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कार्यकारी व्यक्तियों का कहना है कि जेनरेटिव ए.आई. अभी भी मुख्यत: प्रयोगात्मक है, इसमें और प्रयोग और परीक्षण की आवश्यकता है।

कार्यकारी व्यक्तियों का कहना है कि जेनरेटिव ए.आई. अभी भी मुख्यत: प्रयोगात्मक है, इसमें और प्रयोग और परीक्षण की आवश्यकता है।

Generative AI still mostly experimental, say executives.

Executives acknowledge that generative AI remains largely experimental, indicating its ongoing development and testing phases before it achieves widespread and reliable application in various domains.

  • Technology
  • 75
  • 11, Nov, 2023
Jivika Chawla
Jivika Chawla
  • @JivikaChawla

Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, and code. It is a rapidly developing field with the potential to revolutionize many industries.

However, generative AI is still mostly experimental. Executives at leading tech companies have said that the technology is not yet ready for widespread use.

There are a number of challenges that need to be addressed before generative AI can be used in production environments. One challenge is that generative AI systems can be biased. This is because they are trained on data that is created by humans, and this data can reflect the biases of the people who created it.

Another challenge is that generative AI system can be easily fooled. For example, a generative AI system can be used to create fake images or videos that are indistinguishable from real ones. This could be used to spread misinformation or to create deepfakes.

Despite the challenges, executives at leading tech companies are optimistic about the future of generative AI. They believe that the technology has the potential to solve some of the world's biggest problems, such as climate change and disease.

Potential applications of generative AI

Generative AI has the potential to be used in a wide range of industries. Here are a few examples:

  • Healthcare: Generative AI could be used to develop new drugs and treatments, diagnose diseases, and personalize care for individual patients.
  • Education: Generative AI could be used to create personalized learning experiences for students, provide feedback on student work, and develop new educational materials.
  • Finance: Generative AI could be used to develop new financial products and services, assess risk, and detect fraud.
  • Marketing: Generative AI could be used to create personalized marketing campaigns, generate creative content, and target ads more effectively.
  • Media and entertainment: Generative AI could be used to create new forms of entertainment, such as personalized movies and TV shows, and to generate realistic special effects.

Challenges to be addressed

There are a number of challenges that need to be addressed before generative AI can be used in production environments. Here are a few examples:

  • Bias: Generative AI systems can be biased because they are trained on data that is created by humans. This bias can be reflected in the outputs of the system.
  • Safety: Generative AI systems can be used to create harmful content, such as fake images or videos that are used to spread misinformation or to create deepfakes.
  • Explainability: Generative AI systems are often complex and opaque. It can be difficult to understand how they work and why they generate the outputs that they do.
  • Fairness: Generative AI systems can be used to create unfair outcomes. For example, a generative AI system could be used to generate a list of potential job candidates that is biased against certain groups of people.
Jivika Chawla

Jivika Chawla

  • @JivikaChawla