What is generative AI?
Generative AI is a form of artificial intelligence that can produce text, images, and varied content based on the data it is trained on.
Generative AI explained
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 far and wide. Generative AI can be used to create a short story based on the style of a particular author, generate a realistic image of a person who doesn't exist, compose a symphony in the style of a famous composer, or create a video clip from a simple textual description.
To better understand the uniqueness of generative AI, it is helpful to understand how it differs from other types of AI, programming, and machine learning:
Traditional AI refers to AI systems that can perform specific tasks by following predetermined rules or algorithms. They are primarily rule-based systems that can't learn from data or improve over time. Generative AI, on the other hand, can learn from data and generate new data instances.
Machine learning enables a system to learn from data rather than through explicit programming. In other words, machine learning is the process where a computer program can adapt to and learn from new data independently, resulting in the discovery of trends and insights. Generative AI makes use of machine learning techniques to learn from and create new data.
Conversational AI enables machines to understand and respond to human language in a human-like manner. While generative AI and conversational AI may seem similar – particularly when generative AI is used to generate human-like text – their primary difference lies in their purpose. Conversational AI is used to create interactive systems that can engage in human-like dialogue, whereas generative AI is broader, encompassing the creation of various data types, not just text.
Artificial general intelligence (AGI), refers to highly autonomous systems – currently hypothetical – that can outperform humans at most economically valuable work. If realized, AGI would be able to understand, learn, adapt, and implement knowledge across a wide range of tasks. While generative AI can be a component of such systems, it's not equivalent to AGI. Generative AI focuses on creating new data instances, whereas AGI denotes a broader level of autonomy and capability.
What sets generative AI apart?
Generative AI has the ability to generate new data instances in various types, not just text. This makes generative AI useful for designing virtual assistants that generate human-like responses, developing video games with dynamic and evolving content, and even generating synthetic data for training other AI models, especially in scenarios where collecting real-world data might be challenging or impractical.
Generative AI is already having a profound impact on business applications. It can drive innovation, automate creative tasks, and provide personalized customer experiences. Many businesses see generative AI as a powerful new tool for creating content, solving complex problems, and transforming the way customers and workers interact with technology.
How Generative AI works
Generative AI works on the principles of machine learning, a branch of artificial intelligence that enables machines to learn from data. However, unlike traditional machine learning models that learn patterns and make predictions or decisions based on those patterns, generative AI takes a step further — it not only learns from data but also creates new data instances that mimic the properties of the input data.
Across the major generative AI models – discussed in more detail below – the general workflow for putting generative AI to work is as follows:
Data collection: A large dataset containing examples of the type of content to be generated is collected. For example, a dataset of images for generating realistic pictures, or a dataset of text for generating coherent sentences.
Model training: The generative AI model is constructed using neural networks. The model is trained on the collected dataset to learn the underlying patterns and structures in the data.
Generation: Once the model is trained, it can generate new content by sampling from the latent space or through a generator network depending on the model used. The generated content is a synthesis of what the model has learned from the training data.
Refinement: Depending on the task and application, the generated content may undergo further refinement or post-processing to improve its quality or to meet specific requirements.
The cornerstone of generative AI is deep learning, a type of machine learning that imitates the workings of the human brain in processing data and creating patterns for decision-making. Deep learning models use complex architectures known as artificial neural networks. Such networks comprise numerous interconnected layers that process and transfer information, mimicking neurons in the human brain.
Types of Generative AI
Types of generative AI are diverse, each with unique characteristics and suitable for different applications. These models primarily fall into the following three categories:
- Transformer-based models: For text generation, transformer-based models such as GPT-3 and GPT-4 have been instrumental. They use an architecture that allows them to consider the entire context of the input text, enabling them 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 these instances for authenticity. Essentially, the two parts engage in a game, with the generator striving to create data that the discriminator can't distinguish from the real data, and the discriminator trying to get better at spotting the fake data. Over time, the generator becomes skilled at creating highly realistic data instances.
- Variational autoencoders (VAEs): VAEs represent another type of generative model that leverages the principles of statistical inference. They work by encoding input data into a latent space (a compressed representation of the data) and then decoding this latent representation to generate new data. The introduction of a randomness factor in the encoding process allows VAEs to generate diverse yet similar data instances.
While transformer-based models, VAEs, and GANs represent some of the most common types of generative AI models currently being used, other models exist as well. Two worthy of consideration include autoregressive models, which predict future data points based on previous ones and normalizing flow models, which use a series of transformations to model complex data distributions
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Examples and use cases of generative AI
Examples and use cases of generative AI are growing in number. With its unique ability to create new data instances, generative AI is leading to diverse and interesting applications across the following sectors:
Arts and entertainment: Generative AI has been used to create unique pieces of art, compose music, and even generate scripts for movies. Specialized platforms have been created that use generative algorithms to turn user-submitted images into art pieces in the style of famous painters. Other platforms use convolutional neural networks to generate dream-like, highly intricate images. Deep learning models can generate musical compositions with multiple instruments, spanning a wide range of styles and genres. And with the proper prompts, generative AI can be used to generate films scripts, novels, poems, and virtually any kind of literature imaginable.
Technology and communications: In the realm of technology and communication, generative AI is used to produce human-like text responses, making the chatbot more engaging and capable of maintaining more natural and extended 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 much more sophisticated and helpful than previous generations of virtual assistant technology.
Design and architecture: Generative AI is being used to generate design options and ideas to assist graphic designers in creating unique designs in less time. Generative AI has also been used by architects to generate unique and efficient floor plans based on relevant training data.
Science and medicine: In life sciences, generative AI is being used to design novel drug candidates, cutting the discovery phases to a matter of days instead of years. For medical imaging, GANs are now being used to generate synthetic brain MRI images for training AI. This is particularly useful in scenarios where data is scarce due to privacy concerns.
E-commerce: Companies are using GANs to create hyper-realistic 3D models for advertising. These AI-generated models can be customized to fit the desired demographic and aesthetic. Generative algorithms are also being used to produce personalized marketing content, helping businesses communicate more effectively with their customers.
Challenges of implementing Generative AI
Challenges in implementing generative AI span a range of technical and ethical concerns that need to be addressed as the technology becomes more widely adopted. Here, we explore some of the primary challenges organizations face today.
Data requirements: Generative AI models require a significant amount of high-quality, relevant data to train effectively. Acquiring such data can be challenging, particularly in domains where data is scarce, sensitive, or protected such as in healthcare or finance. Additionally, ensuring the diversity and representativeness of the data to avoid bias in the generated output can be a complex task. One solution to this challenge could be the use of synthetic data – artificially created data that mimics the characteristics of real data. Increasingly, niche data companies are specializing in generating synthetic data that can be used for AI training while preserving privacy and confidentiality.
Training complexity: Training generative AI models, especially the more complex models such as GANs or transformer-based models, is computationally intensive, time-consuming, and expensive. It requires significant resources and expertise, which can be a barrier for smaller organizations or those new to AI. Distributed training, where the training process is split across multiple machines or GPUs, can help accelerate the process. Also, transfer learning, a technique where a pre-trained model is fine-tuned on a specific task, can reduce the training complexity and resource requirements.
Controlling the output: Controlling the output of generative AI can be challenging. Generative models might generate content that is undesirable or irrelevant. For example, AI models could create text that is imaginary, incorrect, offensive or biased. Improving the model's training by providing more diverse and representative data can help manage this issue. Also, implementing mechanisms to filter or check the generated content can ensure its relevance and appropriateness.
Ethical concerns: Generative AI raises several ethical concerns, especially in terms of the authenticity and integrity of the generated content. Deepfakes, created by GANs, can be misused to spread misinformation or for fraudulent activities. Generative text models can be employed to create misleading news articles or fake reviews. Establishing robust ethical guidelines for the use of generative AI is crucial. Technologies like digital watermarking or blockchain can help track and authenticate AI-generated content. Also, developing AI literacy among the public can mitigate the risks of misinformation or fraud.
Regulatory hurdles: There is a lack of clear regulatory guidelines for the use of generative AI. As AI continues to evolve rapidly, laws and regulations struggle to keep up, leading to uncertainties and potential legal disputes.
Continuous dialogue and collaboration between technologists, policymakers, legal experts, and society at large are needed to shape comprehensive and effective regulatory frameworks. These should aim to promote the responsible use of AI while mitigating its risks.
History of Generative AI
The history of generative AI has been marked by several key developments and milestones. In the 1980s, data scientists seeking to move beyond the predefined rules and algorithms of traditional AI, started to plant the seeds of a generative approach with the development of simple generative models such as the Naive Bayes classifier.
Later in the 1980s and 1990s came the introduction of models such as Hopfield Networks and Boltzmann machines with the aim of creating neural networks capable of generating new data. But scaling up to large datasets was difficult and issues such as the vanishing gradient problem made it difficult to train deep networks.
In 2006, the Restricted Boltzmann Machine (RBM) solved the vanishing gradient problem, making it possible to pre-train layers in a deep neural network. This approach led to the development of deep belief networks, one of the earliest deep generative models.
In 2014, the generative adversarial network (GAN) was introduced, demonstrating an impressive ability to generate realistic data, especially images. Around the same time, the variational autoencoder (VAE) was introduced, offering a probabilistic approach to autoencoders that supported a more principled framework for generating data.
The late 2010s saw the rise of transformer-based models, particularly in the domain of Natural Language Processing (NLP). Models like generative pre-training transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT) revolutionized NLP with an ability to understand and generate human-like text.
Today, generative AI is a vibrant field with active research and diverse applications. The technology continues to evolve, with newer models like GPT-4, and DALL-E pushing the boundaries of what AI can generate. There is also a growing focus on making generative AI more controllable and ethically responsible.
The history of generative AI is a testament to the tremendous progress in AI over the last few decades. It demonstrates the power of combining robust theoretical foundations with innovative practical applications. Moving forward, the lessons from this history will serve as a guide in harnessing the potential of generative AI responsibly and effectively, shaping a future where AI enhances human creativity and productivity in unprecedented ways.
Conclusion
Already, generative AI – a term that once may have seemed like a concept pulled straight out of science fiction – has become an integral part of our everyday lives. It’s emergence within the larger field of AI represents a significant leap forward. To the capabilities of traditional AI – which can learn from data, make decisions, and automate processes – it adds the power of creation. This innovation paves the way for applications that were previously unimaginable.
For companies across all industries, generative AI is leading the way to the emergence of true “business AI” capable of helping organization automate processes, improve customers interactions, and drive efficiencies in myriad ways. From generating realistic images and animations for the gaming industry to creating virtual assistants that can draft emails or write code to creating synthetic data for research and training purposes, business AI can help companies improve performance across lines of business and drive growth well into the future.