An Overview of 7 Types of Generative AI Models by Joanna GoPenAI

As generative AI models are also being packaged for custom business solutions, or developed in an open-source fashion, industries will continue to innovate and discover ways to take advantage of their possibilities. Widespread AI applications have already changed the way that users interact with the world; for example, voice-activated AI now comes pre-installed on many phones, speakers, and other everyday technology. Generative AI models are only as good as the data they are trained on, and if that data is biased or incomplete, the resulting model will be as well. The generative adversarial network (GAN) model was first designed by Ian Goodfellow and his colleagues in June 2014. Today, generative AI is finding applications in a range of fields, such as marketing, education, healthcare, communication, podcasting, and defense security.

This can be a big problem when we rely on generative AI results to write code or provide medical advice. Many results of generative AI are not transparent, so it is hard to determine if, for example, they infringe on copyrights or if there is problem with the original sources from which they draw results. If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong. However, there are many concerns about how these tools work, their lack of transparency and built-in security safeguards, and generative AI ethics in general. This training enables a generative AI model to mimic those patterns when generating new content, making it believable that the content could have been created by or belonged to a human rather than a machine.

Creating customer emails

NLP algorithms can be used to analyze and respond to customer queries, translate between languages, and generate human-like text or speech. This form of AI is not made for generating new outputs like generative AI does but more so concerned with understanding. With the potential to reinvent practically every aspect of every enterprise, the impact of generative AI on business cannot be understated. These technologies will significantly boost productivity and allow us to explore new creative frontiers, solve complex problems and drive innovation. Ultimately, generative AI will fundamentally transform the way information is accessed, content is created, customer needs are served and businesses are run.

Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system. Meanwhile, Microsoft and ChatGPT implementations also lost face in their early outings due to inaccurate results and erratic behavior. Google has since unveiled a new version of Bard built on its most advanced LLM, PaLM 2, which allows Bard to be more efficient and visual in its response to user queries. VAEs are generative models that utilize an encoder-decoder architecture to map input data into a latent space and reconstruct it back to the original data domain.

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When the reverse diffusion process begins, noise is slowly removed or reversed from the dataset to generate content that matches the original’s qualities. Generative models are designed to create something new while predictive AI models are set up to make predictions based on data that already exists. Continuing with our example above, a tool that predicts the next segment of amino acids in a protein molecule would work through a predictive AI model while a protein generator requires a generative AI model approach. The primary difference between generative and discriminative AI models is that generative AI models can create new content and outputs based on their training.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

As with any technology, however, there are wide-ranging concerns and issues to be cautious of when it comes to its applications. Many implications, ranging from legal, ethical, and political to ecological, social, and economic, have been and will continue to be raised as generative AI continues to be adopted and developed. Like any major technological development, generative AI opens up a world of potential, which has already been discussed above in detail, but there are also drawbacks to consider.

Capabilities

The Eliza chatbot created by Joseph Weizenbaum in the 1960s was one of the earliest examples of generative AI. These early implementations used a rules-based approach that broke easily due to a limited vocabulary, lack of context and overreliance on patterns, among other shortcomings. What is new is that the latest crop of generative AI apps sounds more coherent on the surface. But this combination of humanlike language and coherence is not synonymous with human intelligence, and there currently is great debate about whether generative AI models can be trained to have reasoning ability.

Further, transformer-based GANs and GAN-like transformers have been explored successfully for generative vision AI. As we already mentioned NVIDIA is making many breakthroughs in generative AI technologies. One of them is a neural network trained on videos of cities to render urban environments. Pioneering generative AI advances, NVIDIA presented DLSS (Deep Learning Super Sampling).

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Generative models are also used to enhance existing data and to generate new data that is not available in the existing dataset. In this blog, we will discuss the different types of generative models, the sectors where generative models are used, and how generative models can be used in different ways. Generative AI technology uses machine learning to produce content like text, images, or music. It generates new outputs by learning patterns from existing data and creating novel, creative content based on those patterns. Generative AI uses various machine learning techniques, such as GANs, VAEs or LLMs, to generate new content from patterns learned from training data. These outputs can be text, images, music or anything else that can be represented digitally.

Training involves feeding the model data and allowing it to make errors, then adjusting its internal parameters based on those errors. As we traverse the multiple domains where generative AI has its footprint, it becomes clear that Yakov Livshits its impact is both broad and profound. From arts and media to critical sectors like healthcare and engineering, generative AI is driving innovation, optimizing processes, and opening new avenues for exploration and development.