Generative AI Examples: How Companies Innovate Fast with AI
Generative AI can produce outputs in the same medium in which it is prompted (e.g., text-to-text) or in a different medium from the given prompt (e.g., text-to-image or image-to-video). Popular examples of generative AI include ChatGPT, Bard, DALL-E, Midjourney, and DeepMind. Generative AI models are still a relatively new development, so we haven’t seen their long-term effects yet. However, as these models become more advanced and powerful, they will continue to push the limits of what’s possible. That means the benefits and risks of AI models will also continue to grow and evolve as new use cases, and capabilities are discovered. By staying proactive, businesses can position themselves to take advantage of future benefits while being aware of risks before they happen.
In this article, we’ll introduce you to the top 10 generative AI applications that you should know in 2023. The conversational AI chatbot, a ground-breaking AI like Chat GPT – Chatsonic (now with GPT-4 capabilities), overcomes the shortcomings of ChatGPT and ends up being the finest free Chat GPT substitute. Let’s explore the special attributes, working, and advantages of the top 20 tools. The responses might also incorporate biases inherent in the content the model has ingested from the internet, but there is often no way of knowing whether that’s the case.
Top 10 Generative AI Applications Use Cases & Examples
Since each feature is a dimension, it’ll be easy to present them in a 2-dimensional data space. The line depicts the decision boundary or that the discriminative model learned to separate cats from guinea pigs based on those features. In marketing, generative AI can help with client segmentation by learning from the available data to predict the response of a target group to advertisements and marketing campaigns. It can also synthetically generate outbound marketing messages to enhance upselling and cross-selling strategies.
In this way, dangerous diseases like cancer can be diagnosed in their initial stage due to a better quality of images. CQA (Creative Question Asking) is about generating thought-provoking questions to stimulate your mind. It improves over time by incorporating previous answers into future generations of questioning. The AI is trained to accentuate, tone, and modulate the voice to make it more realistic.
Step 3: Gather and process data for training
Additionally, it can help detect and resolve bugs in the generated code by analyzing code patterns and identifying potential issues before suggesting solutions. Furthermore, generative AI can ensure that the code adheres to style guidelines, promoting consistency and readability throughout the codebase. Generative AI can also be used for voice generation by utilizing existing voice sources through speech-to-speech (STS) conversion. This technique allows for the quick and easy creation of voiceovers, which is advantageous for industries such as gaming and film. With these tools, it is possible to generate voiceovers for documentaries, commercials, or games without the need to hire a voice actor.
Therefore, generative artificial intelligence has the potential to generate economic value in trillions of dollars. Transformers, like GPT-3, LaMDA, and Wu-Dao, can simulate cognitive attention and differentially estimate the relevance of the various sections of the input data. They are taught some classification tasks, taught how to generate words or images from enormous databases, and trained to understand the language or the image. Larger enterprises and those that desire Yakov Livshits greater analysis or use of their own enterprise data with higher levels of security and IP and privacy protections will need to invest in a range of custom services. This can include building licensed, customizable and proprietary models with data and machine learning platforms, and will require working with vendors and partners. Artificial Intelligence algorithms are not new, but generative AI has been empowering a new way of using this technology for business automation.
Advanced Prompt Engineering
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.
Furthermore, when combined with virtual reality technology, it can also create realistic simulations that will further engage learners in the process. In this article, we have gathered the top 100+ generative AI applications that can be used in general or for industry-specific purposes. We focused on real-world applications with examples but given how novel this technology is, some of these are potential use cases. For other applications of AI for requests where there is a single correct answer (e.g. prediction or classification), read our list of AI applications. The first neural networks (a key piece of technology underlying generative AI) that were capable of being trained were invented in 1957 by Frank Rosenblatt, a psychologist at Cornell University.
With a diverse range of applications, generative AI is poised to transform numerous industries, including surveillance, healthcare, marketing, advertising, education, gaming, communication, and podcasting. Yakov Livshits As a result, generative AI has become one of the most important technological trends of the year. It uses LaMDA, a transformer-based model, and is seen as Google’s counterpart to ChatGPT.
General Artificial Intelligence: A Brave New World of Opportunities and Challenges
Generative AI services produce outputs using the same medium that is being prompted (e.g.,
text-to-text) or in a different format from the instruction (e.g., text-to-image or
video-to-image). Examples of generative AI are ChatGPT, Bard, DALL-E, Midjourney, and DeepMind. Lablab.ai is a place where you can during 3 or 7 days AI Hackathons create an AI based app! With ChatGPT’s success, many world-renowned companies have rushed into the AI race with their own generative AI solutions. They use generative AI models and tune them to introduce new AI features, addons, and paid subscriptions.
Even as I pen my thoughts, the generative AI space continues to evolve rapidly. New large language models are being released and trained with billions of parameters. Ordinary users are amazed by the capabilities of AI tools like MidJourney in generating vivid, realistic images. Likewise, marketers turn to linguistic models like GPT-4 to create engaging copies.
Now that you know what a generative AI model is, you may want to know what models exists out there. However, let me stress the concept that a model is just a way of selecting which neurons to use, and how to arrange them. If you are a computer scientist specialized in AI, you may be able to create your own model from scratch, or even your own neurons. Examples include drug discovery, diagnosis, research, producing treatment plans, and much more.
- GANs were invented by Jan Goodfellow and his colleagues at the University of Montreal in 2014.
- Whenever there is user input/prompt, the generator will generate new data, and the discriminator will analyze it for authenticity.
- Businesses can use AI models to process and analyze big data sets and produce relevant and targeted ad copy, campaigns, branding, and messaging.
- Telecom virtual assistants can assist customers with inquiries, billing, and account management, providing a personalized experience.
Jokes aside, generative AI allows computers to abstract the underlying patterns related to the input data so that the model can generate or output new content. The interesting thing is, it isn’t a painting drawn by some famous artist, nor is it a photo taken by a satellite. The image you see has been generated with the help of Midjourney — a proprietary artificial intelligence program that creates pictures from textual descriptions. In addition, Generative AI can help corporations streamline their operations and reduce costs.