In the rapidly evolving landscape of artificial intelligence (AI), one of the most groundbreaking advancements is the emergence of generative AI. This innovative technology has not only revolutionized various industries but has also significantly impacted the realm of data. From generating synthetic data for training models to enhancing creativity in content creation, generative AI has ushered in a new era of possibilities and challenges in the data ecosystem. InfoWorld brought this interesting topic to our attention in their article, “How generative AI is redefining data analytics .”
An area where generative AI is reshaping the data landscape is in content generation. With advancements in natural language processing (NLP) and image generation techniques, AI models are now capable of producing high-quality text, images and even music autonomously. These AI-generated content pieces not only showcase remarkable creativity but also have practical applications across various industries, such as marketing, entertainment and design. Moreover, generative AI has empowered individuals and businesses to streamline content creation processes, generate personalized experiences for users and explore new avenues of expression.
The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
|
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
