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The rapid advancement of generative models, including the release of ChatGPT and StableDiffusion, has led to a significant increase in user adoption. Generative Artificial Intelligence (GAI) has opened up new opportunities for both businesses and professionals. Companies and Founders who saw its potential have already embraced generative AI.
Tools such as CoPilot, and Codex have improved the efficiency of developers and fastened software development cycles. Content creators and researchers are increasingly using GAI tools to assist them. These tools automate repetitive tasks and are very useful in Image, music, design and data generation tasks.
On the frontier of early adoption by users, sensitive data leaks lead many companies like Amazon, Samsung and Apple to ban their employees from using tools such as ChatGPT, third-party AI tools and Copilot for Security concerns. Hence, Security and data privacy tops the challenge list which is addressed before companies can tap into the power of Generative AI.
Some early adaptations have seen e-commerce sites successfully automate product description generation on their sites. At the same time, some companies have their custom customer support chatbot running. To Start One might or might not be all certain of the generative AI use case for their company with all the characteristics.
Given ever-evolving AI systems, incorporating and offering one’s own GAI needs the broadest view of one’s data pool. Navigating and determining the best way to add Generative AI to the product roadmap for your business. If you are starting on the expressway of GAI adaptation, read this exciting article about clear steps and challenges ahead on the way.
Type of Generative AI Adoption
There are broadly 2 kinds of Generative AI adoptions. The First type is streamlining existing processes by increasing quality and productivity through GAI. The early adaptations discussed earlier are of type 1. Developing Type 1 projects should be on an individual, single team or Department level. These projects are a must-do for every startup to race ahead in GAI adoption.
For an AI company type 1 initiatives alone will not cut. With Type 2 adoption, companies look at Innovation of vital business aspects and building on GAI. Type 2 efforts are riskier, and bigger and are essential to attract funding. Type 2 initiatives give companies access to new customers, new value chains and a competitive advantage over others who did not deeply embrace it.
An Example of a Type 2 initiative might be a speech generation company that currently provides text-to-natural speech generation in various accents of English. Integrating some local languages and accents will enable non-English-speaking customers to use the service and open up new market opportunities.
If you are looking for funding and don’t yet have 1 or more Type 2 initiatives going on, you will have a difficult time raising money from investors. Expectations of investors are quickly increasing as technology continues to advance.
Empower Teams with Generative AI
The best way to get on board the Generative AI journey is to build a continuous experimenting environment with your high intentions announced to the teams clearly. Motivate the teams by stating the benefits of Type 1 GAI adoption and get their focus on building it. Focus on employee training and safe tool adoption at early stages.
Department-level brainstorming sessions will improve awareness about GAI among the teams. Tell the teams about the strategies and approach of the company towards Type 2 adoption. Type 2 adoption requires rapid cycles of Experiment, test, and feedback loop. Have a team that monitors the complete type 2 adoption process end to end.
The GAI Challenge list
Despite all the boosts that GAI provides an organization, there are several factors that business leaders must consider when choosing GAI technology. Data generation quality, security and privacy risks can expose organizations to risks.
Tick Off the Secure Your Data Checkbox
Data leakage can be addressed in 3 ways. The first way is to choose the right cloud LLM provider and trust them. (Similar to Azure’s OpenAI service).
The second is to work with Large foundational models like GPT4 in a Domain-optimized and secure Architecture. This method uses API calls to OpenAI(if OpenAI LLMs are utilized) to fetch the response. OpenAI won’t track the data passed through API requests or use this data to train their models.
Here is an application of this approach PDF GPT: How to Build a Personal PDF Chat Assistant.
The third method is to Fine-Tune Open-source models or custom models with training data for your use case and host it on a secure cloud. To get an idea about this approach refer to Harnessing the Power of Open Source Models:
Hallucinations and Bias
Hallucinations refer to phenomena where the model generates incorrect text, which is not real and the response doesn’t make sense. This is because of the fact they are trained on huge data and they pick up unwanted patterns. This might be due to vague input or noisy data.
Though efforts have been made to make models less biased, they are still biased. There is a chance that models have caught this bias from unwanted data or patterns. For Example, Demographic Stereotypes in text-to-image generation models.
The model, when asked to generate an image of a doctor might always generate a white male with blue eyes and generate a black slim man when asked to generate a poor person image.
Such Biases are unacceptable and are needed to be addressed. Prompt Engineering is a handy approach that can be used to address hallucinations in text models. It can be also used to control and avoid possible biases in the model.
The Experiment, Measure and Feedback Loop
The teams must monitor the experiments by measuring and setting global metrics for all teams. The adoption of the GAI process is in itself close to a mini startup within, and so are the strategies you want to follow. At the team level weekly reportings and ensuring that you are headed in the right direction are necessary.
Pick a small number of KPIs for baseline measurement(not more than 3 at the top corporate level). The frequency at which you complete the loop indicates how fast the learning process is and the direction you are headed. To test your future long-term plans, start with the PoC of the project right away.
With the power of Generative AI in the hands potential of many teams has grown by leaps and bounds. Do the research before you start talking to a consultant, determine the roadmap from the steps discussed and kick-start your company’s potential as well with GAI tech. Finally, Educate your entire team on GAI, Embrace the change and Experiment with the right way to implement GAI.