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Artificial Intelligence (AI) showcased a monumental event in our history when IBM's Deep Blue supercomputer emerged victorious against Garry Kasparov, the universally-acclaimed World Chess Champion. This extraordinary clash marked a new age, fostering both admiration and scepticism of AI's potential and implications.
Distinguished as arguably the best chess player in history, Garry Kasparov held the world champion status from 1984 until his retirement in 2005. The epochal chess match against Deep Blue prompted significant exploration into AI's capacity, embodiment of human fears and doubts, and the future direction we are collectively driving towards.
The chronicle of AI extends back to Alan Turing's inception of the Bombe to decrypt Nazi codes during WWII. Following a decline in AI investment called the"AI Winter", a resurgence arose, thanks to deep learning capabilities of neural networks enabling AI's self-correction mechanism.
The chess match between Kasparov and Deep Blue, witnessed by approximately 70 million spectators, enthralled the mainstream audience, culminating in a spectacular resurgence in AI research. Deep Blue's triumph indeed signaled an integrated presence of AI in our daily lives, made evident by tech powerhouses such as Google, Amazon, and more.
Despite the convenience AI brings, uncertainties veiled in the mysterious nature of AI's decision-making process persist. The emergence of these concerns poses an essential question: How can we guarantee that the AI systems we instigate will genuinely align with our ethical codes? Thus, tackling AI's "Black Box Problem" urged the need to decipher and regulate AI's decision-making process.
However, we may be misattributing our fears. The crux could lie in the biases embedded in the data provided to the AI systems. The sober truth remains that AI's future direction is sculpted by governments, tech companies, and consumers. Therefore, minimizing inherent biases in the data is crucial to deliver rational outcomes.
In conclusion, AI's path seems clear - the future of AI firmly rests in our hands, which underscores the necessity to understand and responsibly wield the data that dictates it.