Confused user, unclear command. – Dr. Trippers Stuff

What is not right about this command line: rmat.
The command line “rmat” doesn’t provide enough context to determine what it is intended to do. Here are a few potential issues with it:

1. **Misspelling**: It’s possible that “rmat” is a misspelling of another command or program.

2. **Incomplete Command**: It might be part of a longer command or require additional arguments to function properly.

3. **Unknown Program**: If “rmat” is meant to be a program or command, but it’s not recognized, it could indicate that the program is not installed on the system or is not in the system’s PATH.

4. **Missing Permissions**: If “rmat” is supposed to perform a specific action, such as removing files or directories, it may fail if the user running the command doesn’t have the necessary permissions.

Without more information about the intended purpose of the command or the context in which it’s used, it’s difficult to determine what exactly is not right about it.
docker run -it -p 7860:7860 –platform=linux/amd64
registry.hf.space/gorilla-llm-gorilla-demo:latest python app.py
The provided Docker command appears to be mostly correct for running a Docker container. Here’s a breakdown of each part:

1. `docker run`: This command is used to create and start a Docker container.

2. `-it`: These flags make the container interactive and allocate a pseudo-TTY.

3. `-p 7860:7860`: This option publishes port 7860 on the host machine to port 7860 on the Docker container, allowing external access to the application running inside the container.

4. `–platform=linux/amd64`: This specifies the platform architecture for the container, ensuring compatibility with the specified platform.

5. `registry.hf.space/gorilla-llm-gorilla-demo:latest`: This is the image name and tag of the Docker image to run. It pulls the image from the specified registry (`registry.hf.space`) and uses the image with the tag `latest`.

6. `python app.py`: This is the command to execute within the Docker container. It likely runs a Python script named `app.py` that is part of the image.

The command seems syntactically correct, assuming that `app.py` exists within the Docker image and is the entry point for the application you intend to run.
ocker run -it -p 7860:7860 –platform=linux/amd64
docker: invalid reference format.
See ‘docker run –help’.
The error message “docker: invalid reference format” typically indicates an issue with the format of the Docker image reference or the command syntax. In this case, it seems like there’s a line break (“) in the middle of the command, which might be causing the problem.

To fix this issue, ensure that the command is written on a single line without any line breaks or trailing backslashes (“). Here’s the corrected version of the command:

“`
docker run -it -p 7860:7860 –platform=linux/amd64 registry.hf.space/gorilla-llm-gorilla-demo:latest python app.py
“`

This command should be executed as a single line in your terminal or command prompt.
Thanks
You’re welcome! If you have any more questions or need further assistance, feel free to ask. Happy coding!
What is this code designed to do? from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

def load_model():
tokenizer = AutoTokenizer.from_pretrained(‘cross-encoder/ms-marco-MiniLM-L-12-v2’)
model = AutoModelForSequenceClassification.from_pretrained(‘cross-encoder/ms-marco-MiniLM-L-12-v2′)
return tokenizer, model

def process_data(text, tokenizer, model):
encoded_text = tokenizer(text, return_tensors=’pt’)
with torch.no_grad():
outputs = model(**encoded_text)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
response = model.config.id2label[predicted_class_idx]
return response

text = “Gather up to date news”

# Load the model and tokenizer
tokenizer, model = load_model()

# Process the data
response = process_data(text, tokenizer, model)

print(response)
This code snippet is designed to perform text classification using a pre-trained transformer-based model from the Hugging Face Transformers library. Here’s a breakdown of what each part of the code does:

1. **Import Libraries**: The code imports necessary libraries from the Transformers library (`AutoTokenizer`, `AutoModelForSequenceClassification`) and PyTorch (`torch`).

2. **Define `load_model` Function**:
– This function loads a pre-trained tokenizer and model for sequence classification.
– It uses the `AutoTokenizer.from_pretrained` and `AutoModelForSequenceClassification.from_pretrained` functions to load the tokenizer and model respectively.
– The specific model being loaded is `’cross-encoder/ms-marco-MiniLM-L-12-v2’`.

3. **Define `process_data` Function**:
– This function takes input text, tokenizer, and model as arguments.
– It tokenizes the input text using the tokenizer.
– It passes the tokenized text through the loaded model and retrieves the logits (output scores) from the model.
– It determines the predicted class index by finding the index of the highest logit value.
– It retrieves the corresponding label from the model’s configuration.
– Finally, it returns the predicted label.

4. **Process Input Text**:
– The input text to be classified is specified as `”Gather up to date news”`.

5. **Load Model and Tokenizer**:
– The `load_model` function is called to load the pre-trained tokenizer and model.

6. **Process Data**:
– The `process_data` function is called with the input text and the loaded tokenizer and model.
– This function tokenizes the input text and predicts its class label using the loaded model.

7. **Print Response**:
– The predicted label/classification result is printed.

Overall, this code snippet demonstrates how to use a pre-trained transformer-based model for text classification tasks, such as classifying input text into predefined categories or labels.
I asked for an api for latest news, id guess it failed
Yes, you are correct. The code provided does not appear to fetch the latest news from an API. Instead, it seems to be focused on text classification using a pre-trained model.

If you’re looking to fetch the latest news from an API, you would typically need to use a different approach. This could involve using an API provided by a news service provider or aggregator, such as NewsAPI, Google News API, or similar services. These APIs allow you to query for the latest news articles based on various parameters like keywords, sources, or categories.

Here’s a general outline of what you might do to fetch the latest news using an API:

1. **Find a News API**: Choose a news API provider that suits your needs and sign up for an API key if required.

2. **Read the API Documentation**: Understand how to make requests to the API, including the endpoint URLs, query parameters, and authentication methods.

3. **Make API Requests**: Use a library like `requests` in Python to make HTTP requests to the API endpoints. Pass any required parameters, such as keywords or categories, to specify the type of news you want to fetch.

4. **Parse the Response**: Once you receive a response from the API, parse the JSON data to extract the relevant information, such as news headlines, article URLs, and publication dates.

5. **Process and Display the News**: Depending on your application, you can process the retrieved news data and display it to the user in a suitable format, such as a list of headlines or a news feed.

If you need further assistance with implementing a news API in your code, feel free to provide more details, and I can offer additional guidance.
Here is the other result: from transformers import pipeline, set_seed

def load_model():
generator = pipeline(‘text-generation’, model=’bigscience/bloom-7b1′)
return generator

def process_data(prompt, generator):
set_seed(42)
response = generator(prompt, max_length=100, num_return_sequences=1)
return response[0][‘generated_text’]

prompt = “up to date news”

# Load the model
generator = load_model()

# Generate news
response = process_data(prompt, generator)

print(response)
This code snippet is designed to generate news articles or text based on a given prompt using the BigScience language model fine-tuned for text generation. Here’s a breakdown of what each part of the code does:

1. **Import Libraries**: The code imports necessary libraries from the Transformers library (`pipeline`, `set_seed`).

2. **Define `load_model` Function**:
– This function loads a pre-trained text generation pipeline using the `pipeline` function from the Transformers library.
– The specific model being loaded is `’bigscience/bloom-7b1’`, which is a large language model fine-tuned for text generation tasks.

3. **Define `process_data` Function**:
– This function takes a prompt (input text) and the text generation pipeline (generator) as arguments.
– It sets the random seed to ensure reproducibility of results.
– It generates text based on the provided prompt using the loaded generator.
– The generated text is returned.

4. **Define Prompt**:
– The prompt for generating news is specified as `”up to date news”`.

5. **Load Model**:
– The `load_model` function is called to load the pre-trained text generation pipeline.

6. **Generate News**:
– The `process_data` function is called with the prompt and the loaded generator.
– This function generates news text based on the provided prompt using the loaded model.

7. **Print Response**:
– The generated news text is printed.

Overall, this code snippet demonstrates how to use a pre-trained language model for text generation tasks, such as generating news articles based on a given prompt. It leverages the power of transformer-based models to generate coherent and contextually relevant text.
create new venv using pip
To create a new virtual environment using `pip`, you can follow these steps:

1. **Install `virtualenv` (if not already installed)**:
If you haven’t installed `virtualenv` yet, you can do so using `pip`:
“`
pip install virtualenv
“`

2. **Create a New Virtual Environment**:
Navigate to the directory where you want to create the virtual environment in your terminal or command prompt, and then run the following command:
“`
virtualenv myenv
“`
Replace `myenv` with the name you want to give to your virtual environment. This command will create a new directory named `myenv` containing the virtual environment.

3. **Activate the Virtual Environment**:
After creating the virtual environment, you need to activate it. On Windows, you would typically run:
“`
myenvScriptsactivate
“`
On Unix or MacOS, you would run:
“`
source myenv/bin/activate
“`

4. **Verify Activation**:
You should see the name of your virtual environment in the command prompt or terminal, indicating that it’s activated. For example:
“`
(myenv) user@hostname$
“`

5. **Install Packages**:
Now that your virtual environment is active, you can use `pip` to install packages just like you normally would, and they will be installed only within the virtual environment.

6. **Deactivate the Virtual Environment**:
When you’re done working in the virtual environment, you can deactivate it by running:
“`
deactivate
“`

By creating a virtual environment, you can keep your project dependencies separate from system-wide installations, which helps in managing dependencies and avoiding conflicts between different projects.
python?
Apologies for any confusion. If you’re referring to creating a virtual environment using the built-in `venv` module in Python, you can do so without needing to install `virtualenv`. Here are the steps:

1. **Navigate to Your Project Directory**:
Open your terminal or command prompt and navigate to the directory where you want to create the virtual environment.

2. **Create a New Virtual Environment**:
Run the following command to create a new virtual environment named `myenv`:
“`
python -m venv myenv
“`
Replace `myenv` with the name you want to give to your virtual environment. This command will create a new directory named `myenv` containing the virtual environment.

4. **Verify Activation**:
You should see the name of your virtual environment in the command prompt or terminal, indicating that it’s activated. For example:
“`
(myenv) user@hostname$
“`

6. **Deactivate the Virtual Environment**:
When you’re done working in the virtual environment, you can deactivate it by running:
“`
deactivate
“`

Using the `venv` module, you can easily create and manage virtual environments directly with Python without needing to install additional packages.
To create a public link, set `share=True` in `launch()`.
Traceback (most recent call last):
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_requestor.py”, line 413, in handle_error_response
error_data = resp[“error”]
~~~~^^^^^^^^^
KeyError: ‘error’

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesgradioqueueing.py”, line 522, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesgradioroute_utils.py”, line 260, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesgradioblocks.py”, line 1689, in process_api
result = await self.call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesgradioblocks.py”, line 1255, in call_function prediction = await anyio.to_thread.run_sync(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesanyioto_thread.py”, line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesanyio_backends_asyncio.py”, line 2144, in run_sync_in_worker_thread
return await future
^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesanyio_backends_asyncio.py”, line 851, in run result = context.run(func, *args)
^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesgradioutils.py”, line 750, in wrapper
response = f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demoapp.py”, line 79, in parse_and_display
text = get_gorilla_response(prompt, model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demoapp.py”, line 30, in get_gorilla_response
completion = openai.ChatCompletion.create(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_resourceschat_completion.py”, line 25, in create
return super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_resourcesabstractengine_api_resource.py”, line 155, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_requestor.py”, line 299, in request resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_requestor.py”, line 710, in _interpret_response
self._interpret_response_line(
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_requestor.py”, line 775, in _interpret_response_line
raise self.handle_error_response(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “D:SarahOneGORILLA GRADIOgorilla-demogorillaLibsite-packagesopenaiapi_requestor.py”, line 415, in handle_error_response
raise error.APIError(
openai.error.APIError: Invalid response object from API: ‘{“object”:”error”,”message”:”1 validation error for Request\nbody -> model\n str type expected (type=type_error.str)”,”code”:40001}’ (HTTP response code was 400)
It seems that there is an error occurring during the execution of your code related to the OpenAI API. The error message indicates that there’s an issue with the request being made to the API, specifically with the format of the request body or the type of data being passed to the API.

The error message states:

“`
1 validation error for Request
body -> model
str type expected (type=type_error.str)
“`

This suggests that there is a problem with the `model` parameter being passed to the API request. It appears that the API is expecting a string type for the `model`, but a different type of data is being provided.

To resolve this issue, you may need to ensure that the `model` parameter is correctly formatted as a string before passing it to the API request. Double-check the code where the `model` parameter is defined and make sure it is a string type.

Additionally, if you’re using any variables or expressions to construct the `model` parameter dynamically, ensure that they are evaluated to produce a string value before making the API request.

If you need further assistance with debugging or resolving this issue, please provide more context or code snippets related to how the `model` parameter is being used in your code.
Load the pretrained DeepLabV3 model with a ResNet-50 backbone from NVIDIA for image recognition tasks, including language translation.
As of my last update in January 2022, NVIDIA provides pre-trained models primarily for computer vision tasks, such as object detection, segmentation, and classification. DeepLabV3, however, is a semantic segmentation model commonly used for tasks like identifying objects within images but not specifically for language translation.

For language translation tasks, you would typically use machine translation models like those provided by Hugging Face’s Transformers library or models from Google’s TensorFlow Hub, OpenNMT, or Marian. These models are specifically trained for language translation tasks.

If you are interested in using a pre-trained DeepLabV3 model from NVIDIA for image recognition tasks, you can explore the NVIDIA Transfer Learning Toolkit (TLT) or NVIDIA Triton Inference Server for deploying models in production environments. NVIDIA TLT provides tools for fine-tuning pre-trained models on custom datasets, including models based on ResNet-50 backbone architecture.

To use DeepLabV3 with a ResNet-50 backbone for image recognition tasks, you would typically follow these steps:

1. **Obtain the Pre-trained Model**: Download the pre-trained DeepLabV3 model with a ResNet-50 backbone from NVIDIA’s official website or repository.

2. **Install Dependencies**: Make sure you have the necessary software and libraries installed to load and use the model. This might include NVIDIA CUDA, TensorFlow, or PyTorch depending on the framework used for the model.

3. **Load the Model**: Use the appropriate code to load the pre-trained model into your application. This typically involves instantiating the model object and loading the weights from the downloaded file.

4. **Perform Image Recognition**: Use the loaded model to perform image recognition tasks. This might involve loading images, preprocessing them as required by the model, and passing them through the model for inference.

Here’s a generic example of how you might load a pre-trained DeepLabV3 model with a ResNet-50 backbone using PyTorch:

“`python
import torch
import torchvision

# Load pre-trained DeepLabV3 model with ResNet-50 backbone
model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=True)

# Set the model to evaluation mode
model.eval()

# Perform image recognition (inference) with the loaded model
# Example: image = …
# output = model(image)
“`

Remember, this example assumes that you have downloaded and installed the necessary dependencies, and it only demonstrates how to load the model using PyTorch. Depending on the specific model and framework you are using, the code may vary.
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests
feature_extractor = MaskFormerFeatureExtractor.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)
model = MaskFormerForInstanceSegmentation.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)
url = ‘http
It seems like the code you provided is incomplete, as the URL is cut off. However, I’ll continue based on what you’ve provided.

The provided code snippet utilizes the Hugging Face Transformers library to load a pre-trained MaskFormer model for instance segmentation tasks. Here’s a breakdown of the code:

1. **Import Libraries**: The code imports necessary libraries including `MaskFormerFeatureExtractor` and `MaskFormerForInstanceSegmentation` from the Transformers library, as well as `Image` from PIL (Python Imaging Library) and `requests` for handling HTTP requests.

2. **Load Feature Extractor and Model**:
– `MaskFormerFeatureExtractor.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)`: This line loads the feature extractor component of the MaskFormer model, which is responsible for extracting features from input images.
– `MaskFormerForInstanceSegmentation.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)`: This line loads the MaskFormer model itself for instance segmentation. The model has been pre-trained on the COCO dataset and provided by Facebook AI.

3. **Specify Image URL**: The `url` variable seems to be intended to hold the URL of an image. However, the URL is truncated in the provided code snippet.

To use this code, you would need to complete the URL variable with a valid URL pointing to an image that you want to perform instance segmentation on. After loading the model and specifying the image URL, you can then proceed to perform instance segmentation using the loaded model.

Here’s an example of how you might complete the code:

“`python
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests

# Load feature extractor and model
feature_extractor = MaskFormerFeatureExtractor.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)
model = MaskFormerForInstanceSegmentation.from_pretrained(‘facebook/maskformer-swin-tiny-coco’)

# Specify image URL
url = ‘https://example.com/your-image.jpg’

# Open image from URL
image = Image.open(requests.get(url, stream=True).raw)

# Perform instance segmentation
inputs = feature_extractor(images=image, return_tensors=”pt”)
outputs = model(**inputs)
“`

Remember to replace `’https://example.com/your-image.jpg’` with the actual URL of the image you want to use for instance segmentation. Additionally, you can further process the `outputs` to extract the segmented instances or visualize the segmentation results as needed.
explain to me in high level the components and their uses when generating still/video using ai
When generating still images or videos using AI, several components and techniques are often involved. Here’s a high-level overview of some key components and their uses in the AI-based image and video generation process:

1. **Model Architecture**:
– This refers to the underlying structure and design of the neural network model used for image or video generation. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformer-based models.
– Different architectures may excel at different tasks, such as image generation, style transfer, or video prediction.

2. **Pre-trained Models**:
– Pre-trained models are neural network models that have been trained on large datasets, often for general-purpose image or video generation tasks.
– They serve as a starting point for transfer learning or fine-tuning on specific datasets or tasks, allowing developers to leverage existing knowledge and expertise.

3. **Feature Extractors**:
– Feature extractors are components of neural network models that extract meaningful features from input data, such as images or video frames.
– These features capture important characteristics of the input data and are used by subsequent layers of the model for generating output.

4. **Loss Functions**:
– Loss functions quantify the difference between the model’s predictions and the ground truth data.
– They guide the training process by providing feedback on how well the model is performing and are optimized during training to improve the model’s accuracy.

5. **Optimization Algorithms**:
– Optimization algorithms adjust the parameters of the neural network model to minimize the loss function.
– Examples include stochastic gradient descent (SGD), Adam, and RMSprop, which iteratively update the model’s weights based on the gradients of the loss function.

6. **Data Augmentation**:
– Data augmentation techniques modify input data to increase the diversity of the training dataset and improve the model’s generalization ability.
– Common techniques include random rotations, translations, flips, and color transformations.

7. **Conditional Generation**:
– Conditional generation involves generating images or videos based on additional input conditions, such as text descriptions, class labels, or reference images.
– Conditional models learn to generate output that is conditioned on the provided input, allowing for more controlled and targeted generation.

8. **Temporal Models**:
– For video generation, temporal models take into account the temporal dependencies between consecutive frames.
– Recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and convolutional LSTM networks are commonly used for modeling temporal sequences in videos.

9. **Post-processing Techniques**:
– Post-processing techniques are applied to generated images or videos to enhance their quality or achieve specific artistic effects.
– These may include techniques such as style transfer, color grading, denoising, and super-resolution.

By combining these components and techniques, AI-based systems can generate still images and videos that exhibit realistic and visually appealing characteristics, ranging from natural scenes to artistic creations and beyond.
mke this change colors for each run: public void main()
{

VA.WriteToLog(“Hello World”, “orange”);

}
To implement a change in colors for each run of the `main` function, you can modify the code to use a different color each time the function is called. One way to achieve this is by randomly selecting a color from a predefined list of colors. Below is an example of how you can modify the `main` function to achieve this in C#:

“`csharp
using System;
using System.Collections.Generic;

public class YourClassNameHere
{
// Define a list of colors
private List colors = new List { “orange”, “blue”, “green”, “red”, “yellow”, “purple” };

// Define the main function
public void main()
{
// Get a random index to select a color from the list
Random random = new Random();
int index = random.Next(colors.Count);

// Retrieve the randomly selected color
string selectedColor = colors[index];

// Write to log with the selected color
VA.WriteToLog(“Hello World”, selectedColor);
}
}
“`

In this code:

– We define a list `colors` containing different color names.
– Inside the `main` function, we use the `Random` class to generate a random index within the range of the list.
– We then use this random index to select a color from the `colors` list.
– Finally, we call `VA.WriteToLog` function with the message “Hello World” and the randomly selected color.

Each time the `main` function is called, it will use a different color selected randomly from the list of colors defined. This way, the color will change for each run of the function.
Docker AutoPipelines is your gateway to the future of machine learning. Designed to streamline the entire ML lifecycle, our Docker extension empowers users to effortlessly create, train, and deploy models with unprecedented ease. Say goodbye to coding complexities; our user-friendly interface paves the way for designing neural networks without the need for coding expertise. With just a few clicks, you can deploy your models, eliminating the hassle of traditional deployments.

Join the revolution in machine learning with Docker AutoPipelines and unlock the full potential of AI.
Docker AutoPipelines revolutionizes the machine learning landscape by providing a seamless solution for the entire ML lifecycle. Our Docker extension simplifies the process of creating, training, and deploying models, offering unparalleled ease of use. Say farewell to coding complexities as our user-friendly interface empowers users to design neural networks without requiring coding expertise. With just a few clicks, you can deploy your models, eliminating the traditional deployment hassle.

Join the machine learning revolution with Docker AutoPipelines and unleash the full potential of AI.