With GPT suddenly receiving a lot of attention, one may liken it to a highly anticipated blockbuster movie that everyone is eager to watch, given its potential to revolutionize our understanding of AI, language, and communication. However, it begs the question, what is the reason for all the excitement?
It's astonishing to think that AI is advancing so rapidly that it's easy to overlook the fact that GPT and related technologies, such as BERT, have only been in existence for less than five years.
Now that history is boring to some of us, I won't include all the details from those long five years. (I have included in the reference some details). But what we care about is how it helps us today. So if you know about GPT already, feel free to skip over to the next section
A Brief Introduction
GPT is a new type of computer program that's really good at understanding and using language. It was created by a company called OpenAI in 2018, and since then, it's become very popular. GPT is able to learn patterns and structures of language by reading and analyzing a lot of books, articles, and websites. This helps it to write things that sound like they were written by a human, and it can even translate languages and make chatbots. GPT keeps getting better and better, and as more people use it, we'll probably see even more cool things it can do.
One of the most amazing things about GPT is that it can create text all on its own. When it "generates" text, it's basically using what it learned from reading all those books and articles to come up with new ideas and sentences. Now that is Generative, the G in the GPT.
Imagine this - Someone may have great thoughts and ideas, but may not be very articulate. That is where GPT excels. It can take those thoughts and convert them to text. The problem is that sometimes it can be over-imaginative! But more on that later. For now, let’s focus on what it can do.
How does GPT work?
GPT can be thought of as a child learning about the world. Just as a child is exposed to a vast array of experiences, conversations, and interactions with people, GPT is trained on a massive dataset of text, including books, articles, and websites. In the same way that one learns from these experiences, GPT is able to learn the patterns and structures of language through its training process.
Our brain is made up of interconnected neurons that work together to process information. GPT, similarly, uses a neural network, which is made up of interconnected nodes that process information. The neural network enables GPT to learn from the vast amounts of data it has been trained on, and to generate new text that resembles the patterns and structures it has learned.
The transformer architecture is like the child's brain developing over time, becoming more sophisticated and adept at processing information. It allows GPT to process and generate text with incredible speed and accuracy, making it one of the most advanced natural language processing tools available.
When you use GPT, it's like asking a child a question and getting an answer based on what the child has learned so far. GPT processes your input and generates new text that is similar in style and content to what you input. This makes GPT a powerful tool for a wide variety of applications, including language translation, content creation, and even chatbots.
Overall, GPT is a remarkable example of how AI can be used to process and generate natural language, much like a child learning about the world. As AI technology continues to evolve and improve, we can expect to see even more amazing advancements in natural language processing, and GPT and similar models are sure to play an important role in this exciting new field.
Is GPT smarter than us? Not so fast!
Here are some fun facts about GPT
The largest GPT model to date, GPT-3, was trained on over 45 terabytes of text data, which is equivalent to roughly 3 million books. That is comparable to all of the books in the Library of Congress!
According to OpenAI, GPT-3 has over 175 billion parameters. Parameters are like knobs or buttons that can be adjusted in a machine learning model to change the way it works. With 175 billion of these "knobs" or "buttons", you can imagine how fine-tuned GPT is in generating text.
GPT-3 can perform a variety of tasks, including translation, summarization, and answering questions, with high levels of accuracy.
How can I benefit from GPT?
Here are some easy ways in which an enterprise can start using GPT technology:
- Chatbots: Chatbots are one of the most common applications of GPT technology in enterprise settings. By using GPT-powered chatbots, businesses can provide 24/7 customer support, improve customer engagement, and automate routine tasks such as appointment scheduling.
- Content creation: GPT can be used to generate content such as product descriptions, social media posts, and email newsletters. By automating the content creation process, businesses can save time and resources, while also improving the quality and consistency of their messaging.
- Translation: GPT technology can also be used to translate text from one language to another, which can be useful for businesses operating in multiple countries or catering to a global audience.
- Data analysis: GPT can be used to analyze large amounts of text data, such as customer feedback or social media posts, to gain insights into customer preferences and behavior.
- Personalization: GPT can also be used to personalize content and marketing messages for individual customers, based on their previous interactions and preferences.
Overall, there are many easy ways in which an enterprise can start using GPT technology to improve their operations and customer experience. By leveraging the power of GPT, businesses can save time, improve efficiency, and gain valuable insights into their customers and markets.
Yada yada yada! So Why should you care?
- Scale: GPT can process and analyze a vast amount of data much faster than humans. For example, GPT can read and analyze millions of pages of text in a matter of minutes, whereas it would take humans days or even weeks to do the same.
- Consistency: GPT is not subject to the same biases and cognitive limitations as humans. This means that GPT can generate consistent and accurate results without being influenced by personal beliefs, emotions, or fatigue.
- Adaptability: GPT can learn from and adapt to new data much faster than humans. This means that GPT can quickly update and improve its performance based on new information, whereas humans may require additional training or education to do the same.
- Efficiency: GPT can perform tasks much more efficiently than humans, especially in areas that require a high level of repetition or rote memorization. This can free up human resources to focus on more complex and strategic tasks that require creativity and critical thinking.
It can’t be all good news. What’s the catch?
While GPT has shown impressive capabilities in many areas, there are some potential pitfalls that should be considered:
- Bias: GPT can reflect the biases present in the data it was trained on, which can perpetuate and amplify existing societal biases. It's important to ensure that the training data is diverse and inclusive to avoid biased outcomes.
- Misinformation: GPT can generate fake or misleading information if it is trained on data that is inaccurate or intentionally misleading. It's important to carefully monitor and evaluate the quality of the data used to train GPT to avoid propagating misinformation.
- Ethics: GPT can generate content that is unethical or offensive if not properly supervised. It's important to establish clear ethical guidelines and oversight to ensure that GPT is not used inappropriately.
- Transparency: GPT can be difficult to understand and interpret, which can make it challenging to identify and correct errors or biases. It's important to ensure that GPT is transparent and explainable to promote accountability and trust.
- Environmental impact: GPT requires significant computing power, which can have a negative impact on the environment. It's important to consider the environmental impact of GPT and explore ways to make its development and use more sustainable.
- Job displacement: GPT and other AI technologies have the potential to displace human jobs, particularly in areas that involve routine or repetitive tasks. It's important to consider the social and economic impacts of GPT and explore ways to mitigate any negative effects on employment.
How can an organization avoid these pitfalls? That is the subject of this article.
Where do we go from here?
This is the more exciting part. We are just getting started. Some of us old enough to remember, can remember the time when emails were just a new fad. We may even remember when we got our first email id. Or our first smartphones. We are entering a new era here.
That said, enterprises can use GPT today. The key is to bring a level of enterprise grade predictability in responses. That is what Fonor is about!
Want to know some more?
The underlying technologies of modern AI models, such as GPT, BERT, and their variants, are largely based on a set of innovations in deep learning and natural language processing that have emerged over the past decade or so.
One of the key breakthroughs that enabled the development of these models was the introduction of the transformer architecture in the seminal research paper "Attention Is All You Need" by Vaswani et al. (2017). The transformer architecture replaced the traditional recurrent neural networks (RNNs) with a self-attention mechanism that allowed the model to capture long-range dependencies and context more effectively.
Another influential paper in the development of AI language models was "Improving Language Understanding by Generative Pre-Training" by Radford et al. (2018), which introduced the concept of pre-training large neural language models on massive amounts of data before fine-tuning them for specific tasks. This approach has since become a standard technique for many natural language processing tasks.
While there is no one research paper that all modern AI language models can be traced back to, many of the techniques and ideas used in these models build on earlier research in deep learning and natural language processing, and there continues to be ongoing innovation and development in these fields.