An AI tool was experimenting with a frog. It commanded, “Jump, frog.” The frog complied and leaped an impressive ten feet. The AI tool duly recorded, “FROG JUMPS 10 FEET.”
Next, it decided to modify the experiment. It proceeded to remove one of the frog’s legs and repeated its command, “Jump, frog.” Surprisingly, the frog’s jump distance decreased to five feet. The AI tool recorded this observation as “CUT OFF ONE FOOT, FROG JUMPS 5 FEET.”
Undeterred, the AI tool continued its experimentation by removing another leg. Once again, it instructed the frog to jump, and this time the frog managed only a two-foot jump. The AI tool noted down, “CUT OFF TWO LEGS, FROG JUMPS TWO FEET.”
Curiosity driving it further, the AI tool decided to push the experiment to its limits. It removed all of the frog’s legs and issued the command once more. However, to its bewilderment, the frog did not attempt to jump; it simply lay there. Perplexed, the AI tool recorded its conclusion: “CUTTING OFF ALL THE FROGS’ LEGS MAKES THE SUBJECT GO DEAF.”
Driven by curiosity, the AI tool went to extremes, removing all of the frog’s legs. But when it commanded the frog to jump again, it didn’t move at all, leaving the AI tool puzzled. It then made a curious conclusion: “CUTTING OFF ALL THE FROG’S LEGS MAKES THE SUBJECT GO DEAF.”
Conclusion:
The AI’s ability to draw accurate conclusions relies heavily on the quality and integrity of the data it’s provided with.