Will artificial intelligence eliminate human jobs within three years?

Since the spread of artificial intelligence, the fear of it taking over human jobs has become a contemporary concern.

In a short period of time, AI has replaced many humans in jobs such as translation, writing, teaching, and customer service.

We now have robot chefs, robots specializing in complex surgical procedures, and robots capable of performing in vitro fertilization in assisted reproductive centers.

Not to mention factories entirely run by AI-powered robots.

Will AI replace humans within the next three years? This is what we will explore together in the following paragraphs.

Changes in the Labor Market Caused by Artificial Intelligence

Artificial intelligence is revolutionizing the labor market, replacing humans in some jobs, with the potential to create new jobs that require different human skills.

Jobs Significantly Impacted by AI

Jobs that have been replaced or significantly impacted by AI are typically repetitive, rule-based, and are most susceptible to automation by AI. The most notable of these jobs are:

Job market forecasts for the next three years, through mid-2028

Rapid transformations are expected in the job market. The following are the most prominent forecasts:

Continued automation of routine and repetitive tasks:

More jobs that rely on predictable tasks, office work, and administrative work will see increased automation. This doesn’t necessarily mean that jobs will disappear entirely, but rather that they will shift so that humans focus on more complex and creative aspects.

Creating new jobs related to artificial intelligence:

The spread of artificial intelligence will lead to the emergence of new jobs that require specialized skills in developing, deploying, and maintaining AI systems. These jobs include:

Increased Demand for Unique Human Skills:

There will be an increased demand for jobs that require skills that are difficult for AI to replicate. These skills include:

The Importance of Upskilling and Reskilling

Continuous learning, upgrading existing skills, and acquiring entirely new ones will be critical for individuals to remain competitive in the job market. Companies and governments will need to invest in training programs to prepare the workforce of the future.

Accordingly, we expect the following to occur:

Crushing Defeat for AI in Chess

Recently, some modern AI systems, such as ChatGPT, failed to defeat an old version of chess running on the Atari 2600 dating back to 1979.

This incident generated significant interest and highlighted some important points about AI capabilities:

A Crushing Defeat for AI in Chess

AI is not “general intelligence”:

Modern AI programs, such as large language models (LLMs), are designed to be “general” in their linguistic abilities—that is, they are excellent at understanding and generating text, conversing, and even offering simple advice. However, this does not mean they possess “intelligence” in the general human sense that allows them to excel at all tasks automatically.

Specialization vs. Generalization:

Old chess programs, such as those on the Atari 2600, were precisely and specifically programmed to play chess. These systems are designed with powerful search algorithms (such as minimax) and accurate assessments of board state, making them highly effective at their specific task, even if their computational capabilities are primitive compared to modern systems.

Environmental and Representation Limitations:

When trying to play an old Atari game, modern AI systems struggle to interpret the game’s simple, pixelated visual interface and translate it into an accurate understanding of the chessboard state. This problem of “visual context understanding” is one of the challenges AI continues to face.

Memory and Game State:

Unlike specialized chess programs, which maintain an accurate memory of the current game state and piece movements, large language models can struggle to accurately track this “changing state,” leading to illogical errors during play.

Conclusion:

While AI has demonstrated exceptional chess capabilities for decades, such as Deep Blue’s 1997 victory over Garry Kasparov, the recent failure of some modern models to defeat an old Atari chess game confirms that:

This does not diminish the capabilities of AI in general, but it does highlight the subtle differences in how these systems are designed and their ability to handle different types of problems.

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