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Other - Machines Poster (Artificial Intelligence (Inspiration ("AI is…
Other - Machines Poster
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Initial Question 2: Should human believe AI prediction or not? Why should people trust AI?
当年的照相机技术和现在的AI一样存在一开始被人信任但其实并不完全真实的内涵
AI’s decision-making process is usually too difficult for most people to understand. And interacting with something we don’t understand can cause anxiety and make us feel like we’re losing control. Many people are also simply not familiar with many instances of AI actually working, because it often happens in the background. Instead, they are acutely aware of instances where AI goes terribly wrong:
Inspiration
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Artificial intelligence has the potential to make lives easier by understanding human desires or driving people's cars, but if it were uncontrolled, the technology could pose a serious threat to society. Now, Stephen Hawking, Elon Musk and dozens of other top scientists and technology leaders have signed a letter warning of the potential dangers of developing artificial intelligence (AI).
https://www.livescience.com/49419-artificial-intelligence-dangers-letter.html
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Predict/Forecast
Forecast is scientific and free from intuition and personal bias, whereas prediction is subjective and fatalistic in nature. Forecasting is an extrapolation of past into the future while prediction is judgmental and takes into account changes taking place in the future.
https://www.linkedin.com/pulse/difference-between-forecast-prediction-lakshminarayanan-t-p
Forecast is scientific and free from intuition and personal bias, whereas prediction is subjective and fatalistic in nature.
Forecasting is an extrapolation of past into the future while prediction is judgmental and takes into account changes taking place in the future. Therefore, prediction is utilized more in business and economics while forecasting takes place in weather and earthquakes.
Predicting is saying or telling something before the event while forecasting is done on the basis of analysis of the past.
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Briefly, predictability is defined as a property of a random variable in relation to an information set (the conditional and unconditional distributions of the variable do not coincide). It is a necessary, but not sufficient condition for forecastability, as the latter requires knowledge of what information is relevant and how it enters into the causal mechanism.
https://www.researchgate.net/post/What_is_the_difference_between_Prediction_and_Forecast
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Further Questions
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- Why should people trust AI?
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