Products like Chat GPT 3 AI and GitHub Copilot, as well as the AI models that power them like;
- Stable Diffusion
- DALLE 2
- GPT-3
These are bringing technology into areas that were once thought to be only for humans. With generative AI, it’s possible that computers can now be creative. They can use the data they’ve taken in and their interactions with users to come up with original answers to questions.
They can make the following;
- Blogs
- Draw designs for packages
- Write computer code
- Even try to figure out why something went wrong during production
This new generative AI system is based on foundation models, which are large-scale deep machine learning models trained on large, broad, unstructured data sets (like text and images) that cover many topics. Developers can change the models to fit a wide range of use cases, and each task doesn’t need a lot of fine-tuning.
For example, GPT-3.5, the base model for ChatGPT, has also been used to translate text. Scientists have used an earlier version of Open AI GPT-3 to make new protein sequences.
So, the power of these features can be used by anyone, even developers who don’t know much about machine learning algorithms. Or, in some cases, people who don’t know anything about technology.
What you intend to do with the AI and how it will integrate into your business model should be well thought out before you invest in it.
It’s also crucial to consider the ethical and legal considerations that may arise from implementing AI in your organization.
Using ChatGPT (or a comparable AI language model) can help you grow your company in a few key areas:
- First, think about why you would need a chatbot. A chatbot should help you reach your goal, but it shouldn’t be the goal itself. It should fix a problem or make your customers more interested. Still, it has yet to replace your whole customer service department. Knowing the goal of your chatbot will help you decide how the conversation will go and what kind of chatbot you need. There are different chatbots, like simple FAQ bots, so-called “on rails” bots, and chatbots that let you type in free text. The less the chatbot is in charge, the more the user is in charge of where the conversation goes.
- It takes work to make a chatbot’s conversations work well. Not only should you make a persona that fits your brand’s personality, but the conversational interface should also be clean, and the chatbot should try to give people a good time. So, the conversation should be made by someone other than the developer. Instead, it should be made by a copywriter working with the marketing or communication department. It’s important to ensure the conversation flows correctly for the right goal. People feel more at ease having conversations with a chatbot than with a real person.
- There are a lot of different chatbot platforms, from ones that let you make simple FAQ chatbots to more advanced ones that can figure out what’s going on. Such chatbots’ awareness of their surroundings can add a lot of value because they can improve the user’s experience. Once you know what platform you want to use, you need to decide whether to hire outside help.
- The easy part is making a chatbot, partly because there are many platforms and developers. Integrating the chatbot into your systems is a lot harder, but that’s when the added value comes in. If the chatbot is connected to your system (like your CRM or database), and someone wants to change, say, their address, the chatbot can say, “Sure, give me your address, and I’ll change it for you.” Operational efficiency, customer satisfaction, and the NPS go up here.
- Developing is only one part. Testing is a very important part of any software development project. Most companies I talked to test the chatbot’s code, which is good news. Especially the people who make chatbots have strict testing procedures in place. There is a testing environment, an acceptance environment, and a live environment as part of these processes so that everything can be tested properly. You should test not only the code you write for your projects but also the software you use. Unfortunately, many organizations still need to test the third-party tools they use. Instead, they trusted the third party that their tool and the code were correct and had no bugs. People depend on and have a lot of faith in third-party tools. But it’s important to have the right checks in place.
- Conversations are always based on facts and lead to more facts. This information can be analyzed, and the results can be used to improve how the chatbot talks to people. But for any output text to train the chatbot, there must be a thorough testing process. For example, copywriters, not developers, should do any text that needs to be written. Also, especially in large organizations, the content that the chatbot says needs to be governed in some way.
- All of these analytics can help improve the chatbot in important ways. Reviewing the transcripts and looking for places where the chatbot didn’t understand what people were asking helps build up any datasets so that the chatbot can be retrained or places where the chatbot thinks it got it right but got it wrong so that the information can be fixed and the conversations can be made better. This kind of supervised AI machine learning helps the chatbot improve and keeps it from making mistakes like Microsoft’s Tay did when it learned on its own. The goal should be to improve the artificial intelligence chatbot over time, making it more aware of its surroundings and better able to understand what is being said.
Conclusion
It’s fascinating to think about the potential breakthroughs that generative AI could inspire for companies of all sizes and degrees of technological sophistication. But, administrators should always keep in mind the potential dangers associated with new technologies, especially when they are still in the experimental stages.
