Predictive AI and generative AI: the simple and handy guide
It is been just over a year since the launch of ChatGPT and the boom in generative AI: how does it differ from predictive AI?
Predictive AI and generative AI: the simple and handy guide
It is been just over a year since the launch of ChatGPT and the boom in generative AI: how does it differ from predictive AI?
AI: between predictive AI and generative AI
Artificial Intelligence is nothing more than a machine’s ability to solve problems, activities that we know are associated only with humans. In summary, the computer receives data, for example, collected through smart video sensors; processes it, and responds.
In detail, Artificial Intelligence enables systems to understand the environment, relate to what they perceive, solve problems, and act toward a specific goal. AI systems therefore adapt their behavior by analyzing the effects of previous actions and working autonomously and dynamically. One example we have already discussed is continuous machine learning.
This definition of AI is not entirely complete. Indeed, it must be remembered that before 2014, the most common AI applications were based on predictive approaches to decision-making. Only since the introduction of GANs Artificial Intelligence has gained the ability to generate data and creativity.
Recently, however, we have all witnessed the boom of this generative AI.
Just over a year has passed since November 3, 2022, the day ChatGPT, the famous AI chatbot, was launched on the market. This date undoubtedly marked the beginning of a new era of Artificial Intelligence, which can create varied and articulate content.
There is a lot of misinformation around AI, and since generative art tools have been introduced, skepticism and aversion to AI have increased.
One year later, do we know predictive and generative AI correctly? Do we know what their characteristics are? This article clarifies these two concepts once and for all and can be used as a reminder to consult at all times.
From the birth to the boom of Generative AI
Generative and predictive Artificial Intelligence are two distinct worlds of AI. We can perfectly compare them to the hemispheres of the brain. The right hemisphere, that is, the artistic hemisphere, is undoubtedly generative AI. Let us start by analyzing the latter.
Generative AI is designed to generate content based on user input from unstructured data. It enables the creation of new and original content, such as images, text, and other media.
We have been hearing a lot about it in last years. Did you know, however, that this technology is not that recent?
Generative AI was introduced in the 1960s in chatbots. The generative networks behind it had limitations due to a lack of computing power and small data sets. It was not until 2014 with the advent of big data and improvements in computer hardware that neural networks became more performant and GANs were born. Since that time, generative AI has been able to create even more lifelike images, videos, and audio as if humans made them.
Over time, there have been advances in the field of AI that have led to the development of new approaches. We are talking about transformers, large language models (LLMs), and diffusion models. Let us get to know them better.
Transformers can be trained on billions of pages of text and draw connections between words across pages, chapters, and books.
LLMs can create images, write code, create text message responses but also funny sitcoms.
Finally, diffusion models can generate images and videos with higher fidelity from the random noise of input data.
How has generative AI exploded since the release of ChatGPT?
The boom in generative AI has occurred because of the simplicity of the new interfaces for creating quality text, graphics, and video in very few seconds.
ChatGPT, Bard, and Dall-E are some of the popular generative Artificial Intelligence interfaces.
As we all know by now, the first two are AI-based chatbots that mimic human conversation and provide relevant answers to users’ questions. Dall-E, on the other hand, is an AI application that generates images in different styles from user prompts.
It is clear that generative AI has opened up many opportunities in the art world, in the fields of communication, marketing, and beyond. At the same, it has also unlocked concerns about deepfakes, digitally doctored images or videos.
Generative AI tools are still in their infancy, but they have already demonstrated the ability to create readable text and realistic graphics. Generative AI will continue to evolve, radically changing the way companies in a wide variety of fields operate.
Predictive AI: its main features and its union with generative AI
Taking up the structure of the brain, we now delve into the justify hemisphere, the analytic one, which ideally represents predictive AI.
Predictive AI makes predictions of results of events or behaviors using patterns present in historical data. So, by providing useful information it helps in business decision making and strategy formulation, for example.
It is specified that predictive analytics should not be confused with descriptive or prescriptive analytics. The former, in fact, is concerned with anticipating and predicting certain business events. Prescriptive analysis, on the other hand, is about understanding why an event is likely to occur and determining what effective solutions a company can implement.
Predictive AI uses different machine learning (ML) and deep learning (DL) algorithms to perform various tasks:
- Classification: At its core, classification involves categorizing input data into predefined classes or labels. For instance, image classification can identify whether an image contains a cat or a dog. It’s like teaching AI to recognize patterns and assign them to specific categories. It is one of the main tasks of computer vision.
- Pose Estimation: it involves determining the spatial positioning of objects in an environment. In the context of computer vision, this often refers to understanding the position and orientation of human bodies or specific joints. It is particularly useful in applications like gesture recognition, sports analysis, and virtual reality.
- Anomaly Detection: Anomaly detection involves identifying patterns that do not conform to expected behavior. In cybersecurity, for instance, AI can learn to recognize unusual patterns in network traffic that may indicate a cyber attack. It is also applied in industries like manufacturing to detect faulty products in a production line.
- Recommendation Systems: they predict what users might like based on their past behavior and preferences. This is commonly seen in online platforms like Netflix, where the AI suggests movies based on a user’s watch history. This is a powerful tool for personalizing user experiences.
- Sentiment Analysis: the latter concerns the determination of the emotional tone behind a piece of text. This can be used to analyze social media comments or any text data to understand whether the sentiment is positive, negative, or neutral. Businesses often use this to gauge customer satisfaction.
As we can see from all these approaches, predictive Artificial Intelligence offers many advantageous solutions to have interesting data analysis and insights for future scenarios in a short time.
Of course, predictive AI is based on current and past information collected within the company. Without this data, it is obviously impossible to model useful and effective predictions.
At the end of it all, we can say that generative and predictive AI are like two sides of the same coin.
Generative AI is like a painter creating new works of art, while predictive AI is like a scientist making predictions. When combined, these two technologies can create something extraordinary.
For example, a company could use predictive Artificial Intelligence to predict the demand for these products or services and adjust its production accordingly. Then, they could use generative AI to create new products or services that meet customer needs in innovative ways. Thus, the company could increase its chances of success and have a positive impact on the world.
By mixing these two AIs, a powerful tool for transformation is born. What new possibilities does the future of generative and predictive AI hold for us?
© Copyright 2012 – 2023 | All Rights Reserved
Author: Giovanni Trovini, Chief Technology Officer
AI: between predictive AI and generative AI
Artificial Intelligence is nothing more than a machine’s ability to solve problems, activities that we know are associated only with humans. In summary, the computer receives data, for example, collected through smart video sensors; processes it, and responds.
In detail, Artificial Intelligence enables systems to understand the environment, relate to what they perceive, solve problems, and act toward a specific goal. AI systems therefore adapt their behavior by analyzing the effects of previous actions and working autonomously and dynamically. One example we have already discussed is continuous machine learning.
This definition of AI is not entirely complete. Indeed, it must be remembered that before 2014, the most common AI applications were based on predictive approaches to decision-making. Only since the introduction of GANs Artificial Intelligence has gained the ability to generate data and creativity.
Recently, however, we have all witnessed the boom of this generative AI.
Just over a year has passed since November 3, 2022, the day ChatGPT, the famous AI chatbot, was launched on the market. This date undoubtedly marked the beginning of a new era of Artificial Intelligence, which can create varied and articulate content.
There is a lot of misinformation around AI, and since generative art tools have been introduced, skepticism and aversion to AI have increased.
One year later, do we know predictive and generative AI correctly? Do we know what their characteristics are? This article clarifies these two concepts once and for all and can be used as a reminder to consult at all times.
From the birth to the boom of Generative AI
Generative and predictive Artificial Intelligence are two distinct worlds of AI. We can perfectly compare them to the hemispheres of the brain. The right hemisphere, that is, the artistic hemisphere, is undoubtedly generative AI. Let us start by analyzing the latter.
Generative AI is designed to generate content based on user input from unstructured data. It enables the creation of new and original content, such as images, text, and other media.
We have been hearing a lot about it in last years. Did you know, however, that this technology is not that recent?
Generative AI was introduced in the 1960s in chatbots. The generative networks behind it had limitations due to a lack of computing power and small data sets. It was not until 2014 with the advent of big data and improvements in computer hardware that neural networks became more performant and GANs were born. Since that time, generative AI has been able to create even more lifelike images, videos, and audio as if humans made them.
Over time, there have been advances in the field of AI that have led to the development of new approaches. We are talking about transformers, large language models (LLMs), and diffusion models. Let us get to know them better.
Transformers can be trained on billions of pages of text and draw connections between words across pages, chapters, and books.
LLMs can create images, write code, create text message responses but also funny sitcoms.
Finally, diffusion models can generate images and videos with higher fidelity from the random noise of input data.
How has generative AI exploded since the release of ChatGPT?
The boom in generative AI has occurred because of the simplicity of the new interfaces for creating quality text, graphics, and video in very few seconds.
ChatGPT, Bard, and Dall-E are some of the popular generative Artificial Intelligence interfaces.
As we all know by now, the first two are AI-based chatbots that mimic human conversation and provide relevant answers to users’ questions. Dall-E, on the other hand, is an AI application that generates images in different styles from user prompts.
It is clear that generative AI has opened up many opportunities in the art world, in the fields of communication, marketing, and beyond. At the same, it has also unlocked concerns about deepfakes, digitally doctored images or videos.
Generative AI tools are still in their infancy, but they have already demonstrated the ability to create readable text and realistic graphics. Generative AI will continue to evolve, radically changing the way companies in a wide variety of fields operate.
Predictive AI: its main features and its union with generative AI
Taking up the structure of the brain, we now delve into the justify hemisphere, the analytic one, which ideally represents predictive AI.
Predictive AI makes predictions of results of events or behaviors using patterns present in historical data. So, by providing useful information it helps in business decision making and strategy formulation, for example.
It is specified that predictive analytics should not be confused with descriptive or prescriptive analytics. The former, in fact, is concerned with anticipating and predicting certain business events. Prescriptive analysis, on the other hand, is about understanding why an event is likely to occur and determining what effective solutions a company can implement.
Predictive AI uses different machine learning (ML) and deep learning (DL) algorithms to perform various tasks:
- Classification: At its core, classification involves categorizing input data into predefined classes or labels. For instance, image classification can identify whether an image contains a cat or a dog. It’s like teaching AI to recognize patterns and assign them to specific categories. It is one of the main tasks of computer vision.
- Pose Estimation: it involves determining the spatial positioning of objects in an environment. In the context of computer vision, this often refers to understanding the position and orientation of human bodies or specific joints. It is particularly useful in applications like gesture recognition, sports analysis, and virtual reality.
- Anomaly Detection: Anomaly detection involves identifying patterns that do not conform to expected behavior. In cybersecurity, for instance, AI can learn to recognize unusual patterns in network traffic that may indicate a cyber attack. It is also applied in industries like manufacturing to detect faulty products in a production line.
- Recommendation Systems: they predict what users might like based on their past behavior and preferences. This is commonly seen in online platforms like Netflix, where the AI suggests movies based on a user’s watch history. This is a powerful tool for personalizing user experiences.
- Sentiment Analysis: the latter concerns the determination of the emotional tone behind a piece of text. This can be used to analyze social media comments or any text data to understand whether the sentiment is positive, negative, or neutral. Businesses often use this to gauge customer satisfaction.
As we can see from all these approaches, predictive Artificial Intelligence offers many advantageous solutions to have interesting data analysis and insights for future scenarios in a short time.
Of course, predictive AI is based on current and past information collected within the company. Without this data, it is obviously impossible to model useful and effective predictions.
At the end of it all, we can say that generative and predictive AI are like two sides of the same coin.
Generative AI is like a painter creating new works of art, while predictive AI is like a scientist making predictions. When combined, these two technologies can create something extraordinary.
For example, a company could use predictive Artificial Intelligence to predict the demand for these products or services and adjust its production accordingly. Then, they could use generative AI to create new products or services that meet customer needs in innovative ways. Thus, the company could increase its chances of success and have a positive impact on the world.
By mixing these two AIs, a powerful tool for transformation is born. What new possibilities does the future of generative and predictive AI hold for us?
© Copyright 2012 – 2023 | All Rights Reserved
Author: Giovanni Trovini, Chief Technology Officer