With the advances of time, AI (Artificial Intelligence) has developed at a rapid pace. In the forthcoming years, the AI world will be completely different from what it is today. Methods that are mainstream today will have become obsolete by then. Machines will be producing more data at a speed higher than ever before.
One such technology is Generative AI.
Let us go through this blog to learn about its meaning, applications, pros, and cons in detail.
What is Generative AI?
It is a technology that enables computers to produce artificial and similar content. A computer does this by understanding and scanning the pattern of past data input.
There are several techniques of doing that such as:
- Generative Adversarial Network (GANs) works on two neural networks, namely discriminators and generators. The generator is responsible for generating new data that resembles the source data. The discriminator, on the other hand, is responsible for differentiating the source data and the generated data.
- Transformers can understand and generate texts, images, and languages from massive datasets.
- Variational auto-encoders compress the code that stores the information in a much smaller dimensional representation.
Businesses can derive the following benefits from Generative AI:
- lowered rate of risk
- improved quality of output
- reduced bias
Applications of Generative AI
Identity Protection:
- It helps in protecting the identity of individuals using avatars.
Image Processing:
- Using this technology, low-resolution photos can be easily converted into high-resolution ones.
Image Conversion:
- We can convert a human face into an emoji, using this technology.
Media and Entertainment:
- Artist’s original voice can be matched with a lip-sync using deep fake technology.
Health:
- It helps in the early detection of any potential malignancy so that it can be treated effectively.
Other uses of Generative AI are:
- 3D Printing
- Generating photographs of human faces
- Fraud Detection
- Trend Analysis
Limitations
- As discussed earlier, this technology produces content based on past data input. Thus, it cannot produce any latest content.
- Generative AI models, GANs, for instance, do not always produce the desired output.
- It does not offer a hundred percent privacy.
Is Generative AI supervised or unsupervised learning?
It is semi-supervised learning. It means that it uses manually labeled data for supervised learning and unlabeled data for unsupervised learning.
Future of Generative AI
Soon, it is going to shake all the industries as it tries to make its way into crucial industries such as healthcare and defense.
Conclusion
Generative AI is becoming a highly discussed topic in the AI community. The advances in the future are going to increase its applications.
