What is Generative AI? Definition & Examples

Originally built on OpenAI, we’ve now built an in-house semantic search engine based on state-of-the-art AI models. This allows us to be more reliable, scalable, faster, and meet German data regulations. Essentially, generative AI tools like ChatGPT are designed Yakov Livshits to generate a “reasonable continuation” of text based on what it’s seen before. It takes knowledge from billions of web pages to predict what words or phrases are most likely to come next in a given context and produces output based on that prediction.

In-context learning techniques include one-shot learning, which is a technique where the model is primed to make predictions with a single example. In few-shot learning, the model is primed with a small number of examples and is then able to generate responses in the unseen domain. Development of generative AI models is significantly complex due to the high amount of computation power and data required for creating them. Individuals and organizations would need large datasets for training the generative artificial intelligence models. However, generation of high-quality data with such models can be expensive and time-consuming. Here is an overview of how Large Language Models and Generative Adversarial Networks work.

Generate text

Some systems are “smart enough” to predict how those patterns might impact the future – this is called predictive analytics and is a particular strength of AI. AI models treat different characteristics of the data in their training sets as vectors—mathematical structures made up of multiple numbers. 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. 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. Machine learning refers to the subsection of AI that teaches a system to make a prediction based on data it’s trained on. An example of this kind of prediction is when DALL-E is able to create an image based on the prompt you enter by discerning what the prompt actually means.

However, it also can introduce new risks, be they legal, financial or reputational. Many generative models, including those powering ChatGPT, can spout information that sounds authoritative but isn’t true (sometimes called “hallucinations”) or is objectionable and biased. Generative models can also inadvertently ingest information that’s personal or copyrighted in their training data and output it later, creating unique challenges for privacy and intellectual property laws. By carefully engineering a set of prompts — the initial inputs fed to a foundation model — the model can be customized to perform a wide range of tasks. You simply ask the model to perform a task, including those it hasn’t explicitly been trained to do. This completely data-free approach is called zero-shot learning, because it requires no examples.

B. Text Generation and Language Modeling

This training data could include text, images, audio, or any other form of input that the AI system can process. The models analyze the input data and identify underlying patterns, allowing them to generate new content based on the learned characteristics. This process is reminiscent of how the human brain processes information and produces creative outputs.

• A data set of tagged animal photos would be gathered, for instance, if the objective was to create realistic representations of animals.
• With the advancements happening around AI, ML and Data Science, we expect more AI tools coming up in the future.
• In logistics and transportation, which highly rely on location services, generative AI may be used to accurately convert satellite images to map views, enabling the exploration of yet uninvestigated locations.
• Generative AI refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, and even code to create new possible content.
• Deepfake videos are created using generative AI algorithms that learn to mimic the speech and mannerisms of a person to create a video of that person saying or doing something they never actually did.

The amount of data AI can analyze lies far outside the range of rapid inspection by a person. A neural network is a type of model, based on the human brain, that processes complex information and makes predictions. This technology allows generative AI to identify patterns in the training data and create new content.

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.

Typically, it starts with a simple text input, called a prompt, in which the user describes the output they want. Then, various algorithms generate new content according to what the prompt was asking for. Both generative AI and artificial intelligence use machine learning algorithms to obtain their results.

Generative AI has a plethora of practical applications in different domains such as computer vision where it can enhance the data augmentation technique. Below you will find a few prominent use cases that already present mind-blowing results. In healthcare, X-rays or CT scans can be converted to photo-realistic images with the help of sketches-to-photo translation using GANs. In this way, Yakov Livshits dangerous diseases like cancer can be diagnosed in their initial stage due to a better quality of images. Analysts expect to see large productivity and efficiency gains across all sectors of the market. From a user perspective, generative AI often starts with an initial prompt to guide content generation, followed by an iterative back-and-forth process exploring and refining variations.

Audio generation

DALL-E’s take on the subject is artistic and definitely futuristic, but much less conveniently aesthetic than MidJourney’s one. However, there are various hybrids, extensions, and modifications of the above models. There are specialized different unique models designed for niche applications or specific data types. Accenture has identified Total Enterprise Reinvention as a deliberate strategy that Yakov Livshits aims to set a new performance frontier for companies and the industries in which they operate. Centered around a strong digital core, it helps drive growth and optimize operations by simultaneously transforming every part of the business through technology and new ways of working. Embedded into the enterprise digital core, generative AI will emerge as a key driver of Total Enterprise Reinvention.

You may have even observed aesthetically altered selfies that mirror the Renaissance style of art or incorporate surrealist scenarios. This technology that has now gone “viral” is called generative artificial intelligence. Generative AI is also helping e-commerce businesses automate various aspects of their operations, such as price optimization and product recommendations. By analyzing data in real time, generative AI algorithms can adjust prices on the fly and recommend products that are most likely to appeal to each customer. AI-powered marketing automation tools can also help businesses improve their targeting capabilities.

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Until recently, a dominant trend in generative AI has been scale, with larger models trained on ever-growing datasets achieving better and better results. You can now estimate how powerful a new, larger model will be based on how previous models, whether larger in size or trained on more data, have scaled. Scaling laws allow AI researchers to make reasoned guesses about how large models will perform before investing in the massive computing resources it takes to train them. Autoencoders work by encoding unlabeled data into a compressed representation, and then decoding the data back into its original form. “Plain” autoencoders were used for a variety of purposes, including reconstructing corrupted or blurry images.