Is It Possible to Solve the Issue of AI Hallucinations?

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Spend time interacting with chatbots like ChatGPT, and you’ll soon realize they sometimes generate incorrect information.

This problem, known as hallucination or confabulation, poses a challenge for everyone, from businesses and organizations to high school students relying on generative AI systems to compose documents and perform various tasks. These tasks can include high-stakes activities, such as mental health therapy or legal research.

Daniela Amodei, co-founder and president of Anthropic, creator of the chatbot Claude 2, admits that hallucination is a widespread problem in current AI models. “They’re essentially predicting the next word, and sometimes they do this inaccurately,” Amodei noted.

Major developers like Anthropic, OpenAI, and others are striving to make their AI systems more truthful. Yet, whether these models can ever safely offer medical advice or perform other critical tasks is still uncertain.

According to Emily Bender, a linguistics professor at the University of Washington, the problem may be unfixable due to the inherent discrepancy between the technology and its intended applications.

The reliability of generative AI technology is of great importance, with the McKinsey Global Institute estimating its potential contribution to the global economy to be between $2.6 trillion and $4.4 trillion. This includes not only chatbots but also technology that can generate images, videos, music, and code.

Even Google is promoting an AI-driven news-writing product, and others, like Big Big News, are exploring partnerships with OpenAI. In another application, computer scientist Ganesh Bagler has been experimenting with AI to create recipes for South Asian dishes. A single incorrect ingredient could ruin a meal.

During his visit to India, OpenAI’s CEO Sam Altman expressed hope that significant improvements in the hallucination issue are on the horizon, though perfect accuracy may be challenging to achieve.

However, some experts, including Bender, believe that improvements may never be sufficient. Bender describes language models as designed to create things, and any correctness in their output is coincidental. Errors may go unnoticed in obscure cases.

For some, like Shane Orlick, president of Jasper AI, hallucinations might even be seen as a positive aspect, fostering creativity in fields like marketing.

Orlick recognizes the difficulty of fixing hallucinations but expects companies like Google to invest in solutions. “It may never be perfect, but it’s likely to improve continually,” he said.

Optimistic views have been shared by figures like Bill Gates, who expressed confidence in AI’s ability to separate fact from fiction eventually. Some research also points to promising advancements in detecting and removing hallucinated content.

Yet even Altman, who actively promotes these products, remains skeptical about relying on the models for accurate information. During a speech in India, he humorously admitted, “I probably trust the answers that come out of ChatGPT the least of anybody on Earth.”

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More about fokus keyword: AI hallucination

• OpenAI’s official website
• Anthropic’s official website
• McKinsey Global Institute’s reports on AI
• Bill Gates’ blog on AI’s societal risks
• Swiss Federal Institute of Technology’s research publications
• University of Washington’s Computational Linguistics Laboratory website