|Introduction: Explain Generative AI.|
Body: Explain its implications.
Conclusion: Write a way forward.
Generative AI (GenAI) is the part of Artificial Intelligence that can generate all kinds of data, including audio, code, images, text, simulations, 3D objects, videos, and so forth. It takes inspiration from existing data, but also generates new and unexpected outputs. Recently, San Francisco-based AI start-up OpenAI launched ChatGPT (Chat Generative Pre-trained Transformer).
Generative AI works by training a model on a large dataset and then using that model to generate new, previously unseen content that is similar to the training data. This can be done through techniques such as neural machine translation, image generation, and music generation.
- Reduce the burden of human research:It can help shift through numerous legal research materials and produce a pertinent, specific and actionable summary. As a result, it can reduce the countless hours of human research and enable them to focus on more complex and exciting problems.
- Help in designing: It can also help create and simulate complex engineering, design, and architecture. It can help speed up the iterative development and testing of novel designs.
- Personalized Health treatments:It can also help health professionals with their medical diagnosis. AI can generate potential and alternative treatments personalized to patients’ symptoms and medical history. For instance, DeepMind AlphaFold can predict the shape of the protein.
- Agencies can generate personalized social media posts, blogs and marketing text and video copies by providing a text prompt to a Generative AI service like ChatGPT.
- Deepfakes: Generative AI, particularly machine learning approaches such as deepfakes, can be used to generate synthetic media, such as images, videos, and audio. Such AI-generated content can be difficult or impossible to distinguish from real media, posing serious ethical implications.
- Inaccuracy problem: Generative AI uses machine learning to infer information, which brings the potential inaccuracy problem to acknowledge.
- Increase in Biases: Recent evidence suggests that larger and more sophisticated systems are often more likely to absorb underlying social biases from their training data. These AI biases can include sexist, racist, or ableist approaches within online communities.
- Risk of Unemployment: This could happen if generative AI automates tasks or processes previously performed by humans, leading to the displacement of human workers.
- Plagiarism: they are really just making new patterns from the millions of examples in their training set. The results are a cut-and-paste synthesis drawn from various sources—also known, when humans do it, as plagiarism. Either way, what’s missing is uniqueness.
It is important for developers and users of generative AI to consider the potential impacts and ensure that the technology is used ethically and responsibly.