Generative AI

Generative AI, which is rapidly becoming an integral part of artificial intelligence (AI), can produce synthetic data as well as text, images and audio. The recent exposure of generative AI tools over the past few years has played a crucial role in bringing AI into the spotlight. Additionally, the people’s day-to-day use of these tools by people has sparked a broader social discussion on the potential uses of artificial intelligenceAI. 

What is generative AI?

Generative AI definition: Generative AI is a type of artificial intelligence that can create new content in the form of text, video, images and audio. 

Generative AI refers to ​​deep learning models that can produce this new content in a matter of seconds. Generative AI takes many forms, from gimmicky filters that portray prematurely aged faces on social media apps to revolutionary chatbots that write scripts and text. Over the last few years, there have been significant advancements in the practical uses of generative AI, due to the role played by ​natural language processing and ​large language models. 

The inclusion of these tools has allowed generative AI to expand on queries and prompts given by a user. In simple terms, generative AI models predict what the next word, brushstroke or response should be based on the input prompt and the data they were trained on. These models use patterns learned from vast amounts of data to generate new content that is coherent and contextually appropriate. 

How does generative AI work?

Generative AI is a rapidly evolving form of AI that has significantly advanced in capability, efficiency and application over recent years. While it can produce extremely complex outputs, the fundamental process of running a generative AI model remains rooted in core AI principles. 

The key steps in generative AI include training, pattern recognition, generation and retraining. Initially, the models are trained on vast amounts of relevant data. For generative AI applications, which often focus on media output like text, video or audio content, this training data is typically graphic or textual rather than numerical. 

During training, the model learns to identify attributes, features and patterns within the data. This knowledge enables the generative AI model to produce unique, relevant and accurate content when given tasks and prompts. 

Upon receiving a prompt, a generative AI model performs a series of complex calculations to understand the prompt’s meaning and requirements. The model then determines the best way to fulfill the requirements of the prompt and make calculations to generate content accordingly. This process occurs exceedingly quickly, highlighting the power of generative AI as a tool. 

Finally, after completing a full cycle of training, inferencing and output, the model undergoes retraining. This crucial step involves updating the model based on new data and its previous performance. One of the key strengths of generative AI is its ability to improve over time, learning from both its successes and failures. 

What is the history of generative AI?

With the rapid proliferation of generative AI in daily life, it has become a new tool of artificial intelligence. However, forms of generative AI have existed for a century and played a crucial part in AI’s overall development. 

• Early 20th century, origins: The mechanical brain machine, developed in the early 1930s, could translate languages using a mechanical computer. 
• 1950s, work in linguistics: The next significant advancement in generative AI occurred in the late 1950s through Noam Chomsky’s work in linguistics, particularly his theory of generative grammar, including a classification system for formal grammars now known as the Chomsky hierarchy. The Chomsky hierarchy helps define and categorize the complexity of grammar rules for natural language sentences, aiding in the development of efficient language processing algorithms. 
• 1960s, further expansion: Two groundbreaking moments for generative AI and artificial intelligence came in the 1960s. The first chatbot, ELIZA, was created at MIT. It used pattern matching and substitution to respond to user inputs but was unable to understand the content. Two years later in 1968, SHDRLU, a multimodal AI program, could interpret natural language commands to manipulate objects in a simulated environment (blocks world) and understand and execute commands. For example, if a user said, “Pick up the red block,” SHRDLU understood the command and moved the red block accordingly. 
• 1980s, development of neural networks: The first demonstrations of both recurrent ​neural networks and convolutional neural networks were shown in the late 1980s, further exhibiting the uses of generative AI. 
• 21st century, generative AI boom: The 21st century saw the rise of generative AI with innovative neural network training methods that enabled realistic content production. Hardware advancements have finally made it possible to scale AI, making it more intelligent and accessible. The integration of generative AI into wider society began in the 2010s with Apple’s Siri (2011) and Google’s Assistant, both responding to voice requests. In the past five years, OpenAI’s DALL-E and ChatGPT have significantly advanced the field, producing realistic images and text within seconds. 

What are key types of generative AI?

Generative AI currently encompasses six distinct types. However, with continuous advancements in this field, we may soon witness an expanded taxonomy featuring even more categories. 

Generative adversarial networks (GANs) work by pitting two neural networks against each other. A generator network creates synthetic data, while the discriminator network evaluates whether the data is real or generated. Through this adversarial approach, both networks improve, and the outcome is more accurate and realistic. 

Recurrent neural networks (RNNs) process sequential data, allowing them to be used for generative tasks as the RNN can predict the next element in a data sequence based on previous elements. 

Autoregressive models generate data one element at a time by predicting the probability of the next element based on previous inputs. An example of an autoregressive model is ChatGPT. 

• Variational autoencoders (VAEs) are widely used in image generation tasks due to their ability to encode data into a latent space and then decode it to reconstruct the original data. They learn probabilistic representations of the data, enabling them to generate new, similar data. 
• Reinforcement learning can generate data by evaluating the quality of previously generated samples. This method has been applied in various tasks, including text generation. 

Transformer-based models have grown in popularity with generative AI applications due to their use of attention mechanisms to model the relationships between different elements in a sequence. 

How is generative AI used?

The applications for generative AI are growing rapidly with its increase in popularity. An example of this growth is the widespread inclusion of chatbots across many websites. These chatbots are implemented to streamline customer service operations and can help employees focus on other tasks while maintaining strong relationships with customers. 

In customer service, generative AI also enhances the overall customer experience and journey. For instance, travel companies use generative AI to create personalized itineraries for customers booking flights or hotels, autonomously curating a holiday plan. 

Generative AI is also changing the way we use search engines. AI results now provide a wide range of search terms and queries that aim to provide the most relevant information to users. Additionally, generative AI is used to create efficient written content, allowing companies to quickly generate relevant copy for their websites as needed. 

Generative AI is revolutionizing data analysis and insights by enabling the processing and interpretation of vast datasets to uncover patterns, trends and actionable intelligence. These capabilities are crucial for industries like finance, marketing and business intelligence, where timely and accurate insights drive decision-making. Micron’s leadership in storage and memory solutions plays a pivotal role in this transformation. 

Our advanced technologies enable the high-performance and high-bandwidth infrastructure needed to handle intensive AI workloads, ensuring that generative AI applications run efficiently and effectively. This cooperation between generative AI and Micron’s cutting-edge solutions is empowering businesses to harness the full potential of their data.