Assessing different types of generative AI applications

In this article, we will explore 50 practical Yakov Livshits across different industries. ChatGPT can be used in generating sitemap codes producing an XML file that lists all the pages and content on a website. ChatGPT can be used in creating effective meta descriptions by generating summaries of the content that accurately and concisely describe the main topic of a page. A meta description is an HTML attribute that provides a brief summary of a web page’s content. The meta description serves as an advertisement for the page, encouraging users to click on the link and visit the page.

Overall, the applications of generative AI are vast and varied, and it has the potential to transform many different industries and fields. As the technology continues to advance, it will be interesting to see the new and innovative ways in which it is used in the future. Generative models like ChatGPT can help auditors automate repetitive tasks, such as paperwork and reports. Specifically, it can produce standardized reports (such as in the figure below) that offer consistency in how findings are presented. By analyzing this data, generative AI tools can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development. When a customer sends a message, ChatGPT or other similar tools can use this profile to provide relevant responses tailored to the customer’s specific needs and preferences.

> Code-based Applications

The hype will subside as the reality of implementation sets in, but the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life. The tool prompts sellers to enter a few keywords or sentences describing their product. It then spits out a range of content a seller can use to build their listing, such as product titles, bullet points and descriptions. Our deep knowledge of tech markets combined with our actionable insights empower organizations to make smart growth decisions. Among those that have adopted GAI applications, current usage levels are healthy and are expected to increase. A quarter of respondents use GAI applications once a day (10%) or more (15%), while another quarter use GAI apps at least once a week, while 29% use such apps a couple of times per week.

While GANs can provide high-quality samples and generate outputs quickly, the sample diversity is weak, therefore making GANs better suited for domain-specific data generation. Additionally, diffusion models are also categorized as foundation models, because they are large-scale, offer high-quality outputs, are flexible, and are considered best for generalized use cases. However, because of the reverse sampling process, running foundation models is a slow, lengthy process. Generative AI systems can be trained on sequences of amino acids or molecular representations such as SMILES representing DNA or proteins. These systems, such as AlphaFold, are used for protein structure prediction and drug discovery.[36] Datasets include various biological datasets.

Multilingual customer support

Recent breakthroughs in the field, such as GPT (Generative Pre-trained Transformer) and Midjourney, have significantly advanced the capabilities of GenAI. These advancements have opened up new possibilities for using GenAI to solve complex problems, create art, and even assist in scientific research. Chatbots and conversational AI, which are technologies that have been used in various applications on the internet. Chatbots are software programs that are designed to simulate conversation with human users through text or voice interactions.

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.

Audit programs involve the frequent analysis of large swaths of financial and operational data. For more on the use cases and benefits of generative AI for SEO maximization, check our article on ChatGPT SEO scoring. For more, check out our article on the 5 technologies improving fraud detection in insurance.

The best generative AI tools cover a wide range of functions and business use cases, though many of the most prominent tools today are large language models (LLMs) and/or content generation tools. The generative model, when fed a base of real reviews as training data, would attempt to create seemingly real reviews and then pass them, along with real reviews, through the discriminative model. The discriminator acts as an adversary to the generative model, trying to identify the fakes. The discriminator, which is told which inputs were real and which were fake only after evaluating them, then adjusts itself to get better at identifying fakes and not flagging real reviews as fake. The generator gets better at generating undetectable fakes as it learns which fakes the discriminator successfully identified and which authentic reviews it incorrectly tagged.

Morgan Stanley, for example, is working with OpenAI’s GPT-3 to fine-tune training on wealth management content, so that financial advisors can both search for existing knowledge within the firm and create tailored content for clients easily. It seems likely that users of such systems will need training or assistance in creating effective prompts, and that the knowledge outputs of the LLMs might still need editing or review before being applied. Assuming that such issues are addressed, however, LLMs could rekindle the field of knowledge management and allow it to scale much more effectively. Well, for an example, the italicized text above was written by GPT-3, a “large language model” (LLM) created by OpenAI, in response to the first sentence, which we wrote. First, it is sensitive to the prompts fed into it; we tried several alternative prompts before settling on that sentence. Second, the system writes reasonably well; there are no grammatical mistakes, and the word choice is appropriate.

In our fast-paced, technologically advancing world, the realm of artificial intelligence (AI)… Today, 900+ fast-scaling startups and Fortune 500 companies rely on Turing for their engineering needs and business transformation, and so can you. Yakov Livshits Talk to our expert today and get tailored solutions designed for rapid business transformation. While chatbots like ChatGPT and Google Bard have quickly risen in popularity, there are other generative AI use cases that are becoming prominent.

There will be stronger regulations, penalties and improved fake detection algorithms. GANs are not the only approach, but also Variational Autoencoders (VAEs) and PixelRNN (example of autoregressive model). There are already attempts to use text generation engine’s output as a starting point for copywriters. In our case we did an interview with AI and it sounded really interesting and natural. The same applies to computer games which can upscale the resolution to 4K while maintaining high frames per second based on lower resolution textures.