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The Future of AI is at the Human Side

Can machines learn like humans? A dive into the world of AI!

Artificial Intelligence (AI) has undergone significant evolution since its inception in the 1950s, with the introduction of various approaches to enable machines to mimic human reasoning and learning.

Rule-based AI (1950s-1960s)

The first stage of AI, rule-based AI, could mimic human reasoning by applying a set of logical rules. These rules were human instructions.

At this stage, machine reasoning was not data driven and AI was limited by the fact that it could only handle narrow domains of knowledge.

Machine learning (1960s-1990s)

The second stage of AI, machine learning, enabled computers to learn from data and improve their performance over time. This was done using techniques such as decision trees, neural networks, and genetic algorithms.

At this stage, information or data was available for machine to process and shallow learning is applied to identify hidden patterns in data that humans were challenged.

Deep learning (2000s-2010s)

The third stage of AI, deep learning, uses neural networks with multiple layers to learn and represent complex patterns in data. This has led to breakthroughs in various domains, including image and speech recognition, natural language processing, and robotics.

At this stage, big data is available for use and advanced learning has been used to identify more complicated patterns. It simulates human’s learning and perceiving skills by connecting dots from seemly non-relevant events.

In the second and third stages, humans are out of loop; data is the main input to support machine learning. The challenges are obvious when there are not enough data or it requires a lot of computation power when processing big data.

Reinforcement learning (2010s-present)

The current stage of AI, reinforcement learning, involves training an AI agent to learn from feedback received as it interacts with an environment. This process is interactive learning, where humans provide input or instructions to machines during the learning process.

This stage promises to become less artificial and more intelligent, with the ability to reason and generalize like humans.

The future of AI

However, the nature of machine intelligence will take a radically human turn, becoming less artificial and more intelligent. While machines can process more information with less time and broad knowledge, they cannot outperform humans in terms of emotion, common sense, personalized experience, and spirit perspective. Machines can understand, communicate, interpret or recommend things, but they cannot have human emotions, feelings, faith or belief, and spirit.

Although ChatGPT is a huge breakthrough, it is still based on large language models that are overly complicated than the way that humans use language. It still uses probabilities and language syntax or grammar. Machines should forever be a tool to serve humans, just like we, humans, are forever to serve God who created us.

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