Generative AI: What Is It, Tools, Models, Applications and Use Cases

One significant application of generative AI in healthcare is in medical image analysis. AI models are trained to detect patterns and abnormalities in Yakov Livshits medical images, such as X-rays and CT scans. This allows for quicker and more accurate diagnoses, ultimately leading to better treatment outcomes.

• Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms.
• After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.
• Gaming companies can use generative AI to create new games and allow players to build avatars.
• I opted to compose an additional prompt that would get ChatGPT to do a Chain of Thought approach on this answer.
• Automotive companies can use generative AI for a multitude of use cases, from engineering to in-vehicle experiences and customer service.

Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased the potential of machine learning, as well as the need for it. Some examples of foundation models include LLMs, GANs, VAEs, and Multimodal, which Yakov Livshits power tools like ChatGPT, DALL-E, and more. ChatGPT draws data from GPT-3 and enables users to generate a story based on a prompt. Another foundation model Stable Diffusion enables users to generate realistic images based on text input [2]. Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it.

Generative Adversarial Networks

What is new is that the latest crop of generative AI apps sounds more coherent on the surface. But this combination of humanlike language and coherence is not synonymous with human intelligence, and there currently is great debate about whether generative AI models can be trained to have reasoning ability. One Google engineer was even fired after publicly declaring the company’s generative AI app, Language Models for Dialog Applications (LaMDA), was sentient. Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.

Generative AI raises several ethical concerns, including copyright infringement and the creation of fake content. Bias can also be introduced into the model if the training data is not diverse enough, leading to discriminatory outputs. Artists can use these generated pieces as a starting point for their own creative process, manipulating and editing the pieces to fit their vision.

Amazon debuts generative AI tools that helps sellers write product descriptions

The technology uses machine learning algorithms that analyze large datasets, identify patterns and generate new output based on this learned knowledge. The process of training generative AI models involves exposing a machine learning algorithm to large volumes of data, then training it to recognize and replicate patterns, which can then be used to generate new content. Generative AI is a new buzzword that emerged with the fast growth of ChatGPT. Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data.

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.

This is the basis for tools like Dall-E that automatically create images from a text description or generate text captions from images. But it was not until 2014, with the introduction of generative adversarial networks, or GANs — a type of machine learning algorithm — that generative AI could create convincingly authentic images, videos and audio of real people. Examples of foundation models include GPT-3 and Stable Diffusion, which allow users to leverage the power of language. For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request. On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input.

You can then use the computer to store the data and search the data by exploiting a tree-like capacity. Turns out that trees or at least the conceptualization of trees are an important underpinning for the latest innovation in prompt engineering and generative AI. Prompt engineering is gaining on generative AI via the emergence of the Tree of Thoughts (ToT) … In this article, we explore what generative AI is, how it works, pros, cons, applications and the steps to take to leverage it to its full potential. It’s only the beginning of this tech, so it can be hard to make sense of what exactly it is capable of or how it could impact our lives, but so far, it’s impressive. We’re committed to answering the biggest questions surrounding it, and sharing what we know.

By examining those two lines of thought, hopefully, a decision can be made about which of the two is most meritorious. In general, you might want to somehow compare and contrast each of the distinctive lines of thought. For example, you could try to use numeric weights and mentally calculate the winning potential of each line of thought. Another Yakov Livshits approach could be to directly compare side-by-side the lines of these thoughts. Generative AI enables early identification of potential disease to create effective treatments while the disease is still in an initial stage. For instance, AI computes different angles of an x-ray image to visualize the possible expansion of the tumor.

We cannot say for sure what goes on in the human mind when thinking about things such as which chess move to make. In any case, we all have agreed to refer to those human ponderance as thoughts. Generative AI also can disrupt the software development industry by automating manual coding work. Instead of coding the entirety of software, people (including professionals outside IT) can develop a solution by giving the AI the context of what they need. A low-resolution and bad quality picture can be turned into a decent resolution thanks to some Generative AI tools.

If we take a particular video frame from a video game, GANs can be used to predict what the next frame in the sequence will look like and generate it. This approach implies producing various images (realistic, painting-like, etc.) from textual descriptions of simple objects. The most popular programs that are based on generative AI models are the aforementioned Midjourney, Dall-e from OpenAI, and Stable Diffusion. But still, there is a wide class of problems where generative modeling allows you to get impressive results.