AI and Generative AI- the difference and 5 examples of each

For instance, VALL-E, a new text-to-speech model created by Microsoft, can reportedly simulate anyone’s voice with just three seconds of audio, and can even mimic their emotional tone. It’s worth noting, however, that much of this technology is not fully available to the public yet. The speed and automation that generative AI brings to a company not only produces results faster than they would ordinarily be produced, but it also has the potential to save businesses money.

For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images. As good as these new one-off tools are, the most significant impact of generative AI will come from embedding these capabilities directly into versions of the tools we already use.

Turning sketches into color images

Generative AI can generate game content, such as levels, maps, and quests, based on predefined rules and criteria. This can help game developers to create more varied and interesting game experiences. By leveraging generative AI to create a variety of fashion models, fashion companies can better serve their diverse customer base and accurately display their products in a more authentic manner.

generative ai example

As we discussed previously, it’s worthwhile to be at the forefront of this endeavor. These very large models are typically accessed as cloud services over the Internet. In any AI project, the model is the structure that decides how the AI will work. A generative AI model is a special type of model mean for generative types of problems.

Generative AI use cases for airports demonstrate how to analyze passenger data. For instance, it checks flight history and preferred comforts, recommend services, such as dining options or selling offerings. Predicting demand and adjusting stock levels accordingly helps ensure the availability of essential products and services for passengers.

And logging the results in an experiment tracking tool

This will drive innovation in how these new capabilities can increase productivity. For example, business users could explore product marketing imagery using text descriptions. Generative AI produces new content, Yakov Livshits chat responses, designs, synthetic data or deepfakes. Traditional AI, on the other hand, has focused on detecting patterns, making decisions, honing analytics, classifying data and detecting fraud.

generative ai example

It can take all sorts of input data (like images, blogs or articles, and music) and combine and manipulate it creatively to produce something new and unique. While image generation and artificial creativity are both generative AI use cases, they have other goals. Image generation aims to generate new images, while artificial creativity seeks to create something new and original without human input. RedBlink’s Artificial Intelligence Consulting Services offer unmatched expertise in harnessing the potential of this technology. From enhancing creative processes to automating content generation, their solutions drive innovation across industries.

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.

Nora Osman, «Task Performance vs Content Creation», I’m going to steal that line. It’s the perfect way to explain and blend the creative and underlying value of the tools we have at our disposal. The scary and difficult part in AI though is going to be finding trust in any email, phone call, graphic etc that we see these days. The Google DeepMind project that created AlphaGo, a computer program that beat a human champion at the board game Go. Smart home devices that can turn on lights, adjust thermostats, and perform other tasks based on user preferences. Let generative AI take the reins and create some creative ones for you (just like Gmail’s Smart Reply feature).

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Creating realistic pictures, films, and sounds, generating text, developing goods, and helping in developing medicines and scientific research are just a few examples of real-world uses for generative AI. Users upload videos in Type Studio, and it does the heavy lifting, including transcribing spoken words into text, so there is no need to edit videos with a timeline. It is a cloud-based collaborative audio or video editor by a company named Descript in San Francisco.

In March 2023, Bard was made available to the public for access in the United States and the
United Kingdom, and plans were made to make it available to other nations in more languages
later in the future. The widespread use of AI software has already altered how people interact with the world. For
instance, voice-activated AI is now pre-installed on numerous phones as well as speakers, among
other common devices. 🎓 If you want to start a career in Data Science and Artificial Intelligence and you do not know how? The team behind GitHub Copilot shares its lessons for building an LLM app that delivers value to both individuals and enterprise users at scale. You may have heard the buzz around new generative AI tools like ChatGPT or the new Bing, but there’s a lot more to generative AI than any one single framework, project, or application.

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Moreover, these tools can also help create text-based reports and perform complex business calculations. The adoption of generative AI is increasing across business domains, and why not? After all, if harnessed well, it can significantly reduce the overall time, effort, and cost needed to run the business. The fashion industry, for example, is leveraging AI to produce visually stunning one-of-a-kind designs.

Generate videos

Learn how we leverage Generative AI with predictable results for multiple industries. When used as a foundation model, this type of AI can find a number of business applications – for any budget. That’s all possible thanks to the flexibility and availability of generative AI models. A report Economic potential of generative AI from June, 2023 by McKinsey & Co suggests that the most impacted industries could be e-commerce, healthcare, and banking. Previously, integrating AI meant investing in training, improving, and keeping the performance under inspection. Now, generative AI allows businesses to benefit from AI with nearly any budget.

  • Some of the examples of generative AI in code generation refer to OpenAI, Copilot, and Codex.
  • Snapchat has launched a chatbot called “My AI,” which utilizes OpenAI’s text engine, ChatGPT.
  • New and seasoned developers alike can utilize generative AI to improve their coding processes.
  • If you are already familiar with artificial intelligence, you can pick the model you feel suits your need the most and start learning more about it.
  • Chatbots respond to customer requests and inquiries in natural language and can help customers resolve their concerns.

Even more use cases will be discovered and developed as the technology evolves. Their propensity for “hallucinations,” or creating information that is factually inaccurate, can lead to a mass spread of misinformation. Its mass adoption is fueling various concerns around its accuracy, its potential Yakov Livshits for bias and the prospect of misuse and abuse. To be sure, generative AI’s promise of increased efficiency is another selling point. This technology can be used to automate tasks that would otherwise require manual labor — days of writing and editing, hours of drawing, and so on.

generative ai example

The convincing realism of generative AI content introduces a new set of AI risks. It makes it harder to detect AI-generated content and, more importantly, makes it more difficult to detect when things are wrong. 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.