This blog post was originally published at SOYNET’s website. It is reprinted here with the permission of SOYNET.
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 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:
- 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.
- 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.
- 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.
- 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.
- 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.
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- SOYNET Benchmark Comparison
Global Marketing Manager, SOYNET