Generative AI

Predictive AI vs Generative AI: Key Differences and Applications

What is generative AI? Artificial intelligence that creates

The dominant style of generative AI is based on the neural network, which is an estimation of how we think brain works. Generative AI takes data from a training set and then generates new data based on the patterns and characteristics of the training set. That being said, generative AI as we understand it now is much more complicated than what it was half a century ago. Raw images can be transformed into visual elements, too, also expressed Yakov Livshits as vectors. LaMDA (Language Model for Dialogue Applications) is a family of conversational neural language models built on Google Transformer — an open-source neural network architecture for natural language understanding. First described in a 2017 paper from Google, transformers are powerful deep neural networks that learn context and therefore meaning by tracking relationships in sequential data like the words in this sentence.

generative ai vs. ai

Join us as we embark on an illuminating journey, uncovering the frontiers of intelligent machines and envisioning the future where human and machine intelligence converge. While each technology has its own application and function, they are not mutually exclusive. Consider an application such as ChatGPT — this application is conversational AI because it is a chatbot and is generative AI due to its content creation.

For more on conversational AI and generative AI

DLSS samples multiple lower-resolution images and uses motion data and feedback from prior frames to reconstruct native-quality images. 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. So, the adversarial nature of GANs lies in a game theoretic scenario in which the generator network must compete against the adversary.

I see you are also a Project Manager, like me, so I would much like to read more about your vision in how AI can help the business professionals (that do not want a code opt) and what would be the risks of it. Probably explains the disclaimer you get on the front page as you load ChatGPT “Limited knowledge of world and events after 2021”. Anyhow, this article is about the different approaches to artificial intelligence.

What Can ChatGPT Be Used For?

This allows for using algorithms specifically designed to work with images like CNNs for our audio-related task. 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.

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.

  • These possible AI deployments might be better decision making, removing the tedium from repetitive tasks, or spotting anomalies and issuing alerts for cybersecurity.
  • Artificial Intelligence (AI) refers to the ability of machines to perform tasks that typically require human intelligence, such as understanding natural language, recognizing images, and making decisions.
  • These tools enable businesses to reap AI and ML benefits to supercharge their business performance.
  • It uses a conversational chat interface to interact with users and fine-tune outputs.

Designed to mimic how the human brain works, neural networks “learn” the rules from finding patterns in existing data sets. Developed in the 1950s and 1960s, the first neural networks were limited by a lack of computational power and small data sets. It was not until the advent of big data in the mid-2000s and improvements in computer hardware that neural networks became practical for generating content. Generative AI models combine various AI algorithms to represent and process content. Similarly, images are transformed into various visual elements, also expressed as vectors.

Its adversary, the discriminator network, makes attempts to distinguish between samples drawn from the training data and samples drawn from the generator. Because since generative AI is trained on Yakov Livshits data produced by people, it also inadvertently discovers patterns in human behavior that aren’t necessarily great. Compared to other GAN-based tools, MidJourney produces a unique style of art.

VAEs leverage two networks to interpret and generate data — in this case, it’s an encoder and a decoder. The encoder takes the input data and compresses it into a simplified format. The decoder then takes this compressed information and reconstructs it into something new that resembles the original data, but isn’t entirely the same. In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of 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. 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).

How will generative AI impact the future of work?

Ian Goodfellow demonstrated generative adversarial networks for generating realistic-looking and -sounding people in 2014. The Eliza chatbot created by Joseph Weizenbaum in the 1960s was one of the earliest examples of generative AI. These early implementations used a rules-based approach that broke easily due to a limited vocabulary, lack of context and overreliance on patterns, among other shortcomings. 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.

A generative adversarial network or GAN is a machine learning algorithm that puts the two neural networks — generator and discriminator — against each other, hence the “adversarial” part. The contest between two neural networks takes the form of a zero-sum game, where one agent’s gain is another agent’s loss. Popular generative AI tools like ChatGPT, DALL-E, and MidJourney have various professional use cases, including customer service, content creation, market research, and more. These tools automate tasks, improve accuracy, enable personalization, foster innovation, and offer scalability, thereby providing businesses with increased efficiency, competitive advantage, and cost savings. The term “ML” focuses on machines learning from data without the need for explicit programming.

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