What developers need to know about generative AI

Best Generative AI Model with 9 Examples

Generative AI technology automates text or image generation, offering intelligent recommendations in healthcare, arts, social media marketing, and other domains. Synthetic data generation involves creating unique data from the input of the original dataset. This is useful when there is not enough data to train a machine-learning model or when it is difficult to obtain new data. Speech-to-speech conversion is an impactful feature of most generative AI models. This can be useful for various applications, such as language translation and interpretation. Sentiment analysis is another use of generative AI, which involves text analysis to determine the user’s sentiment or emotion.

Google Mandates Deepfake Generative AI Disclaimers on Political … – Voicebot.ai

Google Mandates Deepfake Generative AI Disclaimers on Political ….

Posted: Wed, 13 Sep 2023 12:00:10 GMT [source]

One Google engineer was even fired after publicly declaring the company’s generative AI app, Language Models for Dialog Applications (LaMDA), was sentient. Despite their promise, the new generative AI tools open a can of worms regarding accuracy, trustworthiness, bias, hallucination and plagiarism — ethical issues that likely will take years to sort out. Microsoft’s first foray into chatbots in 2016, called Tay, for example, had to be turned off after it started spewing inflammatory rhetoric on Twitter. Generative AI can be used to generate contracts based on pre-defined templates and criteria. This can save time and effort for procurement departments and help to ensure consistency and accuracy in contract language. HR departments often need to come up with a set of questions to ask job candidates during the interview process, and this can be a time-consuming task.

Creating interview questions

Generative language models can also help businesses with customized advertising copy and product descriptions. Advanced AI technology has dramatically changed how the art and animation industry operates. Art creation has become easy, and a text prompt is enough to create artistic pieces.

generative ai example

The most promising advantage of using generative AI for creating relevant tunes for a project is visible in the ability to create tunes for advertisements. In addition, you can always rely on generative AI for addressing other creative projects. Some examples of generative AI tools for creating music are Soundful, Amper Music, and AIVA.


These use cases across various industries exemplify the transformative potential of generative AI. As this technology continues to evolve, businesses can unlock new realms of innovation Yakov Livshits and drive progress in their respective domains. 3D shape generation refers to the process of creating three-dimensional models of objects using computer algorithms.

Instead of spending hours creating a travel plan, a generative AI application allows you to do so in minutes. It pulls data from the internet and creates an itinerary based on your travel purpose and preference. Financially, businesses have a huge incentive to embark on the generative AI train, whether as a provider or users. The figure is predicted to grow 35.6% over the next few years to $51.8 billion in 2028. Moreover, businesses that adopted AI in their workflow have demonstrated up to a 10% increase in revenue. Understandably, you’ll want to dive deeper into generative AI, particularly how it works and ways it could empower your business.

Generative AI models can generate new financial data or conduct automated financial analysis tasks. One example is the Variational Autoencoder model, which can create artificial financial data to train machine learning models for financial analysis. Generative modeling tries to understand the dataset structure and generate similar examples (e.g., creating a realistic image of a guinea pig or a cat). In the early 2000s, AI started to gain commercial viability with the development of voice assistants like Siri and Alexa. These systems use natural language processing and machine learning to understand and respond to user requests.

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.

For example, generative AI can be used by physicians to develop custom care plans for patients that will improve health outcomes. Generative AI examples are growing rapidly as generative AI moves toward mainstream adoption. Examples are found in nearly every industry, from healthcare to cybersecurity. Transformer architecture has evolved rapidly since it was introduced, giving rise to LLMs such as GPT-3 and better pre-training techniques, such as Google’s BERT. Generative AI can be used for creating job descriptions that accurately reflect the required skills and qualifications for a particular position. ChatGPT code interpreter can convert files between different formats, provided that the necessary libraries are available and the operation can be performed using Python code.

Generative AI is a technology that uses data sets to produce something new in response to a prompt entered by a human. The output could include Yakov Livshits poetry, a physics explanation, an image, or even new music. Generative AI has the potential to transform the Energy and Utilities industry.


Based on the comparison, we can figure out how and what in an ML pipeline should be updated to create more accurate outputs for given classes. Say, we have training data that contains multiple images of cats and guinea pigs. And we also have a neural net to look at the image and tell whether it’s a guinea pig or a cat, paying attention to the features that distinguish them. We just typed a few word prompts and the program generated the pic representing those words. This is something known as text-to-image translation and it’s one of many examples of what generative AI models do.

generative ai example

Generative AI is, therefore, a machine-learning framework, but all machine-learning frameworks are not generative AI. When you click through from our site to a retailer and buy a product or service, we may earn affiliate commissions. This helps support our work, but does not affect what we cover or how, and it does not affect the price you pay.

However, it is not restricted to text generation and there are generative AI tools for different use cases like code generation, data synthesis, video creation, and more. A Transformer-based model is a type of neural network used for various natural language processing tasks such as machine translation, text summarization, and language understanding. Generative Adaptive Networks, or GANs, are also a type of neural network used in machine learning to generate new data from existing information.

  • The smart machines feature the capabilities of machine learning and artificial intelligence.
  • In this blog post, we’ll explore 10 examples of AI-generated artworks that demonstrate the power of machine learning in the world of art.
  • Tools like GPT-4 and Jasper assist users in generating written content or auto-generating content from user prompts.

New and seasoned developers alike can utilize generative AI to improve their coding processes. Generative AI coding tools can help automate some of the more repetitive tasks, like testing, as well as complete code or even generate brand new code. GitHub has its own AI-powered pair programmer, GitHub Copilot, which uses generative AI to provide developers with code suggestions.

Transformers are a type of machine learning model that makes it possible for AI models to process and form an understanding of natural language. Transformers allow models to draw minute connections between the billions of pages of text they have been trained on, resulting in more accurate and complex outputs. Without transformers, we would not have any of the generative pre-trained transformer, or GPT, models developed by OpenAI, Bing’s new chat feature or Google’s Bard chatbot. Elastic provides a bridge between proprietary data and generative AI, whereby organizations can provide tailored, business-specific context to generative AI via a context window. This synergy between Elasticsearch and ChatGPT ensures that users receive factual, contextually relevant, and up-to-date answers to their queries. Data augumentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering.

Leave a Comment

Your email address will not be published. Required fields are marked *