When the ChatGPT wave hit, where were you? What were you doing? If you rushed to create an account with OpenAI or another artificial intelligence tool, then you’ve come to the right place.
This article is not an AI instruction manual, nor is it intended to convince you to use AI at any cost. Instead, it aims to share ideas and use cases related to these conversational robots with our community, in a transparent way, ensuring that technology serves humans, not the other way around.
Did you know that, according to Goldman Sachs, generative artificial intelligence could have a major global impact? They estimate that over the next ten years, global gross domestic product (GDP) could increase by 7%, or around US$7 trillion, while productivity could rise by 1.5%.
In this article, we will discuss the use of artificial intelligence in daily life and how AI tools can redefine the way you and your teams work.
*Uzinakod would like to thank CRIM and Mr. Houmane Zolfaghari for inspiring this article and for the helpful diagrams below.
Case Study: The Evolution of ChatGPT by OpenAI
Generative AI is a technology that creates new content—such as images, text, or sound—that resembles human creations. These conversational agents use advanced algorithms and extensive training data to analyze and learn from large datasets. This enables them to communicate effectively with users and generate new content that respects, mimics, or is inspired by the original data.
Generative AI is often referred to as a “Transformer” because of its ability to examine and decode the structure of input data, reinventing it in a unique and innovative form.
Source: Conference on generative AI by Houman Zolfaghari, CRIM Scientific Director
Optimizing Content Creation with ChatGPT-2
This simple diagram illustrates how ChatGPT-2 functions, highlighting the role of the transformer in utilizing training data. The process involves inputting text into the system, allowing the transformer to suggest an improved version or even a translated version of the text.
However, ChatGPT-2 has its limitations in content creation. Its relatively smaller size compared to newer large language models reduces its ability to capture complex nuances and generate diverse, coherent responses. Additionally, the quality of ChatGPT-2’s output can be influenced by the training data, potentially leading to inaccuracies, synthetic data, or misinformation.
The overstimulation of the amount of data provided to conversational agents each day can lead to the generation of erroneous information if the initial request lacks context. When using ChatGPT-2, one of the best practices was to carry out additional research to verify the accuracy of the information and produce appropriate content. This practice remains relevant with newer versions of ChatGPT, although the tool has become much more accurate.
Source: Conference on generative AI by Houman Zolfaghari, CRIM Scientific Director
Creating Content with ChatGPT-3
This diagram illustrates the rapid evolution of ChatGPT-3. In this new version, the conversational AI “Transformer” now incorporates a new module for adaptation/specialization, allowing it to process various types of input and instructions. This improvement allows ChatGPT-3 to leverage multiple datasets and offer personalized responses, significantly improving the quality of content creation by contextualizing information and adhering to best practices.
Despite these advancements, it remains crucial to maintain a critical approach towards virtual assistants. Ensuring the accuracy and truthfulness of information generated is paramount. While conversational robots have made significant strides in producing human-like content, there are inherent risks in the accuracy of responses that could potentially have far-reaching consequences.
Source: Conference on generative AI by Houman Zolfaghari, CRIM Scientific Director
The Evolution Towards ChatGPT-3+
In the latest versions of ChatGPT, prompt engineering has become increasingly prominent. Consequently, ChatGPT 3+’s add-on module now functions as a fine-tuning tool, enhancing the capabilities described above and reducing the occurrence of inaccurate content.
There are two types of results:
- The initial “raw” result, which must be assessed on four essential points: reliability, conformity, safety, and uncertainty.
- The actions humans take with the raw results.
In short, the training of language models has rapidly evolved from an artificial intelligence system similar to a simple text translator to a model capable of reasoning. These advanced models can analyze and learn from vast datasets, drawing on best practices available on the web.
Many experts in generative AI are continually working on the life cycle and training of tools associated with large language models to ensure the quality of results received by users of conversational robots.
An Overview of Generative AI
Several generative AI tools have recently entered the market, especially since the arrival of ChatGPT. These include:
These language models operate independently and offer a wide range of services, from image and text generation to data processing and video creation.
But why are they so useful? Virtual assistants have many positive aspects for modernizing work and learning methods. They are highly efficient at carrying out simple tasks, significantly increasing productivity and reducing associated costs.
However, it’s important to maintain a critical eye on the communication of sensitive information, the accuracy of the data generated, and the production of inappropriate content to avoid significant repercussions.
Following best practices when using conversational robots will not only save you time but also ensure the security of your data.
Some Uses for Generative AI
There are many uses for artificial intelligence, and some of you are probably already using it regularly. We’ve grouped these uses into three main categories:
- Content Generation: Creating emails, marketing messages, documents, contracts, computer code, and more.
- Document Intelligence: Summarizing documents or entire corpora, analyzing complex content, reformulating, translating, and more.
- Interaction: Implementing chatbots, facilitating real-time dialogue, and interacting with content.
To give you a more concrete idea, here’s an example of what generative AI can do in a customer service center.
Today, artificial intelligence is capable of analyzing a conversation between a customer and a customer service agent. During a call, AI analyzes the customer’s responses and intonation to determine their level of satisfaction in real time.
At the same time, at the end of the interaction, the agent receives an AI-generated summary email that not only summarizes the key points of the conversation but also incorporates the history of previous interactions. As a result, the customer service agent is presented with personalized, contextualized response scenarios, helping to improve customer service.
Here, generative artificial intelligence plays a crucial role in both processing and analyzing information, as well as formulating precise recommendations. This saves considerable time for the customer service agent while maintaining optimal quality of service for the customer.
A basic, potentially free, conversational robot will not be able to meet this kind of mandate. That’s why it’s possible to develop a large language model, known as custom, which can be adapted to your company’s reality. This approach allows for filtering of input data, management of sensitive information, and implementation of safeguards to protect customer data. The information is then stored in databases that may be useful in similar contexts but are not accessible to the general public.
Generative AI Applied to Corporate Training
When we examine the uses of generative AI, it becomes clear that it is a powerful tool seamlessly integrating into our daily lives. On one hand, it serves as an excellent assistant for performing everyday tasks, and on the other, it acts as an effective teacher for learning essential concepts about a product, service, process, Excel formula, or programming lines. This leads to the question: what role should AI play in learning methods?
Generative AI undeniably represents a significant innovation for enhancing your collaborators’ skill levels. Think of it as a pathway for training your employees and advancing them in their professional environment. Generative AI is highly adaptable, allowing you to present essential tasks to your employees, generate complex or simple images for role-playing, or even function as an interactive assistant during training sessions, where employees can ask questions and receive quick answers.
Your Partner for AI Implementation
It’s important to remember that the power of these AIs lies in their ability to imitate by learning from existing models. This means they can create new data without truly understanding the meaning of what they’re doing. Learning occurs through imitation.
At Uzinakod, we believe that successful AI implementation requires a trusted partner who can accurately interpret your needs and translate them effectively. Whether it’s integrating AI into your data processing, IoT, or web development, our AI experts are here to support you every step of the way. Contact them today.
