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What is Generative AI?

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Generative AI has been making a lot of noise lately. The term is used to refer to any type of artificial intelligence system that relies on unsupervised or semi-supervised learning algorithms to create new digital images, video, audio, and text. According to MIT, generative AI is one of the most promising advances in the field of AI in the past decade. 

Through Generative AI, computers can learn fundamental patterns relevant to input, which enables them to output similar content. These systems rely on generative adversarial networks (GANs), variational autoencoders, and transformers. 

The hype around generative AI is growing steadily, with Gartner including it in its “Emerging Technologies and Trends Impact Radar for 2022” report. According to the company, it is one of the most impactful and rapidly evolving technologies on the market. 

Some of the key predictions from that Gartner report include: 

  • By 2025, generative AI will be used by 50 percent of drug discovery and development initiatives.
  • By 2025, generative AI will be producing 10 percent of all data. 
  • By 2027, 30 percent of manufacturers will use generative AI to enhance their product development effectiveness. 

Generative AI Techniques 

Generative AI can create new content by utilizing existing text, audio files, or images. It enables computers to detect the underlying pattern related to the input so it can produce similar content. 

Generative AI achieves this process through various techniques: 

  • Generative adversarial networks (GANs): GANs consist of two neural networks. There is a generator and a discriminator network that are pitted against each other to establish equilibrium between the two. The generator network generates new data or content resembling the source data. The discriminator network differentiates between the source and the generated data to recognize what is closer to the original. 
  • Transformers: Transformer models include big names like GPT-3, and they imitate cognitive attention and can measure the significance of the input data parts. Transformers are trained to understand the language or image. They can also learn classification tasks and generate texts or images from large datasets. 
  • Variational auto-encoders: With variational auto-encoders, the encoder encodes the input into compressed code while the decoder reproduces the initial information from the code. When trained correctly, the compressed representation can store the input data distribution as a smaller dimensional representation. 

Generative AI Applications

There are a wide range of applications for generative AI spanning across many fields like marketing, education, healthcare, and entertainment. 

Here are some of the top applications of generative AI: 

  • Healthcare: Generative adversarial networks are revolutionizing the healthcare industries. They can be taught to produce fake examples of underrepresented data, which can then be used to train and develop the model. GANs are also used for data identification, improving data privacy and security. They address the major problem of a reversal process that can compromise valuable patient data. 
  • Music: Generative AI is also being used in music by creating neural networks that can imitate the human brain. For example, Google’s Magenta software created the first-ever AI song. One of the biggest benefits of generative AI in music is its ability to create new genres. 
  • Motion Picture: The applications of generative AI in the movie industry continue to grow. It enables professionals to capture a frame at any time despite the lighting or weather conditions since the photo can be converted after. Generative AI can also use face synthesis and voice cloning to enable actors’ images and videos to be used with different ages. 
  • Media: Generative AI is used throughout the media industry. For example, it can upscale content through super-resolution. Machine learning techniques can turn low-quality content into high-quality. 
  • Robotics: Generative modeling helps reinforcement machine learning models exhibit less bias and is able to understand abstract concepts in simulation and the real world. 

Challenges of Generative AI

With all of its benefits and applications, generative AI also poses some challenges. For one, it can be used by bad actors to carry out malicious activities like scamming people or creating spammy news. 

Generative AI algorithms need a lot of training data to successfully perform tasks. At the same time, GANs cannot output entirely new images or text, they must take data and combine it together to create a new output. 

Another challenge of generative AI is unexpected outcomes, with some models like GANs being hard to control. When this is the case, the models can be unstable and generate an unexpected result. 

Examples of Generative AI Companies

There are many companies involved with Generative AI for a wide variety of applications: 

  • Synthesia: One of the most well-known generative AI companies is Synthesia, which was an early pioneer of video synthesis technology. The UK-based company was founded in 2017 and implements new synthetic media technology for visual content creation, as well as to reduce the cost, skills, and language barriers needed to leverage the technology. 
  • Mostly AI: Mostly AI developed the Synthetic Data Engine that enables the simulation of realistic and representative synthetic data at scale. It can automatically learn patterns, structure, and variation from existing data. 
  • Synthesis AI: Synthesis AI combines novel generative AI models and evolving CGI technologies. According to the company, their proprietary pipeline enables the generation of vast amounts of data for training sophisticated computer vision models. 
  • Synthetaic: A leading synthetic data company, Synthetaic grows high-quality data for AI. The company’s RAIC (Rapid Automatic Image Categorization) automates the analysis of large, unstructured datasets so you can train and deploy AI models faster than traditional approaches. 
  • Aqemia: A silico drug discovery company, Aqemia relies on uniqe quantum-inspired algorithms to predict affinity combined with AI. This technique helps rapidly discover more innovative molecules with better chances of success. 
  • AiMi: One of the top generative AI companies in the music industry, AiMi delivers a dynamic, endless flow of electronic music that reanimates in real time. You can use AiMi to create music scapes that immerse you in continuous sound and visuals.

These are just a few of the many companies leveraging generative AI models to usher in innovative and constantly evolving technologies.  


Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.