, with self play, sort of go further into the direction, it can evaluate and learn, using this approach very effectively, and it becomes superhuman.

It is based on LaMDA (Language Model for Dialogue Application), a model created by Google. Bard can dialogue with their interlocutors, and according to the company, it can be used as a creative and helpful collaborator, as it can help the user organize and create new ideas that can be used in several environments, from the artistic to the corporate side. This AI constantly learns as it picks up patterns from trillions of words that help predict a reasonable response to the user’s questions or demands.

Key Metrics to Evaluate your AI Chatbot Performance

Afterwards, learning operators are turned off and we evaluate the communicative success of the agent in condition B for the remainder of the interactions. We expect the agents to remain at a stable level of communicative success when making the transition from condition A to B. We again evaluate on both the simulated environment and the noisy environment. Additionally, we vary the amount of training interactions on condition A to test the speed at which the learner agent can acquire useable concepts.

The novelty of the proposed work lies in the modeling of the decoding problem through a reconfigurable system. Since, it is crucial to study the system performance under low SNR, as the systems are more error-prone at lower SNRs. The proposed system can perform well, even under low SNR scenarios, and can be utilized for decoding the users’ data in next-generation PD-NOMA systems, that currently plan to use the SIC decoding process. SIC and SC are the two processes for such systems, with the former at the receiver side, and the latter at the transmitter side, respectively. As per the authors’ knowledge, the PD-NOMA networks employ SIC to differentiate between the users’ messages, and as such the limitations of SIC viz.

An introduction to reconfigurable systems

At the Turing award session and fireside conversation with Daniel Kahneman, there was a clear convergence towards integrating symbolic reasoning and deep learning. Kahneman made his point clear by stating that on top of deep learning, a System 2 symbolic layer is needed. This is reassuring for neurosymbolic AI going forward as a single more cohesive research community that can agree about definitions and terminology, rather than a community divided as AI has been up to now. For example, in 2013, Czech researcher Mikolov co-published Word2Vec paper (later also FastText). Then in 2017, transformer architecture was able to accept multiple words. These models are able to represent entire paragraphs of text in context as a vector, and not only each word individually.

And there is nothing you can glean from the physical properties of these pulses to make what they mean for a human. “Symbols meaning is rendered independent of the properties of the symbol’s substrate.” And we kind of got into that yesterday. And they say, “The idealized notion of a symbol wherein meaning is established purely by convention.” So then if you look at nature as an example for symbols, is it really then of the subjective sense that’s necessary for culture to define what that symbol means? Or just the laws of physics are enough to say, “Yeah, regardless of who interpreted it, it’s a snowflake regardless of how it looks.” But the snowflake is, oh my God, I’m on something, the snowflake is bound to certain laws of physics that define how it can grow.

The term AI, coined in the 1950s, refers to the simulation of human intelligence by machines. It covers an ever-changing set of capabilities as new technologies are developed. Technologies that come under the umbrella of AI include machine learning and deep learning. Instruction tuning is a common fine-tuning method that has been shown to improve performance and allow models to better follow in-context examples. One shortcoming, however, is that models are not forced to learn to use the examples because the task is redundantly defined in the evaluation example via instructions and natural language labels. For example, on the left in the figure above, although the examples can help the model understand the task (sentiment analysis), they are not strictly necessary since the model could ignore the examples and just read the instruction that indicates what the task is.

As of 2020, many sources continue to assert that machine learning remains a subfield of AI. The main disagreement is whether all of ML is part of AI, as this would mean that anyone using ML could claim they are using AI. Others have the view that not all of ML is part of AI where only an ‘intelligent’ subset of ML is part of AI. Google showed over 10 million random YouTube videos to a brain.

Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems. More recent approaches to concept learning are dominated by deep learning techniques.

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Symbolic AI vs Machine Learning in Natural Language Processing

This indicates that the learner’s repertoire of concepts is shaped quickly and is sufficient to have successful interactions. Additionally, when transitioning from condition A to B, there is no decrease in communicative success in the simulated environment and only a minor decrease in the noisy environment. This indicates that the concepts acquired by the agent abstract away over the observed instances. In the first experiment, we validate the learning mechanisms proposed earlier in this paper. We evaluate the learner agent on its ability to successfully communicate and on its repertoire of concepts, both in the more simple, simulated environment and in the more realistic, noisy environment. In Figure 7A, we show the communicative success of the agents in these environments.

• The conjecture behind the DSN model is that any type of real world objects sharing enough common features are mapped into human brains as a symbol.
• Learning macro-operators—i.e., searching for useful macro-operators to be learned from sequences of basic problem-solving actions.
• These languages allow for precise and unambiguous representation of knowledge, making it easier for machines to reason about and manipulate the symbols.
• But whatever new ideas are added in will, by definition, have to be part of the innate (built into the software) foundation for acquiring symbol manipulation that current systems lack.
• Therefore, this article aims to build a system capable of distinguishing between several cuneiform languages and solving the problem of unbalanced categories in the CLI dataset.

So if we look at these symbols here, not to get too way out there, metaphysically, they are all these different interlinking things that come to a definition of what it may be, to describe something. A better meaning, one that makes a lot more sense is that one can use some kind of “interrelated physical pattern” to represent anything. The immensely complicated and intense concept of love is symbolized with a heart. If anyone in virtually any culture anywhere sees a small disc with a person’s head on it, they know they are looking at some kind of money. 1) Hinton, Yann LeCun and Andrew Ng have all suggested that work on unsupervised learning (learning from unlabeled data) will lead to our next breakthroughs.

Symbol tuning improves in-context learning in language models

It is a conversation between a human, a computer, and another person, but without knowing which of the two conversationalists is a machine. The person asks questions to the chatbot and another person, and in case of not distinguishing the human from the machine, the computer will have successfully passed the Turing test. This computational model can be adapted to simulate the logic of any algorithm.

For instance, if you take a picture of your cat from a somewhat different angle, the program will fail. To think that we can simply abandon symbol-manipulation is to suspend disbelief. Similar axioms would be required for other domain actions to specify what did not change. 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.

What is symbolic AI?

We have introduced a two-part algorithm for data-efficiently learning an abstract symbolic representation of an environment which is suitable for planning with high-level skills. The first part of the algorithm quickly generates an intermediate Bayesian symbolic model directly from data. The second part guides the agent’s exploration towards areas of the environment that the model is uncertain about. This algorithm is useful when the cost of data collection is high, as is the case in most real world artificial intelligence applications. Our results show that the algorithm is significantly more data efficient than using more naive exploration policies.

Nonetheless, progress on task-to-task transfer remains limited. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems. 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.

HyperMask: Adaptive Hypernetworks for Continual Learning

First-order logic statements are therefore mapped onto differentiable real-valued constraints using a many-valued logic interpretation in the interval [0,1]. The trained network and the logic become communicating modules of a hybrid system, instead of the logic computation being implemented by the network. Scientifically, there is obvious value in the study of the limits of integration to improve our understanding of the power of neural networks using the well-studied structures and algebras of computer science logic.

For example, if Toolformer needs a arithmetic calculation, then you can teach it to call a calculator function. This external system which it can use to do the calculation precisely and return the result. Language models are not so good for calculation tasks, but you can train them to call some external tool.

• Deep learning systems are black boxes; we can look at their inputs, and their outputs, but we have a lot of trouble peering inside. We don’t know exactly why they make the decisions they do, and often don’t know what to do about them (except to gather more data) if they come up with the wrong answers. This makes them inherently unwieldy and uninterpretable, and in many ways unsuited for “augmented cognition” in conjunction with humans. Hybrids that allow us to connect the learning prowess of deep learning, with the explicit, semantic richness of symbols, could be transformative. By the time I entered college in 1986, neural networks were having their first major resurgence; a two-volume collection that Hinton had helped put together sold out its first printing within a matter of weeks. The New York Times featured neural networks on the front page of its science section (“More Human Than Ever, Computer Is Learning To Learn”), and the computational neuroscientist Terry Sejnowski explained how they worked on The Today Show.

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