What is GPT-4?
If you’ve followed the artificial intelligence community closely, you might have come across the term “GPT-3” before.
This AI model, created by OpenAI, is a large language model used by a huge amount of people.
With tools in AI writing, AI marketing, AI sales, meme generation, and other areas being powered by GPT-3, the bar was set pretty high for its successor – GPT-4.
While the details surrounding this new AI model are still relatively scarce, we know that it will be designed to be even more powerful and efficient than its predecessor.
In this article, I’ll share with you what the AI world currently knows about GPT-4 and what potential implications this new model could have on the future of artificial intelligence.
Let’s get right into it.
What is GPT-4?
OpenAI opened up its powerful GPT-3 AI language model to the public in May 2020, and the model quickly became a sensation in the AI community.
GPT-3 is a neural network trained on a large text dataset, and it can generate human-like text.
This made it a potent tool for tasks such as machine translation, text summarization, and even creating new long-form articles from scratch.
While a few months ago, it was said that GPT-4 could be coming around mid-2022, it has still not yet been released.
What is known is that the number of machine learning parameters on this new model will likely be similar to GPT-3.
While initially, the number of parameters was said to be as high as 100 trillion possibly, this was denied by Sam Altman, CEO of OpenAI.
At first glance, this is a relatively small number of parameters, especially compared to other models.
For example, Nvidia and Microsoft released Megatron-Turing NLG last year, the largest and most dense neural network ever.
Coming in at 530B parameters, this model contains many data points.
However, smaller models have proven that companies don’t have to go as large to get excellent results.
Smaller models are much better at doing few-shot learning, where a model can classify and learn from a limited amount of data.
For example, some say that models such as Gopher or Chinchilla are better at doing various tasks than GPT-3, and companies have realized this when developing their models.
When comparing it to GPT-4, we’ll have to wait and see what the final model looks like.
However, it might be safe to say that the company has learned from the successful elements of such models.
Accuracy Vs. Cost
One crucial aspect that people tend to forget when discussing AI models is the balance between accuracy and cost.
More extensive models take an incredible amount of time, money, and resources to train, as computing resources that need to be put into preparing these larger models are immense.
However, the results are usually not much better than those of smaller models that can use the provided data to improve.
For example, GPT-3 was only trained once on a dataset, and while some errors were made, the model could still generate human-like text.
Seeking optimal models rather than the largest ones will likely be the way forward with artificial intelligence.
GPT-4 will likely be a good example of this, and it will be interesting to see how the model performs once it’s finally released.
Text-Only Vs. Multimodal Model
These two concepts refer to the type of data that is used to train the model.
A text-only model is trained on, you guessed it, textual data.
On the other hand, a multimodal model is trained on multiple types of data.
This can include images, videos, and even audio.
The benefit of a multimodal model is that it can better understand the data’s context.
For example, if you were to ‘show’ a dog picture to a text-only model, it would have no idea what it’s looking at.
However, if you were to show the same picture to a multimodal model, it would be able to understand that it’s looking at a dog and act accordingly.
The benefits of a multimodal model are evident, but the downside is that they are much more challenging to train.
Altman clarified in one question and answer segment that GPT-4 would not be multimodal (which is the model used by DALL·E and MUM), but a text-only model.
Again, this might go back to the fact that OpenAI is trying to make the model more efficient – rather than larger.
Sparsity and GPT-4
Sparse models that use different parts of the model to process various types of inputs have recently found success.
This might be because they can quickly scale beyond the 1T-parameter mark without suffering from high computing costs.
The benefits of sparsity also include the ability to process multiple types of inputs and data.
That said, a sparse model also leads to needing more resources; thus becoming such a big model is unlikely for GPT-4.
All indications seem to be that OpenAI has found a balance with GPT-4 in model size, and it will be very curious to see how the final product turns out.
Despite that, I can’t envision a future where this is repeated with other future models.
Since our brain relies on sparse processing to function, and artificial intelligence is based on imitating the brain, future models may, in fact, operate this way.
Aligning artificial intelligence with human values is a huge challenge that has yet to be solved entirely.
While GPT-3 was already pretty good at this, there are still some concerns about how GPT-4 will fare.
One of the main problems with artificial intelligence is that it can’t understand intentions or values.
It can only understand the data that it is given.
This is why there has been a lot of focus on creating value-aligned artificial intelligence.
GPT-4 will likely be a big step in the right direction.
However, there are still fundamental questions to be answered.
Solving challenges from a mathematical and philosophical point of view is still required to create a genuinely value-aligned artificial intelligence.
That said, with OpenAI’s commitment toward a healthy future for all, GPT-4 will likely be a big step in the right direction.
GPT-3 vs GPT-4
The most significant difference is the number of machine learning parameters.
GPT-3 uses as many as 175 billion, while GPT-4 will use as many as 100 trillion.
This is approximately 500 times the size of GPT-3.
As I mentioned earlier, bigger is not always better for AI models, so it will be interesting to see how the final product turns out.
GPT-4 For Users And Businesses
Whether you’re someone who uses the internet in your career, or simply someone that uses the internet to stay updated on what’s happening around you, get ready to see more artificial intelligence in the content you read on the internet.
For the former type of internet users, you should consider using GPT-4 to automate some of your business processes.
In addition, GPT-4 is likely to be integrated into many different applications, so it’s essential to be prepared for its release.
Here are some examples.
GPT-4 For Content Writers
Content writers will be happy to hear that GPT-4 is a transformer-based model for natural language.
This means that it uses deep learning to understand and generate text.
GPT-4 also uses AGI, or artificial general intelligence.
This means it can learn any intellectual task that a human being can.
Content writers will probably find that GPT-4 can help them generate content faster and more accurately than ever.
GPT-4 For Developers
Codex, the GPT-based model that generates source code, is a step closer to artificial general intelligence for developers.
Combining natural language processing and programming languages such as Python can make the development process easier for everyone involved.
This is a big step forward for industries such as robotics.
Traditionally, developers have had to hand-code every instruction for a robot.
With GPT-4, a robot could potentially learn how to code itself.
Of course, there is still a long way to go before this is possible, but the industry is moving in that direction.
GPT-4 For Artists And Designers
Artists and designers are two professions that have been impacted by artificial intelligence for some time now.
DeepMind, a subsidiary of Google, has been working on artificial intelligence for years, and their results have been impressive.
With AI art generators already able to take text input and generate images, GPT-4 is likely to have a similar impact.
This means that artists will probably be able to either use GPT-4 to generate ideas or create entire pieces of art on its own.
GPT-4 For Translators
Translators might be interested in this GPT language model because it uses the API from OpenAI to improve NLP capabilities.
This is important because it means they can help improve the accuracy of translations.
In addition, one might consider the way a person learns new languages.
Since the human brain can learn a new language by using synapses, GPT-4 might work similarly, as it uses a generative pre-trained transformer to learn from data.
This makes it possible for GPT-4 to learn from a large amount of data quickly.
This could be a big help for translators as they can get more work done in a shorter period.
GPT-4 For Marketers
Marketers need to know about GPT-4 because it’s a cutting-edge tool that can help them automate many tasks.
From labellers to chatbots, the benchmark for what is possible has been raised.
Wired magazine said that The Future of the Web as it pertains to marketing is AI-generated content, and GPT-4 might just be the tool making that future a reality.
GPT-4 For Salespeople
Salespeople have been some of the earliest and most enthusiastic users of artificial intelligence.
With the release of GPT-4, they are likely to find even more ways to use it to increase their productivity.
Fine-tuning of AI language models is an integral part of the sales process and allows for more targeted and accurate results.
From lead generation to customer segmentation, GPT-4 will likely have a significant impact on the sales industry.
GPT-4 For Data Scientists
The release of GPT-4 provides another step towards data science at an order of magnitude.
This always involves more training data than was previously available.
This will allow for the development of more accurate algorithms.
Additionally, GPT-4 might provide data scientists with access to a wider variety of training data sources.
This will allow for more AI research and development of robust algorithms.
GPT-4 – FAQ
How Does A Machine Learning Model Help In Writing Aid Apps?
A machine learning model uses a language modeling solution to generate natural language text automatically.
From inferring users’ intentions to generating all the automated copy one needs, such techniques can be beneficial in making a writing aid app.
Why Are More Parameters Not Always Better In Artificial Intelligence Models?
Having more data points can help improve the performance of a machine-learning model.
However, having too many parameters can sometimes lead to overfitting.
Overfitting is defined as a machine-learning model that performs well on the training data but does not generalize well to unseen data.
This means that adding more parameters would not improve the situation.
With Openai’s GPT-3 and GPT-4 models, we are seeing some of the most advanced artificial intelligence to date.
These new models are changing the landscape of many industries, creating previously impossible opportunities.
With the ability to input natural language and get an output of code, generate 3D images, or even create marketing copy, the applications for these new models are endless.
While the exact launch date of GPT-4 is currently unknown, I believe that we are only at the beginning of what is possible with machine learning and its impact on our everyday lives.
Further reading on AdamEnfroy.com: A GPT-3 chatbot can be one of the best content marketing tools for businesses dealing with customer experience software.
The great news is that building AI chatbots with GPT-3 is relatively simple with the right tool, so doing your research is vital when choosing the tool for your business.