Breaking down the advantages and disadvantages of artificial intelligence
10 January 2024
5 min read
Chrystal R. China Writer

Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. For example, learning, reasoning, problem-solving, perception, language understanding and more. Instead of relying on explicit instructions from a programmer, AI systems can learn from data, allowing them to handle complex problems (as well as simple-but-repetitive tasks) and improve over time.

Today’s AI technology has a range of use cases across various industries; businesses use AI to minimize human error, reduce high costs of operations, provide real-time data insights and improve the customer experience, among many other applications. As such, it represents a significant shift in the way we approach computing, creating systems that can improve workflows and enhance elements of everyday life.

But even with the myriad benefits of AI, it does have noteworthy disadvantages when compared to traditional programming methods. AI development and deployment can come with data privacy concerns, job displacements and cybersecurity risks, not to mention the massive technical undertaking of ensuring AI systems behave as intended.

In this article, we’ll discuss how AI technology functions and lay out the advantages and disadvantages of artificial intelligence as they compare to traditional computing methods.

What is artificial intelligence and how does it work?

AI operates on three fundamental components: data, algorithms and computing power. 

  • Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. Data is often divided into three categories: training data (helps the model learn), validation data (tunes the model) and test data (assesses the model’s performance). For optimal performance, AI models should receive data from a diverse datasets (e.g., text, images, audio and more), which enables the system to generalize its learning to new, unseen data.
  • Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming. AI can also work from deep learning algorithms, a subset of ML that uses multi-layered artificial neural networks (ANNs)—hence the “deep” descriptor—to model high-level abstractions within big data infrastructures. And reinforcement learning algorithms enable an agent to learn behavior by performing functions and receiving punishments and rewards based on their correctness, iteratively adjusting the model until it’s fully trained.
  • Computing power: AI algorithms often necessitate significant computing resources to process such large quantities of data and run complex algorithms, especially in the case of deep learning. Many organizations rely on specialized hardware, like graphic processing units (GPUs), to streamline these processes. 

AI systems also tend to fall in two broad categories:

  • Artificial Narrow Intelligence, also called narrow AI or weak AI, performs specific tasks like image or voice recognition. Virtual assistants like Apple’s Siri, Amazon’s Alexa, IBM watsonx and even OpenAI’s ChatGPT are examples of narrow AI systems.
  • Artificial General Intelligence (AGI), or Strong AI, can perform any intellectual task a human can perform; it can understand, learn, adapt and work from knowledge across domains. AGI, however, is still just a theoretical concept.
How does traditional programming work?

Unlike AI programming, traditional programming requires the programmer to write explicit instructions for the computer to follow in every possible scenario; the computer then executes the instructions to solve a problem or perform a task. It’s a deterministic approach, akin to a recipe, where the computer executes step-by-step instructions to achieve the desired result.

The traditional approach is well-suited for clearly defined problems with a limited number of possible outcomes, but it’s often impossible to write rules for every single scenario when tasks are complex or demand human-like perception (as in image recognition, natural language processing, etc.). This is where AI programming offers a clear edge over rules-based programming methods.

What are the pros and cons of AI (compared to traditional computing)?

The real-world potential of AI is immense. Applications of AI include diagnosing diseases, personalizing social media feeds, executing sophisticated data analyses for weather modeling and powering the chatbots that handle our customer support requests. AI-powered robots can even assemble cars and minimize radiation from wildfires.

As with any technology, there are advantages and disadvantages of AI, when compared to traditional programing technologies. Aside from foundational differences in how they function, AI and traditional programming also differ significantly in terms of programmer control, data handling, scalability and availability.

  • Control and transparency: Traditional programming offers developers full control over the logic and behavior of software, allowing for precise customization and predictable, consistent outcomes. And if a program doesn’t behave as expected, developers can trace back through the codebase to identify and correct the issue. AI systems, particularly complex models like deep neural networks, can be hard to control and interpret. They often work like “black boxes,” where the input and output are known, but the process the model uses to get from one to the other is unclear. This lack of transparency can be problematic in industries that prioritize process and decision-making explainability (like healthcare and finance).
  • Learning and data handling: Traditional programming is rigid; it relies on structured data to execute programs and typically struggles to process unstructured data. In order to “teach” a program new information, the programmer must manually add new data or adjust processes. Traditionally coded programs also struggle with independent iteration. In other words, they may not be able to accommodate unforeseen scenarios without explicit programming for those cases. Because AI systems learn from vast amounts of data, they’re better suited for processing unstructured data like images, videos and natural language text. AI systems can also learn continually from new data and experiences (as in machine learning), allowing them to improve their performance over time and making them especially useful in dynamic environments where the best possible solution can evolve over time.
  • Stability and scalability: Traditional programming is stable. Once a program is written and debugged, it will perform operations the exact same way, every single time. However, the stability of rules-based programs comes at the expense of scalability. Because traditional programs can only learn through explicit programming interventions, they require programmers to write code at scale in order to scale up operations. This process can prove unmanageable, if not impossible, for many organizations. AI programs offer more scalability than traditional programs but with less stability. The automation and continuous learning features of AI-based programs enable developers to scale processes quickly and with relative ease, representing one of the key advantages of ai. However, the improvisational nature of AI systems means that programs may not always provide consistent, appropriate responses.
  • Efficiency and availability: Rules-based computer programs can provide 24/7 availability, but sometimes only if they have human workers to operate them around the clock.

AI technologies can run 24/7 without human intervention so that business operations can run continuously. Another of the benefits of artificial intelligence is that AI systems can automate boring or repetitive jobs (like data entry), freeing up employees’ bandwidth for higher-value work tasks and lowering the company’s payroll costs. It’s worth mentioning, however, that automation can have significant job loss implications for the workforce. For instance, some companies have transitioned to using digital assistants to triage employee reports, instead of delegating such tasks to a human resources department. Organizations will need to find ways to incorporate their existing workforce into new workflows enabled by productivity gains from the incorporation of AI into operations.

Maximize the advantages of artificial intelligence with IBM Watson

Omdia projects that the global AI market will be worth USD 200 billion by 2028.¹ That means businesses should expect dependency on AI technologies to increase, with the complexity of enterprise IT systems increasing in kind. But with the IBM watsonx™ AI and data platform, organizations have a powerful tool in their toolbox for scaling AI.

IBM watsonx enables teams to manage data sources, accelerate responsible AI workflows, and easily deploy and embed AI across the business—all on one place. watsonx offers a range of advanced features, including comprehensive workload management and real-time data monitoring, designed to help you scale and accelerate AI-powered IT infrastructures with trusted data across the enterprise.

Though not without its complications, the use of AI represents an opportunity for businesses to keep pace with an increasingly complex and dynamic world by meeting it with sophisticated technologies that can handle that complexity.

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