A Multi-Modal Conversational Model for Image Understanding and Generation

Visual ChatGPT allows users to perform complex visual tasks using text and visual inputs. With the rapid advancements in AI, there is a growing need for models that can handle multiple modalities beyond text and images, such as videos or voices. One of the challenges in building such a system is the amount of data and computational resources required. But worry no more because the newly released Visual ChatGPT has got us covered; it is based on ChatGPT and incorporates a variety of Visual Foundation Models (VFMs) to bridge the gap between ChatGPT and visual information. It uses a Prompt Manager that supports various functions, including explicitly telling ChatGPT the capability of each VFM and handling the histories, priorities, and conflicts of different VFMs.

The Prompt Manager

The Prompt Manager in Visual ChatGPT aims to help the language model accurately understand and handle various visual language tasks by providing clear guidelines and prompts for different scenarios. The Prompt Manager distinguishes among the various Visual Foundation Models (VFMs) available in Visual ChatGPT and helps select the appropriate model for a specific task. It defines the name, usage, inputs/outputs, and optional examples for each VFM to help the model decide which VFM to use for a particular task.

The Prompt Manager also handles user queries by generating a unique filename for newly uploaded images and appending a suffix Prompt to force Visual ChatGPT to use foundation models instead of relying solely on its imagination. Using these prompts encourages the model to provide specific outputs generated by the foundation models rather than generic responses and to use the appropriate VFM in a particular scenario. This helps improve the accuracy and relevance of Visual ChatGPT’s responses to user queries, particularly those related to visual language tasks.

Visual foundation model (VFM)

It is a machine-learning model that processes visual information, such as images or videos. VFMs are usually trained on large amounts of visual data. They can recognize and extract features from visual inputs. Other machine learning models, such as ChatGPT, can then use these features to perform more complex tasks requiring language and visual understanding. For example, a VFM could identify objects in an image. Then ChatGPT could generate a caption describing the objects in natural language. VFMs can be trained using various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Sources: Visual ChatGpt Paper

Example of Visual ChatGPT in Action

To understand how Visual ChatGPT works, consider the following scenario: a user uploads an image of a yellow flower and enters a complex language instruction “please generate a red flower conditioned on the predicted depth of this image and then make it like a cartoon, step by step.” With the help of Prompt Manager, Visual ChatGPT starts a chain of execution of related Visual Foundation Models.

In this case, it first applies the depth estimation model to detect the depth information, then utilizes the depth-to-image model to generate a figure of a red flower with the depth information, and finally leverages the style transfer VFM based on the Stable Diffusion model to change the style of this image into a cartoon.

During the above pipeline, Prompt Manager serves as a dispatcher for ChatGPT by providing the type of visual formats and recording the information transformation process. Finally, when Visual ChatGPT obtains the hints of “cartoon” from the Prompt Manager, it will end the execution pipeline and show the final result.

Customized System Principles

Visual ChatGPT is designed to assist with various text and visual-related tasks, such as VQA, image generation, and editing. The system relies on a list of VFMs to solve various VL tasks. Visual ChatGPT is designed to avoid ambiguity and be strict about filename usage, ensuring that it retrieves and manipulates the correct image files.

Filename sensitivity is critical in Visual ChatGPT since one conversation round may contain multiple images and their updated versions. To tackle more challenging queries by decomposing them into subproblems, Chain-of-Thought (CoT) is introduced in Visual ChatGPT to help decide, leverage, and dispatch multiple VFMs. Visual ChatGPT must follow strict reasoning formats.

The intermediate reasoning results are parsed using elaborate regex matching algorithms to construct the rational input format for the ChatGPT model to help determine the next execution, e.g., triggering a new VFM or returning the final response. Visual ChatGPT must be reliable as a language model and not fabricate image content or filenames. Therefore, prompts are designed to ensure that the model is loyal to the output of the vision foundation models.

Limitations

Visual ChatGPT’s limitations are its dependence on ChatGPT and VFMs. The accuracy and effectiveness of individual models invoked heavily influence the performance of Visual ChatGPT. Additionally, the heavy prompt engineering required to convert VFMs into language and make them distinguishable can be time-consuming and involves computer vision and natural language processing expertise.

Another limitation of Visual ChatGPT is its limited real-time capabilities. Since it automatically decomposes complex tasks into several subtasks, handling a specific task may involve invoking multiple VFMs, resulting in limited real-time capabilities.

To deal with the unsatisfactory results due to some VFMs failure, a self-correction module is suggested in the paper; this is for checking the consistency between execution results and human intentions and making the corresponding editing. This self-correction behavior can lead to more complex thinking of the model, significantly increasing the inference time.

Despite the limitations, Visual ChatGPT has demonstrated great potential and competence for different tasks despite these limitations. Its integration of visual information into dialogue tasks holds great promise for the future of AI systems.

Sweta Chaturvedi
Marketing Manager, SOYNET

Sources: Original Paper Visual ChatGpt

The post Visual ChatGPT Explained appeared first on Edge AI and Vision Alliance.

Generative AI is an artificial intelligence capable of generating new data, images, text, or other content similar to what it has been trained on. It uses complex algorithms and machine learning techniques to learn patterns from existing data and then generates new data based on these patterns.

Generative AI is important because it has the potential to revolutionize a wide range of industries, from art and entertainment to healthcare and manufacturing. Creating new and diverse data sets can help researchers and businesses uncover insights and new possibilities that would have been impossible with traditional methods.

One of the main advantages of generative AI is that it can create original content, which can be particularly useful in industries such as design and advertising, where uniqueness and originality are highly valued. It can also help automate tasks that previously required human input, such as creating captions for images or generating personalized content for marketing campaigns.

However, there are also potential drawbacks to generative AI. One of the main concerns is that it could be used to create misleading or false content, which could have serious consequences. Additionally, there are ethical concerns around the use of generative AI, particularly regarding privacy and ownership of the generated content.

The development of generative AI is worth investing in, as it has the potential to create new opportunities and drive innovation across a wide range of industries. However, it is essential to approach its development and deployment cautiously and consider the potential risks and benefits before adopting it.

Deep Fakes

Deep fakes are an example of generative AI. Deepfakes use a type of generative AI called Generative Adversarial Networks (GANs) to generate fake images or videos that appear to be accurate.

In a GAN, two neural networks are trained simultaneously: a generator network, which generates fake images or videos, and a discriminator network, which tries to distinguish between real and fake images or videos. The two networks are trained together. The generator network constantly tries to create better fake images or videos that can fool the discriminator network.

Deepfakes can be created by training a GAN on a large dataset of images or videos of a particular person and then using the generator network to create fake images or videos of that person doing or saying things they never actually did. This technology has raised concerns about its potential to be used for malicious purposes, such as spreading misinformation or impersonating individuals for fraud or blackmail.

Training generative AI models can be costly regarding time and computational resources. Generative models often require large datasets and complex architectures, making training time-consuming and computationally expensive. However, several optimization methods can help reduce the size and increase the accuracy of generative AI models:

1. Transfer Learning: Transfer learning involves using a pre-trained model to train a new model. By using a pre-trained model, you can save the time and resources that would have been required to train the model from scratch. This is particularly useful for generative models, which often require large datasets and complex architectures.
2. Regularization: Regularization is a technique that helps prevent overfitting, which occurs when a model becomes too complex and starts to memorize the training data instead of learning general patterns. Regularization techniques, such as L1 or L2 regularization, penalize large weights in the model, which can help prevent overfitting and improve the model’s accuracy.
3. Architecture Optimization: Optimizing the architecture of the generative model can also help reduce the size and increase the model’s accuracy. This involves selecting the appropriate number of layers, neurons, and activation functions for the model and experimenting with different architectures to find the best one.
4. Data Augmentation: Data augmentation involves creating new data from existing data by applying rotation, scaling, and cropping transformations. By augmenting the training data, you can increase the size of the dataset and help the model learn more robust features.
5. Progressive Growing: Progressive growing is a technique that involves gradually increasing the size of the generative model during training. By starting with a small model and gradually adding layers and neurons, you can reduce the computational cost of training and improve the model’s accuracy.

Overall, these optimization methods can help reduce the cost and time required to train generative AI models while improving their accuracy and performance.

The High Cost of Machine Learning

Machine learning is a valuable technology that companies are using to generate insights and support decision-making. However, the high cost of computing is a challenge that the industry is facing as venture capitalists seek companies that can be worth trillions. Large companies like Microsoft, Meta, and Google use their capital to develop a lead in the technology that smaller challengers cannot match.

The high cost of training and “inference” (running) large language models is a structural cost that differs from previous computing booms. Even when the software is built, it still requires a massive amount of computing power to run large language models because they do billions of calculations every time they return a response to a prompt.

These calculations require specialized hardware, and most training and inference occur on graphics processors (GPUs), which were initially intended for 3D gaming but have become the standard for AI applications because they can do many simple calculations simultaneously. Nvidia makes most of the GPUs for the AI industry, and its primary data center workhorse chip costs $10,000.

Training a large language model like OpenAI’s GPT-3 could cost more than $4 million. Meta’s largest LLaMA model released last month used 2,048 Nvidia A100 GPUs to train on 1.4 trillion tokens, taking about 21 days and nearly 1 million GPU hours. With dedicated prices from AWS, that would cost over $2.4 million.

Many entrepreneurs see risks in relying on potentially subsidized AI models that they don’t control and merely pay for on a per-use basis. It’s still being determined if AI computation will stay expensive as the industry develops. Companies making foundation models, semiconductor makers, and startups see business opportunities in reducing the price of AI software.

SOYNET, a software startup based in South Korea, is actively working in this business to make AI affordable and lighter. Their optimized models are proven to run faster and consume comparatively less memory than other frameworks.

For More Information

• SOYNET Benchmark Comparison
• https://www.cnbc.com/2023/03/13/chatgpt-and-generative-ai-are-booming-but-at-a-very-expensive-price.html
• https://www.linkedin.com/pulse/framework-evaluating-generative-ai-use-cases-barak-turovsky/

For model optimization, check out Model Market or contact sales@soynet.io

Sweta Chaturvedi
Global Marketing Manager, SOYNET

The post Generative AI: Costs and Benefits appeared first on Edge AI and Vision Alliance.

To deliver the best end-user experience for your consumers, lower the cost of AI installations, and increase ROI for your AI initiatives, AI inference performance at scale is crucial. Currently, businesses face many problems in implementing AI in real-time.

SoyNet is an inference-only framework

Current business applications run on devices requiring inference/output to be fast and require substantial memory. In most frameworks like Tensorflow, and Pytorch, training and inference are combined to run an AI model. It takes a considerable portion of memory, making the inference slower. Hence, although perfect for training models, these frameworks may not be suitable for inference.

SOYNET focuses on inference only by eliminating the training engine. SoyNet is a proprietary technology that reduces memory consumption and optimizes AI models to run faster than other frameworks.

Benefits of SoyNet

• It can support customers to provide AI applications and AI services in time (Time to Market)
• It can help application developers execute AI projects without additional technical AI knowledge and experience.
• It can help customers to reduce H/W (GPU, GPU server) or Cloud Instance cost for the same AI execution (Inference)
• It can support customers to respond to real-time environments that require very low latency in AI inference.

Features of SoyNet

• Supports NVIDIA and non-NVIDIA GPUs (based on technologies such as CUDA and OpenCL, respectively)
• Optimization for models in computer vision, NLP, time series, and GAN available.
• Library files to be easily integrated with customer applications DLL file (Windows), so file (Linux) with header or *.lib for building in C/C++
• Integration with the application using SoyNet APIs is easy.
• It can be deployed easily on the cloud, on-premises and edge devices.

Process:

1. Input to SoyNet is the pre-trained weights from common frameworks like ONNX, Pytorch and Tensorflow.
2. SoyNet converter converts these into SoyNet weight files.
3. It combines the converted weight with the model-specific configuration to make an engine file for Inference.
4. Weight extraction codes may vary depending on the structure of the AI framework or custom usage used by the model. Hence SoyNet can support various AI models, including computer vision, NLP and GAN.
5. SoyRT is a component that uses CUDA and cuDNN to optimize the model structure according to the inference code.
6. We provide the SoyNet-optimized model in a bin folder which can be containerized in a docker file.
7. To integrate with your application, you can use C++, Java or Python to call SoyNet APIs.

What makes SoyNet faster than other frameworks?

SoyNet’s proprietary technology makes the model lightweight and faster to run under any setup and environment. SoyNet uses pointer * to save memory when referring to big images. SoyNet accelerates speed by reducing the bottleneck between CPU and GPU (often, custom layer causes to run them on CPU instead of GPU). Along with this, SoyNet also works on model optimization from 32-bit to 16-bit.

With SoyNet, the AI developer/engineer job will now focus only on research and AI development tasks. They no longer need to worry about the application side and vice-versa.

To learn about how to use SoyNet in detail, please check our document “Getting Started with SoyNet” which has a detailed explanation of hardware and software requirements along with the other processes to deploy SoyNet.

Benchmarks

Some of the benchmarks SoyNet has achieved are listed below. More on the Model Market page.

Pricing

Currently, we use a subscription pricing approach.

Free: Optimized models ready to download with a 5-GPU license.

Standard: Optimized models ready to download, along with consultation and an unlimited-GPU license.

Enterprise: Custom model consultation for optimization and other partnership opportunities. Contact us on sales@soynet.io or post a message on our contact-us page.

Sweta Chaturvedi
Global Marketing Manager, SOYNET

The post SoyNet, a Fast and Affordable Solution for Inference Optimization appeared first on Edge AI and Vision Alliance.