Artificial Intelligence (AI) has been an intriguing subject, often wrapped in an enigma, driven by speculation, and enhanced with Hollywood glitz.

While movies paint a picture of AI as sentient entities, filled with emotions and consciousness, one must wonder if today’s AI mirrors such representation. It’s time to uncover the reality of AI in the contemporary era.

Understanding the Basics of AI

To decode AI, a foundational understanding is pivotal. Artificial Intelligence fundamentally revolves around creating computer systems capable of executing tasks that traditionally mandate human intelligence. From speech recognition to decision-making, these tasks have one essence - they perform rather than genuinely understand or feel.


Consider your smartphone’s voice assistant, like Siri or Google Assistant. When you ask it about the weather, it swiftly fetches the information and responds. This rapid retrieval and response make it seem intelligent.

However, it doesn’t “understand” weather in the way humans do; it doesn’t feel the warmth of the sun or the chill of the wind. Instead, it recognizes your voice command, processes the data, and delivers the requested information based on its programming and the data it has access to.

This is AI in action, a combination of voice recognition and data processing, delivering a task that mimics human-like interaction.

Not Sentient, But Highly Efficient

Hollywood might suggest AIs with deep emotions and intricate feelings, but today’s AI models, like GPT-4 by OpenAI, are strictly computational. Functioning as intricate mathematical models, they process vast datasets at incredible speeds. Their “decisions” are more about instant computations than genuine emotional manifestations.

Regrettably, the dream of an emotionally sentient AI eludes us. To quote Wikipedia:

Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of human beings or animals. AI applications include advanced web search engines (e.g., Google Search), recommendation systems (used by YouTube, Amazon, and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Waymo), generative or creative tools (ChatGPT and AI art), and competing at the highest level in strategic games (such as chess and Go).

Such definitions burst the bubble of having a conversational AI companion that can resonate with our emotions and authentically mirror human behavior.

Machine Learning: The Underpinning of Modern AI

Modern AI owes much to Machine Learning (ML). ML, by analyzing extensive data, enables machines to acquire the ability to execute certain tasks without direct programming.

However, it’s essential to note that this is more about recognizing patterns statistically rather than truly understanding them, which, admittedly, may disappoint fans of cinematic AI.


Imagine you’re browsing through a music streaming platform like Spotify. Over time, you might notice that the platform suggests songs or playlists that align closely with your musical tastes. This isn’t magic; it’s Machine Learning at work.

Every time you listen to a song, skip one, or add one to your playlist, Spotify gathers this data. The ML algorithms then analyze your preferences and identify patterns. Based on these patterns, the platform can predict and recommend other songs or genres you might enjoy.

So, the more you use the platform, the better it gets at understanding your musical preferences, all thanks to the power of Machine Learning (ML).

From Narrow AI to AGI: Where Are We?

There are two broad categories of AI: Narrow AI and General AI.

  • Narrow AI: What we predominantly have today. These AIs excel in a specific task, whether that’s translating languages or playing a game. They don’t have general understanding or consciousness.
  • General AI (AGI): The hypothetical future AI that can outperform humans at nearly every cognitive task. It’s more akin to the AI from movies, but we’re not there yet, and it’s debatable if we ever will be.

To illustrate the difference:

  • Narrow AI:

Think about the chess-playing computer program, Deep Blue, that beat world chess champion Garry Kasparov in the late ’90s. Deep Blue was exceptional at playing chess, even beating one of the best human players, but that’s all it could do.

Ask it to make you a cup of coffee or write a poem, and it would be utterly lost.

  • General AI (AGI):

Picture the fictional AI, Data, from “Star Trek: The Next Generation.” Data possesses vast knowledge across countless subjects, from literature and science to music and human behavior.

He can paint, play instruments, engage in philosophical debates, and adapt to new situations. This flexibility and adaptability across various domains represent the idea of AGI.

Why the Confusion?

Hollywood and media hype have inadvertently crafted a magnified AI image. When AIs take on historically human-exclusive tasks, it becomes tempting to anthropomorphize them, leading to misinterpretations.

Childhood memories filled with cartoons and movies painted a future teeming with AI. But reality tells a different story. Our technological priorities seem misplaced, with genuine innovation often sidelined for monetary gains.

The Future of AI: What to Expect

Even with monumental strides in AI, its core remains computational. The aspiration (or fear) of sentient machines is still anchored in fiction. Yet, AI’s promise to revolutionize industries and elevate living standards stands undisputed.

The real evolution towards sentient AI, in my view, might only see the light with the advent of Quantum Computers . A noteworthy development that could supercharge AI’s progression is the advent and refinement of Quantum Computers.

Traditional computers utilize bits as the smallest unit of data, represented by either a 0 or a 1. Quantum Computers, however, leverage quantum bits or qubits, which can exist in a state of 0, 1, or both simultaneously due to superposition. This characteristic exponentially enhances their computational power.


Imagine trying to solve a complex maze. A traditional computer would test each possible route one at a time to find the solution. A Quantum Computer, in contrast, can explore multiple routes simultaneously, dramatically reducing the time needed to find the solution. When applied to AI, this could mean faster data processing, more sophisticated algorithms, and breakthroughs in areas that are currently computationally prohibitive.

It’s speculated that with Quantum Computing, AI could inch closer to achieving feats previously considered unattainable. Some experts even suggest that true AI might only be realized once we’ve harnessed the full potential of Quantum Computing. While we’re still in the early days of this technology, its implications for AI are profound and eagerly anticipated.


Today’s AI, a marvel in its own right, is altering the very fabric of our society. Although it’s far from the sentient entities depicted in films, its computational prowess cannot be undermined. As we navigate this era of technological marvel, understanding and judiciously utilizing AI becomes paramount.

In a world riddled with misinformation, AI often falls victim to misconceptions. It’s imperative for the believer to discern the actual capabilities of AI, avoiding misinterpretations. This article aims to shed light on the reality of AI, its implications, and its future trajectory.