Generative AI vs Traditional AI Explained

Taking the same example from earlier, we could group pictures of pizzas, burgers and tacos into their respective categories based on the similarities or differences identified in the images. A deep-learning model requires more data points to improve accuracy, whereas a machine-learning model relies on less data given its underlying data structure. Enterprises generally use deep learning for more complex tasks, like virtual assistants or fraud detection. Classic or “non-deep” machine learning depends on human intervention to allow a computer system to identify patterns, learn, perform specific tasks and provide accurate results.

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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. 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). Popular examples of generative AI include ChatGPT, Bard, DALL-E, Midjourney, and DeepMind.

Improved Decision-Making

Generative AI is a branch of artificial intelligence centered around computer models capable of generating original content. By leveraging the power of large language models, neural networks, and machine learning, generative AI is able to produce novel content that mimics human creativity. These models are trained using large datasets and deep-learning algorithms that learn the underlying structures, relationships, and patterns present in the data. The results are new and unique outputs based on input prompts, including images, video, code, music, design, translation, question answering, and text. Generative AI works by using machine learning algorithms to analyze existing data and generate new outputs based on that data.

generative ai vs ai

For example, a generative AI algorithm trained on a dataset of cat images can generate entirely new and realistic images of cats. For instance, a machine learning algorithm can be trained on a dataset containing images of cats and dogs, enabling it to identify cats and dogs in new images. Unlike predictive AI, Generative AI is generally used to create Yakov Livshits new content, including audio, code, images, text, simulations, and videos. This transforms the given input data into newly generated data through a process involving both encoding and decoding. The encoder transforms input data into a lower-dimensional latent space representation, while the decoder reconstructs the original data from the latent space.

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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 Yakov Livshits infringe on copyrights or if there is problem with the original sources from which they draw results. If you don’t know how the AI came to a conclusion, you cannot reason about why it might be wrong.

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.

In some cases, AI systems can be programmed to automatically take remediation steps following a breach. Artificial intelligence is a technology used to approximate – often to transcend – human intelligence  and ingenuity through the use of software and systems. Computers using AI are programmed to carry out highly complex tasks and analyze vast amounts of data in a very short time. An AI system can sift through historical data to detect patterns, improve the decision-making process, eliminate manually intensive task and heighten business outcomes.

Software and Hardware

Essentially, generative AI takes a set of inputs and produces new, original outputs based on those inputs. When it comes to generative AI vs. machine learning, think of AI as an umbrella term for all types of AI, including generative AI. Similarly to how there are many types of AI, there are also plenty of machine learning models, such as transformer models, diffusion models, or generative adversarial networks (GANs). GANs are made up of two neural networks known as a generator and a discriminator, which essentially work against each other to create authentic-looking data. As the name implies, the generator’s role is to generate convincing output such as an image based on a prompt, while the discriminator works to evaluate the authenticity of said image.

generative ai vs ai

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. While conversational AI is a specific application of generative AI, generative AI encompasses a broader set of tasks beyond conversations such as writing code, drafting articles or creating images. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content.

What is a neural network?

Like many fundamentally transformative technologies that have come before it, generative AI has the potential to impact every aspect of our lives. As technology advances, increasingly sophisticated generative AI models are targeting various global concerns. AI has the potential to rapidly accelerate research for drug discovery and development by generating and testing molecule solutions, speeding up the R&D process. Pfizer used AI to run vaccine trials during the coronavirus pandemic1, for example. Notably, some AI-enabled robots are already at work assisting ocean-cleaning efforts.

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