The 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 of transforming 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 is surprising us every day with the potential of technology and creativity of human mind 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 AI-generated vocals of Drake and The Weekend (almost) would have run for Grammy nomination!
This blog is the first of a series of blogs where we present our point of view on Generative AI. These blogs will focus on simplifying technology concepts, giving a peak under the hood, and discussing the opportunities and challenges that it presents.
Let’s first set the context with a simple definition of what Generative AI is and what it can do?
What is Generative AI?
Generative AI are artificial intelligence algorithms that can generate fresh content from already existing data. The generated content can be text, audio, or even images. These models are trained on large data sets to form their knowledge base. The models use this knowledge base to create new content on-the-go.
Traditionally, AI has mostly been about using past data to perform tasks such as classification or prediction. Consider a simple example of classification. You have tons of labeled data of images of cats and dogs. With such data, you can train your AI model to identify a cat or a dog accurately. With GenAI, you can now take the intelligence to the next level and ask the Gen AI to even generate a new image of a cat and write a story about it as well.
What can Generative AI do?
GenAI finds its applications in three broad areas:
1. Creative content generation: 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!
2. Efficiency improvement: It can seemingly automate repetitive tasks such as writing emails, or summarizing documents. It also has the capability to understand programming languages, thus 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.
3. 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 experiences in the form of personalized chat-bots, or advertisements, or even emails and notifications. This can form a significant step towards improving the adoption of AI amongst the 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. Some of the trending functions that are actively adopting GenAI include marketing and sales, customer service and contact centers, graphic design and video production, healthcare, entertainment, legal and government, fashion, retail, and e-commerce.
Following are some 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 that keep revolving in fixed loop. GenAI can add a layer of intelligence to these chatbots assistants 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 looking forward to exploring this capability.
- 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, that accepts simple language instructions, and that 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, a song, or a 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. Here is an AI-generated painting of the Harry Potter famed Hogwarts school, painted in the style of Van Gogh!
- AI-generated vocals were created 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 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 in their operations 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.
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 models 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 explainability, 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 shape 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 eco-system to foster GenAI.
Key operationalization aspects to address 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 traditional AI solutions provide a better fitment. Avoid over-engineering an otherwise simpler solution.
- Identify data sources: Identify what type of data do 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 datasets? 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, while some can be fine-tuned. Select the right model by assessing your needs for hosting, performance, customization, and privacy.
- Design prompts: Next critical step is prompt engineering. This will significantly decide the effectiveness of your GenAI solution. 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 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 while using it run-time.
- Assess costs and benefits: GenAI solutions come with a cost — this could be the cost of querying an online LLM API, the infra cost of hosting a local LLM model, or the manpower 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 entire 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 boundaries of what is conceivable.
