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AI, especially generative AI and large language models (LLMs), has made significant technical progress and is now poised for widespread industry adoption. According to McKinsey, top-performing AI companies are fully embracing artificial intelligence, making it clear that others must adopt these technologies or risk falling behind.
Despite these advances, AI safety remains underdeveloped, posing significant risks for companies. Instances of AI and machine learning (ML) malfunctions are common. In areas like medicine and law enforcement, algorithms intended to be neutral have shown biases, worsening societal inequalities and harming reputations. A notable example is Microsoft’s Tay chatbot, which was hijacked by internet trolls and made to produce offensive content, leading to a major public relations crisis. Even ChatGPT has faced criticism for its perceived limitations.
Corporate leaders understand the transformative potential of generative AI, but many are unsure how to begin identifying use cases and prototypes while navigating the complex landscape of AI safety.
The solution lies in targeting “Needle in a Haystack” problems. These are scenarios where finding solutions is challenging for humans, but verifying them is relatively simple. These problems are well-suited for early AI adoption. Recognizing this pattern reveals numerous such opportunities.
Examples include:
1. Copyediting: Spotting grammar and spelling errors in lengthy documents is difficult for humans but easier for AI. Services like Grammarly use LLMs to identify mistakes, which humans can then verify.
2. Writing boilerplate code: Learning the syntax and conventions of new APIs or libraries is time-consuming. AI tools like GitHub Copilot and Tabnine generate boilerplate code, allowing engineers to focus on verification.
3. Searching scientific literature: With millions of papers published annually, keeping up with scientific research is daunting. AI can assist by generating novel insights from vast amounts of data, which humans can then review and validate.
Human verification is critical in all these use cases. While AI can generate solutions, human oversight ensures accuracy and safety. This approach reduces risks and enhances the benefits of AI, allowing companies to gain experience while addressing safety concerns.
By focusing on “Needle in a Haystack” problems, companies can effectively integrate AI into their operations, leveraging its capabilities while mitigating potential dangers.
Tianhui Michael Li is the president of Pragmatic Institute and the founder of The Data Incubator, a data science training and placement firm.
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