Artificial intelligence (AI) is reshaping industries, powering innovations and driving transformative changes across various domains. At the heart of AI lies its ability to learn from data, a process known as training. Traditionally, AI models have been trained to accumulate knowledge over time. However, emerging research suggests that selective forgetting, a technique inspired by human memory mechanisms, can significantly improve AI performance and adaptability. Quanta Magazine brought this topic to our attention in their article, “How Selective Forgetting Can Help AI Learn Better.

In AI, selective forgetting involves dynamically adjusting the importance of past experiences during training to adapt to new information or tasks. By mimicking human memory mechanisms, AI models can learn continuously, adapt to changing conditions and avoid catastrophic forgetting. As research in this field continues to evolve, selective forgetting holds the promise of unlocking new frontiers in AI capabilities, powering innovations and driving transformative changes across industries.

On a personal note, I certainly wish my forgetting was selective. But alas.

Access Innovations knows information science and scholarly publishing. We also know AI and are uniquely positioned to get the most out of the new AI engines. We use various techniques to enhance your data and train focused language models so that you get better results.

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.