An Intro to AI Image Recognition and Image Generation by@annalisa42

An Intro to AI Image Recognition and Image Generation

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Anna Lisa HackerNoon profile picture

Anna Lisa

I'm a content marketer with 3 years of experience in the technology world. I am passionate about the latest technology.

For decades, artificial intelligence has been the subject of research for both engineers and scientists. They have been making extraordinary efforts to disclose the mystery of getting computers and machines to recognize and understand our world well enough to act appropriately and serve humanity. The most significant aspect of this research is getting computers to comprehend visual data (videos and images) created every day around us. This area of getting smart devices to identify and understand visual data is known as image recognition. AI-powered image recognition is getting popular day by day, and by 2021 its market is expected to reach $39 billion.

AI Image Recognition

Between the 1950s to the 1980s, the time known as the rising period for artificial intelligence, computers were instructed manually on the ways to recognize images, objects present in images, and the other features to look out for. This method was called “Expert Systems” due to the involvement of traditional algorithms. They needed human interference to recognize and represent features in mathematical models that computers could perceive. The process required a lot of tedious work because of course, there are hundreds and thousands of ways an object can be interpreted and there are thousands or even millions of existing objects. Therefore, finding accurate mathematical models to present all the possible features of an object and for all the possible objects is more work that will last forever.

After the concept of machine learning was introduced, it enabled humans to design algorithms that would benefit computers in recognizing images on their own. It took computer scientists almost two decades to teach machines that could perceive and see their surrounding world.

Real-Life Examples of Image Recognition

Facial recognition technology is now employed in many different industries. One of its most common uses is seen in the retail business. Retailers deploy image recognition technology to enhance their marketing and selling to their target audience. Some Chinese office complexes have vending machines that detect buyers through image recognition technology. The machines track the items buyers take from the machines to consequently bill the shopper's accounts. Even unknown information about shoppers gathered from cameras such as gender, age, and body language can assist merchants in enhancing their marketing efforts and offering an improved experience for their customers.

Using AI, an iPhone app employs artificial intelligence to assist partially-sighted and blind people to navigate their surroundings by using computer vision. From face identity to unlocking the iPhone XIII to the algorithms that enable social media websites to detect who is in the photo, image recognition is everywhere.

AI Image Generation

As artificial intelligence is now able to perceive what an image represents and can easily tell the difference between a dog and a stop sign, an elephant from a dog, and more, the next step to innovation is AI image generation.

One of the barriers to getting artificial intelligence to generate real-looking images was the need for massive datasets for training. However, this barrier was crossed with today’s significant computing powers and the amount of data collected now.

When tasked with drafting an image, AI deploys GAN (Generative Adversarial Nets) introduced in 2014. Invented by a Google researcher, Ian Goodfellow, GAN employs two neural networks. One that actually creates an image and the other one to judge, based on real-life examples, how close the image is to the real thing. After scaling the image for correctness, it sends that information back to the original artificial intelligence system. The system learns from the feedback and drafts an altered image for the next session of scoring. This process continues until the machine assures that the AI-generated image resembles the real image.

Real-Life Examples of Image Generation

These days, artificial intelligence can generate realistic images and videos of almost anything from hamburgers and cats to any unique artwork, representation of words, and even faces.

Google hosted an art show to support AI’s artwork and represent the work composed by its software DeepDream. The sentimental part attached to this exhibition was that it was organized to benefit the charity. AI-made pieces of art were sold for $8K plus others. The artist, Memo Akten, who played a role in this collaboration, expressed that Google had created a better “paintbrush” tool. However, the artist (human creator) was still significant for creating artwork that would command an $8K price tag. Portrait of Edmond de Bellamy, another AI-generated artwork was sold for $610,000.

Akten’s sentiment expresses how other businesses have taken full advantage of AI to complement and improve the work done by humans without even involving them. It may motivate artists to pursue directions they may not have seen without computer collaboration.

Key Takeaways

Artificial intelligence, undoubtedly, is altering the ways we live, work, and even create. It enhances productivity, quality, and speed of work. Image recognition that used to be tedious work has now been performed by AI-enabled machines. The image-generating feature of artificial intelligence has opened ways for people to go in directions they have never heard of.

Anna Lisa HackerNoon profile picture
by Anna Lisa @annalisa42.I'm a content marketer with 3 years of experience in the technology world. I am passionate about the latest technology.
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