Generative AI

Recent advancements in artificial intelligence (AI) technology could lead to drastic changes in the world's approach to—and perspective on—content creation. But what is generative AI, and what does it do?

What is generative AI?

Generative AI refers to artificial intelligence models that create new content when given various inputs, such as user prompts. The content these models generate may be text, images, art, music, or other media. Generative AI is a machine learning framework. These models create novel content based on the structures and patterns they absorb from immense datasets during their training. 

How does generative AI work?

Most generative AI models use neural networks, especially Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). 

Generative Adversarial Networks (GANs)

GANs work on a principle of two neural networks—a generator and a discriminator—competing against each other. The generator creates new data instances, and the discriminator evaluates them to determine if they are real or generated. Over time, through this competition, the generator improves its ability to create more realistic data.

Variational Autoencoders (VAEs)

VAEs learn a latent space representation of input data. They consist of an encoder network that maps the input data to a latent space and a decoder network that reconstructs the input from the latent space representation. VAEs can generate new data by sampling points from the learned latent space and decoding them into meaningful outputs.

Non-neural network-based generative models

Neural networks commonly factor into generative AI models because they can learn complex patterns and generate new content. However, not all generative models rely on neural networks. Some non-neural network-based generative models include:

• Probabilistic models: Researchers base these models on probability distributions and statistical techniques to generate new data. Examples involve Markov models, Hidden Markov Models (HMMs), and Bayesian networks.
• Rule-based systems: These systems generate content based on predefined rules and logical operations rather than learning from data. They often involve expert knowledge encoded into a set of rules or procedures.
• Genetic algorithms and evolutionary strategies: These models simulate evolution to generate content by iteratively evolving and selecting the best solutions based on a fitness function.
• Monte Carlo methods: These methods use random sampling techniques to approximate solutions and generate new data based on statistical sampling. 

How do developers train generative AI models?

In the case of both GANs and VAEs, developers train the AI on a dataset, learning its patterns and structures. When generating new content, the AI uses this learned information to create new outputs that resemble the original dataset. The quality of generated content depends on various factors, such as the dataset's quality, the model's architecture, and the techniques used during training.

What can generative AI do?

Generative AI has an impressive range of capabilities. It offers creative solutions and novel outputs across various domains. 

Content generation

Generative AI can create realistic images, videos, and audio. From generating lifelike faces to producing art, music, and even entire scenes, it can generate diverse content indistinguishable from human-created output. AI-generated art and music contribute to the creative sphere, producing unique compositions, visual arts, and styles influenced by various artists or genres.

Text generation

Natural Language Processing (NLP) models, like the GPT (Generative Pre-trained Transformer) series, excel in generating content and contextually relevant text. They power applications like chatbots, content creation, translation, and summarization. 

Drug discovery and molecular biology

Generative models aid in molecular design, predicting properties, and generating novel drug compounds. They accelerate drug discovery by simulating molecules with specific properties or potential therapeutic effects. They can even assist with gene sequencing by predicting gene expression.

Gaming and virtual environments

AI generates game environments, characters, and narratives, enhancing gameplay experiences. Procedural generation techniques create vast, dynamic virtual worlds, offering uniqueness and complexity.

Design and creativity

Generative AI assists in design tasks, generating prototypes, architectural designs, fashion concepts, and product iterations based on specified criteria or learned patterns.

Anomaly detection and data augmentation

In data analysis, generative models detect anomalies by learning standard patterns. They can aid in fraud detection, cybersecurity, and quality control. They can also augment datasets by generating synthetic data for training models.

Personalization and recommendation systems

These models power recommendation engines by generating personalized content, product recommendations, and tailored experiences based on user preferences and behavior.

What are the advantages of generative AI?

Generative AI offers multiple benefits, some of which include:

• Creativity: Generates diverse and original content autonomously
• Time and resource efficiency: Speeds up creative tasks, aiding rapid prototyping and idea generation
• Pattern learning: Learns complex patterns from data for improved recommendations and model training
• Healthcare advancements: Accelerates drug discovery by simulating molecular structures and properties
• Innovation and problem-solving: Facilitates innovation, automation, and efficiency across industries

What are the limitations and concerns of generative AI?

As with any form of artificial intelligence, generative AI is not without its considerations. Here are some possible limitations and concerns of generative AI.

• Ethical quandaries: Ownership, authenticity, and potential misuse of generated content raise ethical dilemmas and legal uncertainties., such as those surrounding intellectual property and copyrighted material.
• Quality control challenges: Outputs may lack coherence, realism, or accuracy, necessitating meticulous validation and supervision.
• Data dependency: Generative AI's performance relies heavily on the quality and quantity of training data. This can limit application in certain contexts or domains.
• Resource intensiveness: Training and running complex generative models require substantial computational power and time, which can pose resource challenges.
• Bias and fairness issues: Biases in the training data could lead to biased or unfair outputs. Mitigation strategies are necessary to foster fairness and inclusivity.
• Security risks: Some people use generative AI to create realistic deepfakes and forgeries. This amplifies security concerns regarding misinformation, privacy breaches, and cybersecurity threats.

How can generative AI affect the customer experience?

In an episode of the Insights Unlocked podcast, UserTesting's Michelle Huff spoke with Constellation Research's Liz Miller about the role of generative AI in customer experience. Click here to listen to the episode.