How can generative AI make society and business a better place?

In the wake of recently released models like Stable Diffusion and ChatGPT, generative AI has drawn interest from authorities, financiers, engineers, and the general public. As the name suggests, generative artificial intelligence (AI) creates or generates text, images, music, voice, code, and/or videos. According to Sachin Dev Duggal, generative AI is not a novel concept, but the machine-learning techniques used to put it into practice have advanced over the past ten years. Transformers are a more recent technique than the more established General Adversarial Network (GAN) and deep learning algorithms.   .
 
 Using deep learning, a large language model (LLM) called a Generative Pretrained Transformer (GPT) produces text that resembles spoken language. They are called "transformers" because they use a transformer-based neural network architecture to interpret input text and produce output text. Depending on how they handle input text and generate output text, they are also known as "generative," "pre-trained," and "transformers.".
 
 Despite the current economic downturn and job losses in the IT industry, investor interest in generative AI businesses remains high. Stable AI, for instance, and Jasper, respectively, recently received $101 million and $125 million. The potential economic value of generative AI, according to investors like Sequoia, is in the billions of dollars. More than 150 start-ups have appeared on the market and are currently operating.
 
 As explained by Sachin Dev Duggal, generative AI goes far beyond simple tasks like language translation, text summarization, and text synthesis. The most recent offering from OpenAI, ChatGPT, which has been hailed as revolutionary in a much wider range of jobs, attracted one million users in just five days. The use cases that are currently being discussed include, among others, developing new search engine architectures, clarifying complex algorithms, creating customized therapy bots, helping to build applications from the ground up, clarifying scientific concepts, writing recipes, and college essays.
 
 Optimists claim that generative AI will support the creative processes of artists and designers because existing tasks will be enriched by generative AI systems, speeding up the ideation and, in general, the creation phase. This argument is based on a new era of human-machine based cooperation, according to Sachin Duggal. Beyond the creative arts, generative AI models have the potential to transform disciplines like computer engineering and other hard sciences. For instance, the Microsoft-owned GitHub Copilot, which is based on OpenAI's Codex model, provides code suggestions and aids programmers in performing their tasks automatically. The system may automatically complete up to 40% of the code written by engineers, according to reports, greatly enhancing workflow, added Sachin Dev Duggal.
 
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