The Limitations of Generative AI, According to Generative AI

Generative AI tools can do all sorts of things, such as write blog posts like this one, albeit partially and with a lot of edits by a human writer. This post uses three text-to-text AI writers (anonymized for obvious reasons) to write articles about the limitations of generative AI.

What Generative AI can and can't do, according to AI writer #1

Generative AI is a powerful technology that has the potential to revolutionize almost every sector of our lives. From writing blog posts, creating images and videos, building songs based on a short melody, and helping developers plug code into their programs—generative AI can do it all. However, this raises the question—what are the limitations of generative AI? Let’s take a closer look at what generative AI is capable of and its boundaries.

What can generative AI do?

Generative AI can be used to automate tasks that would otherwise require human labor. It can be used to analyze large sets of data to identify patterns or trends that may not be obvious to humans, then implement those patterns and trends to create similar yet entirely new data. New data can take the form of novel digital content and data insights, such as insights into customer preferences and behavior which could help businesses better serve their customers and stay ahead of trends.

What can’t generative AI do?

Generative AI is still limited in what it can accomplish due to its reliance on data-driven algorithms. While these algorithms may be able to recognize patterns or trends within data sets, they have difficulty understanding context when presented with new information or scenarios outside of their training parameters. This means that generative AI cannot draw conclusions or make decisions based on complex situations — something that only humans can do at present. Furthermore, generative AI cannot replace human creativity completely as it lacks the ability to come up with novel ideas or recognize abstract concepts such as humor or irony — all things which require a human touch.

In conclusion, while generative AI has the potential to revolutionize many aspects of our lives by taking over time-intensive creative tasks and providing business insights — it still has its limitations. There will always be some tasks which will require human intervention in order for them to truly succeed. As such, we must ensure that we use this tool responsibly if we want it to reach its full potential without sacrificing our own ingenuity in the process.

The limitations of generative AI, according to AI writer #2

Generative AI relies on pre-existing data to learn and identify patterns that it will then use to synthesize new data. For example, a machine learning algorithm can be used to generate new images based on a dataset of existing images. But as AI evolves and becomes more sophisticated, so does our understanding of its limitations.

Limited training leads to limited range of outputs

For illustration purposes, let's focus on generative AI tools that can create images. While such tools can create novel images (i.e., images that aren't found in the AI's training dataset), there are limitations to what it can do. For example, a machine learning algorithm can only generate new images based on a dataset of existing images. This means that if the training dataset is limited in scope, so too will the generated images be. 

For instance, if the AI's training dataset is comprised of run-of-the-mill bicycles, it'll be highly unlikely for the AI to create an image of a bike with hubless and spokeless wheels.

Generative AI can't generate new ideas or solutions

One of the key limitations of AI is its inability to generate new ideas or solutions. This lack of creativity is a result of the way AI is typically developed. Most AI systems are based on pre-existing data and rules, and the concepts of "breaking rules" and "thinking outside the box" are completely contrary to any computer programming.

To illustrate, if you train a generative AI-powered robot on the principles of how a Rubik’s cube can be twisted in order to be solved, it can generate countless solutions for every jumbled configuration you present it. However, it would never propose to smash the cube on the ground and reassemble it back together into its solved configuration.

Therefore, generative AI can only produce results that are similar to what has been done before. While this isn’t necessarily a bad thing, it does mean that AI still has some way to go before it can be truly considered intelligent in the way humans are.

The limitations of generative AI: What we can and can't create, according to AI writer #3

Generative AI is a field of AI concerned with artificial intelligence that can generate new data that is similar to training data. There are many potential applications of this technology, including data augmentation, computer vision, and natural language processing.

However, there are also limitations to what generative AI can do. In this blog post, we will explore the limitations of generative AI and what we can and can't create with this technology.

The limitations of Generative AI

Generative AI is still in its infancy, and there are some limitations that need to be considered. Firstly, generative AI is limited by the quality of its training data. The more accurate and diverse the training data is, the more accurate and diverse the generated output will be. Secondly, generative AI is limited by the computational power available. Generative AI requires a lot of computational power to generate realistic images or text, and this can be expensive and time-consuming.

Why are these limitations important?

These limitations are important because they can affect the accuracy of the generative AI’s generated output. Poor quality or low quantity training data can lead to inaccurate or incomplete output. Similarly, low computational power can keep an AI from producing high-quality results.

What does the future hold for generative AI?

The future of generative AI lies in its ability to generate increasingly accurate and diverse data. It is likely that it will continue to improve as more powerful computers become available and better training datasets are developed. It is also beginning to be used in more creative contexts, such as creating music, art, and virtual reality environments.

Generative AI has only just begun

Generative AI is still a fledgling technology, and there are some technical and practical limitations that need to be addressed. However, it has the potential to generate realistic and diverse data in a variety of fields. With more powerful computers and improved training datasets, generative AI is likely to become increasingly powerful in the future.

More limits of generative AI, as observed by humans

All three text-to-text AI writers more or less agree that generative AIs are limited by the inherent nature of their training (i.e., AIs are confined by the parameters of their training), the size of their training dataset, and the quality of their training dataset. What they don’t mention, however, is a limitation they’ve implicitly demonstrated in their outputs, namely the dubiousness of their veracity. How much did they conform to facts or the latest knowledge? They didn’t cite sources. They just provided output and left it up to the user to verify their claims through research, which is problematic because users may just accept the AI’s output without putting it through a rigorous verification process.

Users who are easily impressed by generative AI or overvalue the AI’s output may suffer from the “It's Perfect” effect. That is, they just assume it to be flawless without question. This cognitive bias is analogous to the Dunning-Kruger Effect, where individuals overestimate their abilities and knowledge despite lacking expertise or experience. This overconfidence in the AI can lead to errors in marketing content that can negatively impact a brand's reputation.

Users who aren’t native speakers of a language that the AI writes in are particularly vulnerable to the “It's Perfect” effect and might miss subtle nuances in the language, leading to inappropriate content and PR crises. For instance, when a well-known American fast-food chain introduced a new product in China, their marketing slogan was mistranslated to "Eat your fingers off." Similarly, when HSBC launched their "Assume Nothing" campaign in various countries, it was mistranslated in many languages as "Do Nothing" or "Do Not Assume Anything", leading to confusion among customers.

Therefore, it is crucial for businesses to proofread, fact-check, and consider cultural and contextual appropriateness when using text-to-text AI for marketing purposes. By taking these precautions, businesses can avoid PR disasters and maintain a positive brand image across global markets.

Furthermore, when we wonder about how they were able to claim what they claimed, we arrive at yet another limitation, namely explainability. To clarify, let us imagine having business performance data, and then letting a generative AI tool create sales predictions. Can we plumb the depths of the AI’s logic to be able to justify its output? Or did it process the input data in such an inscrutable manner that we can’t know with certainty that the AI’s predictions can be trusted?

It is still early days for generative AI, and just like with any technological solution, it is creating more problems to be solved. Yet it is only when we surpass our current limitations that any progress is made.

Disclaimer: This article was written when GPT 4 wasn’t released yet. GPT 4 — and Microsoft 365 Co-Pilot, which is built on GPT 4 — surpass many of the limitations listed in this post. We’ll explore more of this soon.