“The Future of AI-Generated Images: Challenges and Possibilities”
The use of Artificial Intelligence is altering the way various industries operate. One such area where AI is making a substantial impact is image generation. We can now create images that are visually appealing, realistic, and extremely hard to distinguish. Industries such as Gaming, Fashion, Art, and even Medical are getting revolutionized by this technology.
Does that mean we do not need artists? No!
While AI image generators present some mind-blowing results, it is not without challenges. This article will help you navigate through these challenges and explore the opportunities of AI image generation.
The science behind AI image generation
The technology used in AI image generation is called artificial neural networks. Humans can perceive different objects and images intuitively and abstractly. But not machines, they can only see numbers or pixels. AI neural networks solve this by taking the input data through several processing layers of artificial neurons stacked up on top of each other. It includes developing algorithms or large codes that can distinguish between real-world images, lighting, and texture. Running such lengthy codes requires exceptional memory and computational power, it is only then that a truly realistic image is generated.
Most AI generators use the Generative adversarial networks (GANs) which is a deep learning model that trains two neural networks besides each other to generate convincing images. This is where the role of data plays a crucial role which can impact the output of the AI image generators. While some AI image generators like MidJourney and Dall-E 2 can create realistic images, researchers and developers are unceasingly evolving AI image generation techniques. Moreover, the outcomes of GANs present some challenges that need to be addressed to produce indistinguishable images.
Challenge #1: Developing algorithms and training data
Real-world images may seem like a normal picture, but when seen from a technical point of view there are several complexities. Developing algorithms that can stimulate factors such as texture, lighting, and perspective is a complex task alone, which requires a vast amount of skills. Let’s for instance consider this done, the algorithms are useless until they are fed data to work upon.
Providing the algorithm with large datasets helps it to recognize features and learn patterns that are common in real life. However, it is not true that the larger the dataset, the more accurate the image is. Because the relevance, diversity, and quality of the training data play a crucial role. Discrepancies in the data can hurt the output of the AI image generator.
Challenge #2: The Uncanny Valley Phenomenon
This term was first coined by Masahiro Mori in 1970 which refers to the unsettling feeling people feel when androids and audio/visual simulations are the same in many aspects but not quite believably realistic.
In simpler words, it is when you see this image below of a bear, and for instance, it looks real. But after a few minutes, you understand that this is an AI-generated image. This unsettling feeling that this looks so real! Is the uncanny valley feeling.
While the researchers try to make AI image generators more realistic, they need to be considerate about the uncanny valley to occur.
Challenge #3: Ethical implication of deep fakes
As the images start to become more realistic, deepfakes stay at the top of the development and use of images generated by AI. Potential for misuse and biases remain a common question that needs to be addressed.
Although we are talking about a single AI- image, to help you understand the severity of this below is a video of MrBeast, who has the world’s most subscribed YouTube channel, created using deep fake technology to scam people into buying an iPhone 15 Pro, for just 2 dollars.
The fact that one can’t tell the difference between the real person or AI talking, can have some dangerous repercussions. Regulators need to work with developers to establish safeguards and guidelines to certify the ethical and responsible use of AI-generated images.
On one hand, there are many challenges for AI image generators, the possibilities are endless. This is because of their application in industries like Art, Gaming, Fashion, and science like medical diagnosis.
Possibility #1: Endless customization and creativity
An average graphic designer takes up to 3 weeks to complete a standard graphic design, while an artist creates Charcoal art within 6–8 hours.
Now, imagine an artist or a graphic designer who can present realistic, unique, and personalized images within a few clicks and only in a few minutes or even seconds.
Seems drastic for artists, right? No!
By leveraging these AI tools artists and creative professionals can start by producing unique starting points, streamline their creative process, and also aid in image manipulation.
Possibility #2: Revolutionize scientific research
Complex data or scenarios can be visualized by AI-generated images which otherwise would be challenging to capture.
One practical application of AI-generated images is in the medical industry. Using GANs to generate tissues and organs images for medical diagnosis. This will allow doctors to see the images of tissues and organs without needing to conduct invasive procedures.
Possibility #3: Improve time and cost efficiency across industries
Industries like Fashion and Gaming require images generated based on the characteristics of their brand or their product very frequently.
Using AI image generators will allow users of fashion brands to upload images of themselves to visualize outfits.
Similarly, for gaming industries, coming up with a character, scenery or graphics takes a lot of time and cost to hire skilled graphic designers and animators. With AI image generators, companies can generate for their digital media and video games.
Conclusion
Rapid improvements in AI image generation are becoming more widespread across industries. Parallelly, working on ethical challenges and finding a way beyond the uncanny valley should be the focus of developers and regulators. As AI develops more, we can see more use cases in the world from the creative field to scientific discoveries, AI images have endless possibilities. One thing to remember is to consider AI as an ally, not a foe, USE AI rather than Loose from it!
