Analyze the advantages and disadvantages of artificial intelligence.

Artificial intelligence (AI) refers to the converging field of computer and data science that focuses on creating machines with human intelligence to perform tasks that previously required humans. Examples include learning, reasoning, problem solving, perception, and language understanding. Rather than relying on explicit instructions from programmers, AI systems can learn from data to handle complex problems (even simple but repetitive tasks) and improve over time.

Today’s AI technologies have a variety of use cases across a variety of industries. Businesses use AI to minimize human error, reduce high operational costs, provide real-time data insights, and improve 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 our daily lives.

However, despite the numerous benefits of AI, there are some notable drawbacks when compared to traditional programming methods. AI development and deployment can involve data privacy concerns, job displacement, and cybersecurity risks, not to mention the massive technical work of ensuring AI systems work as intended.

This article discusses how AI technology works and explains the advantages and disadvantages of artificial intelligence compared to traditional computing methods.

What is artificial intelligence and how does it work?

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

• data: AI systems learn and make decisions based on data, and machine learning (ML) models in particular require large amounts of data to train effectively. Data is often divided into three categories: training data (to help train the model), validation data (to tune the model), and test data (to evaluate model performance). For optimal performance, AI models must receive data from diverse datasets (e.g. text, images, audio, etc.), which allows the system to generalize learning to new, unseen data.
• algorithm: An algorithm is a set of rules that an AI system uses to process data and make decisions. The AI ​​algorithm category includes ML algorithms that learn and make predictions and decisions without explicit programming. AI can also operate on deep learning algorithms, a subset of ML that uses multilayer artificial neural networks (ANNs) (i.e. “deep” descriptors) to model high-level abstractions within big data infrastructure. And reinforcement learning algorithms allow agents to learn behavior by performing functions, receiving punishments and rewards based on accuracy, and iteratively adjusting the model until it is fully trained.
• Computing Performance: AI algorithms, especially deep learning, often require significant computing resources to process large amounts of data and run complex algorithms. Many organizations rely on specialized hardware, such as graphics processing units (GPUs), to streamline this process.

AI systems also tend to fall into two broad categories:

• artificial narrow intelligenceAlso called narrow AI or weak AI, it performs specific tasks such as image or speech recognition. Virtual assistants like Apple’s Siri, Amazon’s Alexa, IBM’s watsonx, and even OpenAI’s ChatGPT are examples of narrow AI systems.
• Artificial General Intelligence (AGI), or Strong AI can perform all intellectual tasks that humans can perform. Ability to understand, learn, adapt, and work with knowledge across domains. However, AGI is still only a theoretical concept.

How does traditional programming work?

Unlike AI programming, traditional programming requires programmers to write explicit instructions for the computer to follow in all possible scenarios. The computer then executes commands to solve the problem or perform the task. This is a deterministic approach, similar to a recipe, where a computer executes step-by-step instructions to achieve the desired result.

Traditional approaches are suitable for well-defined problems with a limited number of possible outcomes, but when the task is complex or human-like perception (e.g. image recognition, natural language processing, etc.). This is where AI programming offers a clear advantage over rule-based programming methods.

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

The real potential of AI is enormous. Applications of AI include diagnosing diseases, personalizing social media feeds, running sophisticated data analytics for weather modeling, and powering chatbots to handle customer support requests. AI-powered robots can also assemble cars and minimize radiation from wildfires.

Like all technologies, AI has advantages and disadvantages compared to traditional programming techniques. In addition to fundamental differences in how they work, AI and traditional programming differ significantly in terms of programmer control, data processing, scalability, and availability.

• Control and Transparency: Traditional programming gives developers full control over the logic and behavior of the software, allowing precise customization and predictable, consistent results. And if a program doesn’t work as expected, developers can backtrack through the codebase to identify and fix the problem. AI systems, especially complex models such as deep neural networks, can be difficult to control and interpret. This often acts like a “black box” where the inputs and outputs are known, but the process the model uses to move from one to the other is unclear. This lack of transparency can be problematic in industries that prioritize process and decision explainability (such as healthcare and finance).
• Training and data processing: Traditional programming is rigid. They rely on structured data to run their programs and typically have difficulty handling unstructured data. To “teach” a program new information, programmers must manually add new data or adjust processes. Traditionally coded programs also struggle with independent iteration. That said, it may not be able to accommodate unexpected scenarios without explicit programming for those cases. Because AI systems learn from massive amounts of data, they are better suited to processing unstructured data such as images, videos, and natural language text. Additionally, AI systems can continuously learn from new data and experiences (like machine learning), allowing them to improve their performance over time, making them especially useful in dynamic environments where the best solutions can evolve over time. .
• Stability and Scalability: Traditional programming is stable. Once a program is written and debugged, it performs tasks in exactly the same way every time. However, the stability of rule-based programs comes at the expense of scalability. Because existing programs can only learn through explicit programming intervention, programmers must write code at scale to scale their work. For many organizations, this process can be difficult, if not impossible, to manage. AI programs offer greater scalability but less stability than traditional programs. The automation and continuous learning capabilities of AI-based programs allow developers to scale processes quickly and relatively easily, which is one of the key advantages of AI. However, due to the ad hoc nature of AI systems, programs may not always provide consistent and appropriate responses.
• Efficiency and Availability: Rules-based computer programs can provide 24/7 availability, but sometimes only if you have personnel available to operate 24 hours a day.

AI technology can run 24/7 without human intervention, allowing business operations to run continuously. Another benefit of artificial intelligence is that AI systems can automate tedious or repetitive tasks (such as data entry), freeing up employees’ bandwidth for higher-value tasks and lowering a company’s payroll costs. However, it is worth mentioning that automation can result in significant job losses for employees. For example, some companies have switched to using digital assistants to catalog employee reports rather than delegating this task to the human resources department. Organizations must find ways to integrate AI into their operations to improve productivity, thereby unifying their existing workforce into new workflows.

Maximize the benefits of artificial intelligence with IBM Watson

Omdia predicts that the global AI market will reach $200 billion by 2028.1 This means that businesses should expect an increased reliance on AI technologies and an increase in the complexity of enterprise IT systems. But with the IBM watsonx™ AI and Data Platform, organizations have a powerful tool in their toolbox for scaling AI.

IBM watsonx allows teams to manage data sources, accelerate responsible AI workflows, and easily deploy and embed AI across the business. All in one place. watsonx offers a variety of advanced features, including comprehensive workload management and real-time data monitoring, designed to help you scale and accelerate your AI-driven IT infrastructure with trusted data across your enterprise.

The use of AI is not without complexity, but it represents an opportunity for businesses to keep pace with an increasingly complex and dynamic world with sophisticated technologies that can handle that complexity.

Leverage AI with Watsonx