OpenAI is a community of researchers that uses open source hardware and software to build and test AI systems. The team has developed core infrastructure for running thousands of experiments. While the project is in its infancy, it has already outdone its original expectations. After the initial build, OpenAI researchers were approached with a highly ambitious project that would test the OpenAI infrastructure to its limits.
GPT-3 is a tool for analyzing text and extracting meaning from it. It works by analyzing the millions of snippets of text that are available online. The result is a text that is often like an eclectic scrapbook with well-known facts, half-truths, and straight lies mixed in. The program can even write poetry.
GPT-3 has an unprecedented ability to analyze digital prose. Having spent months looking for patterns in enormous texts posted on the Internet, the system can predict the next word in a sequence and complete the thought with entire paragraphs of text. This is hugely beneficial for the field of software development and could be a big game changer.
To train the model, OpenAI fed it with a database of over seven thousand published book texts. This database contains nearly 1 million words and five gigabytes of data. The model was trained to compress and decompress these texts. It can then analyze up to eight million web pages, which is 40GB in size.
OpenAI and machine learning are two terms used interchangeably, but there is a significant difference between them. The former are open-ended algorithms that continue to develop and solve problems, while the latter are closed-ended. The latter type of algorithm, which is sometimes called “generative,” is a general-purpose machine learning algorithm that works by generating text based on an input text. It uses internet data and can produce any type of text.
The OpenAI and machine learning community is made up of people who are willing to share their work in order to advance the field. For instance, Vicki Cheung, an engineer and researcher at the OpenAI Research Foundation, was motivated by her own experiences of cheating in her high school physics class. She wrote a bot that filled in blanks on a test, and shared it with her peers.
The next step for OpenAI and machine learning is to create more powerful and larger models. They plan to combine different types of data to create more sophisticated models. They are also planning to include more diverse texts in their models. They will also look at ways to incorporate their model into different applications.
AI for adversarial training involves modifying machine-learning models to be more robust and to handle inputs that are different from those expected. A malicious actor can use these techniques to find the weaknesses in a machine-learning system and destroy it. IBM’s Adversarial Robustness Toolbox supports several popular ML frameworks and provides 39 attack modules. The toolbox includes a taxonomy for adversarial machine-learning.
GPT-3 is an example of such an algorithm. The project was developed by DeepMind AI researcher Sandy Huang. His team simulated a Pong game and then added a single rogue pixel to make it lose. The AI then learned how to beat the human opponent and became an adversary.
While it may seem like an ordinary task, Goodfellow has been doing a lot of machine learning research. He recently conducted an experiment to classify adversarial examples in a machine-learning program. He had previously read a controversial research paper and decided to put the claims in the paper to the test. He left the lab to get lunch with his manager and came back to find that the algorithm he’d created had set a record for classification accuracy when given normal examples.