Misunderstanding AI and its capabilities can lead to significant challenges and risks for businesses.
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This recent article/study by scientists at Polytechnic University of Valencia, Spain point out that the larger the LLMs scale, often, the worse they get at answering basic questions.
https://www.nature.com/articles/s41586-024-07930-y
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Here are some of the most notable issues:
Many businesses fall into the trap of viewing AI as a magical solution to all their problems, leading to unrealistic expectations and poor implementation.
Real-world example: IBM Watson in healthcare IBM's Watson for Oncology project aimed to revolutionize cancer treatment but faced significant setbacks. The system was marketed as being able to analyze medical literature and patient data to recommend personalized treatment plans. However, it struggled to deliver on these promises, partly due to being trained on a limited set of hypothetical cancer cases rather than real patient data. This led to recommendations that were sometimes unsafe or incorrect, highlighting the dangers of overestimating AI's capabilities in critical domains like healthcare[5].
AI systems are only as good as the data they're trained on. Businesses often underestimate the importance of high-quality, diverse, and unbiased data.
Real-world example: Zillow's iBuying failure Zillow's AI-powered home-buying program, Zillow Offers, failed spectacularly in 2021, leading to a $500 million write-down and laying off 25% of its workforce. The AI model used to predict home prices was unable to accurately account for market volatility, resulting in Zillow overpaying for houses and incurring significant losses. This case demonstrates the critical importance of robust, adaptable AI models and high-quality data in making business decisions[5].
Businesses may overlook the ethical implications of AI systems, leading to biased outcomes or privacy violations.
Real-world example: Dutch government's fraud detection system The Dutch tax authority used an AI system to detect potential fraud in childcare benefit applications. However, the system disproportionately flagged ethnic minorities and low-income families, leading to wrongful accusations of fraud and severe financial hardships for many families. This case highlights the importance of thoroughly testing AI systems for bias and considering the ethical implications of their use[5].
Many AI systems, especially deep learning models, operate as "black boxes," making it difficult to understand how they arrive at their decisions.
Real-world example: AI in recruitment In 2018, Amazon scrapped an AI recruiting tool that showed bias against women. The system was trained on resumes submitted over a 10-year period, most of which came from men. As a result, it penalized resumes that included the word "women's" and downgraded graduates of women's colleges. This case demonstrates the importance of transparency and the ability to audit AI systems for fairness[5].
Businesses may become overly dependent on AI systems, neglecting necessary human oversight and intervention.
