Large Vision Models (LVM)

In the fast-evolving realm of artificial intelligence, a new frontier has emerged, and its name is LVM—Large Vision Models. This cutting-edge technology was brought into the spotlight by none other than Andrew Ng, a prominent figure in the field of AI. In a recent interview with EE Times in September 2023, Ng discussed the imminent AI revolution, particularly in the domain of images, hinting at a future dominated by LVMs.

Understanding LVM: Large Vision Models

So, what exactly is LVM? At its core, LVM stands for Large Vision Models, also referred to as Vision Language Models (VLM). Unlike their predecessors, LVMs aren't confined to language processing alone; they extend their prowess to vision-based tasks. Essentially, these models are trained using a rich dataset comprising images, videos, and visual information.

Large Vision Models exhibit a unique ability to analyze and comprehend vast volumes of intricate data encompassing text, images, and various forms of information. Leveraging deep learning techniques, these models excel at discerning patterns, predicting future trends, and delivering high-quality outcomes. One standout feature of LVMs is their capacity to generate natural language content that closely emulates human writing. This capability proves invaluable for applications such as language translation, content generation, and chatbots, where the models can generate coherent and persuasive written passages across diverse subjects.

Similarly, when it comes to visual recognition, LVMs demonstrate exceptional precision. They can recognize and classify images with remarkable accuracy, offering detailed descriptions of what they perceive. Whether it's identifying objects, scenes, or even discerning emotions depicted in photographs, LVMs showcase a remarkable ability to understand visual content.

Some key ways in which LVMs differ from LLMs:

• Data Modality: LVMs are trained on huge datasets of images, digital video footage, and other visual inputs rather than text corpora. This allows them to develop visual perception skills.
• Architectures: LVMs use convolutional neural networks optimized for spatially processing pixel inputs rather than recurrent networks common in NLP models. They also utilize transformers and attention mechanisms though.
• Tasks Targeted: LVMs aim to master computer vision abilities like image classification, object detection, image generation etc rather than language skills.
• Evaluation: Evaluating the visual cognition of LVMs requires different kind of metrics analyzing aspects like pixel accuracy for classification/segmentation, image fidelity and diversity for generative tasks etc.

Visual Prompting: The Training Technique

One fascinating aspect of LVMs is the training technique known as Visual Prompting. In this method, users prompt the model to produce desired outputs by suggesting specific patterns or images. The model, having been trained to recognize and respond to these visual cues, generates responses in a predefined manner. This technique enhances the versatility and adaptability of LVMs, making them a powerful tool for various applications.

In conclusion, Large Vision Models mark the next frontier in AI evolution, combining language processing and visual recognition to create versatile, intelligent systems. As we stand on the brink of this AI revolution, the potential applications of LVMs—from language generation to image recognition—are vast and promising, paving the way for a future where machines comprehend and interact with the world in ways that were once purely the realm of human understanding.

LVMs to be Valuable for Modern Manufacturing

My perspective on how Large Vision LMs (LVMs) could prove transformative for the manufacturing industry across the entire product lifecycle - from initial design to final delivery.

Fundamentally, LVMs work just like language models such as GPT-3, but they are trained on massive datasets of images, videos and other visual data rather than text corpora. As a result, they develop a very robust visual understanding and can generate new vivid imagery as well.

Here are some ways I envision LVMs to drive innovation across manufacturing product lifecycles:

Design:

LVMs can rapidly analyze visual data on past designs, simulate millions of 3D permutations for the product geometry and topology, evaluating aesthetic, structural and fabrication feasibility to automatically generate multiple optimized, novel designs for engineers to select from.

Production:

The keen visual cognition and pattern recognition capabilities of LVMs can enable real-time monitoring of production quality by identifying microscopic defects, wear and tear in equipment, anomalies etc. This allows both improving and maintaining consistent quality.

Testing:

LVMs can automatically visually validate that the manufactured products match the quality, specifications, safety standards defined for them based on visual data from dealing with regulations in the past. This makes compliance and testing efficient.

As you can see, the cross-domain visual intelligence offered by models, combined with reasoning abilities of PLMs open doors for next-generation, self-learning manufacturing all through from design to delivery!

LVMs are far from perfect and have a few issues related to hallucinations, label issues, biases but these will continue to evolve.

For more insights into the AI revolution and LVM technology, you can read Andrew Ng's interview with EE Times here.

What is Multimodal Search: "LLMs with vision" change businesses here.

Nomic AI and Google have a visualization demo