Hybrid search on distributed signals for multimodal understanding

Multimodal information refers to data that comes from multiple sensory inputs or modalities. In the human experience, this includes visual, auditory, tactile, olfactory, and gustatory inputs. In the digital realm, multimodal data can include text, images, audio, video, and other forms of structured and unstructured data.

Distributed Signal Processing in the Human Brain

The human brain provides an excellent model for understanding distributed signal processing and multimodal integration:

1. Distributed Processing: Different regions of the brain specialize in processing specific types of sensory inputs. For example:
   • The occipital lobe primarily processes visual information
   • The temporal lobe handles auditory signals
   • The parietal lobe integrates sensory information and spatial awareness
2. Parallel Processing: The brain processes these different sensory inputs simultaneously, allowing for real-time integration of multiple data streams.
3. Distributed Storage: Memories and learned patterns are not stored in a single location but are distributed across neural networks in the brain.
4. Holistic Retrieval: When recalling a memory or making a decision, the brain retrieves and integrates information from these distributed networks to form a cohesive understanding or response.

Artificial Multimodal Understanding Systems

Drawing inspiration from the human brain, advanced AI systems like Mixpeek implement multimodal understanding through a similar architecture of distributed processing and holistic retrieval.

Distributed Indexing

In systems like Mixpeek, the indexing process mirrors the brain's distributed processing and storage:

1. Modality-Specific Processing: Different components of the system specialize in processing specific types of data:
   • Text processing modules handle transcriptions and on-screen text
   • Computer vision modules process visual elements, identifying objects, scenes, and faces
   • Audio processing modules analyze speech and non-speech audio elements
2. Feature Extraction: Each module extracts relevant features and metadata from its specific modality, creating a rich, multidimensional representation of the data.
3. Embeddings Generation: The system generates embeddings (high-dimensional vector representations) for various elements, capturing semantic and contextual information.
4. Distributed Storage: These processed features, metadata, and embeddings are stored in a distributed manner, often across multiple databases or indexes optimized for different types of data.

Hybrid Search for Holistic Retrieval

The power of multimodal understanding comes from the ability to perform hybrid searches across these distributed signals:

1. Query Understanding: The system interprets user queries, which may themselves be multimodal (e.g., text and image inputs).
2. Cross-Modal Matching: Hybrid search algorithms match query elements against the distributed indexes, looking for relevance across different modalities.
3. Signal Fusion: Results from different modalities are merged and ranked based on relevance and confidence scores.
4. Contextual Integration: The system considers the relationships between different modalities, much like how the brain integrates various sensory inputs to form a complete perception.

Mixpeek: Vision infrastructure

Mixpeek exemplifies this approach to multimodal understanding in the context of video analysis:

Customizable Indexing Pipeline

Users can configure which features to extract from videos, including transcriptions, visual descriptions, embeddings, scene detection, face recognition, object labeling, and structured JSON output.

import requests
import json

url = "https://api.mixpeek.com/index/url"
headers = {
    "Authorization": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
payload = {
    "url": "https://example.com/sample-video.mp4",
    "collection_id": "my_video_collection",
    "metadata": {
        "title": "Sample Video",
        "tags": ["demo", "example"]
    },
    "video_settings": [
        {
            "interval_sec": 10,
            "transcribe": {
                "model_id": "polyglot-v1"
            },
            "describe": {
                "model_id": "video-descriptor-v1"
            },
            "embed": {
                "model_id": "multimodal-v1"
            }
        }
    ]
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())

Distributed Processing

These various features are extracted and indexed separately, allowing for efficient processing and storage of diverse data types.

Hybrid Search

Users can perform complex queries that span multiple modalities, such as searching for specific spoken phrases combined with visual elements.

import requests
import json

url = "https://api.mixpeek.com/search/text"
headers = {
    "Authorization": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
payload = {
    "collection_ids": ["my_video_collection"],
    "query": "people walking in the park",
    "model_id": "multimodal-v1",
    "group_by_file": True,
    "filters": {
        "AND": [
            {
                "key": "metadata.tags",
                "operator": "in",
                "value": ["demo"]
            }
        ]
    }
}

response = requests.post(url, headers=headers, data=json.dumps(payload))
print(response.json())

Holistic Retrieval

The system merges results from different modalities to provide comprehensive insights, enabling developers to build sophisticated vision-enabled applications.

Implications and Future Directions

The development of multimodal understanding systems has far-reaching implications:

1. Enhanced AI Capabilities: By mimicking the brain's ability to process and integrate multiple sensory inputs, AI systems can achieve more human-like understanding and decision-making capabilities.
2. Improved Human-Computer Interaction: Multimodal interfaces that can process natural language, gestures, and contextual information can lead to more intuitive and efficient human-computer interactions.
3. Advanced Content Analysis: In fields like media production, marketing, and surveillance, multimodal understanding enables deeper, more nuanced analysis of complex content.
4. Cognitive Science Insights: The development of these systems can provide valuable insights into human cognition, potentially leading to advancements in neuroscience and psychology.

As we continue to refine these systems, we can expect even more sophisticated multimodal understanding capabilities, bringing us closer to AI systems that can perceive and interact with the world in increasingly human-like ways.