Generative AI and Predictive AI - a quick summary of new trends

Introduction

Generative AI and Predictive AI are two different types of artificial intelligence techniques with distinct functionalities.

Generative AI is a type of AI that is capable of creating new content, such as images, music, and text. It uses complex algorithms and deep learning techniques to generate new content that is like the training data it has been fed.

Predictive AI, on the other hand, uses statistical algorithms and machine learning to analyze data and make predictions about future events or behaviors. It learns from historical data to identify patterns and make predictions about future outcomes.

The main difference between predictive AI and generative AI is that predictive AI is used to make predictions based on historical data, while generative AI is used to create new content or data based on existing patterns and trends.

Predictive models infer information about different data points so that they can make decisions.?Is this an image of a dog or a cat? Is this tumor benign or malignant??A human supervises the model’s training, telling it whether its outputs are correct. Based on the training data it encounters, the model learns to respond to different scenarios in different ways.

Generative models produce new data points based on what they learn from their training data. These models typically train in an unsupervised manner, analyzing the data without human input and drawing their own conclusions.

For years, generative models had the more difficult tasks, such as trying to learn to generate photorealistic images or create textual information that answers questions accurately, and progress moved slowly.?

Then, an increase in the availability of compute power enabled machine learning (ML) teams to build foundation models: Massive unsupervised models that train vast amounts of data (sometimes all the data available on the internet). Over the past couple of years, ML engineers have calibrated these generative foundation models — feeding them subsets of annotated data to target outputs for specific objectives — so that they can be used for practical applications.?

ChatGPT-3/4 is a good example. It’s a version of Chat GPT, a foundation model that’s trained on vast amounts of unlabeled data. To create ChatGPT,?OpenAI?hired thousands of annotators to label an appropriate subset of data, and its ML engineers then used that data to fine tune the model to teach it to generate specific information.? With these sorts of fine-tuning methods, generative models have begun to create outputs of which they were previously incapable, and the result has been a swift proliferation of functional generative models.

Advancements around large language models and generative AI?in healthcare are ramping up quickly, and it's challenging to keep up with the evolving news. The company - Insilico - for example, has developed AI platforms that utilize deep generative models, reinforcement learning, transformers, and other modern machine learning techniques for novel target discovery and the generation of novel molecular structures with desired properties.?There are numerous such examples across the entire healthcare and clinical research spectrum.

So, what are the new trends in Generative AI?

Generative AI is revolutionizing many industries and is bringing several new digital trends. Here are some of the key digital trends brought by generative AI:

Personalization

Generative AI can create personalized content, products, and services based on user preferences and behaviors. This trend is especially evident in e-commerce, where AI can create custom recommendations and product designs based on individual shopping patterns.

Automation

Generative AI automates repetitive and time-consuming tasks, freeing up human workers to focus on more complex and creative tasks. This trend is already visible in industries such as manufacturing, where AI-powered robots are being used to automate assembly line processes.

Creativity

Generative AI assists with creative tasks such as art, music, and writing. This trend is evident in fields such as graphic design, where AI can create custom logos and designs based on user preferences.

Prediction?

Generative AI can analyze large datasets to make predictions about future trends and behaviors. This trend is especially useful in fields such as finance and marketing, where AI can analyze market trends and consumer behavior to make data-driven decisions.

Human-AI Collaboration

Generative AI can collaborate with humans to create new and innovative products and services. This trend is evident in industries such as medicine, where AI can assist with diagnosis and treatment planning.

Interactive experiences

Generative AI creates interactive experiences that engage users in new ways. This trend is evident in applications such as virtual and augmented reality, where AI can generate realistic and immersive environments.

Data Augmentation

Machine learning models require large amounts of high-quality data to perform accurately, which can be difficult to obtain. However, generative AI can be used to create synthetic data that can be used to augment existing datasets, allowing for more robust and accurate models to be trained. For example,?this can help to improve the accuracy of machine learning models in areas such as healthcare, finance, and transportation, leading to better predictions and outcomes.

Product Design

Generative AI can be used to create new and innovative designs based on existing patterns and trends, allowing companies to quickly develop new products and services. This can lead to more efficient and effective product development processes. For example,?generative AI can automatically design neural networks for machine learning tasks, reducing the time and cost associated with building and training neural networks. This can help companies to develop and deploy new products more quickly, leading to increased innovation and competitiveness.?

What are the new trends in Predictive AI ?

Predictive AI can be used in a wide range of applications, including financial forecasting, fraud detection, healthcare, and marketing. It is also used in recommendation systems, which provide personalized recommendations based on a user's past behavior and preferences.

Improved Decision-Making

Predictive AI uses statistical algorithms and machine learning to analyze data and make predictions about future events or behaviors. It can help organizations make data-driven decisions by providing insights into future trends and behaviors. For example, retailers can use predictive AI to predict which products are likely to sell the most, adjust their inventory accordingly, and optimize their supply chain processes.

Personalized Experiences

Predictive AI can be used to create personalized experiences for customers. For instance, it can help, e-commerce companies, recommend products based on the customer's past purchases, and preferences, and streaming services suggest movies or TV shows based on the user's viewing history.

Fraud Detection

Predictive AI can be used to identify potential fraudsters by analyzing data and identifying patterns that are indicative of fraudulent behavior. This can help financial institutions prevent fraudulent activities and save millions of dollars in losses.

Healthcare

Predictive AI can help healthcare in predicting disease outbreaks, identifying patients who are at high risk of developing certain diseases, and personalizing treatment plans based on patient data. predictive AI?can be used to predict which patients are likely to be readmitted to the hospital, allowing healthcare ?providers to intervene early and prevent readmissions.

Marketing

Predictive AI can help marketers identify the best channels and messages to reach their target audience. By analyzing customer behavior and preference data, predictive AI can predict which customers are most likely to make a purchase and personalize marketing messages accordingly.

Looking Ahead

As we continue to explore the possibilities of Generative AI and Predictive AI, it’s clear that these techniques and technologies have the potential to transform the way we live and work. From creating new forms of art to improving healthcare outcomes through AI-enabled drug discovery, Generative AI and Predictive AI are opening up new frontiers of innovation and discovery.

Lastly, If a task feels routine or laborious today, it will likely be a target of algorithmic advancement tomorrow. Mimicking human intelligence and performance, however, requires having one system that is both predictive and generative, and that system will need to perform both of these functions at high levels of accuracy with full responsibility (Responsible AI) for AI-transition to be truly pervasive, effective and successful at scale.