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The term 'Generative Adversarial Networks (GANs)' is often used in the context of
- a)Health care infrastructure
- b)Deepfake technology
- c)Telecommunication infrastructure
- d)Money laundering network
Correct answer is option 'B'. Can you explain this answer?
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The term Generative Adversarial Networks (GANs) is often used in the c...
- A generative adversarial network (GAN) is a class of machine learning framework and a prominent framework for approaching generative AI. In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.
- Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. GANs are similar to mimicry in evolutionary biology, with an evolutionary arms race between both networks.
- Deepfakes are fake videos and image of people created using artificial intelligence by swapping faces or altering voices. The deepfake technology swaps the face of a person in a video or an image with another persons' face through AI algorithms. The deepfake algorithm runs through a process called Generative Adversarial Networks (GANs). Two machine learning models work together to create forgeries and to detect them, resulting in convincing fake videos.
- Hence option (b) is the correct answer.
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The term Generative Adversarial Networks (GANs) is often used in the c...
Deepfake technology:
Generative Adversarial Networks (GANs) are often used in the context of deepfake technology.
Explanation:
Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm that is used to generate realistic-looking images, videos, or audio. In the context of deepfake technology, GANs are used to create fake content that is often difficult to distinguish from real content. This technology has raised concerns about its potential misuse, such as creating fake news, spreading misinformation, or impersonating individuals.
How GANs work:
- GANs consist of two neural networks - a generator and a discriminator.
- The generator creates fake content, while the discriminator tries to distinguish between real and fake content.
- The two networks are trained together in a competitive process, with the generator trying to fool the discriminator, and the discriminator trying to correctly identify fake content.
- Over time, the generator learns to create increasingly realistic content, while the discriminator improves its ability to detect fakes.
Applications of GANs in deepfake technology:
- GANs are used to create realistic-looking videos of individuals saying or doing things they never actually did.
- This technology is often used in entertainment, but it also has the potential for malicious uses such as spreading misinformation or creating fake content for political purposes.
In conclusion, GANs play a significant role in the development of deepfake technology, which has both positive and negative implications for society. It is important to be aware of the potential misuse of this technology and to take steps to mitigate its negative effects.
Generative Adversarial Networks (GANs) are often used in the context of deepfake technology.
Explanation:
Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm that is used to generate realistic-looking images, videos, or audio. In the context of deepfake technology, GANs are used to create fake content that is often difficult to distinguish from real content. This technology has raised concerns about its potential misuse, such as creating fake news, spreading misinformation, or impersonating individuals.
How GANs work:
- GANs consist of two neural networks - a generator and a discriminator.
- The generator creates fake content, while the discriminator tries to distinguish between real and fake content.
- The two networks are trained together in a competitive process, with the generator trying to fool the discriminator, and the discriminator trying to correctly identify fake content.
- Over time, the generator learns to create increasingly realistic content, while the discriminator improves its ability to detect fakes.
Applications of GANs in deepfake technology:
- GANs are used to create realistic-looking videos of individuals saying or doing things they never actually did.
- This technology is often used in entertainment, but it also has the potential for malicious uses such as spreading misinformation or creating fake content for political purposes.
In conclusion, GANs play a significant role in the development of deepfake technology, which has both positive and negative implications for society. It is important to be aware of the potential misuse of this technology and to take steps to mitigate its negative effects.
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The term Generative Adversarial Networks (GANs) is often used in the context ofa)Health care infrastructureb)Deepfake technologyc)Telecommunication infrastructured)Money laundering networkCorrect answer is option 'B'. Can you explain this answer?
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