10 Things to Know About GenAI
✅️ Generative AI: A technology that can learn from existing artifacts to generate new, realistic content that reflects the characteristics of the training data but doesn’t repeat it.
✅️ Foundation Models: These are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning.
✅️ Enterprise Use Cases: Includes innovations in drug and chip design and material science development.
✅️ Generative AI Benefits: Faster product development, enhanced customer experience, and improved employee productivity.
✅️ Generative AI Risks: Lack of transparency, accuracy, bias, intellectual property and copyright, cybersecurity and fraud, and sustainability.
✅️ Generative AI Use Cases: Written content augmentation and creation, question answering and discovery, tone, summarization, simplification, classification of content for specific use cases, chatbot performance improvement, and software coding.
✅️ Generative AI Emerging Use Cases: Creating medical images that show the future development of a disease, synthetic data helping augment scarce data, mitigate bias, preserve data privacy and simulate future scenarios, and applications proactively suggesting additional actions to users and providing them with information.
✅️ Generative AI Impact: Will affect the pharmaceutical, manufacturing, media, architecture, interior design, engineering, automotive, aerospace, defense, medical, electronics, and energy industries by augmenting core processes with AI models.
✅️ Generative AI Costs: Range from negligible to many millions depending on the use case, scale, and requirements of the company.
✅️ Generative AI Future: Predicted that 40% of enterprise applications will have embedded conversational AI by 2024, 30% of enterprises will have implemented an AI-augmented development and testing strategy by 2025, and 15% of new applications will be automatically generated by AI without a human in the loop by 2027.
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