What is Generative AI VS AI

What is generative AI?

Generative AI refers to artificial intelligence models designed to generate new content in the form of written text, audio, images, or videos. Applications and use cases are diverse.Generative AI can be used to write a short story in the style of a particular author, generate a realistic image of a person who does not exist, compose a symphony in the style of a famous composer, or create a music video from a simple text description.

To better understand the uniqueness of generative AI, there’s nothing better than looking at how it differs from other types of artificial intelligence, programming, and machine learning:

Traditional AI refers to AI systems that can perform specific tasks by following predefined rules or algorithms. These are mostly rule-based systems that cannot learn from data or improve over time. In contrast, generative AI can learn from data and create new data instances.

Machine learning allows a system to learn from data rather than through explicit programming. In other words, theMachine Learning Generative AI is the process by which a computer program can adapt and learn autonomously from new data, leading to the discovery of trends and insights. Generative AI uses machine learning techniques to learn from new data and create data.
Conversational AI allows machines to understand human language and respond to it in a human-like manner. While generative AI andConversational AI While the two may seem similar, especially when generative AI is used to generate text as written by a human, their main difference lies in the purpose. Conversational AI is used to create interactive systems capable of participating in human conversations, while generative AI is broader and encompasses the creation of various types of data, not just text.

Artificial general intelligence refers to highly autonomous systems (currently hypothetical) capable of outperforming humans for a more cost-effective outcome. If realized, artificial general intelligence would be able to understand, learn, adapt, and apply knowledge to a wide range of tasks. While generative AI can be a component of these systems, it is not the same as artificial general intelligence. Generative AI focuses on creating new instances of data, while artificial general intelligence offers a higher level of autonomy and functionality.

How is generative AI different?

Generative AI is capable of creating different forms of data instances, not just text. Generative AI is therefore very useful for designing virtual assistants that generate human responses, for developing video games with dynamic and evolving content, and even for generating synthetic data for training other artificial intelligence models, especially in scenarios where real-world data collection would be complicated or impractical.

Generative AI is already having a significant impact on business applications. It can drive innovation, automate creative tasks, and deliver personalized customer experiences. Many companies are seeing generative AI as a powerful new tool for creating content, solving complex problems, and transforming how customers and workers interact with technology.

How Generative AI Works

Generative AI works on the same principles as machine learning, a branch of artificial intelligence that allows machines to learn from data. However, unlike traditional machine learning models that memorize patterns, make predictions, or make decisions based on those patterns, generative AI goes further: it not only learns from data, but also creates new data instances that mimic the properties of the input data.

Regarding the main generative AI models (detailed below), the general workflow for operating generative AI is as follows:

Data collection: A large dataset is collected, including examples of the type of content to be generated. For example, a set of images to generate realistic images, or a set of text to generate coherent sentences.
Model training: The generative AI model is built using neural networks. The collected dataset is used to train the model to learn the underlying patterns and structures.
Generation: Once the model is trained, it can generate new content by sampling the latent space or using a generative network depending on the model. The generated content is a synthesis of what the model has learned from the training data.
Adjustment: Depending on the task and application, the generated content may be subject to further adjustment or post-processing to improve its quality or meet specific requirements.

The cornerstone of generative AI is Deep Learning , a type of machine learning that is inspired by how the human brain works in processing data and creating patterns for decision-making purposes.Deep Learning modelsrely on complex architectures known as artificial neural networks. These networks consist of multiple interconnected layers that process and transform information, like neurons in the human brain.

Types of Generative AI

Generative AI takes various forms. Each has its own characteristics and lends itself to different applications. These models primarily fall into the following three categories:

So-called “transformer” models: These models, such as GPT-3 and GPT-4, play an important role in text generation. They are based on an architecture that allows them to take into account the entire context of the input text in order to generate highly coherent and contextually appropriate text.
Generative Adversarial Networks (GANs) : GANs consist of two parts: a generator and a discriminator. The generator creates new data instances, while the discriminator evaluates the authenticity of these instances. The two parties engage in a kind of game in which the generator strives to create data that the discriminator cannot differentiate from real data, and the discriminator strives to better identify fabricated data. Over time, the generator becomes increasingly adept at creating hyper-realistic data instances.
Variational autoencoders (VAEs) : VAEs are another type of generative model that leverages the principles of statistical inference. They encode input data into a latent space (a compressed representation of the data) and then decode that latent representation to generate new data. Introducing a random factor into the encoding process allows VAEs to generate diverse, but similar, data instances.

While so-called “transformer” models, VAEs, and GANs are among the most widely used types of generative AI models today, other models exist. Two of these are worth mentioning. First, autoregressive models, which predict future data points based on past data points, and second, “Normalizing Flow” models, which use a series of transformations to model complex data distributions.

Discover what’s new in generative AI

Content creators and business leaders have a wealth of new possibilities at their fingertips. Learn how to use generative AI to create more than just text.

Examples and use cases of generative AI

Examples and use cases for generative AI are multiplying. With its unique ability to create new data instances, generative AI lends itself to a variety of interesting applications in the following sectors:

Arts and Entertainment: Generative AI is being used to create unique works of art, compose music, and even write film scripts. Specialized platforms have been created to use generative algorithms to transform user-submitted images into works of art like famous painters. Other platforms use convolutional neural networks to generate highly complex, dream-like images. Deep learning models are capable of composing music with multiple instruments in a wide range of styles and genres. And with the right prompts, generative AI can generate film scripts, short stories, poems, and virtually any type of literature.

Technology and communications: In the technology and communications sector, generative AI is used to produce responses similar to those of a human being, making thechatbotmore interesting and better able to hold more natural and in-depth conversations. It has also been used to create more interactive and engaging virtual assistants. The model’s ability to generate human-like text makes these virtual assistants more sophisticated and useful than previous virtual assistance technologies.

Design and Architecture: Generative AI is used to generate design options and ideas to assist graphic designers in creating unique designs faster. Generative AI has also been used by architects to create unique and efficient floor plans from training data.

Science and medicine: In life sciences, generative AI is being used to design new drug candidates, reducing discovery phases to days instead of years. In medical imaging, GANs are now being used to generate synthetic MRI images of the brain to train AI. This method is particularly useful in scenarios where data is scarce due to confidentiality concerns.

E-commerce: Businesses are using GANs to create hyper-realistic 3D models for advertising. These AI-generated models can be customized to appeal to target demographics or match a desired aesthetic. Generative algorithms are also used to produce personalized marketing content, helping businesses better connect with their customers.

Challenges of implementing generative AI

The challenges of implementing generative AI encompass a variety of technical and ethical issues that need to be addressed as the technology gains wider adoption. Here, we examine some of the key challenges businesses face today.

Data requirements: Generative AI models require a large volume of relevant, qualitative data for effective training. Collecting such data can be challenging, especially in sectors where data is scarce, sensitive, or protected, such as healthcare or finance. Furthermore, ensuring data diversity and representativeness, which are essential to avoid bias in the generated results, can be challenging. The use of synthetic data—i.e., artificially created data that mimics the characteristics of real data—is emerging as one solution to this challenge. A growing number of niche data companies are specializing in generating synthetic data for AI training while respecting privacy.

Training complexity: Training generative AI models, especially more complex ones such as GANs or so-called “transformer” models, is computer-intensive, time-consuming, and expensive. They require significant resources and expertise, which can be a barrier for smaller companies or those new to artificial intelligence. Distributed training, when the training process is spread across multiple machines or GPUs, can speed up the process. Transfer learning, a technique in which a pre-trained model is adapted to a specific task, can also reduce training complexity and resource requirements.

Controlling results: Controlling the results of generative AI can be challenging. Generative models are prone to generating unwanted or irrelevant content. For example, AI models could create imaginary, incorrect, offensive, or biased text. Addressing this problem involves improving model training with more diverse and representative data. Implementing mechanisms to sort or verify generated content is also a way to ensure its relevance and appropriateness.

Ethical concerns: Generative AI raises several ethical questions regarding the authenticity and integrity of the generated content. Deepfakes created by GANs can be misused to spread misinformation or engage in fraudulent activities. Generative text models can be used to create misleading articles or fake reviews. The development ofstrong ethical guidelineson the use of generative AI is essential. Technologies such as digital watermarking or blockchain can help track and authenticate content generated by artificial intelligence. Raising public awareness of AI can limit the risks of misinformation or fraud.

Regulatory hurdles: There are no clear regulatory guidelines governing the use of generative AI. As artificial intelligence continues to evolve rapidly, laws and regulations are struggling to keep pace, raising concerns and potential legal disputes.

Continued dialogue and collaboration between technologists, legislators, legal experts, and society at large are necessary to achieve comprehensive and effective regulatory frameworks that should promote the responsible use of AI while limiting associated risks.

History of Generative AI

The history of generative AI has been marked by several key developments and milestones. In the 1980s, data scientists sought to move beyond the predefined rules and algorithms of traditional artificial intelligence. They laid the foundations for a generative approach with the development of simple generative models such as naive Bayesian classification.

Later in the 1980s and 1990s, new models were introduced, including the Hopfield network and Boltzmann machines, with the goal of creating neural networks capable of generating new data. However, scaling to larger datasets proved difficult, and problems such as gradient vanishing complicated the training of deep networks.

In 2006, the restricted Boltzmann machine (RBM) solved the vanishing gradient problem and thus made it possible to pretrain layers in a deep neural network. This approach led to the development of deep belief networks, one of the first deep generative models.

In 2014, the generative adversarial network (GAN) emerged and demonstrated impressive capabilities in generating realistic data, particularly images. Around the same time, the variational autoencoder (VAE) emerged, offering a probabilistic approach to autoencoders that supported a more principled framework for data generation.

The end of the 2010s was marked by the rise of so-called “transformer” models, particularly in the field ofnatural language processing (NLP)Models such as generative pre-trained transformers (GPTs) and bidirectional encoder representations from transformers (BERTs) have revolutionized NLP with their ability to understand and generate human-like text.

Today, generative AI is a dynamic field undergoing intense research and suitable for various applications. Technologies continue to evolve, with new models such as GPT-4 and DALL-E pushing the boundaries of AI. There is also a growing desire to make generative AI more controllable and ethically responsible.

The history of generative AI is a testament to the considerable progress made in the field of artificial intelligence over the past few decades. It demonstrates the power of combining solid theoretical foundations with innovative practical applications. Going forward, lessons learned from the past will be used to harness the potential of generative AI effectively and responsibly to shape a future in which artificial intelligence enhances human creativity and productivity in unprecedented ways.

Conclusion

Generative AI, a concept that once seemed like science fiction, is already an integral part of our daily lives. Its emergence within the broader umbrella of artificial intelligence represents a significant leap forward. In addition to traditional AI capabilities—learning from data, making decisions, and automating processes—generative AI adds the power of creation. This innovation opens the door to applications that were previously unimaginable.

For businesses across all sectors, generative AI lays the foundations for a true ” Business AI » able to help them automate processes, improve customer interactions, and drive efficiency in multiple ways. From generating realistic images and animations for the video game industry to creating virtual assistants capable of composing emails or writing code for creating synthetic data for research and training purposes, business AI can improve business performance across all business lines and drive growth for the future.