Generative artificial intelligence (AI) is all the buzz right now. The Scholarly Kitchen brought this popular topic to our attention in their article, “The Intelligence Revolution: What’s Happening and What’s to Come in Generative AI.“
Speculation around how generative AI will evolve and how it will further impact our world – for good or bad – is a common discussion point . Big tech companies are already integrating generative AI and large language models (LLMs) into their existing commercial products to improve collaboration and productivity for users.
Current generative AI models require vast amounts of data to produce high-quality outputs. Progress in data-efficient training techniques, such as few-shot or one-shot learning, could enable generative models to achieve impressive results with limited training data.
Combining generative models with reinforcement learning techniques could also lead to AI systems that learn to improve their outputs iteratively through interactions with users or the environment.
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. Generative AI is no different.
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 its 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. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
