What is Generative AI?
Generative AI constitutes a category within Artificial Intelligence that employs intricate mathematical algorithms and robust computational capabilities to construct anticipatory models derived from diverse datasets. It can then generate unique content, such as images, videos, music, text, software code & product designs based on these models. Generative AI finds applications in enterprise contexts such as the design of pharmaceuticals and chips, as well as the advancement of material science. Additionally, it enables the capability to respond to natural language queries without the need for coding expertise.
Generative AI operates through the utilization of neural networks, employing them to discern patterns and structures inherent in existing data. This process enables the generation of new content based on the identified patterns. These models can employ different learning techniques, such as unsupervised or semi-supervised learning, to train quickly using large amounts of unlabeled data. Examples of these foundation models are GPT-3 & Stable Diffusion, which allow people to generate an essay from a short request with applications like ChatGPT or photorealistic images with Stable Diffusion.
How Can We Assess Generative AI Models?
Requirements for a successful Generative AI Model include:
· Having high-quality outputs is essential for applications that interact with users. For example, speech generation should have clear & understandable results & image generation should create outputs that look similar to natural images.
· A generative model that captures minority modes in its data distribution without compromising quality can help prevent bias in the models it learns.
· Interactive applications often need to generate things quickly in order to be helpful, such as real-time image editing for content creation workflows.
How Can I Create Generative AI Models?
Generative models come in many forms & when their positive attributes are combined, more powerful models can be created. This is a list of various types:
Diffusion models are generative models that use a two-step process during training to create vectors in latent space. The first step is forward diffusion, which adds noise to training data. The second step is reverse diffusion, which reverses this noise to recreate the data samples. This process can also be used to generate new data by starting with random noise & running the reverse denoising process.
Diffusion models are considered the best choice for general use cases as they offer high-quality outputs that can be created through a two-step process, allowing hundreds or even infinite layers to be trained. However, due to the involved reverse sampling process, training these models is slower & more time-consuming than other generative AI models such as Variational Autoencoders (VAEs).
· VAEs are composed of two neural networks – an encoder & a decoder. The encoder takes an input & converts it into a condensed form, preserving essential information needed to reconstruct original data while discarding extraneous details. Encoder & decoder work together to learn a simplified representation of latent data. This allows users to sample new latent representations, which can then be transformed into novel outputs, such as images, through a decoder. Compared to diffusion models, VAEs generate outcomes faster but with less detail.
· GANs are a type of machine learning method developed in 2014. They involve two opposing neural networks: a generator that creates new examples & a discriminator that learns to tell the difference between authentic (from domain) & fake (generated) data.
Generative Artificial Intelligence (AI) relies on two models that work together – a generator & a discriminator. The generator creates new content, while the discriminator judges it & gets better at identifying generated content after every iteration. This process continues until the generated content is indistinguishable from existing data. GANs are best suited for creating domain-specific data, but their sample diversity needs to be stronger. A famous architecture for generative AI is a transformer network, which needs to be understood in order to get the most out of it.
Transformers are neural networks that process input data non-sequentially. They enable text-based generative AI applications by using two techniques: self-attention & positional encoding. Self-attention assigns a weight to each part of an input to determine its importance for the rest of the information. At the same time, positional encoding allows for the representation of the order in which words appear. This will enable algorithms to focus on how terms relate to each other over long distances.
A transformer is a machine learning model that uses multiple layers, such as self-attention layers, feed-forward layers, & normalization layers, to decipher & predict data streams like text, protein sequences, or image patches.
What are the uses of Generative AI?
Generative AI can take existing inputs such as text, images, audio, video & code & use them to create new content across any of these modalities. For instance, it can transform text into an image, an image into a song, or video into text. This technology has many potential applications for creatives, researchers, engineers & more in all industries.
Most commonly used Generative AI applications:
· Generative AI models often use text as the basis of their operations, making it one of the most sophisticated areas of artificial intelligence. One type of language-based generative model is a Large Language Model (LLM), which can be used to generate essays, write code, translate languages & even interpret genetic sequences.
· Generative AI is creating new opportunities in the fields of music, audio, & speech. Examples include models that can generate songs from text inputs, identify objects in videos & create sound effects for them, & compose custom music.
· Generative AI can be used to create a variety of visuals, such as 3D images, avatars, videos, graphs & other illustrations. It can also edit & modify existing visuals. Applications include producing graphs to discover new chemical compounds or molecules for drug discovery, creating realistic images for virtual or augmented reality, designing logos & creating 3D models for video games.
· Synthetic data is an excellent way for businesses to train Artificial Intelligence (AI) models when data does not exist, is restricted, or is not accurate enough. Generative AI models can reduce the cost of labeling by automatically creating more training data or learning an internal representation of data that requires less labeled data. This process is called label-efficient learning & it covers all use cases & modalities.
· Generative AI, when used as a productivity tool, is an example of augmented artificial intelligence. Real-world applications for this type of AI include:
Generate &/or modify images quickly to discover new creative opportunities.
Generate news articles & other types of texts in different writing styles for text generation.
Generating additional data to train machine learning models when there is not enough or it is expensive to obtain accurate data is known as Data Augmentation.
Generating virtual molecular structures & chemical compounds can help speed up the process of discovering new pharmaceuticals.
Generating original pieces of music can help composers explore new musical ideas.
Apply different artistic styles to the same content using Style Transfer.
Develop virtual avatars & environments for video games, augmented reality platforms, & metaverse gaming for use in VR/AR development.
Analyze medical images & create reports based on analysis.
Creating personalized recommendations for e-commerce & entertainment platforms is a content recommendation.
I am translating text from one language to another.
Generate virtual product designs & concepts to save time & money.
Creating virtual models of standard data patterns can help make it easier for AI programs to detect anomalies, such as defects in manufactured products or unusual practices in finance & cybersecurity.
Generative chatbots can be used to help customers by answering their questions & responding to their feedback. This is known as Customer Experience Management.
Generate treatment plans tailored to individual patients using data from multiple sources.
Generative AI is having a significant impact on many different fields. In transportation, natural sciences, & entertainment, it is helping to drive advancements & transformations. Here are some examples of how it is being used in these areas.
· Generative AI in the automotive industry is being utilized to create 3D worlds & models for simulations & car development. Synthetic data is then used to help train autonomous vehicles, allowing them to be tested in realistic 3D worlds, which increases safety, efficiency, & flexibility while reducing risk & overhead.
· Generative AI can have a significant impact on the field of natural sciences. In healthcare, generative models can be used to create new protein sequences for drug discovery & automate tasks such as scribing, medical coding & imaging. They can also be utilized in the weather industry to simulate the planet & assist with accurate weather forecasting & disaster prediction, ensuring more safety for people. These applications help scientists prepare better for natural disasters.
· Creative professionals in the entertainment industry, such as video game developers, filmmakers & animators, are using AI-generated models to help speed up their content production. Generative models can be used as a tool to enhance creativity & make the creative process more efficient.
What difficulties does Generative AI present?
Generative models are still relatively new, allowing for potential advances in the future.
· development of generative AI models requires significant capital investment, technical expertise, & large-scale compute infrastructure. Such models can require billions of parameters & need fast data pipelines to train them. To train datasets with millions or billions of images, massive computing power is necessary & practitioners must have access to hundreds of GPUs.
· Generative models frequently find application in interactive contexts, including chatbots, AI voice assistants, and customer service applications. However, their extensive scale can sometimes introduce latency issues. but they can introduce latency due to their large scale. Diffusion models create high-quality samples but can be slow regarding sampling speed. This raises an issue for applications that require prompt & accurate conversations.
· Generative AI models often create synthetic data for different purposes. However, not all data can be used to train AI models since it needs to be of high quality & without bias. Additionally, some areas, such as 3D assets, have very little available data & require a lot of resources to reach maturity.
· Organizations often struggle to obtain a commercial license for existing datasets or create custom datasets to train generative models, making it difficult to access high-quality data. This licensing process is essential for avoiding intellectual property infringements.
Companies like NVIDIA, Cohere, & Microsoft are helping to advance generative AI models with tools & services. These products simplify the process of setting up & running AI models at a large scale.
What benefits does Generative AI offer?
Generative AI has many benefits, including:
· Generative AI algorithms can be used to create content, such as images, videos, & text, that looks & sounds like it was made by a human. This technology has applications in entertainment, advertising & creative arts.
· Generative AI algorithms can be used to enhance existing AI systems, such as natural language processing & computer vision. These algorithms can generate synthetic data, which can then be used to train & evaluate other AI models.
· Generative AI algorithms enable businesses & researchers to discover patterns & trends that would otherwise remain hidden in complex data. These algorithms provide a new way to analyze data, allowing deeper insight.
· Generative AI algorithms can make tasks & processes faster & more efficient, saving businesses & organizations time & resources.
Generative AI has the potential to revolutionize many industries & applications. It is a key area of research & development in AI. To demonstrate its capabilities, this section entitled “What are Benefits of Generative AI?” was written by ChatGPT, a generative AI model.
What are the potential dangers associated with generative AI?
The use of generative AI carries several risks. Deep fakes, copies of products & complex scams have already been created through this technology. ChatGPT & other similar tools are built using public data, meaning they do not comply with privacy regulations like GDPR or copyright laws. Companies need to be aware of potential risks when utilizing such platforms.
Risks to monitor for oversight include:
· lack of transparency surrounding generative AI & ChatGPT models makes them difficult to predict, & even companies that created them may not fully understand how they work.
· Check the accuracy, appropriateness, & usefulness of all outputs generated by generative AI systems before relying on or sharing them.
· To avoid bias, it is important to have policies & controls in place that can identify any biased outputs & take action that aligns with company policy & applicable laws.
· It is essential to be aware that any data or queries you enter into ChatGPT & its competitors may become public information.
· Enterprises must protect themselves against cyber & fraud attacks that use AI systems such as deep fakes for social engineering. Speaking with your cyber insurance provider to determine whether your policy covers any potential AI-related breaches is important.
· To meet sustainability goals, choose vendors that reduce power consumption & use high-quality renewable energy when using generative AI.
