It appears that there are several competing technologies in AI models. What are they?
As of my last update in September 2021, there were several competing technologies in the field of AI models. It’s essential to keep in mind that the landscape may have evolved since then. Some of the prominent AI model technologies up to that point included:

1. **Transformers**: Transformer models, particularly the BERT (Bidirectional Encoder Representations from Transformers) architecture, revolutionized natural language processing (NLP) by introducing attention mechanisms. These models have been widely used for tasks such as language understanding, text generation, and sentiment analysis.

2. **GPT (Generative Pre-trained Transformer)**: GPT models, including GPT-3, are part of the Transformer family and are notable for their language generation capabilities. They can generate human-like text and have found applications in various areas, including chatbots, content creation, and language translation.

3. **CNN (Convolutional Neural Networks)**: CNNs are commonly used in computer vision tasks. They are designed to recognize patterns in visual data, making them effective in tasks like image classification, object detection, and facial recognition.

4. **RNN (Recurrent Neural Networks)**: RNNs are a class of models used for sequential data analysis, making them suitable for tasks like language modeling, speech recognition, and time series prediction.

5. **LSTM (Long Short-Term Memory)**: LSTM is a specific type of RNN architecture that can effectively handle long-range dependencies in sequential data. It has been widely used in NLP tasks and time series analysis.

6. **Autoencoders**: Autoencoders are a type of neural network used for unsupervised learning. They can reconstruct input data, making them useful for tasks like data compression, anomaly detection, and feature learning.

7. **Capsule Networks**: Capsule Networks are an alternative to CNNs, proposed to overcome certain limitations in recognizing hierarchical patterns within images.

8. **GANs (Generative Adversarial Networks)**: GANs consist of two neural networks competing against each other—one generating data, and the other evaluating its authenticity. They are used in generating realistic data, such as images, audio, and text, and have been applied in areas like art generation and data augmentation.

9. **Meta-Learning**: Meta-learning, or “learning to learn,” involves training models to be able to adapt quickly to new tasks with minimal data. This approach has potential applications in few-shot learning and adapting models to new environments.

10. **Capsule Networks**: Capsule Networks are an alternative to traditional Convolutional Neural Networks (CNNs), designed to better handle hierarchical and spatial relationships in images.

11. **Sparse Transformers**: Sparse Transformers are a variant of the standard Transformers that aim to reduce computational complexity by focusing on relevant parts of the input sequence.

It’s important to note that this list may not be exhaustive, and AI research is a dynamic field with continuous advancements. Additionally, some technologies mentioned above might have further evolved or been combined with others to create more advanced models.