GPT3 The Dream Machine In Real World

Like Sam’s tweets, there is a lot of hype in the tech community about the latest GPT-3 released by OpenAI in June 2020, but it is still powerful and impressive when you interact with it. GPT-3 is the largest language model ever trained and achieved good results on several NLP tasks like language generation and language translation, with huge potentials for many other creative and functional tasks.

Here we will go over a couple of highlights and have a more clear view of what the model can and cannot do, and how to utilize it to empower various applications with examples.


Why it is disruptive?

Gigantic

GPT-3 use the Transformer framework and Attention architecture, which is the same model architecture as GPT-2. It comes with different sizes, the largest (or “GPT-3”) has 175B trainable parameters, 96 layers, 96 heads in each layer, each head with a dimension of128. Even the batch size is huge at 3.2M.

It is trained on huge text corpus from sources like

  • filtered Common Crawl(410B tokens * 0.44)
  • WebText2(19B * 2.9)
  • 2 internet Books(12B * 1.9 + 55B * 0.43)
  • English Wikipedia(3B * 3.4)

According to the paper, to train the largest GPT-3(175B parameters) needs 36400 petaflop/s-day, and 1 petaflop/s-day is equivalent to 8 V100 GPUs at full efficiency of a day.

_“The supercomputer developed for OpenAI is a single system with more than 285,000 CPU cores, __10,000 __GPUs and 400 gigabits per second of network connectivity for each GPU server,” check out how Microsoft built the GPU-accelerated _supercomputer system for OpenAI

Few shots learner 👏👏👏

This is the main breakthrough of the GPT-3, it means the model can quickly learn a task by observing zero, one, or few examples of the task, makes it more capable of predicting for tasks that it has never seen. The bigger model can learn the task better with contextual information from few examples.

Image for post

When comparing with traditional task agnostic architecture (BERT), which requires tens of thousands task specific examples to fine tune on a downstream task. GPT-3 enables us to perform on a new task (classification, question answering, translation, reasoning and many others) from just a few example or instructions without fine tuning. This is a huge advantage over other mainstream language models like BERT, ELMO, XLM.

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Even more, OpenAI released the GPT-3 API. Sending the text prompt, the API will return the completion, attempting to match the pattern you gave it in the examples. The API is so simple that developers can just plug and play to bring the intelligence of remote model to their products, without worry about ML pipeline, infrastructure, and hosting. This is a huge breakthrough with many benefits:

  1. It makes the State-of-the-Art model more accessible to developers for exploring potential use cases.
  2. Significantly lowered the barrier of adopting NLP/machine intelligence. Developers no longer need to spend extra efforts on acquiring task specific dataset, mastering the complicated ML and fine tuning process, handling the ML infrastructure.

The API is not ready for mass production use, OpenAI and developers are still learning and evaluating various applications and the potential social impact.


Use cases

Many creative prototypes enabled by GPT-3 are popping up in the past few weeks, a lot of impressive demos can be found on Twitter. Now, let’s have a deeper dive of how GPT-3 helps with traditional NLP tasks as well as some of the creative applications from community.

** In the following examples, we will use blue 🔵 color to indicate the context or prompt (task descriptions, examples) as a prefix. Then the orange 🍊 color to indicate the users’s input and the rest will be the model’s predictions.

Language generation

One of the most impressive use cases is language generation/text completion. Here is an example of having GPT-3 to help us expand one sentence to create a short essay. It even provides concrete examples of how to express gratitude.

Image for post

Writing essay with practical knowledge and zero example

Question Answering

With just 5 QA examples, the model is able to answer all the following questions with accurate answers while maintaining the context information.

#gpt-3 #machine-learning #deep-learning #artificial-intelligence #nlp #deep learning

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GPT3 The Dream Machine In Real World
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  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
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  • Rise of Robots

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