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Artificial intelligence is poised to be perhaps the most impactful technology of modern times. The recent advances in transformer technology and generative AI have demonstrated a potential to unlock innovation and ingenuity at scale.
However, generative AI is not without its challenges, which can significantly hinder adoption and the value that can be created with such a transformative technology. As generative AI models grow in complexity and capability, they also present unique challenges, including the generation of outputs that are not grounded in the input data.
These so-called “hallucinations” are instances when models produce outputs that, though coherent, might be detached from factual reality or from the input’s context. This article will briefly survey the transformative effects of generative AI, examine the shortcomings and challenges of the technology, and discuss the techniques available to mitigate hallucinations.
The transformative effect of generative AI
Generative AI models use a complex computing process known as deep learning to identify patterns in large sets of data and then use this information to create new, convincing outputs. The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time.
Generative AI models like OpenAI’s GPT-4 and Google’s PaLM 2 have the potential to accelerate innovations in automation, data analysis, and user experience. These models can write code, summarize articles, and even help diagnose diseases. However, the viability and ultimate value of these models depends on their accuracy and reliability. In critical sectors like healthcare, finance, or legal services, reliable accuracy is of paramount importance. But for all users, these challenges need to be addressed to unlock the full potential of generative AI.
Shortcomings of large language models
LLMs are fundamentally probabilistic and non-deterministic....