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What is AI? What is deep learning?
Since the 1960s, the dream of sci-fi-level artificial intelligence—like the iconic HAL from *2001: A Space Odyssey*—has captivated imaginations. However, for decades, computers and robots remained far from intelligent, often appearing more like advanced calculators than thinking machines. Today, the AI revolution is no longer a distant fantasy. Major tech companies and startups are unveiling groundbreaking applications such as self-driving cars, AI-powered doctors, algorithmic investors, and more. According to PricewaterhouseCoopers, by 2030, AI could contribute a staggering $15.7 trillion to the global economy.
In 2017, “AI†became a buzzword, much like “.com†in 1999. Everyone claims to be interested, but not all AI promises are genuine. The question remains: is this a real breakthrough or just another bubble? Compared to past AI trends, what makes today’s developments different?
Artificial Intelligence is not something that can be implemented easily or quickly. Many of the most impressive AI achievements come from top universities or big tech firms. Claims by self-proclaimed AI experts that they can revolutionize your business with cutting-edge AI may be misleading. Some of them are simply rebranding old technologies as AI. While services from Google, Microsoft, and Amazon offer glimpses of AI’s potential, true deep learning is still complex and difficult to implement within large organizations. Most people lack sufficient high-quality data to train reliable AI models, which means human involvement remains crucial in training and testing these systems.
Today, AI can "see" and excel at visual tasks like identifying cancer from medical images, often outperforming human specialists. It can also drive cars, interpret lip movements, and even create art in the style of Picasso or your own paintings. AI can analyze a photo and reconstruct missing parts, or look at a web page and generate similar code. It can "hear" and understand speech, recognize music, and even compose new songs or mimic any voice it hears. In some cases, it's impossible to distinguish between a human-made and an AI-generated piece of art or music.
AI has also made strides in language translation, creating intermediate languages to bridge gaps between different dialects. It can play games like poker, learning to bluff, deceive, and adapt strategies. Machine Learning (ML), a subset of AI, allows systems to learn from experience rather than relying on fixed rules. The more data it processes, the better it becomes. However, many AI systems still rely on rule-based programming, which lacks the ability to learn from data.
Deep learning, a form of ML using layered neural networks, has gained popularity, though the term "deep" is sometimes overused. Successful ML solutions often combine multiple techniques, such as decision trees and neural networks, to achieve better results. Unlike traditional AI, which was based on rigid if-then logic, modern ML systems can adapt and improve over time.
The key difference between old AI and current AI lies in learning. Early AI systems were rule-based, while modern AI, especially ML, learns from data. This shift has led to breakthroughs in areas like image recognition, natural language processing, and game playing. For example, in 1997, IBM’s Deep Blue defeated a chess champion, but it wasn’t until 2016 that AlphaGo beat a Go champion—a game with exponentially more possibilities than chess.
Despite its power, ML isn’t perfect. It relies on labeled data for supervised learning, and errors in labeling can lead to flawed outcomes. Unsupervised learning, where the system finds patterns without labels, is useful but limited. Reinforcement learning, used in games and robotics, involves trial and error, allowing AI to improve through experience.
AI has also sparked debates about creativity, ethics, and bias. The “AI effect†occurs when people dismiss AI as just computation rather than true intelligence. Similarly, biased training data can lead to unfair outcomes, such as discriminatory algorithms. While AI can detect anomalies like fraud or cyber threats, it struggles with understanding context and nuance.
As AI continues to evolve, it will reshape industries, automate tasks, and create new job roles. However, it won’t replace humans entirely. Instead, it will complement human skills, requiring collaboration between people and machines. The future of AI is not about replacing humans but enhancing their capabilities, making work more efficient and creative.