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  • Writer's pictureLahiru Fernando

Generative AI: Unleashing the Creative Revolution of Tomorrow

Generative AI is a technology that stands at the forefront of the world where innovation knows no bounds. It enables machines to blend with human ingenuity, transcending boundaries to generate remarkable and thought-provoking creations.



Generative AI: Unleashing the Creative Revolution of Tomorrow


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Introduction



Artificial intelligence has made remarkable strides in recent years, revolutionizing various industries, and transforming how we interact with technology. Generative AI is one of the most attractive branches of AI. Generative AI is a field that holds immense promise for shaping the future by creating content, such as images, videos, and text.

Generative AI takes AI technology a step further by enabling machines with the power of creativity to produce unique content. It utilizes advanced machine learning algorithms to learn from vast amounts of data and convert the knowledge into innovative results. This remarkable capability has opened new possibilities across every industry.


In this article, we will explore Generative AI, diving deep into its current landscape, unveiling the future possibilities, examining industry adoption, and discussing how individuals can prepare themselves for the exciting road ahead.




Current Landscape of Generative AI



Artificial Intelligence is a broad area that includes multiple subfields. For instance, AI dealing with language usually falls within Natural Language Processing (NLP) and this may be further broken down into natural language understanding (NLU) or natural language generation (NLG). There are many other types of AI disciplines.


AI is based on various types of models. These models use "features" to understand "things" (like a picture of a face or a negative blog review).


A Feature is an individual measurable property or characteristic of input data

Models are trained using formulas to analyze sample data and extract the features needed to understand things. Initially, these models were designed and constructed by data scientists. As mathematical algorithms and computing capabilities evolved, machines have learned how to take over many of the tasks previously performed by data scientists (i.e., evaluating which combination of features and algorithms provide the best accuracy and reliability). Large Language Models (LLM) are now able to leverage billions of features to analyze massive amounts of data. These LLMs also can respond with astonishingly human-like responses.


Models can be trained using supervised or unsupervised techniques.



Supervised Learning


Supervised learning uses historical data to train a model and predict future values. In general, the training data must be labeled before being fed into the model for training. For example, if you wanted to train a “credit approval” model, you would have to gather a set of credit applications labeled as Approved or Rejected. Two of the most common approaches to supervised learning are described below.

  • Classification: Use historical data to categorize data based on specific parameters available in the dataset. A real-world example of this is classifying emails as spam or redirecting emails to different departments based on their context.

  • Regression: It is used to identify the relationships between different data points in the dataset. A real-world example of this is trying to predict numerical values based on different values, such as sales revenue projections.

One of the benefits of supervised learning is that models can be trained based on real-world data examples. Supervised learning can be helpful in two scenarios:

  • When there are a minimal number of factors to consider

  • When adequate training data is readily available.

Supervised learning is often performed by experts specialized in determining the type of model used, the training data to be used, and the reliability of the model. In addition, this specialist must periodically review these models for potential adjustments.



Unsupervised Learning


Recent trends indicate a move towards models that “train themselves.” In other words, you point the model to a stack of files, and the training system analysis the content and determines what factors should be included in the model to predict desired outcomes (i.e., credit approval or denial).

Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets. Clustering is one of the techniques used to identify hidden patterns in data. Such techniques minimize the need for human specialists. Unsupervised learning often relies on the advanced computational capabilities of chip manufacturers. These capabilities are used to evaluate different options previously performed manually.



Deep Learning


Deep learning is a subset of machine learning. It uses neural networks and advanced computing capabilities to analyze data patterns extensively. Neural networks use both labeled and unlabeled data to learn. Generative AI is a subset of Deep Learning that uses artificial neural networks to process data using supervised, unsupervised, and semi-supervised methods.


Artificial Intelligence Tech Hierarchy

Deep learning models are categorized into two main types:

  • Discriminative: Used to classify and predict information by learning the relationship between various aspects of data points.

  • Generative: Uses historical data to learn and generate new data based on the knowledge gained through training.


Generative AI


Generative AI is an artificial intelligence technology that takes creativity to the next level, enabling machines to produce text, images, video, and audio content. The recent technological advancements in Generative AI resulted in trends seen with ChatGPT, Google Bard, MidJourney, etc.


MidJourney is a platform that can bring your imagination to life by converting text into high-quality images.


Generative AI powering MidJourney for automatic text to image generation

ChatGPT and Google Bard are strong Large Language models (LLM) that can understand natural language and generate human-like conversations and code.


Transformers and Large Language Models (LLM) are two terms we cannot exclude when talking about Generative AI. The advancements in transformers and LLMs enabled machines to generate creative, high-quality content. A transformer model is a neural network that learns context and meaning by tracking the relationships in sequential data, like words in a sentence. It applies a technique named "Attention" to extend its relationship-building capabilities. The "Attention" enables the model to identify and track connections between words across large portions of text data rather than just in individual sentences.


Large Language Models (LLM) are trained with billions of data sources, opening a new era for Generative AI. These models are trained to work with multiple media types, such as text, graphics, video, audio, and software code. The model uses the vast amount of knowledge it possesses to generate content in all possible combinations. These are known as Multimodal AI, as they can work with multiple data types, enriching their creativity. All the popular Generative AI platforms use these concepts to create high-quality content.




Unveiling the Future Possibilities



Generative AI is a field that evolves continuously, generating more opportunities for further adoption in various domains. As of today, it provides so many benefits for its users. One of the most significant benefits is the efficiency of performing activities. In addition, it also assists (but is not limited to) in creating new content, generating new ideas, writing, enhancing customer support and satisfaction, facilitating decision-making, and streamlining research and development.


The potential of Generative AI will shape our lives and industries in many ways soon. The predictive power and accurate content creation ability can revolutionize industries such as healthcare, education, entertainment, finance and banking, manufacturing, environment, marketing, and many more. One of the exciting points about Generative AI is that it can create personalized content. Imagine using personalized support in previously mentioned industries to create custom content without much effort. For example, doctors and laboratory staff can get personalized and specialized assistance in experimenting with medications and identifying sicknesses.


Generative AI works with large amounts of data enabling it to generate accurate content. Many improvements are taking place in techniques such as Reinforcement Learning with Human Feedback to enhance the LLM models. While the AI models are effective in creating content, ensuring the content is accurate and human-friendly is also important. Reinforcement learning supports the model to react to particular situations and provide precise information while limiting abuse and hallucinations. More research and improvement will take place in these technology areas to enhance the capabilities of Generative AI.


Virtual Reality (VR) and Augmented Reality (AR) are some of the cutting-edge technologies we are always interested in exploring. Generative AI combined with these technologies can take the virtual world to a different level by enabling users to create dynamic environments seamlessly. Some exciting examples could include creating avatars, interactive experiences for organizations, kids, and people with special needs, virtual shops, architecture simulations, and many more visualizations.


Besides technological advancements, many organizations are interested in adopting and using Generative AI. Several organizations have already started incorporating Generative AI capabilities to enhance their customer experience and service/ product quality. Product-based organizations have begun using the technology to create specialized LLMs to support various requirements. These include LLMs specialized in proofreading, generating software code, image generation, video generation, and many more. For example, popular online photography companies like Shutterstock have started using Generative AI to create images and videos based on user prompts. In addition, Adobe, one of the leading multimedia tools, now includes a feature to create high-quality photos with Generative AI. Another exciting adoption of Generative AI is Microsoft coming up with Microsoft Copilot for their Windows 11 operating system.


Here is a showcase of a Generative Storytelling application I tried recently.



Explore the story: Click here.


The examples mentioned in this section are just a glimpse of how Generative AI has already started shaping how we work. These technologies will soon be a part of our daily lives (work and personal), changing how we interact with the world.




Current Challenges and Limitations



While Generative AI holds tremendous potential, it also faces several challenges and limitations.



Data Availability


Deep learning uses large-scale neural networks that process millions of data. However, obtaining large volumes of data within organizations is difficult for many reasons. The data can either be unavailable, data is only accessible for authorized users or may require manual cleansing and labeling before submitting for training. Additionally, training models with limited data may make the models biased or generate wrong information. It is important to train the model with accurate and non-biased information in large volumes to produce accurate content.



Data Privacy


Maintaining data privacy and ensuring data diversity while training generative models pose ethical considerations. It is always important to ensure the models are trained, maintaining accountability, transparency, and privacy of personal data.



Explainability


Explainability is not a new challenge for AI systems. Explainability is the ability to explain how a model reached a specific decision point. It becomes difficult as the models become larger and more complex. While the models promise to deliver accurate information, it is still important to meet regulatory requirements. These requirements include auditing and understanding the facts behind the reasoning. While the models are evolving and improving, explainability is still a limitation that slows down the adoption of AI.



Computational Requirements and Resource Constraints


Training and deploying complex generative models often require significant computational resources. These requirements can be a barrier for organizations or individuals with limited access to such expensive resources.


While these challenges and limitations exist, researchers and practitioners actively work on overcoming them.




Preparing for the Future



As Generative AI continues to advance at an unprecedented pace, preparing ourselves for the exciting possibilities it holds is crucial. Preparation for a future with Generative AI requires upskilling ourselves with the necessary skills and knowledge, fostering interdisciplinary collaboration, gaining domain expertise with AI literacy, and embracing lifelong learning while staying updated with the latest advancements.


Technically proficient people can focus more on learning AI and developing a solid foundation in machine learning, deep learning, and neural networks. Additionally, upskilling in areas such as data preprocessing, model training, and evaluation will enable individuals to unlock the creative power of Generative AI. This also includes exploring and getting hands-on experience in leading Generative AI platforms to understand how to use and train those models. There are many educational resources and learning platforms that can help us enhance our understanding of Generative AI. Some of those include paid/ free online courses, books, and research papers.


Note: Google recently introduced a free learning course to learn about Generative AI

Google Course on Generative AI

Generative AI can be applied to various domains and disciplines. However, each domain has its own specific challenges, opportunities, and ethical implications that require domain expertise and contextual knowledge. Therefore, it is important to collaborate with experts in multiple domains to get a better understanding. Use the available opportunities to connect with domain experts and gain knowledge and expertise in different fields where Generative AI can provide value. Use the expertise gained to research solutions to address complex business problems. It is a great way to enhance your expertise in problem solving using Generative AI capabilities.




Conclusion



Generative AI represents an exciting technological frontier, offering exciting prospects for innovation, creativity, and personalization. However, it has its own challenges and limitations that require careful attention. As technology evolves, it is important that we also upskill ourselves to embrace future opportunities. Understanding the full potential of Generative AI and being prepared for the future will enhance our lives in ways we never thought possible.




 

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Written by:

Lahiru Fernando

Practice Lead for Document Understanding and AI at Boundaryless Group

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