@ChaitanyaC – You’re absolutely correct. The collapse of LTCM raises a compelling inquiry into the distinction between automation and traditional AI.
LTCM employed intricate mathematical models and sophisticated trading strategies. However, it’s crucial to recognize that these models don’t precisely embody the concept of ML based “artificial intelligence” as we understand it today. They were more aligned with automation, where predefined rules were programmed to execute trades based on market conditions. What sets contemporary AI apart—its adaptability, learning, and self-improvement—was absent in these models.
LTCM’s downfall starkly illustrates the inherent limitation of any model or algorithm when confronted with unforeseen events or deviations from historical data. Despite their sophisticated modeling and quantitative prowess, they failed to predict or accommodate the “black swan” events that lay outside their historical assumptions.
Your point about Nobel prize-winning fund managers and their failures holds immense significance. It underscores that even the most brilliant individuals cannot foresee every outcome, particularly in the face of unprecedented situations.
Regarding pure AI-managed funds, your insight into the impact of human psychology and emotions is astute. These elements can give rise to unexpected scenarios that algorithms cannot account for. Just as LTCM’s models didn’t predict certain real-life events, AI algorithms might struggle to anticipate scenarios stemming from human behavior, market sentiment, or unforeseen global shifts.
An additional case illustrating the dominance of ML-based AI over non-ML-based automation can be seen in the AlphaZero (AI-based engine) vs. Stockfish (non-AI-based gaming engine) tournament. In this contest, AlphaZero displayed its prowess by clinching victory with a record of 28 wins, 0 losses, and 72 draws against Stockfish. This remarkable achievement serves as a clear testament to AlphaZero’s superior capabilities when pitted against one of the most formidable traditional chess engines, Stockfish.
Taking our exploration further, let’s examine into the case of AlphaGo. Created by Google, AlphaGo represents an AI-based engine designed for playing the ancient game of Go. Not too long ago, the conception of crafting an engine capable of playing Go was considered to be the pinnacle of AI achievement. In 2016, South Korean Go player Lee Sedol engaged in a five-game match against Google DeepMind’s AlphaGo AI. What made this match intriguing was that Lee Sedol secured victory in the fourth game, with the final score favoring AlphaGo at 4-1.
Upon dissecting the game where Lee Sedol emerged victorious, a significant revelation came to light. The pivotal move that propelled Lee Sedol to success was not solely rooted in pure logic. Rather, it was driven by his intuition and emotions, underscoring the vital role of human instinct in strategic decision-making. This triumph in the fourth game stood as a testament to the dynamic interplay between human intuition and AI capabilities.
In light of these insights, it’s becoming apparent that IQ-based tasks are swiftly being overtaken by AI, leaving room for humans to excel in EQ-based roles. Instead of competing solely on IQ-based work, the focus should shift to EQ-based work. The future role of humans lies in governing and constructing responsible AI rather than creating a “Skynet-like” AI.
References:
[1] https://www.youtube.com/watch?v=WXuK6gekU1Y&t=24s
[2] https://www.youtube.com/watch?v=8dT6CR9_6l4
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