For this week’s entry on our blog, we will explore the essential information you need to know to decide whether ChatGPT — the increasingly popular and slightly controversial new AI software program— will kill your company.

I’ll explain what it is, how it came to be, and the goal behind it. We would delve into the larger discussion of artificial intelligence, and in the end, by the closing paragraphs, I would have helped you answer some of the most pressing questions you have regarding this new phenomenon called LLMs, including the one that serves as the foundation for the program that is the focus of this article.

That said, let’s start with an understanding of what ChatGPT is.

Think NLP, then ChatGPT

“Generally speaking, AIs pre-train using two principle approaches: supervised and non-supervised. For most AI projects up until the current crop of generative AI systems like ChatGPT, the supervised approach was used. —David Gewirtz. 

When I began the research for this article, one of the things that became obvious to me was that people didn’t truly understand what goes on behind the scenes of the new ChatGPT platform that we’ve all been excited about for weeks on end now.

The huge amount of time it would take to fully explain how it works and the tension around whether it should be allowed to exist at all due to its potential as a threat to humans and job security doesn’t help the case either. 

On the basic level, ChatGPT represents a class of artificial intelligence chatbots -developed by a company called OpenAI – that provides semantically meaningful responses to natural language queries as humans do, based on the context and intent behind these queries.

One brief exploration I love about what ChatGPT is, how it works, and what its purpose is comes from David Gewirtz, who is a computer scientist and distinguished lecturer at UC Berkeley. 

David, in one of the articles he contributes to various websites, mentioned how the technology’s unique power, which is its ability to mimic human communication, lies in its ability to parse questions and produce fully fleshed-out answers that are pulled from publicly available information or data. 

According to him, the reason for the buzz surrounding the technology is due to the use of a revolutionary new training approach.

The ‘non-supervised’ training approach for large language models, contrary to popular belief, became suddenly feasible through OpenAI’s research leading up to ChatGPT’s development and introduction.

This fact is reflected in its “ChatGPT” name, as in ‘Generative Pre-trained Transformer’. 

To dive a little into the technical description, ChatGPT is what you can call a computer program that leverages natural language processing methods, which aim to allow computers to harness massive amounts of data to generate ‘learned intelligence’ that gives it the ability to respond to any query as a human would.

A big part of the reason for ChatGPT’s success is that the program is believed to be better than any other before it at producing sentences that sound like they were written by a human. 

Since it’s the non-supervised training approach that was used to develop ChatGPT’s underlying model, which makes it so different from others like itself, we’ll move on to look at what this approach is in the next section for a more comprehensive understanding. 

The Non-supervised Training Approach

It would be impossible to anticipate all the questions that would ever be asked, so there really is no way that ChatGPT could have been trained with a supervised model. Instead, ChatGPT uses non-supervised pre-training — and this is the game changer. —David Gewirtz. 

In layman’s terms, what ChatGPT adopting a non-supervised training approach meant is that the program is built in a way that lets it understand the syntax and semantics of human language and uncover patterns from the underlying structure of data from the one it was given to memorize on its own before launch, and which serves as the repertoire where words are taken from to form human-language sentences response to users. 

It might help to read that again. 

It’s the sudden success and the apparent massive potential that came with the threat to human survival, should these models develop consciousness, that have led people to the kind of question that is the title of this article.

Due to how this training approach confers ChatGPT, a computer program, with an almost limitless knowledge in a way that has never been possible before – since its developer can just continue to train newer versions of the program with more and more information or data – these kinds of existential questions became necessary to ask.

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In contrast, where a supervised training approach is adopted, an AI program is trained to map certain outputs or answers to specific inputs or questions. 

An explanation of this approach was given by David, who noted that “For example, an AI could be trained on a dataset of customer service conversations, where the user’s questions and complaints are labeled with the appropriate responses from the customer service representative. To train the AI, questions like «How can I reset my password?» would be provided as user input, and answers like ‘You can reset your password by visiting the account settings page on our website and following the prompts’ would be provided as output.”

Where ChatGPT Gets Its Data From

According to OpenAI, the creators of ChatGPT, the program was trained on a dataset known as WebText2, which is a library of high-quality, well-annotated, and very large text data from websites around the internet, like Wikipedia and lots of books.  

It’s reliably reported that ChatGPT’s GPT model was trained on about 45 terabytes(1 terabyte = 1,000 gigabytes) of exclusively text data from these sources. 

This massive amount of data, according to David Gewirtz in a ZDNet article, is what “allowed ChatGPT to learn patterns and relationships between words and phrases in natural language [Human language] at an unprecedented scale, which is one of the reasons why it is so effective at generating coherent and contextually relevant responses to user queries.”

So, will it Kill Your Company?

When you think about it, the shrewd capability of ChatGPT and newer AI models like it that are certain to be fed with all the books and other such data that represent the entirety of the raw human knowledge that has ever existed in is likely to remain unknown for a while. 

At the moment, the potential that it could kill your company is there. People are already seeing how several occupations like writing, coding, law, etc., and companies that provide services in these industries could be heavily affected by the growing capabilities and adoption of artificial intelligence technologies such as ChatGPT. 

Although as a language model, ChatGPT in particular was mainly designed as a tool to provide information and assistance to individuals and organizations in terms of business insight discovery and recommendations, as well as to provide solutions, such as helping automate their customer service, improve their marketing strategies, optimize their operations, and so on, still, we cannot rule out the potential for doom. 

In other words, the possibility that AI models would ever be able to harm you or your business is there.

Although it is more likely that your company would face more serious challenges and struggle to compete with competitors that offer AI-powered solutions in the future, and as a result, die a natural death if it fails to constantly evolve and incorporate these AI tools,

When I asked ChatGPT itself this same question, it suggested that instead of worrying about the program killing your company, you should rather focus on how to use AI and machine learning programs like it to your advantage, just as I did with this article, and hope that you can continue to keep enough pace with the evolution of AI tools and technology in general to remain relevant for years to come.  

Final Thoughts

To proceed, you can learn how to create prompts and use ChatGPT efficiently from the next article on our blog, where you can also find insights from other articles on business, technology, and how we engineer some of our most interesting projects.