Discover the Surprising Dangers of Positional Encoding in AI and Brace Yourself for Hidden GPT Threats.
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Understand the concept of Positional Encoding in AI. | Positional Encoding is a technique used in Natural Language Processing (NLP) to add positional information to the input sequence. It is used in Transformer models, which are neural network architectures used for language modeling tasks. | If the Positional Encoding is not properly implemented, it can lead to errors in the output sequence. |
| 2 | Be aware of the limitations of Transformer models. | Transformer models have limitations in handling long sequences and complex relationships between words. | If the model is not trained properly, it can lead to poor performance and inaccurate results. |
| 3 | Understand the importance of Attention Mechanism in Transformer models. | Attention Mechanism is a key component of Transformer models that helps the model to focus on relevant parts of the input sequence. | If the Attention Mechanism is not properly implemented, it can lead to errors in the output sequence. |
| 4 | Be aware of the risk of overfitting in machine learning algorithms. | Overfitting occurs when the model is trained too well on the training data and performs poorly on the test data. | Overfitting can lead to poor performance and inaccurate results. |
| 5 | Understand the importance of data augmentation techniques in deep learning. | Data augmentation techniques are used to increase the size of the training data by creating new data from the existing data. | If the data augmentation techniques are not properly implemented, it can lead to overfitting and poor performance. |
| 6 | Be aware of the hidden risks of GPT models. | GPT models can generate biased and offensive content if they are not properly trained and monitored. | If the GPT models are not properly managed, they can lead to reputational damage and legal issues. |
| 7 | Understand the importance of managing the risks associated with GPT models. | It is important to monitor the output of GPT models and implement measures to prevent biased and offensive content. | Failure to manage the risks associated with GPT models can lead to reputational damage and legal issues. |
Contents
- What are the Hidden GPT Risks and How to Brace for Them?
- Understanding Neural Network Architecture in Relation to Positional Encoding
- Transformer Model Limitations: Implications for Positional Encoding
- The Role of Natural Language Processing (NLP) in Positional Encoding and AI Safety
- Machine Learning Algorithms and Their Impact on Positional Encoding Accuracy
- Deep Learning Techniques for Enhancing Positional Encoding Performance
- Attention Mechanism Flaws: Addressing Potential Issues with Positional Encoding
- Overfitting Prevention Methods in Relation to Positional Encoding Implementation
- Data Augmentation Techniques for Improving the Quality of Training Data Used in Positional Encodings
- Common Mistakes And Misconceptions
What are the Hidden GPT Risks and How to Brace for Them?
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Identify potential risks | GPT models can be biased, unethical, lack privacy, and be vulnerable to security breaches | Bias can lead to unfair outcomes, unethical behavior can harm individuals or society, lack of privacy can lead to data breaches, and security vulnerabilities can lead to attacks or misuse of the model |
| 2 | Evaluate training data | Training data can be biased or contain misinformation | Biased training data can lead to biased models, and misinformation can lead to inaccurate or harmful predictions |
| 3 | Validate model accuracy | Models can have low accuracy or be susceptible to data poisoning | Low accuracy can lead to incorrect predictions, and data poisoning can manipulate the model to produce biased or harmful outcomes |
| 4 | Implement accountability and transparency measures | Lack of accountability and transparency can lead to misuse or manipulation of the model | Accountability and transparency can help prevent unethical behavior and ensure fair outcomes |
| 5 | Consider regulation and oversight | Lack of regulation and oversight can lead to misuse or manipulation of the model | Regulation and oversight can help ensure ethical behavior and prevent harm to individuals or society |
Understanding Neural Network Architecture in Relation to Positional Encoding
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Understand the basics of neural network architecture | Neural networks are machine learning models that are designed to mimic the way the human brain works. They consist of layers of interconnected nodes that process information and make predictions. | None |
| 2 | Learn about natural language processing (NLP) | NLP is a subfield of AI that focuses on teaching machines to understand and generate human language. It is used in applications such as chatbots, language translation, and sentiment analysis. | None |
| 3 | Study the transformer model | The transformer model is a type of neural network architecture that was introduced in 2017. It is used in NLP tasks such as language translation and text summarization. | None |
| 4 | Understand the attention mechanism | The attention mechanism is a key component of the transformer model. It allows the model to focus on specific parts of the input sequence when making predictions. | None |
| 5 | Learn about positional encoding | Positional encoding is a technique used in the transformer model to give the model information about the position of each word in the input sequence. | None |
| 6 | Understand the self-attention mechanism | The self-attention mechanism is a type of attention mechanism that allows the model to attend to different parts of the input sequence at different times. | None |
| 7 | Study the multi-head attention mechanism | The multi-head attention mechanism is a variation of the self-attention mechanism that allows the model to attend to different parts of the input sequence using multiple attention heads. | None |
| 8 | Learn about feedforward neural networks (FFNN) | FFNNs are a type of neural network architecture that consist of multiple layers of interconnected nodes. They are commonly used in machine learning tasks such as image recognition and classification. | None |
| 9 | Understand the backpropagation algorithm | The backpropagation algorithm is a method used to train neural networks. It involves adjusting the weights of the network based on the error between the predicted output and the actual output. | None |
| 10 | Study the training, testing, and validation data sets | These data sets are used to train, test, and validate the performance of a neural network model. The training data set is used to adjust the weights of the model, while the testing and validation data sets are used to evaluate its performance. | Overfitting can occur if the model is trained too well on the training data set and does not generalize well to new data. |
| 11 | Understand gradient descent | Gradient descent is an optimization algorithm used to minimize the error between the predicted output and the actual output of a neural network model. It involves adjusting the weights of the model in the direction of the steepest descent of the error function. | None |
Transformer Model Limitations: Implications for Positional Encoding
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Explain the Transformer model | The Transformer model is a deep learning algorithm used for sequence modeling and natural language processing (NLP) tasks. It uses an attention mechanism to focus on relevant parts of the input sequence and a self-attention mechanism to capture dependencies between different parts of the sequence. | The Transformer model is a complex algorithm that requires significant computational resources and large amounts of training data. |
| 2 | Describe the role of positional encoding | Positional encoding is used to provide the Transformer model with information about the position of each token in the input sequence. This is important because the Transformer model does not have an inherent sense of order in the input sequence. | Positional encoding can be a source of error if it is not properly implemented or if the input sequence is too long. |
| 3 | Discuss the limitations of the Transformer model | The Transformer model has limitations in its ability to capture long-term dependencies and to handle out-of-vocabulary (OOV) words. Additionally, the model’s performance can be limited by the quality and quantity of training data. | These limitations can lead to errors in the model’s predictions and can make it difficult to use the model in real-world applications. |
| 4 | Explain the implications for positional encoding | The limitations of the Transformer model have implications for how positional encoding is used. Specifically, the use of non-linear transformations in positional encoding can exacerbate the model’s limitations in capturing long-term dependencies. Additionally, the use of contextualized embeddings can make it more difficult to encode positional information. | These implications highlight the need for careful consideration of how positional encoding is implemented in the Transformer model, and for the development of new techniques to address the model’s limitations. |
| 5 | Discuss the risks associated with these implications | If positional encoding is not properly implemented in the Transformer model, it can lead to errors in the model’s predictions and limit its usefulness in real-world applications. Additionally, the use of non-linear transformations and contextualized embeddings can make it more difficult to interpret the model’s predictions and to identify sources of error. | These risks highlight the need for ongoing research and development in the field of NLP, and for the development of new techniques to address the limitations of the Transformer model. |
The Role of Natural Language Processing (NLP) in Positional Encoding and AI Safety
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Natural Language Processing (NLP) is a subfield of AI that deals with the interaction between computers and human language. | NLP is essential in AI safety because it enables machines to understand and process human language, which is crucial in many applications such as chatbots, virtual assistants, and sentiment analysis. | The risk of using NLP in AI safety is that it can lead to biased models if the training data is not diverse enough. |
| 2 | Positional Encoding is a technique used in NLP to add positional information to the input sequence. | Positional Encoding is crucial in NLP because it allows the model to differentiate between words with the same meaning but different positions in the sentence. | The risk of using Positional Encoding is that it can lead to overfitting if the model is trained on a small dataset. |
| 3 | AI Safety is the field of study that focuses on ensuring that AI systems are safe and reliable. | AI Safety is essential because AI systems can have unintended consequences that can be harmful to humans. | The risk of AI Safety is that it can limit the development of AI systems, which can have significant benefits for society. |
| 4 | Machine Learning Algorithms are algorithms that enable machines to learn from data without being explicitly programmed. | Machine Learning Algorithms are essential in AI because they allow machines to learn from data and improve their performance over time. | The risk of Machine Learning Algorithms is that they can be biased if the training data is not diverse enough. |
| 5 | Neural Networks are a type of Machine Learning Algorithm that are inspired by the structure of the human brain. | Neural Networks are essential in NLP because they can learn to represent words and sentences in a way that captures their meaning. | The risk of Neural Networks is that they can be computationally expensive and require a lot of data to train. |
| 6 | Text Classification Techniques are techniques used in NLP to classify text into different categories. | Text Classification Techniques are essential in many applications such as spam detection, sentiment analysis, and topic modeling. | The risk of Text Classification Techniques is that they can be biased if the training data is not diverse enough. |
| 7 | Sentiment Analysis Models are models used in NLP to determine the sentiment of a piece of text. | Sentiment Analysis Models are essential in many applications such as social media monitoring, customer feedback analysis, and market research. | The risk of Sentiment Analysis Models is that they can be biased if the training data is not diverse enough. |
| 8 | Word Embeddings are a technique used in NLP to represent words as vectors in a high-dimensional space. | Word Embeddings are essential in NLP because they can capture the semantic and syntactic relationships between words. | The risk of Word Embeddings is that they can be biased if the training data is not diverse enough. |
| 9 | Contextualized Word Representations are a type of Word Embeddings that take into account the context in which a word appears. | Contextualized Word Representations are essential in NLP because they can capture the meaning of a word in different contexts. | The risk of Contextualized Word Representations is that they can be computationally expensive and require a lot of data to train. |
| 10 | Transformer Architecture is a type of Neural Network architecture that uses self-attention mechanisms to process input sequences. | Transformer Architecture is essential in NLP because it can capture long-range dependencies between words in a sentence. | The risk of Transformer Architecture is that it can be computationally expensive and require a lot of data to train. |
| 11 | Attention Mechanisms are mechanisms used in Neural Networks to focus on specific parts of the input sequence. | Attention Mechanisms are essential in NLP because they can improve the performance of the model by focusing on the most relevant parts of the input sequence. | The risk of Attention Mechanisms is that they can be computationally expensive and require a lot of data to train. |
| 12 | Language Modeling Tasks are tasks used in NLP to predict the next word in a sequence given the previous words. | Language Modeling Tasks are essential in NLP because they can improve the performance of the model by training it to predict the most likely next word. | The risk of Language Modeling Tasks is that they can be biased if the training data is not diverse enough. |
| 13 | Pre-training Strategies are strategies used in NLP to train models on large amounts of data before fine-tuning them on specific tasks. | Pre-training Strategies are essential in NLP because they can improve the performance of the model by providing it with a better understanding of the language. | The risk of Pre-training Strategies is that they can be computationally expensive and require a lot of data to train. |
| 14 | Fine-tuning Techniques are techniques used in NLP to adapt pre-trained models to specific tasks. | Fine-tuning Techniques are essential in NLP because they can improve the performance of the model on specific tasks. | The risk of Fine-tuning Techniques is that they can lead to overfitting if the model is trained on a small dataset. |
| 15 | Bias Mitigation Methods are methods used in NLP to reduce the impact of bias in the training data. | Bias Mitigation Methods are essential in NLP because they can improve the fairness and accuracy of the model. | The risk of Bias Mitigation Methods is that they can lead to a loss of performance if the training data is not diverse enough. |
| 16 | Explainability and Interpretability are important aspects of AI Safety that enable humans to understand how AI systems make decisions. | Explainability and Interpretability are essential in AI Safety because they can help humans to identify and correct errors in the AI system. | The risk of Explainability and Interpretability is that it can limit the performance of the AI system if the model is too complex to explain. |
Machine Learning Algorithms and Their Impact on Positional Encoding Accuracy
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Apply data preprocessing techniques to clean and prepare the data for machine learning algorithms. | Data preprocessing techniques such as normalization and feature scaling can improve the accuracy of positional encoding. | Incorrect data preprocessing techniques can lead to inaccurate results and negatively impact the accuracy of positional encoding. |
| 2 | Use feature extraction methods to identify the most relevant features in the data. | Feature extraction methods such as principal component analysis (PCA) can reduce the dimensionality of the data and improve the accuracy of positional encoding. | Incorrect feature extraction methods can lead to the loss of important information and negatively impact the accuracy of positional encoding. |
| 3 | Select appropriate supervised or unsupervised learning models based on the nature of the data and the problem at hand. | Supervised learning models such as decision trees and random forests can be used for classification tasks, while unsupervised learning models such as k-means clustering can be used for clustering tasks. | Choosing the wrong type of learning model can lead to inaccurate results and negatively impact the accuracy of positional encoding. |
| 4 | Implement deep learning architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) to improve the accuracy of positional encoding. | Deep learning architectures can learn complex patterns in the data and improve the accuracy of positional encoding. | Deep learning architectures require large amounts of data and computational resources, and can be prone to overfitting if not properly regularized. |
| 5 | Use overfitting prevention strategies such as regularization techniques and early stopping to prevent overfitting in deep learning architectures. | Regularization techniques such as L1 and L2 regularization can prevent overfitting by adding a penalty term to the loss function, while early stopping can prevent overfitting by stopping the training process when the validation loss stops improving. | Overfitting can lead to inaccurate results and negatively impact the accuracy of positional encoding. |
| 6 | Use hyperparameter tuning methods such as grid search or random search to optimize the hyperparameters of the machine learning algorithms. | Hyperparameter tuning can improve the performance of the machine learning algorithms and improve the accuracy of positional encoding. | Improper hyperparameter tuning can lead to overfitting or underfitting and negatively impact the accuracy of positional encoding. |
| 7 | Implement gradient descent optimization to minimize the loss function and improve the accuracy of positional encoding. | Gradient descent optimization can find the optimal weights for the machine learning algorithms and improve the accuracy of positional encoding. | Improper implementation of gradient descent optimization can lead to slow convergence or getting stuck in local minima and negatively impact the accuracy of positional encoding. |
| 8 | Use transfer learning approaches to leverage pre-trained models and improve the accuracy of positional encoding. | Transfer learning can save time and resources by using pre-trained models and improve the accuracy of positional encoding. | Transfer learning may not be applicable to all problems and may require fine-tuning to achieve optimal results. |
| 9 | Implement ensemble modeling techniques such as bagging or boosting to improve the accuracy of positional encoding. | Ensemble modeling can combine multiple models to improve the accuracy of positional encoding. | Ensemble modeling can be computationally expensive and may require additional resources. |
Deep Learning Techniques for Enhancing Positional Encoding Performance
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Implement Neural Networks | Neural Networks are a type of machine learning algorithm that can learn from data and make predictions. | Neural Networks can be computationally expensive and require a lot of training data. |
| 2 | Use Attention Mechanism | Attention Mechanism allows the model to focus on specific parts of the input sequence, improving performance. | Attention Mechanism can be difficult to implement and may require additional computational resources. |
| 3 | Utilize Transformer Architecture | Transformer Architecture is a type of neural network that uses self-attention to process input sequences. | Transformer Architecture can be complex and may require additional training data. |
| 4 | Apply Multi-Head Attention | Multi-Head Attention allows the model to attend to different parts of the input sequence simultaneously, improving performance. | Multi-Head Attention can be computationally expensive and may require additional training data. |
| 5 | Incorporate Feedforward Network | Feedforward Network is a type of neural network that can improve the model’s ability to learn complex patterns in the input sequence. | Feedforward Network can be computationally expensive and may require additional training data. |
| 6 | Use Residual Connections | Residual Connections allow the model to learn from the input sequence more effectively, improving performance. | Residual Connections can be difficult to implement and may require additional computational resources. |
| 7 | Apply Layer Normalization | Layer Normalization can improve the model’s ability to learn from the input sequence by reducing the impact of input variations. | Layer Normalization can be computationally expensive and may require additional training data. |
| 8 | Utilize Dropout Regularization | Dropout Regularization can improve the model’s ability to generalize to new data by reducing overfitting. | Dropout Regularization can be difficult to implement and may require additional computational resources. |
| 9 | Apply Gradient Descent Optimization | Gradient Descent Optimization can improve the model’s ability to learn from the input sequence by adjusting the model’s parameters. | Gradient Descent Optimization can be computationally expensive and may require additional training data. |
| 10 | Use Training Data Augmentation | Training Data Augmentation can improve the model’s ability to learn from the input sequence by increasing the amount of training data. | Training Data Augmentation can be time-consuming and may require additional computational resources. |
| 11 | Incorporate Batch Normalization | Batch Normalization can improve the model’s ability to learn from the input sequence by reducing the impact of input variations. | Batch Normalization can be computationally expensive and may require additional training data. |
| 12 | Apply Regularization Techniques | Regularization Techniques can improve the model’s ability to generalize to new data by reducing overfitting. | Regularization Techniques can be difficult to implement and may require additional computational resources. |
Overall, Deep Learning Techniques for Enhancing Positional Encoding Performance involve implementing various neural network architectures and techniques to improve the model’s ability to learn from the input sequence. These techniques include Attention Mechanism, Transformer Architecture, Multi-Head Attention, Feedforward Network, Residual Connections, Layer Normalization, Dropout Regularization, Gradient Descent Optimization, Training Data Augmentation, Batch Normalization, and Regularization Techniques. While these techniques can improve performance, they also come with potential risks such as increased computational resources, additional training data, and implementation difficulties.
Attention Mechanism Flaws: Addressing Potential Issues with Positional Encoding
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Identify potential issues with positional encoding in attention mechanisms | Positional encoding is a technique used in transformer architecture to represent contextual information in natural language processing systems | Input sequence length limitations may impact the effectiveness of positional encoding |
| 2 | Assess the impact of input sequence length limitations on positional encoding | Longer input sequences may result in positional encoding becoming less effective in representing contextual information | Overfitting prevention techniques may not be effective in addressing this issue |
| 3 | Evaluate model generalization capabilities in relation to positional encoding | Models may struggle to generalize to longer input sequences due to limitations in positional encoding | Training data quality assessment may be necessary to ensure models are trained on diverse and representative data |
| 4 | Implement hyperparameter tuning strategies to optimize positional encoding | Hyperparameters such as the number of encoding dimensions and the encoding function may impact the effectiveness of positional encoding | Computational resource requirements may increase with more complex hyperparameter configurations |
| 5 | Consider the challenges of model interpretability in relation to positional encoding | The complex nature of positional encoding may make it difficult to interpret the impact of specific encoding configurations on model performance | Error propagation risks may also be increased due to the complexity of the encoding process. |
Overfitting Prevention Methods in Relation to Positional Encoding Implementation
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Implement Positional Encoding | Positional Encoding is a technique used to add positional information to the input data in order to help the model understand the sequence of the data. | If the implementation of Positional Encoding is not done correctly, it can lead to overfitting. |
| 2 | Use Regularization Techniques | Regularization techniques such as Dropout, Weight Decay, and Batch Normalization can be used to prevent overfitting. | If the regularization parameters are not chosen correctly, it can lead to underfitting or poor performance of the model. |
| 3 | Use Early Stopping | Early Stopping is a technique used to stop the training of the model when the validation loss starts to increase. | If the stopping criteria are not chosen correctly, it can lead to stopping the training too early or too late. |
| 4 | Use Cross-Validation | Cross-Validation is a technique used to evaluate the performance of the model on multiple subsets of the data. | If the number of folds is not chosen correctly, it can lead to overfitting or underfitting. |
| 5 | Use Data Augmentation | Data Augmentation is a technique used to increase the size of the training data by creating new data from the existing data. | If the augmentation techniques are not chosen correctly, it can lead to creating unrealistic data or data that is too similar to the existing data. |
| 6 | Use Feature Selection | Feature Selection is a technique used to select the most important features from the input data. | If the feature selection criteria are not chosen correctly, it can lead to selecting irrelevant or redundant features. |
| 7 | Use Ensemble Learning | Ensemble Learning is a technique used to combine multiple models to improve the performance of the overall model. | If the ensemble models are not chosen correctly, it can lead to creating models that are too similar or too different from each other. |
| 8 | Use Hyperparameter Tuning | Hyperparameter Tuning is a technique used to find the optimal values for the hyperparameters of the model. | If the hyperparameters are not chosen correctly, it can lead to poor performance of the model. |
| 9 | Split Data into Training, Validation, and Testing Sets | Splitting the data into training, validation, and testing sets is a technique used to evaluate the performance of the model on unseen data. | If the data is not split correctly, it can lead to overfitting or underfitting. |
| 10 | Manage Model Complexity | Managing the complexity of the model is a technique used to prevent overfitting. | If the model is too simple, it can lead to underfitting. If the model is too complex, it can lead to overfitting. |
Data Augmentation Techniques for Improving the Quality of Training Data Used in Positional Encodings
| Step | Action | Novel Insight | Risk Factors |
|---|---|---|---|
| 1 | Identify the training data used in positional encodings. | The quality of training data is crucial for the performance of positional encodings. | The training data may not be diverse enough, leading to overfitting. |
| 2 | Determine the data preprocessing procedures required for the training data. | Data preprocessing procedures can improve the quality of training data. | Preprocessing procedures may introduce bias into the data. |
| 3 | Apply image manipulation techniques such as rotation, flipping, translation, and scaling adjustments to the training data. | Image manipulation techniques can increase the diversity of the training data. | Overuse of image manipulation techniques can lead to unrealistic data. |
| 4 | Inject noise into the training data to increase its robustness. | Noise injection strategies can improve the generalization of the model. | Overuse of noise injection strategies can lead to noisy data. |
| 5 | Modify the contrast and brightness of the training data to increase its diversity. | Contrast and brightness modifications can improve the performance of the model. | Overuse of contrast and brightness modifications can lead to unrealistic data. |
| 6 | Apply random cropping approaches to the training data to increase its diversity. | Random cropping approaches can improve the generalization of the model. | Overuse of random cropping approaches can lead to unrealistic data. |
| 7 | Transform the color space of the training data to increase its diversity. | Color space transformations can improve the performance of the model. | Overuse of color space transformations can lead to unrealistic data. |
| 8 | Generate synthetic data to increase the diversity of the training data. | Synthetic data generation can improve the generalization of the model. | Synthetic data may not accurately represent the real-world data. |
| 9 | Augment the dataset by combining the original and augmented data. | Augmented dataset creation can improve the performance of the model. | The augmented dataset may not be representative of the real-world data. |
| 10 | Evaluate the performance of the model using the augmented dataset. | The augmented dataset can provide a more accurate evaluation of the model’s performance. | The augmented dataset may not accurately represent the real-world data. |
In summary, improving the quality of training data used in positional encodings can be achieved through various data augmentation techniques such as image manipulation, noise injection, and synthetic data generation. However, overuse of these techniques can lead to unrealistic data and bias. It is important to evaluate the performance of the model using the augmented dataset to ensure that it accurately represents the real-world data.
Common Mistakes And Misconceptions
| Mistake/Misconception | Correct Viewpoint |
|---|---|
| Positional encoding is a new concept in AI. | Positional encoding has been around for several years and is commonly used in natural language processing tasks such as machine translation and text classification. It is not a new concept. |
| Positional encoding can solve all problems related to sequence modeling. | While positional encoding can improve the performance of models that rely on sequential data, it cannot solve all problems related to sequence modeling. Other techniques such as attention mechanisms may also be necessary depending on the task at hand. |
| Using positional encoding will always result in better model performance. | The effectiveness of using positional encoding depends on the specific task and dataset being used, so it may not always result in better model performance compared to other methods or no positional encoding at all. It should be evaluated empirically rather than assumed to be universally effective. |
| There are no risks associated with using positional encoding in GPT models. | While there are benefits to using positional encodings, there are also potential risks such as overfitting or introducing bias into the model if not implemented correctly or if inappropriate parameters are chosen during training. |