Generative artificial intelligence is a branch of AI that focuses on creating new data instead of making predictions or classifications. GenAI works for any kind of data, from realistic images to complex texts to videos known asGenAI has become one of the most discussed technologies today. However, despite its potential, this technology holds fundamental limitations and risks that often go unaddressed in the mainstream hype, which is what I'll be focusing on today.
LLMs can also reproduce sensitive or proprietary information when using approaches such as retrieval augmented generation and/or fine-tuning, posing privacy and security risks. This can be particularly problematic in areas like news dissemination, education, healthcare and legal advice where accuracy is crucial.Despite their impressive output capabilities, GenAI applications are limited in their ability to tackle complex, multi-dimensional societal issues.
By adopting use-case-specific AI, we can propel technology forward in a manner that is not just innovative but also aligned with the nuanced demands of human values and ethical considerations. Of course, this is not technology that is already available and there are plenty of challenges and barriers.One of the primary challenges in developing AI that truly understands and interacts with the real world is the need for comprehensive, multi-domain data.
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