
Recent discussions highlight the potential drawbacks of 'unlearning' techniques in artificial intelligence, which aim to make AI models forget undesirable data. According to various sources, including a blog post by Kyle Wiggers, these techniques may negatively impact the performance of generative AI models. The concept of making AI forget specific information, such as sensitive private data, has raised concerns about the overall effectiveness of AI systems. Additionally, a research paper published in Nature warns that despite significant advancements in AI, there is a risk that these models could become less capable over time. This sentiment is echoed in a report from Gartner, indicating that skepticism is growing regarding the return on investment (ROI) of generative AI projects, with about one-third of such initiatives facing doubts about their viability.
This Week in AI: Companies are growing skeptical of AI’s ROI: https://t.co/MiCsGgxJWx by TechCrunch #infosec #cybersecurity #technology #news
This Week in AI: Companies are growing skeptical of AI’s ROI: Hiya, folks, welcome to TechCrunch’s regular AI newsletter. This week in AI, Gartner released a report suggesting that around a third of generative AI projects in the… https://t.co/PAczpuHSNk #AI #AInews #AItips
Making #AI models ‘forget’ undesirable data hurts their performance. #MachineLearning https://t.co/ulJE16Zv2a

