You’ve likely encountered applications of Generative AI, from generating images to consulting with AI for interview preparation.
ChatGPT, OpenAI’s flagship product, along with excellent ChatGPT alternatives such as Google Gemini, Microsoft Copilot, and Anthropic’s Claude, are typical examples of Generative AI models.
Generative AI technology has become an indispensable part of many people’s personal and professional lives. But what exactly is Generative AI (frequently shortened to GenAI)? How does it differ from other types of artificial intelligence? And how does it work? If you haven’t gotten around to asking ChatGPT these questions yourself, this article will provide you with the answers.
What is Generative AI?
Perhaps against my journalistic instincts, I’ll defer here to ChatGPT to define Generative AI:
“Generative AI is a type of artificial intelligence that creates new content, such as text, images, music, or code, by learning the patterns in existing data. It uses models like generative adversarial networks (GANs) and transformers to generate realistic, human-like outputs, supporting creative applications in art, design, writing, and other fields.”
Or, more succinctly: AI that generates content is Generative AI.
While the term "Generative AI" has only recently gained popularity, the concept has been around for much longer. As far back as the 1950s, computer scientist Arthur Samuel coined the term "machine learning," which can be seen as a precursor to Generative AI.
Despite decades of research and exploration, the biggest advances in Generative AI as we know it today occurred a little over a decade ago, thanks to generative adversarial networks (GANs, as in the definition above) developed by engineer Ian Goodfellow.
That was soon followed by the "transformer architecture," proposed by Google scientists in 2017, which is the foundation for the most commonly used Generative AI tools today.
Examples of Generative AI Applications
If you’ve used popular chatbot tools like ChatGPT, Gemini, Copilot, or Claude, you’ve already experienced Generative AI. For example, when you ask it for restaurant recommendations, help writing a term paper, or a template letter complaining to your landlord.
Its uses are broad, ranging from harmless entertainment (writing original poems and songs, or generating fantastical images) to professional applications (creating presentations, designing product prototypes, developing strategies), and even potentially life-saving ones (drug discovery).
Many social media trends—such as visualizing yourself as a doll, or turning your pet dog into a human—are products of Generative AI.
However, Generative AI is also used for nefarious purposes. "Deepfakes" are used to spread disinformation, damage reputations, or create "nude photos" for sextortion scams. This is one of the reasons why the rapid popularization of Generative AI has many people worried, especially as the technology becomes increasingly realistic and easy to use.
How Generative AI Works
Rest assured that I won’t delve into the complexities of probabilistic modeling and high-dimensional outputs. In practical, simplified terms, you can think of Generative AI models as performing two core functions.
The first and foremost task is learning patterns from vast datasets. These datasets consist of text, images, webpages, code, and really anything that can be fed into the model; this is often referred to as “training.”
The AI model then identifies patterns in this data, effectively acquiring knowledge and understanding techniques. For instance, if the model were fed 100 of the greatest horror novels of all time, it would cross-reference the data, extracting structures, language, themes, and narrative devices that are common to these books.
Next, it applies this training to generate entirely new content. So, when you ask ChatGPT to plan your next vacation, it extracts all the information it has gathered and uses a method called "learning probability distributions" to compose an answer.
For written responses, it does this word by word, using all the data it possesses to choose the most suitable next word in a sentence. Or for images, a Generative AI tool using transformer-based models receives the colors and composition of countless real-world images that it has seen. For example, asking Midjourney to create a cartoon might have it considering all the training samples it previously received to generate something that fits the request accurately.
It’s often easy to confuse the terms "artificial intelligence" and "Generative AI." Artificial intelligence is an umbrella term encompassing all forms of AI. Generative AI is a branch of AI that specifically refers to AI tools capable of generating content.
IBM’s ‘Deep Blue’ chess computer, famously defeating Garry Kasparov—one of the greatest chess players of all time—in 1997 is a prominent example. “Deep Blue” used what’s known as symbolic AI to learn moves, assess the chess board, and make strategic decisions, but it can’t be categorized as Generative AI because it didn’t create anything new.
Another common, non-Generative AI example is discriminative AI. It’s applied in facial recognition software to group photos in your smartphone’s gallery, or identify spam email and hide it from your inbox.
So, while chatbots like ChatGPT, Copilot, and Gemini certainly fall under the broad umbrella of artificial intelligence, they are more accurately categorized as Generative AI models.
Challenges Facing Generative AI
In addition to the malicious uses of Generative AI mentioned above, other drawbacks are more inherent to how the technology works. These models are only as good as the information they’re trained on. Believe it or not, there’s a huge amount of outdated, misleading, or outright false information on the Internet—all of which could be ingested by a chatbot and spat back out as fact. These errors are also known as "hallucinations."
For the same reason, Generative AI models can also fall into the trap of reinforcing biases or stereotypes. As ChatGPT itself gave as an example: "Text-to-image models may commonly associate professions like ‘nurse’ with women, while associating ‘CEO’ with men."
Academic institutions have been scrambling to deal with students using tools like ChatGPT to write their term papers and dissertations. And the challenges it poses to the creative industries—will Generative AI really make writers, actors, musicians, and artists completely redundant?—is an ongoing point of contention.
Generative AI presents the potential to reshape creative industries, but also raises concerns about its impact on the job market. The ability of machines to generate content raises important questions about the value of human skills and creativity in the future economy.
Beyond the Hype: The Future Trajectory of Generative AI
While discussions around Generative AI often center on its capabilities and potential pitfalls, it’s important to consider its broader implications and the key considerations that will shape its trajectory. Here are some important aspects to consider:
Ethical Considerations and Responsible Development
As Generative AI becomes more powerful, ethical considerations become paramount in guiding its development and deployment. Issues such as bias, misinformation, and intellectual property need to be carefully addressed to ensure that these technologies are used responsibly and ethically. Prioritizing transparency, accountability, and fairness is essential to building trust in Generative AI systems and their outputs.
Human-AI Collaboration
The future of Generative AI lies not in completely replacing humans, but in augmenting human capabilities and fostering human-AI collaboration. By leveraging AI’s strengths to automate repetitive tasks, generate creative ideas, and provide insights, humans can focus on higher-level activities that require critical thinking, emotional intelligence, and domain expertise. This collaborative approach can unlock new levels of productivity and innovation.
Industry Transformation and New Opportunities
Generative AI has the potential to disrupt various industries, from healthcare and finance to entertainment and education. By automating processes, personalizing experiences, and unlocking new creative possibilities, organizations can leverage Generative AI to drive efficiency, reduce costs, and gain a competitive edge. As businesses adapt to these technologies, job roles are expected to evolve, creating new opportunities that require expertise in developing, deploying, and maintaining Generative AI systems.
Upskilling and Workforce Development
As Generative AI becomes more prevalent, individuals need to acquire new skills and competencies to thrive in the evolving job market. Emphasis should be placed on fostering skills such as critical thinking, problem-solving, creativity, and communication, as well as an understanding of the ethical implications and responsible use of AI. Upskilling and training programs can help workers adapt to new job roles and capitalize on the opportunities presented by Generative AI.
Addressing Challenges and Mitigating Risks
Generative AI is not without its challenges and risks. Addressing issues such as bias, misinformation, and misuse requires a multifaceted approach that includes technical safeguards, regulatory frameworks, and public awareness campaigns. Continuous monitoring and evaluation of the impact of Generative AI systems is crucial for identifying and mitigating potential negative consequences.
Conclusion: Embracing Responsible Innovation
Generative AI represents a significant leap forward in technological innovation, offering immense potential for industries and individuals alike. By addressing ethical concerns, fostering human-AI collaboration, embracing industry transformation, investing in upskilling, and mitigating risks, we can unlock the full benefits of Generative AI while minimizing its risks. As we continue to explore the possibilities of Generative AI, it is essential to approach innovation with a responsible, human-centered, and forward-thinking mindset.