Recently, people worldwide have been concerned about artificial intelligence (AI) and its eventual “takeover” of society. Still, few could have predicted that it would start in literature and art.

Thanks to ChatGPT, a chatbot created using OpenAI’s proprietary GPT-3 technology, OpenAI is back in the spotlight after dominating the internet with its AI image generator, Dall-E 2.

Since ChatGPT’s release via an API, Twitter and LinkedIn have been flooded with creative examples of people finding exciting and innovative ways to use this groundbreaking technology. From writing children’s books, offering weight-loss plans, and even providing programming advice!

^{Screenshot from a response given by ChatGPT}

To try ChatGPT yourself, follow this link, sign in, and ask anything. During the research preview, using ChatGPT is free.

Why all this excitement?

Many say it could be better than it promises. Others love it and argue that it can surpass Google’s search engine.

Nonetheless, we will let that discussion for the latter is essential to grasp that for many, especially those agnostic to Machine Learning and Artificial Intelligence, having a platform (API) that allows for a taste of the potentials of AI creates a high demand for these types of services.

GPT is not new in the community. Other versions have been around for a while, but this model has crossed a threshold since it is genuinely helpful for various tasks, from creating code to generating business ideas to writing a wedding toast and e-mails.

While previous generations of the system could technically do these things, the quality of the outputs was much lower than that produced by an average human.

But what is GPT?

GPT is short for “Generative Pre-trained Transformer,” a significant language model developed by OpenAI that has been fine-tuned for responding to prompts conversationally.

It is a type of transformer model based on a neural network architecture that was introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017.

A transformer model consists of multiple attention layers, allowing it to process input sequences in parallel rather than sequentially, making it much faster to train and use than previous models such as RNNs (recurrent neural networks).

GPT is, therefore, a machine-learning model that uses deep learning techniques to generate human-like text. It does this by predicting the next word in a sequence based on the words that came before it.

Training GPT on a large text dataset allows it to learn the patterns and structure of language and generate coherent text that resembles human writing.

It can be used for various natural language processing tasks, including language translation, text summarization, and question-answering. It has also been used to generate synthetic text, such as news articles and stories, and has even been used to create code.

How does it work?

Using the same techniques as InstructGPT, Chat GPT trained this model using Reinforcement Learning from Human Feedback (RLHF), with minor variations in the data collection configuration.

The model was first trained in text databases from the Internet. These contained a staggering 570GB of material that was collected from books, web texts, Wikipedia, articles, and other online literature. Even more precisely, the algorithm was fed 300 billion words.

The initial model was trained through supervised fine-tuning, in which human and AI instructors acted as both the user and the AI assistant in discussions. The trainers then had access to sample written recommendations to aid in creating their responses.

The InstructGPT dataset, which was converted into a dialogue format, is then combined with the new dialogue dataset.

^{Image by OpenAi}

The model is planned to produce a good result, but more is needed to guarantee it will. When it makes a mistake, the OpenAI team returns the correct response to the program to teach it and aid in knowledge development.

This is called the reward system in reinforcement learning models; here, in particular, according to the quality of the responses, a rank is given, and the model learns.

OpenAI collected this data by listening to all AI trainers talk with the chatbot. They randomly picked a model-written message, sampled many potential conclusions, and asked AI trainers to rank them. Finally, using these reward models, they fine-tuned the model using Proximal Policy Optimization.

To become the ultimate know-it-all, this technology constantly improves its comprehension of prompts and inquiries while making educated guesses about the next word.

Imagine it as a far more advanced, intelligent version of the autocomplete software you frequently see in writing software or emails. Your email program prompts you to begin typing a sentence before you have finished it.

Limitations Pointed by OpenAi

Despite the impressive performance of GPT-3, there is still room for improvement. Some of the limitations of the model can be found in OpenAi’s Blog post, and others are reported by users and can be found spread across social media posts:

• The model needs to gain more knowledge of the world after 2021. It needs to be made aware of world leaders who came into power in 2021 and cannot answer questions about recent events.
• Sometimes provides answers that are correct but need to be revised or revised. Fixing this problem is difficult because:

1. There is currently no source of truth during RL training;
2. Making the model more cautious makes it decline questions that it can answer correctly;
3. Supervised training deceives the model because the best response depends on the model’s knowledge rather than the demonstrator’s.

• • It is sensitive to repeated attempts at the same question. For instance, the model might claim not to know the answer if the question is written one way, but with a simple rewording, it could respond accurately.
  • The model frequently employs unnecessary words and phrases, such as repeating that it is a language model developed by OpenAI. Over-optimisation problems and biases in the training data cause these.
  • The model should ask clarifying questions when the user provides an uncertain query. Instead, it typically makes assumptions about what the user means.
  • The model attempts to decline unsuitable requests, although it occasionally follows lousy advice or behaves biasedly. OpenAi anticipates some false negatives and positives for the time being by leveraging the Moderation API to alert users or prohibit specific categories of hazardous content.

The Opportunities of AI from a Business Perspective.

Companies that wisely utilize AI technology might benefit significantly from tools like ChatGPT. Chat-based AI can improve human productivity by automating monotonous jobs and fostering more interesting user interactions.

Here are a few of the ways companies can use tools like ChatGPT:

• Compiling research
• Drafting marketing content
• Brainstorming ideas
• Writing computer code
• Automating parts of the sales process
• Delivering aftercare services when customers buy products
• Providing customised instructions
• Streamlining and enhancing processes using automation
• Translating a text from one language to another
• Smoothing out the customer onboarding process
• Increasing customer engagement, leading to improved loyalty and retention

Many businesses have much room to grow in customer service and internal productivity. Companies can automate numerous operations traditionally carried out by people and significantly reduce response times using AI-powered technology to produce responses for their customer care chatbots.

According to research by Opus Research, 48% of consumers do not care whether a human or an artificial chatbot assists them with a customer service inquiry, and 35% of consumers want to see more businesses use chatbots.

ChatGPT and AI can be helpful in any situation where you need to generate natural-sounding text based on input data.

On the other hand, the opportunities of AI can turn into threats if your competitors successfully leverage this technology and your company does not.

What Comes Next?

If ChatGPT made you excited about the potential that Artificial Intelligence and Machine Learning can have on your business, contact us via email.

While you wait to try what GPT-4 can do, you can leverage your data. Take a look at our success stories!

https://bi4allconsulting.com/en/knowledgecenter_categoria/success-cases/