In our ongoing series of blogs “Unravelling the AI mystery” Digitate continues to explore advances in AI and our experiences in turning AI and GenAI theory into practice. The blogs are intended to enlighten you as well as provide perspective into how Digitate solutions are built.
Please enjoy the blogs
2. Prompt Engineering – Enabling Large Language Models to Communicate With Humans
3. What are Large Language Models? Use Cases & Applications
4. Harnessing the power of word embeddings
written by different members of our top-notch team of data scientists and Digitate solution providers.
Leveraging Generative AI to Develop Production-Ready Solutions
The Generative Artificial Intelligence (Generative AI) buzz has been driven by the simplicity of its usage, as well as its ability to create high-quality content in a matter of seconds.
Come to think of it, any tech that comes with the inherent simplicity of use, easy accessibility, and deep societal penetration holds the power to transform the world in a never-before-seen manner. Decades ago, Google had this effect! The entire world of the Internet was made accessible with a simple textbox on a deceptively simple page!
The ease of access to GenAI is opening new avenues for creativity. It surprises us every day with the potential of technology and the human intelligence in using it! While tech itself will keep evolving, a parallel revolution has started where the end-user is getting increasingly creative in using this tech in never-before-seen ways! A decade back, who would have thought that the AI-generated vocals of Drake and The Weekend (almost) would have run for a Grammy nomination?
In this chapter, we will present our point of view on Generative AI technology, a subset of artificial intelligence. The subsequent chapters will focus on simplifying technology concepts, giving a peak under the hood, and discussing the opportunities and challenges that Generative AI (GenAI) can present.
Let’s first set the context with a simple definition of what Generative AI is and what it can do.
- In this article, we discuss the fundamentals:
- What Is Generative AI?
- What Can Generative AI Do?
- How Are Industries Using Generative AI?
- Pushing the Boundaries of Creativity
- Challenges in Using Generative AI
- Key Operationalization Aspects to Consider While Using Generative AI
What Is Generative AI?
Generative AI refers to artificial intelligence algorithms that can generate fresh content from already existing data, such as text, audio, images, etc. These models are trained on large data sets to create new content. GenAI uses a neural network architecture to train these models with new content using a machine learning process called deep learning.
Unlike traditional AI, which focuses on using past data for tasks like classification or prediction, Generative AI goes beyond these limitations. For instance, in a conventional setup, you might have extensive labeled data of cat and dog images used to train an AI model to identify these animals. However, with the advent of Generative AI tools, such as AI copilots, Bard, Dall-E, Stable Diffusion, MidJourney, GPT (Generative Pre-trained Transformer), and advanced interfaces, it’s now possible to not only classify but also to create realistic images based on text data. These tools are capable of natural language processing, which is the main component in creating new human-like content. You could ask Generative AI to produce new data, such as a new image of a cat, and write a story about it.
What Can Generative AI Do?
GenAI is a type of artificial intelligence that finds application in three broad areas:
- Creative Content Creation: With its solid understanding of language and ability to generate fresh content, it is turning out to be a powerful tool for creative content generation. Be it articles, blogs, advertisements, or even stories, songs, and screenplays!
- Efficiency Improvement: It can seemingly automate repetitive tasks such as writing emails or summarizing documents. It also has the capability to understand programming languages. Therefore, it can automatically generate code, debug it, and even write test cases for the same (albeit under human supervision), thus allowing human experts to focus on exceptional cases and customizations.
- Experience Personalization: It can provide a touch of personalization to any end application. This way, it can cater to individualistic choices. For example, it can create personalized customer experiences in the form of personalized chatbots, advertisements, or even emails and notifications. This can form a significant step toward improving the adoption of AI amongst non-technical users.
How Are Industries Using Generative AI?
Industries are actively exploring GenAI to produce fresh ideas, automate jobs, and push the boundaries of creativity even more, especially in product design. Some of the trending functions that are actively adopting GenAI include marketing and sales, customer service and contact centers, graphic design and video production, image generation, cybersecurity, healthcare, entertainment, legal and government, fashion, retail, and e-commerce.
Following are some of the trending areas that are adopting GenAI:
- AI Assistants, Chatbots, and Search: GenAI can transform the experience of communicating with chatbots. Existing chatbots do feel naïve and keep revolving in a fixed loop. GenAI can add a layer of intelligence to these chatbots, thereby making them interact with a human-like experience.
- Developer Tasks: GenAI is significantly contributing to this space by introducing features like automated code generation, testing, and documentation. It holds the potential to significantly reduce the human effort in software development so that software developers can focus more on validating the output, handling new and exception cases, and training the AI engine.
- Creative Content Generation: GenAI is creating waves with its ability to summarize content, explain difficult concepts, write essays and blogs, generate social media feeds, etc. Sales and Marketing teams are eagerly exploring this capability with text-generation tools likeChatGPT, a text-generation platform developed by OpenAI. GPT-3.5 and GPT-4 are already being used extensively by marketing and advertising professionals to create groundbreaking promotional copy.
- Computer Vision: GenAI is pushing the boundaries of computer vision. Tools such as Carrot and Groundlight are now using the tech for automated captioning of images and doing image Q&A. Voice and Audio Synthesis: GenAI is also opening up new avenues for audio processing. Tools such as AudioGPT offer a dialogue assistant to which you can talk!
Pushing the Boundaries of Creativity
GenAI is coming across as a tool that is user-friendly, accepts simple language instructions, and easily takes feedback for customization. These very qualities of GenAI are enabling artists to further their creative attempts from conceptualization to execution. We can create a story, comic, song, or video with just a few words of instructions! Here are some cool examples:
- An AI engine is fed hundreds of Batman comics to generate a new Batman comic!
- GenAI is being used to create new art forms. For example, we can create an AI-generated painting of the Harry Potter famed Hogwarts school, painted in the style of Van Gogh!
- AI-generated vocals that sound just like Drake and The Weekend.
GenAI is opening a world where imagination meets algorithms and where creativity is both the question and the answer, paving the way for algorithmic masterpieces.
Challenges in Using Generative AI
We are still in the early days of Generative AI. The potential seems enormous, and business leaders are exploring ways to incorporate GenAI into their corporate workflows and reap its benefits. But the technology is so new that we are yet to see the long-tail effect of generative AI models. There is still a lot of skepticism in the executive ranks regarding the adoption of GenAI.
The use of GenAI presents copyright infringement risks with respect to both the input data on which these models are trained and the output that they generate. If the training data set was copyrighted, then the portions of it being reproduced or included in the output without the authorization of the copyright holder can lead to infringement risk.
It is important to keep in mind that the generated content is not necessarily accurate or up-to-date. Gen AI systems often suffer from hallucinations where the model “imagines” or “fabricates” information that does not directly correspond to the provided input.
The models often provide an output without any explanation of how they reached that response or which data sources were used. The lack of explicability, traceability, and reproducibility of the GenAI outcomes are some of the biggest concerns, as they risk the possibility of incorrect decision-making.
Many positive developments are taking place to mitigate some of these challenges. Restricting the knowledge base of these models to use specific custom data sets is turning out to be a very effective lever to improve reproducibility. Detailed recording of data sources, pre-processing, and configurations can help establish transparency. Prompt engineering is being used in creative ways to limit hallucinations. Well-defined guardrails are being established to develop a secure ecosystem to foster GenAI.
Key Operationalization Aspects to Consider While Using Generative AI
Generative AI presents some unique challenges and opportunities when it comes to operationalizing it in addressing real-world business problems. Below are some key aspects that need to be addressed while operationalizing GenAI.
- Assess Suitability: Assess if your use case really needs a GenAI solution or if traditional AI solutions provide a better fitment. Avoid over-engineering an otherwise simpler solution.
- Identify Data Sources: Identify what type of data you need for your use case. Can your use case work with generic datasets available out there, or do you need to fine-tune with custom specialized new data sets? Also, assess if the required data available is of good quality and does not carry any infringement risks.
- Select the Right GenAI Tool: There are different GenAI models out there. Some can be locally hosted, some have high infra needs, some are generic, and some can be fine-tuned. Select the right model by assessing your needs for hosting, performance, customization, and privacy.
- Design Prompts: The next critical step is prompt engineering. This will significantly decide the effectiveness of your GenAI Assess different types of prompts and see what works well. Prompts also offer an effective way to limit the responses to a certain context.
- Assess Trustworthiness: Another crucial step is to assess trustworthiness. Assess if there are ways you can measure the output quality. Can you quantify measures to compute the completeness, correctness, and quality of the output? Assess the best- and worst-case scenarios. Design error mitigation strategies to address cases where GenAI generates incorrect output. Often, a good design choice is to use GenAI at compile time and keep human-in-loop during run-time.
- Assess Costs and Benefits: GenAI solutions come with a cost—this could be the cost of querying an online large language model (LLM) API, the infra cost of hosting a local LLM model, or the cost of training an LLM. Assess this cost against the potential benefits of using GenAI.
- Design the Machinery Around GenAI: GenAI is only a part of the process. You would still need the rest of the machinery of data engineering, traditional AI solutions, user experience, last-mile automation, and a learning engine to develop your solution. Do not underestimate it.
- Keep Human in the Loop: GenAI solutions are still evolving. While using it for business-critical solutions, it is advisable to keep human-in-loop to monitor, validate, and improve until the solution hardens.
Closing Notes
We are witnessing a definite wave of interest and investment in GenAI. Every new game-changing technology demands caution, but nevertheless, it needs exploration and investment. There are open questions with respect to transparency and trustworthiness, but true GenAI potential should be harnessed by responsible development, ethical use, and continuous exploration. GenAI brings us to an exciting intersection where machine intelligence meets human creativity, and this intersection empowers us to push the boundaries of what is conceivable.
