GPT-3: What is GPT-3 and what can it do for your business?

There’s been a lot of talk about GPT-3 and generative AI in the news, on social media, and probably from every AI practitioner or vendor you talk to lately.

Everyone is very excited about the future that such AI tools hold.

But what exactly is this AI technology and what does it mean for your business and your AI problems? Let’s explore!

What is GPT-3?

GPT-3 is great language model Developed by Open AI. It is the successor to Open AI’s older language model, GPT-2, which was relatively small.

So what is a language model? A language model is a probability distribution over sequences of words derived from data. This probability distribution can then be used Complete the sentences, Confirmation of the correctness of the sentence, Validation of speech recognition predictionsTTranslation from one language to anotherand more.

As you can see, language models are quite powerful and are not new in concept. Language models have been around for decades.

Here’s an example of how the language model completes the sentence:

The car is about to ______

crash => probability 0.08 

stop => probability 0.92 

Predicted answer: stop

Using this general language model concept, GPT-3 is a giant language model that can create word order, Code, Translations, resumesor other types of data from a source input called a request.

Traditionally, language models are trained on small datasets because it is computationally expensive to train large language models. GPT-3, however, is trained on large chunks of data from the Internet, books, and Wikipedia, meaning it trains on billions of words. Additionally, GPT-3 is trained using a very deep and sophisticated neural network that helps it learn complex relationships between words.

Such training is not something we can easily replicate, as it can cost millions of dollars for each iteration of the training. Actually, it was about worth it 4.6 million dollars Preparation of GPT-3 using Tesla V100 cloud instance for 9 days. But what this level of sophistication means is that the GPT-3 can answer all kinds of questions and complete sophisticated tasks with a flick of the wrist. You can think of GPT-3 as a A super intelligent question and answer machine.

What can GPT-3 do?

Some of the capabilities of GPT-3 include:

• Predicting categories on textual data
• Generate appropriate source code based on description only
• Extracting relevant information from unstructured data to make it more structured
• Become your own therapy chatbot
• Translating text in one language to understand it in several other languages
• Writing content paragraphs with prompts
• Copying article titles
• Spelling correction
• and much more

Business benefits of GPT-3

So what are the benefits of GPT-3 for business applications?

In short: one model that can be completed multiple tasks. Years ago, we had to develop one specialized model for every task we tried to solve with AI. We needed training data, an appropriate ML algorithm, and a data scientist.

But with large language models like GPT-3, you can use this one model in a nutshell for many tasks teaching A model with examples of what kind of output it should produce. For some tasks you don’t even need it. You can simply describe the task and provide the input, and GPT-3 will generate the corresponding output. So almost anyone can do AI “development”.

For example, if you are performing a classification task, you can set the model to the expected category types. If you want generated content, you can say what type of content you expect. So it essentially democratizes AI development and takes less time.

Imagine an evolved sentiment classifier with only 5 queries. Is it too good to be true? The only way to know if your data is holding water is to evaluate, evaluate, and evaluate. You will never escape evaluation, no matter how sophisticated the model, which I talk about repeatedly in my book.

Will traditional ML disappear because of GPT-3?

No. Task-specific models, smaller language models, and classic ML aren’t going anywhere anytime soon. GPT-3 only works on tasks it understands well or tasks you can understand (see examples below). If you have very specific domain tasks, you will still need to build specialized models that are fine-tuned just for those tasks.

It just means that it will be much easier to develop ML solutions for certain well thought out tasks. Or these models can be used to generate additional information for your specialized ML tasks.

What are the risks of GPT-3?

Now let’s talk about the hard stuff. While GPT-3 has great potential, we still need to consider its broader impact on your AI applications and business. Some of the risks of GPT-3 include:

• Prevalence of bias— Since GPT-3 mainly trained on web data, it learned both the good and the bad side of the web. This means that any built-in biases, errors in data, and non-factual content can easily creep into your applications.
• Potential plagiarism— Knowledge of the entire web (almost) also means that GPT-3 can publish content verbatim from various sources without attribution. So if you see familiar content in a third-party app, don’t be surprised—it could be your content. Unfortunately, you may not be able to report plagiarism, and there’s not much we can do about it since the model is already open for public use.
• Unpredictable performance— GPT-3 is essentially a multitasking language generator. And because it’s not well-tuned to the specific task of your application, its performance on one “narrow” task can be unpredictable. One small mistake can lead to incorrect results.
• hallucinations— Because GPT-3 calculates the probability of generating meaningful output, it is very good at connecting related concepts that IT thinks make sense. This may end up with unfactual and inaccurate information. If you use GPT-3 to generate content, you should confirm the facts produced on particularly unusual or time-dependent topics and topics that are open to interpretation.

These risks are real and people are already raising these issues in various formats.

GPT-3 examples

Here are two examples of GPT-3 in action.

sentence correction

In this example, GPT-3 is asked to edit English sentences.

GPT-3: Correction of sentence

Classification of sentiments

In this example, GPT-3 gives examples of how sentences are classified, and then it does so on the last task.

GPT-3: Prediction of Mood Orientation

GPT-3 Key Takeaways

• GPT-3 is a great language model that can help you perform many tasks with little supervision.
• GPT-3 cannot solve all AI problems. It’s only as good as the needs you feed it and the tasks it understands, and it may not work well at specialized tasks like predicting market movements.
• Although GPT3 has great potential, it has its fair share of problems, like any ML model. Some potential problems include spreading bias, hallucinations, and unpredictable performance.
• As with any AI solution, evaluation is critical to the success of any initiative, and GPT-3 is no exception.

That’s all for this article!

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