Generative AI

Generative AI Techniques

Autoencoders

Autoencoders help people automatically encode data and are made up of two distinct components, an encoder, and a decoder. Autoencoders reside in unsupervised artificial neural networks that memorize and quickly encode data that can then be reconstructed at a later date. 

Generative Adversarial Networks

A general adversarial network (GAN) is a type of machine learning framework that places two neural networks in a contest. A training set is given and allows AIs to generate new data with the same statistics as the training set.

Generative AI Applications

Generative AI has applications in many fields, including marketing, education, healthcare, and entertainment. It could be used to create fake news stories, or it could be used to produce original content like music or movies.

Use Case One: The Medical Field 

Generative AI-powered applications allow computers to generate new content based on existing information. For example, this is useful for creating new medical images like those used in retinopathy diagnosis. Medical professionals could also use it to create new patient records, which can be fed into the system to improve accuracy.

These applications use deep learning techniques to train themselves on large amounts of data from actual patients. It then creates new images based on those patterns. This process allows it to generate new data sets that humans could have never developed.

One of the most formidable use cases for Generative AI-powered applications is new content creation based on existing information. They can then compare generated content against real-world data to improve accuracy. This means they can analyze large amounts of data quickly and efficiently, allowing them to provide better insights into diseases than ever before. 

Use Case Two: Data Augmentation 

The most common form of data augmentation involves altering images by applying small changes. For example, it may involve changing an image’s brightness, contrast, saturation, hue, or color balance. It could also include rotating, flipping, or cropping an image.

The key idea behind generative AI is that it allows us to train neural networks without having access to all the training examples. Instead, we only need to provide the network with enough examples to learn the problem’s underlying structure. Then, once the model has learned this structure, we can generate additional samples based on this knowledge.

Generative AI Healthcare

Generative adversarial networks have revolutionized the medical industry and offer doctors and healthcare professionals a range of intuitive patient treatment and privacy-protecting applications. 

Generative adversarial networks are crucial to healthcare providers because they can be taught to produce fake examples of underrepresented data, which helps to train, educate and develop the model. You can also use generative adversarial networks (GANS) for data identification, which helps with security and data privacy. 

GAN offers a promising solution to data de-identification. It solves a major problem for healthcare analysts who have experienced difficulties with a reversal process, which can compromise valuable data and patient records.

Generative Intelligence

The primary aim of generative intelligence is to recognize new cases before they materialize while simultaneously developing an appropriate course of action. It works by pairing the decision-making capabilities of artificial intelligence with human understanding and the scientific practices of dynamic complexity and perturbation theory. 

Generative intelligence is supported by casual reconstruction, which helps to create a logical collection of practical, impartial knowledge that transcends human intelligence. When generative intelligence is practically applied, it enables machines to automatically regulate, control and scrutinize environments by taking action to accomplish various definable objectives. The generative AI spectrum covers known and unknown patterns and is expressed through mathematical emulation, stress testing, and sensitivity analysis.

Generative AI Music

Generative AI is redefining the convergence between music and software by creating neural networks which try to imitate and mimic the human brain. Neural networks can learn complex patterns in the same repetitive nature as the human brain. Neural networks are growing at a phenomenal pace and are becoming harder for humans to understand. 

Google’s Magenta created the first-ever AI song in 2016 and has been innovating at a record pace ever since. The biggest change Magenta has predicted in terms of generative AIs’ impact on music is the creation of new genres. Research is being conducted that considers the impact of the amalgamation of two or more genres, opening the doors for AI to become more of a co-creator than a tool.

Generative AI Companies

The following are three industry-leading examples of generative AI companies: 

Synthesia 

Synthesia is a UK-based company founded in 2017 that is one of the earliest pioneers of video synthesis technology. They are implementing new synthetic media technology to revolutionize visual content creation, reducing applications’ cost, skills, and language barriers. 

Mostly Ai 

Mostly-AI is paving the way for realistic simulations and representative synthetic data at scale. They’ve created state-of-the-art generative technology that automatically learns new patterns, structures, and variations from existing data.  

Genie Ai 

Genie is a machine learning expert who shares and organizes reliable, relevant information within a legal firm, team, or structure. This helps empower lawyers to draft with the collective intelligence of the entire firm. 

Generative AI Statistics

• By 2025, generative AI will account for 10% of all data produced 
• According to Gartner, 71% of respondents said the ROI of intelligent automation is high within their organizations 
• The forecasted AI annual growth rate between 2020 and 2027 is 33.2%
• By 2030, AI will lead to an estimated $15.7 trillion, or 26% increase in global GDP 

Generative AI In The Digital Transformation Era

The widespread explosion of artificial intelligence (AI) technologies has heightened the need for processes to be put in place that fully utilizes the field’s growing capabilities. AI tech is a huge part of digital transformation and is used by businesses to create diverse working practices and positive change to constantly shifting processes. 

An organization’s ability to quickly deploy AI technologies helps to enable digital transformation in the four key dimensions of technology, boundaries, activities, and goals. These dimensions facilitate the readiness of the AI framework and allow for a better theorization of the roles AI will play in the future of digital transformation.