ΑΙhub.org
 

If AI image generators are so smart, why do they struggle to write and count?


by
17 July 2023



share this:

AI image produced using the prompt ‘hyper-realistic ten hands on a picture with text saying hello’. Midjourney, author provided.

By Seyedali Mirjalili, Torrens University Australia

Generative AI tools such as Midjourney, Stable Diffusion and DALL-E 2 have astounded us with their ability to produce remarkable images in a matter of seconds.

Despite their achievements, however, there remains a puzzling disparity between what AI image generators can produce and what we can. For instance, these tools often won’t deliver satisfactory results for seemingly simple tasks such as counting objects and producing accurate text.

If generative AI has reached such unprecedented heights in creative expression, why does it struggle with tasks even a primary school student could complete?

Exploring the underlying reasons helps sheds light on the complex numerical nature of AI, and the nuance of its capabilities.

AI’s limitations with writing

Humans can easily recognise text symbols (such as letters, numbers and characters) written in various different fonts and handwriting. We can also produce text in different contexts, and understand how context can change meaning.

Current AI image generators lack this inherent understanding. They have no true comprehension of what any text symbols mean. These generators are built on artificial neural networks trained on massive amounts of image data, from which they “learn” associations and make predictions.

Combinations of shapes in the training images are associated with various entities. For example, two inward-facing lines that meet might represent the tip of a pencil, or the roof of a house.

But when it comes to text and quantities, the associations must be incredibly accurate, since even minor imperfections are noticeable. Our brains can overlook slight deviations in a pencil’s tip, or a roof – but not as much when it comes to how a word is written, or the number of fingers on a hand.

As far as text-to-image models are concerned, text symbols are just combinations of lines and shapes. Since text comes in so many different styles – and since letters and numbers are used in seemingly endless arrangements – the model often won’t learn how to effectively reproduce text.

AI-generated image produced in response to the prompt ‘KFC logo’. Imagine AI.

The main reason for this is insufficient training data. AI image generators require much more training data to accurately represent text and quantities than they do for other tasks.

The tragedy of AI hands

Issues also arise when dealing with smaller objects that require intricate details, such as hands.

Two AI-generated images produced in response to the prompt ‘young girl holding up ten fingers, realistic’. Shutterstock AI.

In training images, hands are often small, holding objects, or partially obscured by other elements. It becomes challenging for AI to associate the term “hand” with the exact representation of a human hand with five fingers.

Consequently, AI-generated hands often look misshapen, have additional or fewer fingers, or have hands partially covered by objects such as sleeves or purses.

We see a similar issue when it comes to quantities. AI models lack a clear understanding of quantities, such as the abstract concept of “four”.

As such, an image generator may respond to a prompt for “four apples” by drawing on learning from myriad images featuring many quantities of apples – and return an output with the incorrect amount.

In other words, the huge diversity of associations within the training data impacts the accuracy of quantities in outputs.

Three AI-generated images produced in response to the prompt ‘5 soda cans on a table’. Shutterstock AI.

Will AI ever be able to write and count?

It’s important to remember text-to-image and text-to-video conversion is a relatively new concept in AI. Current generative platforms are “low-resolution” versions of what we can expect in the future.

With advancements being made in training processes and AI technology, future AI image generators will likely be much more capable of producing accurate visualisations.

It’s also worth noting most publicly accessible AI platforms don’t offer the highest level of capability. Generating accurate text and quantities demands highly optimised and tailored networks, so paid subscriptions to more advanced platforms will likely deliver better results.The Conversation

Seyedali Mirjalili, Professor, Director of Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia

This article is republished from The Conversation under a Creative Commons license. Read the original article.




The Conversation is an independent source of news and views, sourced from the academic and research community and delivered direct to the public.
The Conversation is an independent source of news and views, sourced from the academic and research community and delivered direct to the public.




            AIhub is supported by:


Related posts :



monthly digest

AIhub monthly digest: May 2025 – materials design, object state classification, and real-time monitoring for healthcare data

  30 May 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.

The Good Robot podcast: Transhumanist fantasies with Alexander Thomas

  28 May 2025
In this episode, Eleanor talks to Alexander Thomas, a filmmaker and academic, about the transhumanist narrative.

Congratulations to the #ICRA2025 best paper award winners

  27 May 2025
The winners and finalists in the different categories have been announced.

#ICRA2025 social media round-up

  23 May 2025
Find out what the participants got up to at the International Conference on Robotics & Automation.

Interview with Gillian Hadfield: Normative infrastructure for AI alignment

  22 May 2025
Kumar Kshitij Patel spoke to Gillian Hadfield about her interdisciplinary research, career trajectory, path into AI alignment, law, and general thoughts on AI systems.

PitcherNet helps researchers throw strikes with AI analysis

  21 May 2025
Baltimore Orioles tasks Waterloo Engineering researchers to develop AI tech that can monitor pitchers using low-resolution video captured by smartphones

Interview with Filippos Gouidis: Object state classification

  20 May 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.



 

AIhub is supported by:






©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence