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What is AI? What is deep learning?
Since the 1960s, people have dreamed of sci-fi-style AI like HAL, but for decades, computers and robots remained limited in their capabilities. However, in recent years, a new wave of AI has emerged, driven by tech giants and startups. Innovations such as self-driving cars, robotic doctors, and machine investors are now becoming reality. PricewaterhouseCoopers predicts that by 2030, AI could contribute up to $15.7 trillion to the global economy. In 2017, "AI" became a buzzword, much like ".com" did in 1999. But is this a genuine revolution or just another hype cycle? What makes today’s AI different from previous attempts?
AI isn’t something that can be implemented quickly or easily. The most impressive AI breakthroughs often come from universities or big tech companies. Many so-called AI experts promise revolutionary changes, but they may simply be repackaging old techniques under a new label. While services like Google, Microsoft, and Amazon have made AI accessible, most businesses still struggle to apply deep learning effectively due to a lack of quality data. This means AI doesn't replace jobs entirely—it often requires human oversight during training and testing.
Today's AI can "see" and understand visual information, excelling in tasks like medical imaging, where it outperforms human specialists in detecting diseases. It can also recognize speech, generate art in various styles, and even write code based on screenshots. AI can "hear" and understand music, creating new compositions or mimicking voices with uncanny accuracy. In some cases, it becomes impossible to distinguish between human and machine creations.
AI can also "think" strategically, as seen in games like poker, where it learns to bluff and deceive. Similarly, translation AI can bridge language gaps without relying on pre-defined rules, sometimes even developing its own intermediate language. Machine learning, a subset of AI, allows systems to improve over time through experience, unlike traditional rule-based AI that follows fixed instructions.
While older AI was rigid and predictable, modern machine learning systems are more adaptive. They rely on large datasets and complex algorithms, often combining multiple methods to achieve better results. Despite this progress, AI still lacks general intelligence and common sense. It excels in narrow tasks but struggles with broader understanding or creative thinking.
The "AI effect" occurs when people dismiss AI as not truly intelligent, even after it surpasses human performance in specific areas. This bias leads to redefining what counts as "intelligence." Meanwhile, ethical concerns arise, such as biased training data leading to unfair outcomes. AI can also produce original content, raising questions about authorship and intellectual property.
Training AI remains expensive and complex, making it feasible only for large organizations. However, as technology advances, costs will decrease, and more industries will adopt AI. While AI might eliminate some jobs, it will also create new roles, such as AI trainers and developers. The future of work will likely involve collaboration between humans and machines, with AI augmenting rather than replacing human skills.
Ultimately, AI is neither a miracle nor a threat. It is a powerful tool that, when used responsibly, can enhance productivity, creativity, and decision-making across various fields. As we continue to explore its potential, the key lies in balancing innovation with ethics, ensuring that AI serves humanity in meaningful and equitable ways.