From the outside looking in, artificial intelligence (AI) can be simple—more like a predicting tool that can automate processes. But it is much more than that. It can, in fact, initiate changes that would define our future, such as improving productivity across industries. And that is what GPT-3 is trying to achieve. But before we go into the nitty-gritty of this revolutionary technology, let’s first explain what it is.
What Is GPT-3?
GPT-3 is the work of the San Francisco-based AI research laboratory OpenAI. This technology is an autoregressive language prediction model that employs deep learning to come up with human-readable text. In simple terms, this neural network technology can produce text that can be indistinguishable from how humans use language.
It is the third version of the prediction model in the GPT series. With it, OpenAI hopes to initiate artificial general intelligence (AGI) or computers that can mimic the human mind with high accuracy.
While GPT-3 is not yet the AGI that OpenAI is hoping for, it does serve as a good stepping stone toward that goal.
How Does GPT-3 Work?
The GPT-3 working model starts with training. In this phase in supervised learning, the machine is taught to respond to an input with a desired output. When asked a question like “What is the first law of robotics?” It should reply with “A robot may not injure a human being, or through inaction, allow a human being to come to harm.”
In unsupervised learning, instead of teaching the machine to reply with a definite output, you give it an example to follow (focusing on the answer’s format) and allow it to choose the correct output from a database.
The example here is very simplistic, though. The processing that occurs is much more complicated since GPT-3 machines learn from 175 billion parameters and work on problems token by token (or in our example, word for word).
If you are wondering how GPT-3 actually works, you can watch this video:
Celebrating Its Strengths: Potential Applications of GPT-3
People are talking about GPT-3 because it is way better than any language program in existence. It can produce text that any human can read. And this breakthrough can be critical for companies that wish to automate most tasks.
GPT-3 can respond to text inputs contextually. For instance, companies can use it to amplify its customer service in a way that won’t make customers feel like they are talking to a computer. Today, the program is in private beta mode, and interested parties can sign up on a waitlist. Organizations with access to the program can develop some applications that include the following:
Language Translators
In a paper, the authors claimed that GPT-3 could be programmed to translate English to Spanish and French using context. One example is Revtheo, which uses a GPT-3-based dictionary, allowing users to look up the meanings of words depending on how they are used.
More than that, GPT-3 can rephrase entire English paragraphs to simple text. This technology is particularly useful in specialized industries, such as medical and legal, where the language used can be challenging for ordinary people to understand.
Mathematical Decoders
More than language learning, GPT-3 can learn math. Note that the program will not know all mathematical theories, but it can generate accurate answers to given equations, such as those used in accounting. Surprisingly, the program can provide the correct answers to two-number addition problems.
Perhaps this is because GPT-3 has been exposed to tons of structured data while in training. It seems that the system is configured to react to English inputs that come in structured data forms, such as eXtensible Markup Language (XML) or JavaScript Object Notation (JSON).
No-Code Programmers
GPT-3 can also directly generate computer programs. Numerous projects have been currently successful in doing so. Apple’s senior frontend engineer Antonio Gomez developed a three-dimensional (3D) scene using a JavaScript (JS) API by merely describing the parameters and elements to use.
These are just some of the potential applications of GPT-3, which show how priming is critical in generating structured outputs. This same scenario, however, also makes the program imperfect.
Dissecting Its Flaws: Challenges That GPT-3 Has Yet to Overcome
With enormous potential comes numerous drawbacks. Like most deep neural networks, GPT-3 remains a black box to humans. There is no way to study and inspect how its algorithms accomplish what they do. As mentioned, priming is important for producing structured outputs but it is mostly a trial-and-error process that can be time-consuming, making GPT-3 a resource hogger. Its processes require tremendous amounts of computing power, memory, and storage, putting a strain on users.
Some claim that the text that GPT-3 produces seems impressive at first but when working on long compositions, the system’s language becomes senseless and loses coherence.
Many are also concerned that GPT-3 can be used to amplify social biases, such as sexism and racism, because it cannot differentiate fact from fiction. GPT-2, for instance, was not released to the public because it can be used for some malicious applications like spamming and proliferating fake news. Considering that GPT-3 is more advanced than GPT-2, can it have a higher potential for misuse and abuse? That’s a question that proponents need to answer.
Is GPT-3 Different from ChatGPT? How?
Yes, GPT-3 and ChatGPT are different, but they are both based on the same underlying architecture, the GPT model.
GPT-3 is a large-scale language model developed by OpenAI that contains over 175 billion parameters, making it one of the largest language models in existence. It is trained on a massive corpus of text data and is capable of generating highly realistic human-like text in a wide range of domains, including language translation, question-answering, and text completion.
On the other hand, ChatGPT is a version of the GPT model that has been fine-tuned specifically for conversational applications, such as chatbots and virtual assistants. It has been trained on a dataset of conversational data and is optimized for generating natural and coherent responses to user input.
While both GPT-3 and ChatGPT share the same underlying architecture, there are some differences in their training data and optimization objectives. GPT-3 has been trained on a much larger and more diverse dataset, and is designed to generate high-quality text across a wide range of tasks. ChatGPT, on the other hand, has been fine-tuned for conversational applications and is optimized for generating natural and engaging dialog.
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Now that we know the answer to “What is GPT-3?” we now understand that despite being an exciting technology, GPT-3 also has its flaws. It needs to be accountable for its outputs. And its developers need to ensure that it doesn’t end up in the hands of people with sinister motives.
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