A Few Big Astonishing Reasons When AI Fails and why?
Is AI Really as Smart as We Think?
What if it has failed, and there’s a precedent for it?
We rely on AI to predict trends and guide decisions, but how much can it really foresee?
AI’s promise to bring a sense of certainty can be alluring, yet it is highly deceptive. The belief that an abundance of data and AI can predict and control the future is often misleading. AI and big data rely heavily on analyzing past trends to forecast future outcomes, operating under the assumption that future conditions will mirror those of the past and that’s not always true.
An example of a known failure is called brittleness. An AI can only recognize a pattern it has seen before If you show it a new pattern, it is easily fooled. These are more tech aspects but we are going to discuss cases when human behaviour or events takes CenterStage.
There are many cases where it is proven that AI has limitations and it has failed time and again on the predicting the future events. One such example where AI and big data often struggle to account for disruptive innovations, such as the rise of smartphones. Nokia has all the data in the world, yet Apple & Android replaced it so rapidly which was beyond comprehension.
AI also struggles with predicting rare, high-impact events like pandemics, (Covid) economic crashes, or natural disasters. These “black swan” events are so infrequent or unprecedented that AI lacks even the basic data to make accurate forecasts. The below HBR Article Nicely dissects the reasons when AI is expected to perform and why it failed during the recent Covid Pandemic.
https://hbr.org/2022/03/why-ai-failed-to-live-up-to-its-potential-during-the-pandemic
AI-powered big data tools may identify short-term trends but often struggle with longer-term patterns. Emerging trends that develop slowly over time and sometimes they are so often hidden to be noticed unless they become a norm.
Cultural changes can drive significant trends that AI and big data may overlook. For instance, the rise of mindfulness, wellness, care of environment & recycled items have led to increased demand. While AI might detect a spike in wellness product sales, it may not capture the underlying cultural shift unless it includes qualitative insights like consumer sentiment or global trends.
AI and big data also don’t account for generational shifts and as a result they may fail to identify specific preferences, between a Gen Z’s or Millennials. There are other Psychological factors like emotional responses based on brand perceptions, that is hard for AI and big data to quantify like FOMO or Aspirational or Luxury brands. If AI focuses solely on purchase frequency, it may miss the psychological factors like FOMO that drive scarcity-based marketing strategies.
Similarly, since People don’t always act rationally, and AI may fail to account for biases like overconfidence, anchoring, or the bandwagon effect.
The sheer volume of data processed by AI does not always lead to better insights. In fact, larger datasets can introduce more noise, making it harder to extract meaningful conclusions. This is known as the Volume-Quality Paradox.
“It’s unpredictable which problems artificial intelligence will be good at, because we don’t understand intelligence itself very well,” says computer scientist Dan Hendrycks at the University of California, Berkeley.
While AI excels at pattern recognition, predictions, and automation, its limitations stem from its inability to fully understand complex, unpredictable, or deeply human aspects of the world.
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