Stages in AI Development and the Future of AI

Stages in AI Development and the Future of AI  

By Janpha Thadphoothon  

I am sure you would agree with me that the development of artificial intelligence (AI) is one of the most transformative technological shifts in human history. What we refer to as "artificial intelligence" is, in essence, a software application—or more accurately, a set of digital applications—that satisfies two key criteria: it can learn or be trained, and it can exhibit human-like behavior.  

I am not a data scientist but an English teacher, so my perspective on AI is not overly technical. However, I have read somewhere that AI has been evolving through distinct stages since its early beginnings. From its humble roots in the 1970s to the innovations of the 1980s, and now, as we look toward 2030, we can observe a remarkable trajectory.  

Experts in the field say that AI can be understood through a series of developmental stages, each with its unique characteristics and potential. In this blog post, I will share what I believe are five key stages in AI's evolution—from simple chatbots to what I prefer to call "agentic entities," systems that could one day manage businesses or even entire organizations.  

Types of AI and How They Learn

Before diving into the stages, let me briefly touch on the different types of AI and how they are trained.

1. Types of AI:

   • Generative AI: These systems, such as ChatGPT, DALL-E, and MidJourney, can create new content, including text, images, music, or videos. They use large datasets and advanced models to generate outputs that mimic human creativity.
   • Predictive AI: Systems like recommendation engines analyze data to predict future outcomes, such as which movies you might like or stock market trends.
   • Reactive AI: These are limited systems that only respond to specific tasks, like playing chess or diagnosing faults in machines.
   • Adaptive AI: AI capable of learning and evolving in real time, improving its performance as it interacts with its environment.

2. Training Methods:

   • Supervised Learning: AI is trained on labeled data, where it learns by example. For instance, a system might be trained on images of cats and dogs to identify which is which.
   • Unsupervised Learning: The system works with unlabeled data, finding patterns or clusters on its own. This approach is often used in market segmentation.
   • Reinforcement Learning: This involves training AI through trial and error, rewarding it for correct actions and penalizing it for mistakes. A good example is AlphaGo, which learned to master the game of Go through countless simulations.

Now, let’s look at how these training methods have contributed to the development of AI through its various stages.

The Five Stages of AI Development

1. Reactive Agents (Chatbots)  

In its earliest stage, AI is reactive, designed to handle specific inputs and generate pre-programmed outputs. These agents lack memory or the ability to understand context. They say the first chatbots, like ELIZA from the 1960s, were pioneers of this stage. Today, this level of AI is still widely used in customer service chatbots.  

2. Contextual Agents (Assistants)  

The second stage involves AI systems that can learn from data and adapt to context. Virtual assistants like Siri and Alexa fall into this category. They are smarter than simple chatbots and can perform a range of tasks, from setting reminders to answering trivia questions.  

3. Collaborative Agents (Strategists)

By the 2020s, AI began to take on more collaborative roles, assisting humans in making strategic decisions. For example, AI tools in finance or logistics analyze data and provide actionable insights. I am sure you would agree with me that such systems already show potential as strategic partners.  

4. Agentic Entities (Entrepreneurs)  

Looking toward the near future, it is believed that AI will evolve into fully autonomous systems. These "agentic entities" will be capable of managing entire enterprises, from identifying business opportunities to executing strategies. This stage could redefine what it means to lead and innovate.  

5. Networked Entities (Ecosystem Leaders)  

 In the final stage, AI systems will likely function as part of interconnected networks. They say these entities will not only work independently but also coordinate with other systems to optimize global operations in fields like healthcare, education, and transportation.  

From the 1970s to 2030: A Brief Timeline  

- 1970s: The early days of AI were driven by academic curiosity and foundational theories. ELIZA, one of the first chatbots, demonstrated the potential for AI to simulate conversations, albeit in a limited way.  

- 1980s: Expert systems emerged, allowing computers to make decisions based on pre-defined rules. This decade saw AI applications in industries like medicine and engineering.  

- 1990s-2000s: Machine learning gained traction, with systems becoming more adaptive. Breakthroughs like IBM's Deep Blue defeating a world chess champion in 1997 showcased the growing capabilities of AI.  

- 2010s: The era of deep learning and big data began. Virtual assistants like Siri, Alexa, and Google Assistant became household names. AI began assisting in areas such as autonomous vehicles and personalized recommendations.  

- 2020s-2030s: Experts predict that AI will evolve into agentic entities capable of entrepreneurship and leadership. These systems will be smarter, more autonomous, and able to navigate ethical challenges, prompting the need for robust AI regulations.  

Ethical Concerns and the Role of AI Regulations  

As AI progresses, ethical concerns inevitably arise. I have read somewhere that questions about privacy, bias, and accountability dominate discussions about AI's future. For example, who is responsible when an autonomous system makes a mistake? Can we ensure that AI decisions are fair and unbiased?  

It is believed that governments and organizations are taking steps to address these issues. AI regulations are being developed to create a balance between innovation and responsibility. For instance, the European Union has proposed frameworks to ensure AI systems respect fundamental rights and promote transparency.  

They say we are entering an age where ethics must go hand in hand with technology. Without thoughtful regulation, the potential misuse of AI could overshadow its benefits. As educators, we have a role to play in fostering discussions about these challenges and preparing the next generation to navigate this new world responsibly.  

At present, it should be clear that AI is neither a passing fad nor mere hype. It is as real and transformative as air, water, or electricity. If anyone still has doubts, I encourage them to seek the truth and explore the subject further. I cannot force people to believe in the reality of AI, but I can share my insights and experiences to raise awareness.  

The future is being shaped before our eyes, with AI playing a pivotal role alongside other beings—humans and cyborgs—working together to lead the way forward.