Yes - you've heard of ChatGPT. Now, imagine being able to easily train ChatGPT on data for a specific use case - creating a customized version of it for your own application.
For example, you train ChatGPT on your company's sales and marketing information and then almost instantly, create an advanced chatbot for your website that is as powerful as ChatGPT but is also fine-tuned to have all the specific knowledge about your business. Though this chatbot example is a very simple case of this custom AI building, it showcases a very important point. People will find it incredibly powerful and useful to be able to “stand on the shoulders of giants” and piggyback off the work of the most powerful open source models when building their own AI applications. We aim to make that easy and possible with xMagic.
In a way, this is similar to the increase in accessibility of computers over time. Analogous to AI nowadays, in the past, computers took up whole entire rooms and were extremely costly to run. They were only accessible to and practically applied by large corporations and governments. Its uses were restricted for certain functions that either had a substantial return on investment or were well funded. The full potential of the computer would not be realized until later when it became more accessible to be experimented with by the public. Over time computers became smaller and smaller, and its parts, programming, and code became more publicly available. Today, anyone with some knowledge of computers and access to the internet can custom build their PCs and create new applications on these PCs with ease. Individuals have capability to build new innovative solutions with the computer - in ways beyond the imagination of what the original creators of computers had intended.
That's what we intend to do with AI at Stochastic with xMagic - give individuals and companies the ability to easily build their own little AI agents using powerful open source models, but fine-tuned for more creative and innovative use cases in ways beyond what big tech currently envisions for AI.
How costly is it to build a chatbot now? Let's look at further at the chatbot example. According to professional chatbot developer BotsCrew, when building a chatbot for a business one currently has to deal with 6-24 months of time,$150k in costs or more, and funding and managing a development team consisting of 4-6 people, including machine learning engineers and data scientists.
Currently, in order to make a chatbot that isn't terrible, it is necessary to follow and perfect 5 general steps.
As you can see, this is a long and painstaking process. For most companies, even ones that are technology based, doing this at scale is challenging. In practice, training and tuning a chatbot can be a time-consuming and resource-intensive process, particularly for larger datasets and more complex use cases. Overall, the current cost of building and maintaining a custom chatbot can cost up to hundreds of thousands of dollars, take 12-24 months of time, and a team of 4 to 6 data scientists.
With xMagic, the steps to building a chatbot with xMagic would potentially look like this:
xMagic streamlines the chatbot creation process from 5 long steps to 3 quick steps, and particularly the last few time consuming steps of building, training, testing, refining, deploying, and maintaining the chatbot will be significantly faster.
To summarize, xMagic would allow you the ability to easily dump in your personal data in any format, pdf, txt files, csvs, and put it into something called a knowledge base. Once this knowledge base is sufficient enough, xMagic will automatically fine-tune and optimize open source LLMs like ChatGPT to be proficient for your specific application. These would retain the same power and accuracy as before but be additionally trained on your personal data, within a short time be ready to be deployed and used. Over time after deployment, as more users use your custom AI application more, your system's knowledge base grows and your custom AI will actively learn and improve from this knowledge. This includes being trained on additional data, human in the loop feedback, learning from user preferences, and behaviors.
As a result, users will have more budget, manpower, and time to contribute to other important functions. Your company won’t have to spend months of hard work and brain power, while also getting a chatbot that is likely more powerful than making it the traditional way due to using an already pre-trained open source LLM model.
This is our long term vision of xMagic. Creating the chatbot or any sort of powerful AI application with xMagic would take no more than thirty minutes, making it a feasible option for individuals or teams of any size. For creating a chatbot, the five-step process is reduced to three steps, which are expedited by xMagic. By simplifying this process, you can get a very intelligent and powerful chatbot running in 30 minutes, which is a small fraction of the time it would take normally - also only one data scientist is needed, and the costs are significantly cheaper. With this tool, businesses can integrate AI effortlessly, without requiring deep knowledge of AI, and with significantly less time, opportunity cost, and monetary investment.
Ultimately, our driving principle with xMagic is your data, your cloud, and your models. No one else is touching your data. You can integrate and connect xMagic to existing or new pipelines or workflows.
Right now, xMagic is in its very early stages. Currently it is an AI assistant for your own files - a chatGPT-like app for your personal documents. Upload any text files - a research paper, cooking manual, or transcript you don't have time to read, and ask your question. It'll find the answer you're looking for fast while also providing knowledge from outside sources - all with AI. This makes it easier to sort through information, whether you're a student, researcher, worker, or a business. Test out xMagic for free in its early stages here.
Finally, subscribe to our newsletter to hear about our weekly progress with xMagic. We are working every day on improving the platform to get us closer to the goal described in this blog post.