AI has reached yet another massive milestone. Throughout the course of 2021, we’ve seen developments in quantum computing, xenobots (tiny living robots that can reproduce) and even the discovery of new exoplanets.
But this time, it’s all about big advances in Natural Language Processing (NLP).
You know. That ability computers have to understand, process and even respond to human language. The same technology that listens in on your conversations, ‘reads your mind’ and surprises you with the most ideal recommendations.
It’s only getting better. And the use cases are absolutely remarkable.
What is Natural Language Processing?
Computers communicate through code. And humans communicate through language. Natural Language Processing (NLP) is a way to bridge this gap.
Under the umbrella of AI, NLP seeks to interpret, understand and make use of human language in a way that is understandable to us. It essentially allows machines to understand what we say or write and respond to it in discernable human language.
And because people are so diverse and have a large variety of methods to communicate. Whether through formal or informal language, jargon, or with accents, NLP and text analytics allow for computers to understand a user’s intent and act upon it.
The Benefits of NLP
Having the ability to communicate with a computer is a massive benefit in and of itself.
The possibilities become virtually endless, as interoperability allows for the seamless exchange of information between users and devices.
You’re able to gauge, capture and play with customer intent. You’re able to automate and optimise the exchange of information within an organisation. You even have access to digital assistants that can do purchasing, answer questions and make bookings.
Making effective use of NLP comes with huge competitive advantages, especially in an increasingly digitised world.
History of GPT
When OpenAI released the General Pre-Trained Transformer (GPT-1) model back in 2018, NLP was to be changed forever. By flattening their competition (outperforming the best language models at the time), GPT-1 took the world by storm.
Before GPT-1, most NLP models were being trained using supervised learning, which posed major limitations. So OpenAI took a different approach. They used a combination of unsupervised learning and supervised learning, resulting in a far more effective model.
And with each passing year, this powerful NLP model is only getting better.
A year later, GPT-2 was released and made an even bigger impact on the world around us. With the effort put into their 40GB dataset (8 million web pages) and 1.5 billion parameters (10x more than GPT-1), it was bound to be a work of art.
And a work of art it is.
With the right training and fine-tuning, GPT-2 is capable of doing some remarkable things. It can write poetry, stories and even scripts (a person trained it on 1000 hours of Batman and got a humorous, but well-formed script for a movie).
On top of that, conversational chatbots also got a major facelift. Together with transfer learning and GPT-2, you can build a chatbot in a matter of days, as opposed to a few months.
Welcome to the Stage, GPT-3
Like the previous versions, GPT-3 was released the following year. However, it was limited to only a select number of people, where a waiting list would have potential users left in the dark.
The reason for the big wait? Safety concerns. OpenAI discovered that they had no real control of their GPT-3 users after an incident concerning a chatbot project.
An early user of GPT-3 developed a chatbot service called Project December that allowed users to tinker with particular GPT-3 instances. A user would later develop a chatbot with the personality of his late fiancee.
Although it did help him deal with his loss, OpenAI saw the potential harm that it could cause and made for a swift intervention.
As of November 2021, the GPT-3 waiting list was no more. Now developers and businesses around the world have full access. With a few countries excluded.
(Here’s the list of supported countries).
Now, with 100x more parameters than GPT-2 (at 175 billion) and 45TB of text data, this state-of-the-art model is about to strengthen our relationship with AI even more.
Real-World Benefits & Use Cases of GPT-3
With the huge potential that NLP offers a wide range of industries and enterprises across the globe, GPT-3 aims to push everything up a notch.
It has the potential to automate tasks that require complex language understanding and specific technical proficiency. It can interpret and translate complex documents, take actions and even generate code.
Think of all the different areas that it could be applied in. Technology (automation), customer service (assisting customers and even strengthening relations), marketing (content creation), or sales (identifying pain points and sentiment).
The use cases are nothing short of incredible. In fact, just a scroll through Twitter will reveal to you a surprising amount of projects that are being built using GPT-3.
Front End Design
Ever wanted an application to understand your requests and to do the code, as well as design work for you?
ML & Deep Learning Framework Design
GPT-3 makes code look easy. By simply punching in a request in English, you can have an entire framework formed in code.
ChatBots & Search Engines
Chatbots are only going to get better at conversations and answering your questions. It could even be the inception of newer search engines that aren’t driven by agendas.
Memes
Indeed. If you’re ever to run out of memes, AI has your back. GPT-3 has made it possible to make funnier, more coherent memes.
GPT-3 Is Taking the world by storm!
It’s everywhere. Developers are having huge amounts of fun with the capabilities that this powerful API offers. And our world is only going full steam ahead with it.
The GPT-3 model promises to inject new meaning and value into our increasingly digitally-driven lives. And at the very least, it will have us in tears with funny memes, or far-fetched scripts.
