During the initial period, OpenAI Codex will be offered for free. We’re now making OpenAI Codex available in private beta via our API, and we are aiming to scale up as quickly as we can safely. But we know we’ve only scratched the surface of what can be done. We’ve successfully used it for transpilation, explaining code, and refactoring code. OpenAI Codex is a general-purpose programming model, meaning that it can be applied to essentially any programming task (though results may vary). The latter activity is probably the least fun part of programming (and the highest barrier to entry), and it’s where OpenAI Codex excels most. Once a programmer knows what to build, the act of writing code can be thought of as (1) breaking a problem down into simpler problems, and (2) mapping those simple problems to existing code (libraries, APIs, or functions) that already exist. OpenAI Codex empowers computers to better understand people’s intent, which can empower everyone to do more with computers. OpenAI Codex has much of the natural language understanding of GPT-3, but it produces working code-meaning you can issue commands in English to any piece of software with an API. GPT-3’s main skill is generating natural language in response to a natural language prompt, meaning the only way it affects the world is through the mind of the reader. It has a memory of 14KB for Python code, compared to GPT-3 which has only 4KB-so it can take into account over 3x as much contextual information while performing any task. OpenAI Codex is most capable in Python, but it is also proficient in over a dozen languages including JavaScript, Go, Perl, PHP, Ruby, Swift and TypeScript, and even Shell. For this example, we will use the same code as in the above example.OpenAI Codex is a descendant of GPT-3 its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories. For this, a combination of “Ctrl” plus “R” is used. In the third example, we will be using our keyboard’s shortcut keys to comment on our narrative lines. In this example, we have used the “%” key present on our keyboard. “comment” these narrative lines, to avoid any error. Therefore, we must use these lines as comments, i.e. If we try to execute our code directly, we will get a run time error in lines to lines 3 & 4 as these lines are not understandable by MATLAB’s compiler. This is how our input and output will look like in MATLAB: Prefix “%” at the end of the narrative lines.Our purpose is to prevent these narrative lines from getting executed by the compiler. In this example also we will be writing a dummy code with some narrative explaining the code. In the second example, we will be using our keyboard’s “%” key to comment on our narrative lines. In this example, we have used the “Comment” button present in the Live Editor for this purpose. Output 1 (After commenting on the narration lines): Click the “Comment” button (labeled as “%” sign) present in MATLAB’s “Live Editor” tab, as shown in the image below.Select the narrative lines which we want to comment (the lines written to explain the code).Write the code along with the narration to describe it.Our purpose is to prevent our narration lines from getting executed by the compiler, for which we will “comment” the narration lines. But, first, let us write a dummy code and provide a narration that will explain our code. In the first example, we will make use of MATLAB’s “Comment” button present in the Live Editor. Let us now understand how to provide comments in MATLAB. Utilizing the “Ctrl + R” short cut keys.Utilizing the ‘%’ sign present in our keyboard.Utilizing the “Comment” button present in the MATLAB Editor (Labelled as “%”).There are 3 ways in which we can provide comments in MATLAB and prevent them from getting executed by the compiler: Hadoop, Data Science, Statistics & others
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