Traditional coding is like following a recipe, step by step, to get the desired outcome. But AI is like a black box, where data goes in and output comes out, but no one knows how it happens in between. Traditional coding is stable but not scalable, while AI is scalable but unstable. Traditional coding offers control and transparency, but AI lacks both. Traditional coding relies on structured data, while AI learns from vast amounts of unstructured data and improves over time. It’s not a zero sum game, both have their place in the tech world. π
AI Systems vs Traditional Coding
Understanding AI and Traditional Programming Components π§©
You know, back in my day, computer programs did what you told them to do. You would code them with specific instructions as to what to do. And the computer would faithfully execute those instructions to perform the task. But applications that use artificial intelligence these days, well, they’re not like that at all. The most advanced ones act like black boxes, data comes in, output comes out. But the process the model uses to get from inputs to output. Well, that’s anybody’s guess.
So first, let’s discuss the differences in AI and traditional programming and the pros and cons of each. Let’s start with defining what AI programming means and discuss the fundamental components within it.
The Fundamental Components of AI π§
Now, the first of those we can consider as data. Now, data is what makes AI systems learn and make better decisions and AI systems, they require large quantities of data to train effectively.
| Data Categories |
|---|
| Training Data |
| Validation Data |
| Test Data |
The second fundamental component of AI are the algorithms that make up an AI system. The algorithms are these sets of rules that AI systems use to process data and make decisions.
And finally, the third component is computing power, or "compute". You’ve probably heard plenty of stories about companies in the news right now scrambling for GPUs. And that’s because AI programs often need significant computing resources to process such large quantities of the data and to run these complex algorithms and specialized GPUs streamline these processes.
How AI Compares to Traditional Programming π»
What about then, how that compares to traditional programming and traditional computing? Traditional programming requires the program to write explicit instructions, or rules, and that’s the things that the computer follows in every possible scenario.
| AI Programming | Traditional Programming |
|---|---|
| Data-driven | Rule-driven |
| Algorithms process data | Explicit instructions |
| Requires significant computing power | Stable and scalable |
That’s an approach that’s well-suited to clearly defined problems that have a limited number of possible outcomes. But it becomes nearly impossible to write rules for every single scenario when tasks are complex or need human like perception, like image recognition, for example.
Advantages and Disadvantages of AI and Traditional Programming π
Let’s consider the advantages and disadvantages of AI programming and traditional programming across three aspects.
Stability and Scalability π
Traditional computing is stable. Once a program is written and debugged, it will perform operations the exact same way every single time. But the stability of rules-based programs comes at the expense of scalability, because traditional programs can only learn through explicit programing interventions. They require programmers to write code at scale.
AI programs offer more scalability than traditional programs, but with less stability. The automation and continuous learning features of AI-based programs allow developers to scale processes quickly and with relative ease. But the nature of AI systems to improvise means that programs may not always provide consistent, appropriate responses. You never quite know what you’re going to get.
Control and Transparency π΅οΈββοΈ
Traditional computing offers developers full control over the logic and behavior of software. If a program doesn’t behave as expected, so it has a bug, developers can trace back through the code base here to identify and correct the issue. But not with AI systems. Complex AI models like deep neural networks often work like black boxes, as I said, where the input and the output are known, but the process the model used to get from one to the other is unclear.
AI systems can be hard to control and interpret, and this lack of transparency can be problematic in industries that prioritize process and decision making, like health care, like finance.
Learning and Data Handling π
Traditional programming is rigid. It relies on structured data to execute programs in order to teach a program new information. The programmer must manually add new data or adjust those processes. Also, traditional programs may not be able to accommodate unforeseen scenarios without explicit programing for those cases. But because AI systems learn from vast amounts of data, they’re better suited for processing unstructured data like images, like videos, and natural language text.
This isn’t a zero sum game. It’s not like traditional programming is going away anytime soon. AI systems, they’re not without their complications, but the use of AI represents an opportunity to keep pace with an increasingly complex and dynamic world by meeting it with sophisticated technologies that can handle that complexity. Even if those pesky AI programs don’t always do precisely what I ask them to.
EN_GB