Commercial Applications of GPT-3

GPT-3 is a language model that uses deep learning to generate human-like text on demand.

GPT-3 is Open AI’s latest natural language prediction model. With the emergence of Artificial Intelligence (AI) in the business landscape, this is one of the tools that will quickly increase in popularity. The Generative Pre-trained Transformer 3 offers limitless access to computing on top of its cloud infrastructure, promoting scalability. Nevertheless, GPT-3 should be the next big thing in tech. Following the rise of deep learning, advanced technology will transform how business gets conducted globally.

GPT-3 Commercial Application KPIs	Description	Formula to Measure	When to Use	Advantages	Drawbacks
Conversion Rate	Measures the percentage of website visitors who take a desired action, such as making a purchase or signing up, influenced by GPT-3-generated content.	(Total Conversions / Total Visitors) x 100%	To assess content effectiveness in driving conversions.	Indicates the impact on revenue.	Low conversion may need content adjustments.
Engagement Rate	Calculates the percentage of users who actively engage with GPT-3-powered chatbots or virtual assistants.	(Total Engagements / Total Interactions) x 100%	To evaluate the level of user interaction and satisfaction.	Measures user engagement and satisfaction.	Low engagement may require improvements.
Cost Savings	Quantifies the reduction in operational costs achieved by using GPT-3 for tasks like customer support or data analysis.	(Cost before GPT-3 – Cost with GPT-3)	When evaluating cost efficiency and ROI.	Demonstrates cost-effective solutions.	Costs vary based on implementation.
Response Time	Measures the average time it takes for GPT-3-powered systems to respond to user queries or requests.	Total response time / Number of interactions	To assess user experience and efficiency.	Indicates promptness in addressing queries.	Slow response can lead to user frustration.
Accuracy Rate	Calculates the percentage of accurate responses or predictions provided by GPT-3 models in specific applications.	(Total Accurate Responses / Total Responses) x 100%	When evaluating model performance and reliability.	Measures AI model accuracy and trustworthiness.	Inaccurate results can erode trust.
User Satisfaction	Assesses user satisfaction levels with GPT-3-powered applications through surveys or feedback analysis.	User satisfaction score or feedback analysis	To gauge user sentiment and experience.	Helps improve user-centric applications.	Subjective and requires ongoing monitoring.
Task Completion Rate	Measures the percentage of tasks or queries successfully completed by GPT-3 in applications like virtual assistants.	(Total Completed Tasks / Total Tasks) x 100%	To evaluate the efficiency and capability of AI systems.	Indicates the AI’s task-handling competence.	Incomplete tasks can frustrate users.
Language Proficiency	Evaluates the language proficiency and fluency of GPT-3 in handling multiple languages and dialects.	Proficiency score or multilingual performance	When targeting diverse language-speaking audiences.	Demonstrates linguistic versatility.	May require additional training for languages.
Task Automation	Quantifies the number of tasks or processes automated by GPT-3, reducing the need for human intervention.	Total automated tasks or processes	To measure the extent of process optimization.	Indicates efficiency and resource savings.	Limited to tasks that can be automated.
User Retention Rate	Calculates the percentage of users who continue using GPT-3-powered applications over time.	[(End Users – Start Users) / Start Users] x 100%	To monitor user loyalty and satisfaction.	Measures user engagement and value.	User retention may fluctuate.
Content Generation Speed	Measures the rate at which GPT-3 generates content, such as articles or product descriptions, in a given time frame.	(Total Content Pieces / Time Taken)	When assessing content production efficiency.	Indicates content creation speed.	Quality may vary with speed.
Data Analysis Efficiency	Evaluates the efficiency of GPT-3 in analyzing large datasets and extracting valuable insights.	(Data Analyzed with GPT-3 / Total Data) x 100%	To measure data processing capabilities.	Enhances data-driven decision-making.	Accuracy depends on data quality.
Customization Flexibility	Assesses the ability to customize GPT-3 models for specific applications or industries.	Customization level or successful customizations	When tailoring AI for unique business needs.	Offers adaptability to diverse use cases.	Customization may require expertise.
System Downtime	Tracks the amount of time GPT-3-powered systems experience downtime or are unavailable for use.	Total downtime duration	To ensure system reliability and availability.	Minimizes disruptions and losses.	Downtime can affect user experience.
Knowledge Base Expansion	Measures the growth of knowledge bases or databases through GPT-3’s ability to provide information and answers.	(New Knowledge Entries / Total Knowledge Entries) x 100%	When expanding information resources.	Enhances knowledge repository value.	Accuracy and relevancy are crucial.
Error Rate	Calculates the percentage of errors or inaccuracies in GPT-3-generated content or responses.	(Total Errors / Total Responses) x 100%	To assess the quality and reliability of AI-generated content.	Identifies areas for improvement.	High error rates can harm credibility.
User Feedback Analysis	Analyzes user feedback and sentiment to identify areas of improvement or enhancement for GPT-3 applications.	Feedback analysis and sentiment score	When aiming to enhance user experience.	Incorporates user input for refinements.	Interpretation may vary.
Task Complexity Handling	Measures GPT-3’s ability to handle complex or intricate tasks, such as legal research or medical diagnosis.	Successful handling of complex tasks	When assessing AI’s suitability for specialized applications.	Demonstrates advanced capabilities.	Limited to AI training and expertise.
Integration Compatibility	Assesses how well GPT-3 integrates with existing systems, applications, or platforms within an organization.	Integration success score or compatibility analysis	When incorporating AI into existing workflows.	Facilitates seamless AI adoption.	Compatibility challenges may arise.
Data Privacy Compliance	Evaluates the level of compliance with data privacy regulations and security standards when using GPT-3.	Compliance score or audit results	When handling sensitive or personal data.	Ensures data protection and legal adherence.	Non-compliance can lead to legal issues.
Sentiment Analysis Accuracy	Measures the accuracy of GPT-3 in sentiment analysis, particularly in understanding user emotions and opinions.	(Correct Sentiment Predictions / Total Predictions) x 100%	When analyzing user sentiment and feedback.	Enhances user experience understanding.	Accuracy may vary with context.
Content Recommendation Success	Quantifies the success rate of content recommendations made by GPT-3 in improving user engagement and satisfaction.	(Successful Recommendations / Total Recommendations) x 100%	When implementing content recommendation systems.	Enhances user interaction and content relevance.	Recommendations may not always align.
Multimodal Content Generation	Evaluates GPT-3’s ability to generate content across multiple formats, such as text, images, or videos, for diverse marketing needs.	(Successful Multimodal Content / Total Multimodal Content) x 100%	When requiring versatile content creation.	Provides flexibility in content production.	Quality may vary across formats.
Compliance Monitoring	Monitors GPT-3-generated content for regulatory compliance, ensuring it aligns with industry-specific guidelines and standards.	(Compliant Content / Total Content) x 100%	When dealing with highly regulated industries.	Mitigates legal and reputational risks.	Requires continuous content monitoring.
Market Trend Analysis	Assesses GPT-3’s effectiveness in analyzing market trends, consumer behavior, and competitive intelligence from available data sources.	(Accurate Market Insights / Total Insights) x 100%	When seeking data-driven market insights.	Facilitates informed strategic decisions.	Accuracy relies on data quality.
Energy Efficiency	Measures the energy consumption or efficiency of GPT-3-powered systems, particularly in data centers and infrastructure.	(Energy Efficiency Score)	When prioritizing sustainable AI solutions.	Reduces environmental impact and costs.	Energy efficiency optimization may be complex.
Training Data Bias Detection	Identifies and quantifies bias in GPT-3 training data, helping to address ethical concerns and promote fairness in AI applications.	(Bias Detection Score)	During AI model development and training.	Addresses ethical AI concerns and bias mitigation.	Bias detection may require specialized tools.
Knowledge Gap Analysis	Evaluates GPT-3’s ability to identify knowledge gaps or areas where additional information or research is needed.	(Identified Knowledge Gaps / Total Gaps) x 100%	When enhancing knowledge base completeness.	Supports ongoing knowledge improvement.	Accuracy depends on knowledge relevance.

What is GPT-3?

The Generative Pre-trained Transformer 3, also known as GPT-3, is developed by OpenAI, a San Francisco startup founded by Elon Musk. The GPT-3 is an extensive neural network formed from the deep learning segment of machine learning technology.

Machine learning is the extension of artificial intelligence (AI), which uses computer algorithms to automate specific actions.

The latest version of GPT offers advanced features that produce lines of text that come off as something created by humans. Imitating human-like actions, this provides a personalized approach to transactions and crucial business decisions.

As a result, a growing number of commercial industries found the potential of leveraging GPT-3 to automate mundane tasks. GPT-3 prompts a generated response to any text that the person enters into the computer. It creates a reaction following the information presented.

For instance, typing a string of statements into the search box can respond within the given statement’s context. GPT-3 has the potential to amplify human effort in a variety of situations. It can be applied in different business processes such as customer service, documentation, and producing various content.

Understanding GPT-3

GPT-3 (Generative Pre-trained Transformer 3) is a language model that was developed by the AI research laboratory OpenAI.

GPT-3 was launched in mid-2020 and returns natural language text completion for any text prompt such as a sentence or phrase.

Note that the GPT-3 algorithms have been pre-trained. Each draws from around 570 GB of text-based information that was sourced from crawling the internet.

The model can generate text of up to 50,000 characters unsupervised and in addition to simple fact-based writing, can also produce code, stories, press releases, technical manuals, and even Shakespeare-esque content.

GPT-3 marks the first instance of a model that can produce text which is virtually indistinguishable from that produced by a person.

Just nine months after it was launched, more than 300 applications were already using GPT-3 to churn out 4.5 billion words per day.

Considered the world’s most powerful language model with over 175 billion trainable machine learning parameters, it has been used in everything from creativity and games to education and productivity.

How does GPT-3 work?

Central to the functioning of GPT-3 are transformers which were released by Google researchers in 2017.

Transformers are machine learning models that are semi-supervised and primarily used with text data.

These models, which have replaced the role of neural networks in natural language processing, take input sequence data and then generate an output sequence one element at a time.

Take a transformer that translates a sentence from English to Spanish. The sentence is essentially treated as a sequence with the words in that sentence serving as its elements.

Transformers have two primary segments.

The first is an encoder that operates on the input sequence while the other segment, the decoder, operates on the output sequence during training and predicts what the next item will be.

Returning to our example, the transformer model may analyze the sequence of words in English and iteratively predict the next Spanish word until the sentence has been accurately translated.

For the sake of accuracy, the model works through the sentence one word at a time whilst respecting the grammar rules inherent to the Spanish language.

What makes GPT-3 special?

Parameters and data

The feature that stands out most about GPT-3 is the 175 billion trainable parameters.

To put this number into perspective, consider that GPT-3 has ten times more parameters than any of its predecessors.

This necessitated that the model was trained with 45 TB of data from numerous sources which include books, web text, all outbound links from Reddit, and the entire Wikipedia content library.

While GPT-3 can perform reading and writing tasks at a level compared to humans, the real power of the model lies in the fact that it has “processed” more information than a person would ever be capable of reading in their lifetime.

Versatility

The versatility of GPT-3 was touched on briefly in the previous section, but to explain this point further, we’d like to mention a blog post by artist Mario Klingemann who used GPT-3 to generate all kinds of interesting output.

Content included songs about Harry Potter by Taylor Swift and Lil Wayne, a user manual for the flux capacitor from Back to the Future, and a post espousing the value of free speech and civility in a democratic society.

GPT-3 commercial applications

Here are a few recent commercial applications of GPT-3.

Replier.ai

Replier.ai is an AI software tool that automates customer support and sales conversations. It uses natural language processing (NLP) and machine learning (ML) to understand customer inquiries and provide accurate responses in real time.

Replier.ai can be integrated with popular messaging and chat platforms such as Facebook Messenger, WhatsApp, and Slack, as well as with websites and mobile apps. It can also be customized to match the branding and tone of voice of the business it represents.

Replier.ai is best suited to repetitive or common customer inquiries that free up human support staff to focus on those that are more complex or sensitive. The tool can also provide data insights to businesses on customer interactions, sentiment analysis, and performance metrics.

The company offers one plan that encompasses all its services, with prices based on the number of business locations the client wants to monitor. The cost for Replier’s Base Plan is $10 per location per month.

Features include:

Auto-generated review responses – the AI can output custom review responses for client use.
Reply analysis – the client can also analyze how their response rates improve over time and the impact it has on their business.
Marketing content – clients can generate marketing content for their reviews and share it on social media as part of brand promotion.
Support for over 20 languages – this includes English, Spanish, Portuguese, German, French, and Italian.
API integration, and
White labeling for agencies.

ABTesting.ai

ABTesting.ai is a software tool that utilizes AI to automate the process of A/B testing, a widely used method for increasing website and app performance.

A/B testing involves comparing two versions of a web page or app to determine which performs better based on user behavior and engagement metrics.

This is how a company can run its first A/B test based on GPT-3:

Create variations – to start, the user enters the URL and provides several variations of the title, copy, and CTA. The AI then recognizes these, suggests further variations, and will track any subsequent conversion events.
Integrate – the user then adds a small JavaScript snippet to their site or installs a proprietary app for Wix or WordPress.
Experiment – an experiment is then run with some of the variations using the multi-armed bandit approach. This is a classic ML technique that seeks to minimize the exploration/exploitation trade-off dilemma. As the experiment is conducted, the algorithm shows successful variations more often.
Find the best combination – when the experiment achieves statistical significance, the most successful variations are incorporated into a so-called “evolutionary” algorithm which mixes and mutates them before the next batch is run. This enables the algorithm to quickly find the best variation via refinement and avoids the need to separately test every possible combination.
Repeat – the company then repeats the process with a new experiment until the combination yielding the highest conversion rate is discovered.

The company offers four plans based on the number of website visitors, experiments, and AI-generated variations. The $19 per month starter plan, for example, is suitable for up to 8,000 unique visitors and 30 AI-generated variations per month.

For more trafficked websites, the business plan can accommodate up to 150,000 unique monthly visitors and comes with GA integration, premium support, API access, and a team-sharing feature.

Key takeaways:

GPT-3 is a language model that uses deep learning to generate human-like text on demand.
Central to the functioning of GPT-3 are transformers, which are machine learning models that are semi-supervised and primarily used with text data.
The ability of GPT-3 to produce text comparable to humans is impressive, but so too is the 45 TB of information the model can access. With access to so much information, the model can produce a diverse variety of texts such as poems, essays, songs, technical manuals, and music lyrics.

Interesting Ways GPT-3 Is Used in Commercial Applications

GPT-3 can help businesses accomplish mundane, repetitive tasks. For instance, developers can build a tool that generates various layouts for the design required in different situations. It can also simplify programming by translating natural language into SQL queries. Formulating complex spreadsheets, building complex CSS, or even deploying Amazon Web Services (AWS) instances, GPT-3 can get applied in almost any business process.

The novel applications mentioned above can automate simple tasks helping employees focus on value-adding projects.

However, the commercial applications that businesses can take advantage of using GPT-3 are even greater.

Let’s take a look at some of them below:

Automate Translation of Various Documents

The automated translation of large volumes of documents from various languages can bring many opportunities to businesses. Apart from streamlining the delivery of services, they can increase their capacity outside the country. GPT-3 can learn over 499 billion words of different languages to produce translations at a given time seamlessly.

As we all know, some statements can get lost in translation even when every word gets translated correctly. Each language has sayings and context that controls the conversation. With GPT-3, that context is taken into consideration to understand further what the statement means.

Priming AI to Deal with Data

One of the applications of GPT-3 that businesses find exceptionally fascinating is priming AI to respond to English inputs with structured data output like JSON or XML. Through this feature, developers can take advantage of its machine learning capabilities to comprehend the theorems and their underlying structure. While it can produce answers to mathematical questions with the highest accuracy, it also considers those values’ context. This consideration provides better results and can help the advancement of the research promptly.

Programming Without the Use of Code

Without the need for manual intervention, GPT-3 generates structured data for developing specialized programs and software. Eliminating the need for writing algorithms using traditional programming languages allows some developers to embrace natural languages. Adopting natural languages becomes vital to prime the AI. Although it does not require expert skills in coding, this opens up new opportunities needing unique skills to expand software development jobs.

Generate Automated Regex

Composed of a sequence of characters that define the search pattern, Regex is vital in creating string-searching algorithms. Through GPT-3, developers can acquire Regex for various use-cases. All you need to do is type the Regular expression that you wish to generate in plain English. You have to enter an example string to help GPT-3 develop relevant Regex in a matter of seconds.

Create a Clone of a Website

GPT-3 works with the UI and UX designer application called Figma to create clones of a website. Cloning websites is necessary to test the updates and the compatibility of certain features safely. This breakthrough in designing websites and generating robust tools can boost their functionalities. All you need to do is to input the URL of the website to produce its clone.

Produce Quizzes and Tests

Now that most of the classes became online, most teachers and students find it challenging to manage their time in the middle of the pandemic. A quiz generator powered by GPT-3 can ease their burden and also improve learning. It produces test questions of different topics and subject matter for students to answer. It also contains a thorough explanation of the answers to help students understand how they find the result.

Formulate Unique Animations

Among many other things you can create from GPT-3, production companies leverage this tool to automate animations. GPT-3 combined with a Figma plugin can produce frame-by-frame animations and a text prompt. It can also formulate 3D scenes with the help of the three.js Javascript API. All you need to do is enter the details of the location that you desire to create and take care of the rest.

Automate Documentation

Even though you don’t have prior accounting knowledge, it is possible to generate financial statements using GPT-3. This tool transforms natural language into Python code to automate balance sheets.

The Future of GPT-3

Everything that involves a particular language structure can get automated through GPT-3. From answering queries, producing content, summarizing a long string of texts, translating languages, to programming code, there are multiple ways for businesses to leverage this tool.

Overall, GPT-3 helps businesses get a few steps ahead into providing a future that makes business data and communication much more manageable. After all, communicating effectively is one of the best business tools, and GPT-3 makes that communication more accessible.

Read Next: History of OpenAI, AI Business Models, AI Economy.