The HiveMindsAI system consists of a centralized Queen AI that oversees and delegates tasks to multiple subordinate AIs, ensuring tasks are performed until completion. I will be using gpt 4o mini for this experiment
Any suggestion for prompts test this HiveMindsAI? It is CLI right now, but I want to make sure it works before making it a web GUI.
Queen AI will provide task to subordinate AI.
How about this?
Could you assist me in planning a 5-day trip to Tokyo? I’m particularly interested in exploring the city’s pop culture, EDM music scene, and popular gourmet food spots. Additionally, could you recommend a hotel that is conveniently located near the Imperial Palace or offers a stunning view of the Rainbow Bridge?
I am thinking the Queen AI will break it down to several tasks and delegate it to mini agents like one for finding EDM events, getting hotels that fits the requirement, etc.
HiveMindsAI response (skipped Queen AI instructions)
Subordinate AI response for Plan a trip to Japan:
Day 1: Arrival & Introduction to Pop Culture
Day 2: Experiencing Harajuku and Shibuya
Day 3: EDM Music Scene Exploration
Day 4: Gourmet Food Experiences
Day 5: Leisure & Culture
Hotel Gracery Shinjuku
Mandarin Oriental Tokyo
Shinjuku Granbell Hotel
This plan should provide a comprehensive experience focused on Tokyo’s pop culture, EDM scene, and culinary delights. Have a fantastic trip!
— What you think of the response?
Any other prompt requests?
Given a dataset containing multimodal inputs (text, image, audio), how would you design a model architecture to effectively process and fuse these inputs for a unified understanding?
HiveMindsAI result (with a few turns to get it right):
Here’s a detailed guide to implementing a multimodal model architecture that integrates text, image, and audio data inputs, following the steps outlined.
Tokenization:
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def tokenize_and_encode(text):
return tokenizer(text, padding='max_length', truncation=True, max_length=512, return_tensors='pt')
Vectorization:
from transformers import BertModel
bert_model = BertModel.from_pretrained('bert-base-uncased')
def get_text_embeddings(input_ids, attention_mask):
outputs = bert_model(input_ids=input_ids, attention_mask=attention_mask)
return outputs.last_hidden_state.mean(dim=1) # Mean pooling
Padding/Truncation: Handled automatically in the tokenization process with padding='max_length'.
Resize & Normalization:
from torchvision import transforms
image_transforms = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) # Normalize to [-1, 1]
])
def preprocess_image(image):
return image_transforms(image)
Data Augmentation:
augmentation_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
# Add more augmentation techniques as needed
])
Sampling:
Ensure audio files are resampled to 16 kHz. Libraries like librosa can be used.
import librosa
def load_audio(file_path):
audio, sample_rate = librosa.load(file_path, sr=16000)
return audio
Feature Extraction:
Generate MFCC or spectrograms.
def extract_mfcc(audio):
mfccs = librosa.feature.mfcc(y=audio, sr=16000, n_mfcc=13)
return mfccs
Normalization:
Normalize MFCC values.
def normalize_mfcc(mfccs):
return (mfccs - np.mean(mfccs)) / np.std(mfccs)
import torch.nn as nn
class TextFeatureExtractor(nn.Module):
def __init__(self):
super(TextFeatureExtractor, self).__init__()
self.bert = BertModel.from_pretrained('bert-base-uncased')
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
return outputs.last_hidden_state.mean(dim=1) # Mean pooling
from torchvision import models
class ImageFeatureExtractor(nn.Module):
def __init__(self):
super(ImageFeatureExtractor, self).__init__()
self.resnet = models.resnet18(pretrained=True)
self.resnet.fc = nn.Identity() # Remove the classification head
def forward(self, x):
return self.resnet(x)
class AudioFeatureExtractor(nn.Module):
def __init__(self):
super(AudioFeatureExtractor, self).__init__()
self.cnn = nn.Sequential(
nn.Conv2d(1, 16, 3, stride=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(16, 32, 3, stride=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(32 * W * H, 128) # Adjust for output dimension
)
def forward(self, x):
return self.cnn(x.unsqueeze(1)) # Assuming x is of shape (Batch, Feature, Time)
class FusionLayer(nn.Module):
def __init__(self):
super(FusionLayer, self).__init__()
self.fc1 = nn.Linear(text_feat_size + image_feat_size + audio_feat_size, 512)
self.fc2 = nn.Linear(512, 256)
def forward(self, text_feat, image_feat, audio_feat):
combined = torch.cat((text_feat, image_feat, audio_feat), dim=1)
x = self.fc1(combined)
return self.fc2(x)
class UnifiedUnderstanding(nn.Module):
def __init__(self, num_classes):
super(UnifiedUnderstanding, self).__init__()
self.fusion_layer = FusionLayer()
self.dense1 = nn.Linear(256, 128)
self.dropout = nn.Dropout(0.5)
self.output_layer = nn.Linear(128, num_classes)
def forward(self, text_feat, image_feat, audio_feat):
fusion_out = self.fusion_layer(text_feat, image_feat, audio_feat)
x = self.dropout(nn.ReLU()(self.dense1(fusion_out)))
return self.output_layer(x)
criterion = nn.CrossEntropyLoss() # For classification
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
Use libraries like sklearn to perform train-test split.
from sklearn.model_selection import train_test_split
train, test = train_test_split(data, test_size=0.2)
Define metrics like accuracy and F1-score using sklearn.
from sklearn.metrics import accuracy_score, f1_score
def evaluate(model, data_loader):
model.eval()
predictions, true_labels = [], []
with torch.no_grad():
for batch in data_loader:
# Perform inference
# Collect predictions and true labels
return accuracy_score(true_labels, predictions), f1_score(true_labels, predictions, average='weighted')
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
for train_index, test_index in kf.split(data):
# Train and evaluate for each fold
Use tools like Optuna or GridSearchCV to find optimal hyperparameters.
Apply L1/L2 regularization in optimizers:
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5) # L2 regularization
torch.save(model.state_dict(), 'multimodal_model.pth')
Use frameworks like Flask or FastAPI for building an API around the model.
Implement logging frameworks or dashboards to monitor model performance.
Conduct a thorough review of the implementation, validating that all steps have been executed effectively, ensuring model robustness, accuracy, and readiness for application. Adjust steps as needed based on insights gained during the evaluation phase.
What you think of the responses from HiveMindsAI?
Make a video with a live demonstration, everyone can do prompts.
What is important to prove your mechanism is flexible and handle various tasks.
Thanks for the feedback. I can create a video.
With so much data output, should I use CLI or web(flask)? (Queen output, Subordinate outputs)
Any other prompts?
Use what you want, you made me curious.
You have a main Assistant (Alpha):
You will give a task to Alpha to deliver to all Agents Tasks:
After you give them tasks, you will print each thread to see if there is an success. Doesn’t matter the tasks, functionality matter, in my case.
That is a good feedback indeed! I have version with multiple subordinates already. They might have their own subordinates too.
Updated: added sub-subordinates
This is perfect!
So if the claim is that if I have framework for autonomous agents, it may be sufficient to show those agents acting in an autonomous way. However (and perhaps obviously) these agents must, somehow, refer back to the original context.
i.e. if they are tasked to work on
“Given a dataset containing multimodal inputs (text, image, audio), how would you design a model architecture to effectively process and fuse these inputs for a unified understanding?”
they cannot be working on quantum theory 
I prompt “Given a dataset containing multimodal inputs (text, image, audio), how would you design a model architecture to effectively process and fuse these inputs for a unified understanding?”
Or
“Given a dataset containing multimodal inputs (text, image, audio), how would you design a model architecture to effectively process and fuse these inputs for a unified understanding?”
they cannot be working on quantum theory“
Either one will work, I think.
FYI In my view of the world, this is how the prompt
" Could you assist me in planning a 5-day trip to Tokyo? I’m particularly interested in exploring the city’s pop culture, EDM music scene, and popular gourmet food spots. Additionally, could you recommend a hotel that is conveniently located near the Imperial Palace or offers a stunning view of the Rainbow Bridge?"
broke down:
Requests: Planning a trip to Tokyo
Facts:
Ideas:
Objectives:
Frames:
Claims:
Actions:
Goals:
Missions:
Purposes:
Outcomes:
Decisions:
I really appreciate your prompts and suggestions. t will take some time for the prompt requests.
HiveMindsAI response with subordinates and sub-subordinates:
"
Subordinate AI response for design a model architecture to effectively process and fuse these inputs for a unified understanding?:
To design a model architecture that effectively processes and fuses multimodal inputs — specifically text, image, and audio — for a unified understanding, we’ll follow a structured approach. Here’s a step-by-step outline to implement this architecture:
Text Input:
Image Input:
Audio Input:
Text Branch:
Image Branch:
Audio Branch:
Dimension Alignment:
Fusion Strategy:
Fusion Layer:
Loss Function:
Optimization:
Training Procedure:
By following this structured architecture design, the resulting model will effectively process, fuse, and interpret multimodal inputs, leading to a unified understanding that enhances decision-making and predictions across diverse applications.
Summary of tasks and responses:
Task: design a model architecture to effectively process and fuse these inputs for a unified understanding?
Queen AI response: Here are detailed instructions for designing a model architecture to process and fuse multimodal inputs (text, image, audio) for a unified understanding. The approach incorporates feature extraction, fusion strategies, and output interpretation.
Text:
Image:
Audio:
Individual Modal Networks:
Feature Extraction Layers:
Early Fusion (Concatenation):
Late Fusion (Decision-Level):
Intermediate Fusion:
By following these instructions, a subordinate AI can effectively design and implement a multimodal model architecture that processes and fuses text, image, and audio inputs for a comprehensive understanding of the input data.
Subordinate AI response: To design a model architecture that effectively processes and fuses multimodal inputs — specifically text, image, and audio — for a unified understanding, we’ll follow a structured approach. Here’s a step-by-step outline to implement this architecture:
Text Input:
Image Input:
Audio Input:
Text Branch:
Image Branch:
Audio Branch:
Dimension Alignment:
Fusion Strategy:
Fusion Layer:
Loss Function:
Optimization:
Training Procedure:
By following this structured architecture design, the resulting model will effectively process, fuse, and interpret multimodal inputs, leading to a unified understanding that enhances decision-making and predictions across diverse applications."
It is awesome output, thanks for the try!
You’re most welcome. HiveMinds AI and I appreciate your inputs.
I updated HiveMinds AI with search capabilities in sub-subordinate just to make sure it gets latest data. Any prompt? To test?
HiveMinds AI have coding capabilities
