Ask Chatgpt How To Create An Ai Model For Controlling Computer Under Human Instruction

GPT

RNN/LSTM

ViT

Screenshots

Natural Language Instructions

Keyboard/Mouse Events

This article discusses an AI model that utilizes GPT, RNN/LSTM, and ViT to process input data from various sources such as screenshots, natural language instructions, and keyboard/mouse events. The model first extracts features using ViT, then encodes instructions with GPT, followed by processing the combined features through LSTM/RNN. Finally, it decodes the output into events utilizing CNNs, RobertaModel, and GPT2LMHeadModel.

Published

April 3, 2023

question

how do i design an ai model using gpt, rnn, lstm, vit to accept computer display screenshots and human instructions as input, keyboard and mouse events as output, trained on random keystrokes and mouse events, also human instructions paired with human demonstrations? show me python code

answers

Claude+

That would be an extremely complex AI system to build. Some high level steps would be:
Collect a large dataset of screenshots, human instructions, and corresponding keyboard/mouse interactions. This dataset would be crucial for training.
Use a CNN to extract visual features from the screenshots. Something like VGG16 or ResNet would work well here.
Use an RNN/LSTM to encode the sequence of instructions. BERT or RoBERTa would also be good options here.
Combine the visual and textual features. Options include concatenation, addition, or a more complex fusion method.
Use a Transformer like GPT-3 or Visual-Transformers to generate the keyboard/mouse interactions from the combined visual+textual features.
Train the entire model end-to-end to minimize the difference between the generated interactions and the ground truth interactions from your dataset.
This would likely take a team of ML engineers and researchers to develop, and would require lots of time, data, and compute.
Some sample Python code would be:
import torchvision.models as models
import torch
import torch.nn as nn
from transformers import RobertaModel, GPT2LMHeadModel
# Feature Extractors
cnn = models.resnet50(pretrained=True)
roberta = RobertaModel.from_pretrained("roberta-base")
# Fusion
fusion = nn.Sequential(nn.Linear(2048 + 768, 1024), nn.ReLU(), nn.Linear(1024, 512))
# Generation Model
generator = GPT2LMHeadModel.from_pretrained("gpt2")
# Forward pass
img_features = cnn(img)
instruction_features = roberta(instructions)[0]
combined_features = fusion(torch.cat((img_features, instruction_features), dim=1))
preds = generator(inputs_embeds=combined_features)[0]
This is just a brief sample, but hopefully gives you an idea of the components and flow. Let me know if you have any other questions!

GPT4

Designing an AI model that combines GPT, RNN, LSTM, and ViT for the task you described is complex and requires deep understanding of these architectures. However, I’ll provide you with a high-level overview and a basic structure of the code to help you get started.

First, process the input data:

For computer display screenshots, use a Vision Transformer (ViT) model to extract features.
For human instructions, use a GPT model to encode the natural language instructions.

Combine the features from both the ViT and GPT models.
Use an LSTM or RNN to process the combined features and maintain the temporal context.
Finally, decode the output of the LSTM/RNN into keyboard and mouse events.

Here’s a basic structure of the code:

import torch
import torchvision
from transformers import GPT2Tokenizer, GPT2Model
# Load pretrained models
gpt_model = GPT2Model.from_pretrained('gpt2')
vit_model = torchvision.models.vit_b_16(pretrained=True)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
class CustomModel(torch.nn.Module):
def __init__(self, gpt_model, vit_model, hidden_size, output_size):
super(CustomModel, self).__init__()
self.gpt_model = gpt_model
self.vit_model = vit_model
self.rnn = torch.nn.LSTM(input_size=hidden_size, hidden_size=hidden_size, batch_first=True)
self.fc = torch.nn.Linear(hidden_size, output_size)
def forward(self, instructions, screenshots):
# Encode instructions using GPT2
inputs = tokenizer(instructions, return_tensors="pt", padding=True, truncation=True)
gpt_output = self.gpt_model(**inputs)
gpt_features = gpt_output.last_hidden_state[:, 0, :]
# Encode screenshots using ViT
vit_output = self.vit_model(screenshots)
vit_features = vit_output["last_hidden_state"][:, 0, :]
# Combine features
combined_features = torch.cat((gpt_features, vit_features), dim=1)
# Process combined features through an RNN
rnn_output, _ = self.rnn(combined_features.unsqueeze(1))
# Decode the output into keyboard and mouse events
output = self.fc(rnn_output.squeeze(1))
return output
# Instantiate the custom model
model = CustomModel(gpt_model, vit_model, hidden_size=1024, output_size=256)
# Train and evaluate the model using your dataset

You’ll need to collect a dataset with computer display screenshots, human instructions, and paired keyboard and mouse events. Then, preprocess the data, split it into training and validation sets, and train the model using an appropriate loss function and optimizer.

Keep in mind that this is just a starting point, and you may need to experiment with different architectures, loss functions, and hyperparameters to achieve the desired performance.