Predictive AI vs Generative AI: The Differences and Applications

Generative AI vs Predictive AI: Unraveling the Distinctions and Applications

Google’s focus for PaLM 2 was to gain a deeper understanding of mathematics, logic, reasoning and science so you can assume the training data set has been altered accordingly. OpenAI simply claims the GPT-4 has been trained using publicly available data or data that they have licensed. By leveraging Generative AI, organizations can uncover hidden patterns, generate synthetic data for testing, and increase the overall accuracy & robustness of their predictive models.

AI refers to the development of models and applications that can perform tasks that simulate human intelligence with computer systems. In this article, we’ve discussed the key aspects of generative machine learning models, particularly their capacity to differentiate between various data types and to create new data that closely resembles existing data. In computer vision, GANs have been used for image synthesis, super-resolution, and image-to-image translation tasks. They have also been employed in generating realistic deepfake videos, where the faces of individuals are swapped in video footage, raising ethical concerns.

Generative tools like ChatGPT can help create compelling and informative product descriptions that resonate with your target audience. By analyzing this data, generative AI tools can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development. From designing syllabi and assessments to personalizing course material based on students’ individual needs, generative AI can help make teaching more efficient and effective. Furthermore, when combined with virtual reality technology, it can also create realistic simulations that will further engage learners in the process.

Generating video ads or product demos

Earlier, generative models had more challenging tasks like trying to produce textual information that responds to questions accurately and learning to produce photorealistic images. For years, generative models had the more difficult tasks, such as trying to learn to generate Yakov Livshits photorealistic images or create textual information that answers questions accurately, and progress moved slowly. Predictive AI systems can read documents, control temperature, analyze weather patterns, evaluate medical images, assess property damage, and more.

Predictive AI, though less flashy, remains crucial for solving real-world challenges and unleashing AI’s true potential. By merging the powers of both AI types and closing the prototype-to-production gap, we’ll accelerate the AI revolution and transform our world. These solutions can accelerate the agility and effectiveness of their business processes.

Discriminative vs generative modeling

Automation powered tools such as AutoGPT (an AI background agent) will continue to evolve allowing AI applications to generate their own prompts to execute very complex tasks. The LLMs of today are all trained on a variety on input data but the open internet is a critical source for almost all of them. We all know the internet is filled with plenty of misinformation and biased data, so it’s critical that any of the answers you get form AI chat interfaces be validated and/or interrogated.

VAEs work by training an encoder network that maps the input data to a latent space and a decoder network that reconstructs the input data from the latent space. By sampling points from the learned distribution in the latent space, VAEs can generate new data samples that resemble the training data. The ability of VAEs to generate novel samples and traverse the latent space allows for creative exploration and synthesis of 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.

Training generative models require substantial computational resources and large datasets, making it resource-intensive. Moreover, ensuring the generated content aligns with ethical and social considerations can be complex. In marketing, content is king—and generative AI is making it easier than ever to quickly create large amounts of it. A number of companies, agencies, and creators are already turning to generative AI tools to create images for social posts or write captions, product descriptions, blog posts, email subject lines, and more. Generative AI can also help companies personalize ad experiences by creating custom, engaging content for individuals at speed. Writers, marketers, and creators can leverage tools like Jasper to generate copy, Surfer SEO to optimize organic search, or albert.ai to personalize digital advertising content.

Generative AI for Healthcare – C3 AI

Generative AI for Healthcare.

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Many pharmaceutical companies like Alter Pharma and Bayer are using predictive AI solutions. By understanding the customer service operations and functions of ChatGPT, many CX leaders Yakov Livshits are looking to stay ahead of the competition. It finds applications in healthcare, assisting in tasks such as medical image analysis, drug discovery, and treatment planning.


The more neural networks intrude on our lives, the more the areas of discriminative and generative modeling grow. 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, dangerous diseases like cancer can be diagnosed in their initial stage due to a better quality of images. The interesting thing is, it isn’t a painting drawn by some famous artist, nor is it a photo taken by a satellite. The image you see has been generated with the help of Midjourney — a proprietary artificial intelligence program that creates pictures from textual descriptions.

generative ai vs predictive ai

Its algorithms are meant to create new content based on the increasingly large data sets it is trained upon, weaving something new from the threads it uncovers. On the other hand, traditional AI continues to excel in task-specific applications. It powers our chatbots, recommendation systems, predictive analytics, and much more.

> Customer Service Applications

It is a broad field that includes many different techniques and applications, including machine learning, natural language processing, robotics, and computer vision. Generative AI is a technology that has the potential to revolutionize the way businesses operate and work. It enables machines to learn from data and create new content without human intervention, thus significantly reducing development time and cost. Generative AI collects various types of relevant data—including text, images, audio files, and videos—and then analyzes it all to identify patterns or trends within this dataset. Generative AI models, such as Generative Adversarial Networks (GANs) and autoregressive models, work by learning the statistical patterns present in a dataset. GANs consist of a generator and a discriminator that compete against each other to create authentic-looking content.

  • These algorithms can analyze vast amounts of data from sensors and cameras to make real-time driving decisions, such as braking, accelerating, and changing lanes.
  • It is a broad field that includes many different techniques and applications, including machine learning, natural language processing, robotics, and computer vision.
  • With its ability to quickly generate personalized experiences based on user input, Generative AI can help companies increase customer loyalty by providing unique and customized solutions.
  • Even lower-stakes predictive AI models, such as email filtering, need to meet high-performance thresholds.
  • This will lead to a rise in demand for “Prompt Engineers” roles in the future as businesses adopt and accept AI tools.

Generative AI can analyze historical sales data and generate forecasts for future sales. So, sales teams can optimize their sales pipeline and allocate resources more effectively. Generative AI can be used to generate contracts based on pre-defined templates and criteria. This can save time and effort for procurement departments and help to ensure consistency and accuracy in contract language.

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For example, a generative AI model trained on a dataset of paintings can create new artwork that resembles the style of famous artists. Machine learning is a subfield of AI that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. The applications of machine learning are wide-ranging and include image recognition, natural language processing, predictive maintenance, fraud detection, and personalized marketing. 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.