Reasons you are reading this: Generative AI can create different types of content. It is also one of the most widely discussed aspects of AI. Concerns about generative AI stealing jobs are still well-discussed and cannot be ignored. ChatGPT, DALL-E, and Bard are all recent models that have proven their potential for image generation, text generation, and music suggestions. Experts believe generative AI has the potential to revolutionize business.
Three things you should know:
- Generative AI is not a new term and has been in industrial use since 2014.
- Various industries around the world, such as media, IT, and healthcare, are increasingly using generative AI to power their operations.
- The overall concept of generative AI is based on the machine learning model and framework of generative AI, GAN (Generative Adversarial Network).
A variety of new applications are being driven by the concept of generative AI. Tech giant Amazon has announced plans to use Generative AI for review summaries, and Meta has also announced plans to use Generative AI in almost all of its products. Content management system WordPress announced that it will use this concept to power content writing. Professionals are enthusiastic about the possibilities.
“The possibilities of generative AI seem endless. It will revolutionize businesses, take new directions and open up new opportunities. , no one can grasp the reality of it.Change will happen,” Spiros Margaris, a venture capitalist and founder of Margaris Ventures, told Republic.
5 Popular Applications of Generative AI
1. Content generation: ChatGPT and Bard are the most discussed products of generative AI. You can search for any general information, and algorithms help generate the information. It is widely used in marketing, games and communications to generate dialogue, headlines and advertisements.
limit
- Lack of information in AI systems does not always give accurate results.
- There are concerns that this system will convey false and biased information.
2. Image generation: This is also an approach that turns words into images. Algorithms in the image generation system analyze text and descriptions provided by users to generate real-time images used in media, design, manufacturing, marketing and education.
Limitations
- AI cannot accurately represent the correct human shape.
- Algorithms have difficulty identifying free images, so copyright issues still remain.
3. Music production: AI systems can generate music based on text descriptions. This also allows you to generate short clips of songs, manipulate existing melodies and arbitrary notes played on your instrument.
limit
- Copyright infringement
- Concerns about training data collected from the web without the author’s knowledge.
4. Coding: Generative AI models, a trait that has been treated exclusively by the IT industry, will benefit anyone who wants to write programming code. This is beneficial for someone who works in a startup company where he relies on websites and applications to run his business.
Limitations
- Understanding multiple programming languages is a concern.
- AI is currently not trained to handle complex problems in the coding domain and will require manual intervention over time.
5. Generate text-to-speech: Even students with visual impairments can do it. Businesses will benefit from this, as they will have a wider choice of languages and no longer have language barriers across borders.
Limitations
- I need to add different languages from around the world to my AI model.
- The interpretation techniques used in AI models are subject to error.
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