AI vs. Generative AI: Exploring the Artificial Intelligence Landscape

Artificial intelligence (AI) technology is quickly evolving. Since 2018, the global market has increased by more than $60 billion, with experts like the investment banking company Goldman Sachs analysts predicting it will reach $200 billion by 2025 [1, 2].

The world has already experienced AI at work, with programs like ChatGPT and image generators leaving a lasting impression about its capabilities. In 2024 and beyond, generative AI will likely expand with new uses and applications that have the power to reshape various sectors, including entertainment and design. Although traditional AI and generative AI aren’t mutually exclusive, it’s essential to understand how they differ to ensure you apply the correct technology for various use cases. 

Read more: What Is Artificial Intelligence? Definition, Uses, and Types

Artificial Intelligence

AI's roots span back to the 1950s when Alan Turing first published Computer Machinery and Intelligence, which later became a tool to measure computers' intelligence. In 1952, scientist Arthur Samuel created the first computer program to learn to play checkers, and AI has slowly developed ever since. 

At the time, computers could only execute commands. They couldn't store that information. Additionally, the technology costs were astronomical, with a single computer costing approximately $200,000 monthly to lease [3].

Modern technology has made incredible progress, with computer memory and speed facilitating the path forward. AI has garnered a lot of buzz and made great strides over the past decade and continues to evolve quickly. 

In perhaps the first significant moment in modern AI, in 2016, Google’s AlphaGo artificial intelligence program bested Lee Se-dol in a game of Go, a Chinese board game Se-dol made a name for himself winning. Today, AI has exceeded human abilities in speech and image recognition, reading comprehension, and understanding language.

What is AI?

Put simply, AI is a computer's ability to learn and make decisions independently, using cognitive abilities typically associated with humans. For that to be possible, multiple disciplines must come together, including computer science, biology, mathematics and statistics, neuroscience, and philosophy.

It goes beyond mimicking human intelligence, with much of the research in AI applied to studying and solving real-world challenges using methods that computers excel in but may be entirely separate from how human intelligence operates. The way AI's intellectual processes work often differs from those of humans, presenting unique benefits.

How AI works

AI works through various processes, such as machine learning (ML), which uses algorithms to aid the computer in understanding information and "learning" it. For example, if the goal was to teach a computer to recognize the difference between a picture of a horse and a cow, humans would need to help the machine identify the differences initially. ML would allow it to learn the features inherent in each data set and apply them moving forward.

ML is just one of the subfields under the AI. Neural networks simulate human nervous systems to help machines learn using interconnected artificial neurons, which can help predict patterns. Deep learning uses multiple layers of artificial neurons that enable the machine to engage in sophisticated learning, such as that necessary for applications like self-driving vehicles, which must analyze factors like distance and depth for the machine to perform effectively.

Advantages of AI

One of the primary reasons AI has gained so much traction over the past few years is its ability to improve our lives in various ways. Although many worry that AI will replace humans in multiple occupations, the technology cannot replace the empathy and critical thinking people are capable of. Instead, it offers its unique strengths that people can use in collaboration with their own to achieve improved results in various areas. 

Five essential advantages include the following:

• Increased productivity and efficiency: Using AI to tackle lower-level, repetitive tasks allows people to focus on more complex tasks.

• Fewer errors: AI can handle many tasks with flawless precision and accuracy by taking human error out of the equation. For example, a computer could quickly parse through a large volume of data to derive the necessary information or locate mistakes. A human performing the same task would require more time and could inadvertently miss critical details and data. 

• Improved decision-making abilities: Humans ultimately make the decisions, applying context to the insights AI provides. However, AI efficiently delivers data-driven information that powers better decisions. Additionally, AI removes human bias, leading to better decisions, particularly in situations like approving loans or reviewing job applications. 

• Automation and 24/7 availability: Staying connected is easier with AI's ability to send reminders and auto-reply texts and emails. It can also handle otherwise monotonous tasks like data entry and collection and provide service day or night for an improved customer experience. 

• Improved health care: Just as AI can reduce human error in other fields, it can also enhance medical testing results by catching even the smallest irregularities that humans could easily miss. For example, AI might note an anomaly in an X-ray or imaging scan, leading to potentially earlier diagnoses or better care. Additionally, wearable technology allows continual monitoring, and AI can support improved clinical care while aiding in making sound decisions regarding treatments and predicting health risks.

Disadvantages of AI

Despite its many benefits, AI also has a potential downside. It’s essential to consider the cons before investing in the technology. 

Five potential disadvantages include the following: 

• High implementation costs: To leverage the benefits, many companies will need to invest in costly hardware and other technology. The expense required for implementing AI may not be feasible for smaller businesses, and it may not produce an adequate return on the investment for a substantial amount of time.

• Wear and degradation: The wear on the machine itself isn't often considered. Machines break down with time and use. So, for example, if you use an AI-powered machine in a manufacturing setting, the parts will eventually need replacing and maintenance and will fail in time. Additionally, continual or regular learning is critical to avoid using outdated AI.

• Lack of original thought: AI can’t duplicate human creativity. Although people can already use it in art, for example, it cannot "think" outside of the existing data pool it has to draw on. It lacks innovation and the necessary creativity to produce original ideas. 

• Cannot consider emotional ramifications: Humans have empathy and emotional intelligence, two qualities necessary to make decisions with emotional components factored in. Machines work solely with the parameters you give them. Humans can apply kindness and compassion to make sounder decisions on sensitive issues or in areas like marketing, where you must consider buyers' emotions throughout the sales process.

• Ethical considerations: Just as AI can't factor emotions into decisions and insights, it also can't incorporate morality or ethics. The larger concern surrounds data privacy and informed consent when using human data. Some of the leading concerns include fair and impartial use of results, transparency among companies using AI regarding the principles they operate under, and willingness to collaborate with others toward creating ethically-minded AI regulations.

Generative artificial intelligence 

While traditional AI is excellent for data analysis, pattern recognition, and making predictions, generative AI can create new data using its training data to recognize patterns and create new ones. 

Like traditional AI, generative AI has been around for decades, although it has only gained sophistication and more widespread use within the last few years. The first chatbots emerged in the 1960s. With the developments in machine learning and generative adversarial networks (GANs), generative AI has entered a new era. Today, generative AI can create images that look realistic, create content such as graphics and text, answer questions, explain complex concepts, and convert language into code. 

ChatGPT's release in 2022 brought generative AI into the conversation. In 2024, many experts anticipate generative AI will become even more powerful, bringing AI-led transformation to various spaces. For example, we will likely see increased capabilities and capacity, more adept use of generative AI in product design, and greater integration in formats like video and audio. 

Read more: What Is Generative AI? Definition, Applications, and Impact

What is generative AI?

This subset of artificial intelligence uses ML models to learn from data and create new content. Some people consider it the next generation of AI because, instead of relying on existing sources, it can predict what comes next in the pattern it identifies to produce new data, images, and other forms of content. 

Developers train systems and applications like DALL-E, which generates text-prompted images, and ChatGPT, which uses vast data sets, adding to the power of generative AI. Engineers develop this technology using several approaches, including GANs, which include components for creating content and another component that evaluates that content for authenticity. 

In doing so, it provides feedback for the program to use to improve the content, continuing the process until the results are as realistic as possible. Autoencoders enhance the way generative AI programs store and process data while reducing noise. Developers incorporate variations into the encoding step of the process to help generative applications with content creation. 

Diffusion models, which DALL-E uses, train on images and apply the laws of diffusion to move pixels and blur the image to a static state. Doing so allows the program to learn by tracing the pixels back to the original image. For example, if you wanted DALL-E to generate an image of a tiger, it would take an existing image and use this process to create a new one. ChatGPT, on the other hand, uses a transformer-based model that focuses on the context and sequence found in data to analyze it and make predictions and translations or create new text.

Advantages of generative AI

Generative AI offers a significant array of abilities. It can respond to questions, revise content, correct code, and create songs and other creative content. Use cases apply across various industries, and developers continue exploring its advantages for businesses of all types. Global research and consulting firm Gartner predicts that 30 percent of businesses will incorporate generative AI by 2025 [4].

Five significant advantages of generative AI include the following:

• Improved creativity and personalization: Traditional AI lacks generative AI’s ability to create new content. It can aid marketers in crafting personalized campaigns; creating engaging written, visual, and audio content; and making interactions with generative AI applications more personal.

• Better customer service: Personalization can enhance the customer experience, as can the use of virtual assistants to provide 24/7 customer service. Generative AI also has the power to analyze customer data to identify trending topics and customer sentiment, which companies can use to provide a better experience.

• Time savings: Generative AI can perform data processing in real-time and automate tasks, helping to create significant savings in both time and operational costs.

• Boost productivity: Generative AI can provide valuable employee support, including answering questions, finding information, and summarizing content. Additionally, because they are free from the repetitive and time-consuming tasks AI can handle, employees can focus more on other tasks. It can generate reports automatically to save time, minimize mistakes, and even help speed up the timeline for app development by making coding suggestions.

• Improved learning: Generative AI uses new data and feedback to refine its performance. This ability to engage in adaptive learning can help users learn more effectively, too. Models can adjust according to individual learners' learning styles and preferences, enhancing education and knowledge discovery in addition to summarizing complex information in an understandable manner.

Disadvantages of generative AI

As with traditional AI, the ethical implementation and use of generative AI remains a concern. With generative AI, the potential for distributing intentionally or unintentionally harmful content, copyright issues, and data privacy concerns are among the potential cons that must be addressed. Other potential drawbacks include the following: 

• Compromised brand reputation: The generation of insensitive polls incorporated in news stories has already occurred. This spotlights the potential for generative AI to have a harmful effect, which can significantly damage a brand's reputation.

• Inadequate sourcing: Generative AI can sometimes identify the sources it bases its results on, but not always. 

• Inappropriate use: Generative AI can be transformative when used responsibly, but it’s not the right choice for every situation. For example, any scenario that requires empathy or moral context or those where health and legality are on the line should have a human in charge, not AI.

• Possible bias: The information created sounds hyperrealistic, which makes it difficult to assess the bias coming from the source. It’s easy to assume AI is always correct, but that’s not always the case, requiring careful use and sensitivity to the potential for bias. 

Taking the next steps with Coursera

Artificial intelligence is here to stay. Whether you’re interested in learning more about AI for personal reasons or your professional development, you can effectively deepen your understanding of AI with courses like AI For Everyone from DeepLearning.AI or the IBM Applied AI Professional Certificate. Alternatively, if you want to focus on generative AI, consider taking Google’s Introduction to Generative AI. You’ll find these courses and more on the Coursera platform.