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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.
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:
- It makes the State-of-the-Art model more accessible to developers for exploring potential use cases.
- 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.
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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- ML and AI can offer high security in the transportation industry.
- It offers high reliability of their services or vehicles.
- The adoption of this technology in the transportation industry can increase the efficiency of the service.
- In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.
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Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.
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- Drug Discovery and Manufacturing
- Medical Imaging Diagnosis
- Personalized Medicine
- Machine Learning-based Behavioral Modification
- Smart Health Records
- Clinical Trial and Research
- Better Radiotherapy
- Crowdsourced Data Collection
- Outbreak Prediction
**
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In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.
- Fraud prevention
- Risk management
- Investment predictions
- Customer service
- Digital assistants
- Marketing
- Network security
- Loan underwriting
- Algorithmic trading
- Process automation
- Document interpretation
- Content creation
- Trade settlements
- Money-laundering prevention
- Custom machine learning solutions
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Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.
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**
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Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.
- Improved Unsupervised Algorithms
- Increased Adoption of Quantum Computing
- Enhanced Personalization
- Improved Cognitive Services
- Rise of Robots
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**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.
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