The Difference Between Generative AI And Traditional AI: An Easy Explanation For Anyone

To improve the odds the model will produce what you’re looking for, you can also provide one or more examples in what’s known as one- or few-shot learning. This ability to generate novel data ignited a rapid-fire succession of new technologies, from generative adversarial networks (GANs) to diffusion models, capable of producing ever more realistic — but fake — images. The applications for this technology are growing every day, and we’re just starting to explore the possibilities.

It will significantly boost productivity among software coders by automating code writing and rapidly converting one programming language to another. And in time, it will support enterprise governance and information security, protecting against fraud and improving regulatory compliance. This training data is then used to generate text, translate languages and answer questions Yakov Livshits via natural language processing (NLP). While LLMs are still being developed, CXOs have noted that they can be used for code generation, technical document creation, marketing and data analysis. Generative AI is a type of artificial intelligence that can produce content such as text, imagery, audio and data based on what it has learned from a massive training set of data.

What are generative AI examples?

The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. End users should be realistic about the value they are looking to achieve, especially when using a service as is, which has major limitations. Generative AI creates artifacts that can be inaccurate or biased, making human validation essential and potentially limiting the time it saves workers. Gartner recommends connecting use cases to KPIs to ensure that any project either improves operational efficiency or creates net new revenue or better experiences.

There are artifacts like PAC-MAN and GTA that resemble real gameplay and are completely generated by artificial intelligence. Pioneering generative AI advances, NVIDIA presented DLSS (Deep Learning Super Sampling). The 3rd generation of DLSS increases performance for all GeForce RTX GPUs using AI to create entirely new frames and display higher resolution through image reconstruction. Generative algorithms do the complete opposite — instead of predicting a label given to some features, they try to predict features given a certain label. Discriminative algorithms care about the relations between x and y; generative models care about how you get x. Let’s limit the difference between cats and guinea pigs to just two features x (for example, “the presence of the tail” and “the size of the ears”).

Generative adversarial networks

AI that is able to create images, videos, and texts is today often used by designers, artists, and other creatives. VC’s also demonstrate a particular interest in generative artificial intelligence startups this year. Experts say that their interest is motivated by the latest improvements in this area and real benefits that generative Yakov Livshits AI can bring across multiple industries. Video is a set of moving visual images, so logically, videos can also be generated and converted similar to the way images can. 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.

Ultimately, generative AI will fundamentally transform the way information is accessed, content is created, customer needs are served and businesses are run. With the complex technology underpinning generative AI expected to evolve rapidly at each layer, technology innovation will be a business imperative. An effective, enterprise-wide data platform and architecture and modern, cloud-based infrastructure will be essential to capitalize on new capabilities and meet the high computing demands of generative AI. Discriminative modeling, on the other hand, is primarily used to classify existing data through supervised learning. As an example, a protein classification tool would operate on a discriminative model, while a protein generator would run on a generative AI model.

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.

The incredible depth and ease of ChatGPT have shown tremendous promise for the widespread adoption of generative AI. To be sure, it has also demonstrated some of the difficulties in rolling out this technology safely and responsibly. But these early implementation issues have inspired research into better tools for detecting AI-generated text, images and video.

Generative AI creates new content, chat responses, designs, images and programming code. Traditional AI has been used for detecting patterns, making decisions, surfacing and classifying data and detecting anomalies to produce a simple result. Generative AI is important not only by itself but also because it makes us one step closer to the world where we can communicate with computers in natural language rather than in a programming language.

What’s a Large Language Model (LLM)?

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. Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities. Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields. Generative AI, as noted above, often uses neural network techniques such as transformers, GANs and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content.

You’ve almost certainly heard about ChatGPT, a text-based AI chatbot that produces remarkably human-like prose. DALL-E and Stable Diffusion have also drawn attention for their ability to create vibrant and realistic images based on text prompts. ‍Large language models are supervised learning algorithms that combines the learning from two or more models.

The future of generative AI

DALL-E is another popular generative AI system in which the GPT architecture has been adapted to generate images from written prompts. Generative AI is a type of artificial intelligence that can produce content such as audio, text, code, video, images, and other data. Whereas traditional AI algorithms may be used to identify patterns within a training data set and make predictions, generative AI uses machine learning algorithms to create outputs based on a training data set. As the name suggests, Generative AI means a type of AI technology that can generate new content based on the data it has been trained on. Generative AI can produce a wide range of outputs based on user input or what we call “prompts“. Generative AI is basically a subfield of machine learning that can create new data from a given dataset.

Yes, generative AI can potentially generate biased content if it is trained on biased or unrepresentative datasets. The biases present in the training data can be learned and perpetuated by the generative model, resulting in generated outputs that reflect those biases. It is essential to carefully curate and address biases in the training data to mitigate this issue and promote fairness in generative AI applications. From there, transformer models can contextualize all of this data and effectively focus on the most important parts of the training dataset through that learned context.

• It’s why companies like Salesforce, Microsoft and Google are all scrambling to incorporate generative AI across their products, and why businesses are eager to find ways to fold it into their operations.
• Because tools like ChatGPT and DALL-E were trained on content found on the internet, their capacity for plagiarism has become a big concern.
• That said, manual oversight and scrutiny of generative AI models remains highly important.

It can also personalize content for individual users, increasing user engagement and retention. Virtual assistants can aid in content discovery, scheduling, Yakov Livshits and voice-activated searches. Overall, generative AI is transforming the media industry, providing a more engaging and personalized experience for users.