What are the Limitations of GPT-3?
During the history of humans, new technologies are often greeted with a combination of enthusiasm and doomsday predictions, and history repeated itself when GPT-3 burst into the technology world.
Open AI, a non-profit startup founded in 2015 ended up becoming a pioneer of the AI landscape in today’s world. OpenAI created numerous AI tools, and GPT-3 is one of its prominent creations. GPT-3 impacted many industries like business, education, e-commerce, entertainment and many more, yet none surpass the popularity ChatGPT-3 has gained within just two months.
(Tokens are units ranging from a single character to multiple arrays. The weight percentage is given from the data set in training this model)
Generative pre-trained transformer 3 popularly known as GPT-3 is a neural network machine-learning model created by OpenAI. GPT-3 was released for beta testing in July 2020 while the world was in quarantine during the Covid 19 pandemic.
GPT-3 was officially released for public use in November 2022 which was a cutting-edge technological advancement for the race of humanity. One of its products, ChatGPT 3, gained 100 million users by the end of January 2023, just two months after its initial launch.
Unlike other Autoregressive language models, 175 billion machine learning parameters were used in the development of this game-changing deep learning neural network. Scaling things up from Microsoft's language learning model Turning which was only created using 17 billion ML parameters.
Compared to its predecessors, GPT-3 is a user-friendly model which will predict texts without the need for users to know explicit programming.
Consuming millions of sample data and information, GPT-3 compresses, combines and transforms information and data from various resources including Wikipedia, Google Books, coding tutorials, Common Crawl and other resources available on the World Wide Web into predictive texts based on complex conditional probability calculation rules.
The ability of GPT-3 to use both Natural Language Processing and Generation to understand the wealth of information available on the World Wide Web in text format and process and transform them into content and text with constructive and useful information has been a landmark achievement for any neural network machine learning model.
Generating content that equals the capacity of humans has been a challenging task for machines for a very long time. Imitating the complexities, intricacies, and nuances of human language was a challenge that many predicted would be near impossible for a machine.
However, GPT-3 has overcome this challenge to generate anything with a text structure. Its capacity is not limited to generating articles, poetry, stories, news reports and dialogue but to text summarization and programming code.
One of the most popular and acclaimed uses of GPT-3 implementation is the ChatGPT language model. A variant of the GTP-3 model further optimized for human dialogue, with the ability to ask questions, admit mistakes, and challenge inaccuracies, the ChatGPT language model also includes the ability to reduce the possibility of harmful or deceitful responses.
Using GPT-3’s ability to generate text structures, ChatGPT generates articles, poetry, stories, news reports, dialogue, and summaries, as well as code using various programme languages based on input prompts. It can also review text structures including human language based paragraphs and code blocks to pinpoint syntax errors and bugs.
An equally popular image-generation neural network is, Dall-E. Built on a 12 billion parameter version of GPT-3 and trained on data sets of text-image pairs, Dall-E generates images based on user-submitted text prompts.
Since GPT-3 is a language prediction model that can output any structured text, not limited to human language, applications that are based on GPT-3 can create workable code that can run without errors.
For example, ChatGPT can also generate, edit, review and explain code-based natural human language instruction. A GPT-3 API integration with the interface prototyping tool Figma enables the development of high-fidelity prototypes and corresponding websites with descriptive yet short prompts.
With GPT-3’s reduced time and increased quality of learning, the model can help to speed up test automation through automated test code and test script generation as well as positive and negative test case generation for various platforms.
Another way to put the massive data analysis ability of GPT-3 into good use is to use GPT-3 to generate concise root cause reports. Automated Root Cause Analysis is critical to reducing Mean Time to Repair (MTTR). However there lies the challenge of identifying the root cause hidden among enormous volumes of logs. With GPT-3 integration, reports generated through autonomous Root Cause Analysis (RCA) can now be easily translated into plain language, helping to understand correlated anomalies and causes.
A simple integration of Open AI API and WEB SERVICE function allows anyone to increase the capabilities of Excel and Word documents as well as Google Spreadsheets and documents. Once integrated, these super powered docs and spreadsheets get the ability to analyze, sort, predict large amounts of data as well as generate content as prompted and input information, making day to day tasks a breeze.
One of the best ways generative AI has transformed everyday life is its ability to make information and answers to simple or complex and specialized questions accessible to everyone from all walks of life.
Today anyone can request ChatGpt to explain a process or formula to them in a format that suits their capacity and perspective by using the prompt ‘ As If I am’. (E.g. As if I am a child, as if I am a chef, as if I am a millennial, etc.).
Brands and businesses are combining GPT-3’s ability of natural language processing, and natural language generation with sentiment analysis to manage customer and user interactions. Smart chat apps built based on GPT-3 have already proven their ability to provide comprehensive and informative feedback to customers while being aware of their sentiments and handling them accurately.
While many worry about the generative AI tools replacing human skills and resources, many GPT-3 powered open source and proprietary applications have added to human capacity and ridden us of mundane tasks of everyday life.
Website based smart search and discovery tool Algolia, specializes in upgrading B2B and B2C ecommerce experience by providing personalized onsite search and product discovery experience.
Since its integration GPT-3 has further enhanced the capacity of well known tools like Figma and Zebrium used in prototyping and root cause detection.
Generative AI is also changing the way creatives do their work. Platforms like Fable Studio which helps to create virtual 3D characters and avatars as well as creative content generation platforms like Jasper allows creative agencies to fine tune and speed up their creative process.
GPT-3 powered meeting software tl;dv combines the ability to record, transcribe, translate, summarize, and report enabling asynchronous communication across teams and organizations.
In addition to the number of applications powered by GPT-3, browser based extensions like Merlin or Emerson AI, a conversation based chatbot that can easily be integrated with mobile and desktop messenger apps like Telegram and Facebook messenger with multiple language capabilities.
Since GPT-3 at its core is a Large Language Model (LLM) and a statistical tool used to predict language outputs or statistically plausible answers without truly understanding them, GPT-3 and its offsprings will occasionally generate inaccurate answers by wrongfully combining ill-matching or outdated information snippets. It is also pre-trained and is not constantly learning, which makes it unable to process, remember or learn from ongoing interactions.
GPT-3 can be a great tool when there is no precisely accurate answer, but its dependability declines when the answer must be 100% accurate to be truly useful.
As reported, Alphabet the parent company of Google lost USD 100 Billion because of its very own AI-powered language model LaMDA's performance on the Twitter ad. AI chatbot Bart is given the prompt “What new discoveries from the Jame Webb Space Telescope can I tell my 9-year-old son about?” its reply that the first picture taken of our solar system was taken from the JWST was challenged by NASA drawing ire from netizens,
Bard is an experimental conversational AI service, powered by LaMDA. Built using our large language models and drawing on information from the web, it’s a launchpad for curiosity and can help simplify complex topics → https://t.co/fSp531xKy3 pic.twitter.com/JecHXVmt8l— Google (@Google) February 6, 2023
Any transformer including GPT-3 has its own restricted input size due to computational and performance limitations caused by fixed dimensionality. While an average prompt limit for pre- GPT-3 transformers were 512 - 1024 tokens, GPT-3 has a prompt limit of 2018 tokens.
Any language model trained on the information available on the World Wide Web tends to demonstrate the same biases that humans exhibit online, may it be imitating religious radicals, conspiracy theorists, or white supremacists. To overcome the error between the average model prediction and the ground truth GPT-3 goes through ongoing intensive training and user feedback.
Whether it was the successful invention of the first ENIAC computer or the birth of Google, people concluded that technological developments would supersede human capability. There is no doubt that AI technologies will be even more powerful.
The novelty of AI makes humans less experienced with its capabilities, it is certain that AI is another innovation. Like numerous jobs that got replaced because of technology, AI will mostly affect automotive repulsive tasks like data entering, customer support, proofreading, and book-keepers.
Critics believe mundanes will be replaced by AI due to their ambiguous perception of technology. Alexander Wang Founder/ CEO of Scale Ai which is a trusted partner of tech giants including Meta, Microsoft states “AI enhances or even supercharges humanity”.
These intelligence and judgment-lacking automation require algorithms created and annotated by humans. No matter How much of a beast people assume AI is, the technology always has been and always will rely on human intelligence.