True Artificial Intelligence Should Not Be Just About Statistics
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The topic of artificial intelligence has been widely debated for a long time. Despite the support of capital and the backing of tech giants, making AI a hot field in the post-internet era, people have yet to experience significant changes brought about by so-called AI in their daily lives. Experiments with autonomous driving continue, and discussions about facial recognition payments remain heated. However, the AI we know seems limited to these superficial applications, falling far short of our expectations. After all, by our definition, AI is meant to be the 'face' of the fourth industrial revolution.
Unfortunately, AI has not developed in the direction we hoped for but has instead leaned more toward the realm of statistics. The so-called statistical approach relies on vast databases and powerful computing to identify deterministic patterns in human behavior and habits, then mechanically replicate tasks originally performed by humans. If AI remains confined to this scope, it would significantly underestimate its true potential. Persisting in this direction may cause us to miss the best opportunities for AI's development.
Clearly, AI is far more than just statistics. Beyond simply mimicking human behavior and habits, it should encompass richer concepts and deeper meaning. Only when AI becomes truly 'intelligent' rather than 'mechanical' can it genuinely serve as the 'face' of the fourth industrial revolution and enhance the next era.
This raises the question: What constitutes true artificial intelligence? In my view, for AI to be genuinely intelligent, it must possess the following characteristics:
Currently, most people’s understanding of AI is limited to the concept of 'unmanned' operations, equating automation with intelligence. But this is not the case. True AI must be intelligent—capable of making appropriate and effective responses based on real situations, rather than merely performing repetitive, mechanical actions. Therefore, for AI to be truly intelligent, it must move beyond simple, repetitive tasks and respond dynamically to specific contexts, avoiding rigid, pre-programmed reactions.
Here, 'forward-thinking' refers to the ability to anticipate what traditional methods cannot foresee, thereby reducing errors, failures, and costs while improving industry efficiency. Simply reacting to past events does not qualify as AI. How can AI achieve this foresight? It requires robust big data and ultra-fast computing power. By leveraging accumulated behavioral and consumption data from the internet era, statistical analysis can predict future events, allowing for proactive measures to minimize losses from blind judgments.
This is only the initial stage. As data accumulation grows, computing power increases, and deep learning advances, AI will eventually predict future trends without relying solely on statistics. This is true 'forward-thinking' and genuine 'intelligence.'
It may sound alarming, but if AI cannot break free from the constraints of mechanical learning and human intervention, it may remain little more than a trendy rebranding of mechanical evolution. Thus, true AI must be capable of self-learning and evolution. This means AI can adapt based on its own experiences and external industry changes without human input. Only through self-learning and evolution can AI transcend the limitations of 'artificial' constraints and enter a new era driven by genuine intelligence.
Based on this analysis, it’s clear that true AI is far more profound than the 'unmanned' and 'mechanical' applications we see today. Current AI remains largely confined to statistical methods—predictive work based on historical data—without achieving self-learning or evolution. As a result, today’s AI resembles a transient phase, destined to be replaced by newer, smarter technologies.
Despite its current popularity, AI harbors hidden concerns. The market will inevitably undergo a reshuffling before it can return to the right path. For the AI industry, the fundamental challenge lies in the imbalance between investment and output. Many AI companies rely heavily on capital due to this disparity. Until AI, as the protagonist of the fourth industrial revolution, deeply transforms traditional industries like manufacturing, logistics, and finance, this imbalance will persist.
The most direct consequence is a vicious cycle where AI companies depend on continuous funding and investment. When capital runs dry, a market shakeout becomes inevitable. Only those who achieve a balance between investment and output will survive this transformation.
Research in AI has reached a mature stage, with advancements in deep learning, neural networks, and image recognition. Yet, the practical application of these technologies remains distant. When AI’s technology and implementation stagnate, the industry’s growth falters. The widening gap between theory and application reflects an unhealthy development trend. Many AI applications being introduced today are already outdated.
During this reshuffling phase, only players who can translate the latest AI research into practical solutions will emerge as true leaders. Those who fail to do so will be淘汰 by the market.
Although AI is a future-oriented field, its adoption remains niche. Current AI applications are sporadic and isolated, lacking规模化效应. While the industry is a blue ocean, it demands higher standards from participants. Only those who find viable development strategies in this largely untapped market will survive. Others will inevitably淘汰.
As the AI industry enters a phase of extreme contrasts, its inherent contradictions become apparent. Despite its火爆 and bright prospects, the industry faces new challenges. After the initial hype fueled by capital and tech giants, AI will likely undergo a market reshuffling before reaching maturity.
After all, AI based on statistical categories is not very innovative, nor does it have a bright future.