Changes between Version 16 and Version 17 of Public/WhitePaperMachineTalk

Timestamp:

May 4, 2023, 9:43:10 PM (20 months ago)

Author:

Boris Horner

Comment:

—

Public/WhitePaperMachineTalk

@@ -49 +49 @@
 
 == Usage cost economy
-openAI charge a fraction of a Cent for 1,000 //tokens// transmitted (request plus response). A token is one to few characters in length. The transform shown in the screenshot costs roughly half a Cent, including the upload of the training data. Unless we want to apply the same rule set on one million of input data sets, we shouldn't worry too much about the transaction cost. 
+openAI charge a fraction of a Cent for 1,000 //tokens// transmitted (request plus response). A token is one to few characters in length. The transform shown in the screenshot costs roughly half a Cent, including the upload of the training data. Unless we want to apply the same rule set on one million of input data sets, we shouldn't worry too much about the transaction cost. On the other hand, reducing the size of training data does not necessarily mean thinning it out in a density sense, but rather, writing the instructions as sentences as short as possible. Short sentences are often clear sentences, so this way of reducing size can rather increase quality.
 
-In the case of many transforms with the same rules, we could use the dialog approach described before to upload the training data only when the context has collapsed. Otherwise, we can safely ignore it.
+Plus, since the models are limited in token length (for example, GPT3.5 turbo to 4k tokens per dialog step (user input, GPT response), this can free space for more examples etc., increasing the transform quality further.
+
+Only in the case of very many transforms with the same rules, we could use the dialog approach described before to upload the training data only when the context has collapsed. Otherwise, we can safely ignore it.
 
 == Protection of intellectual property
@@ -68 +70 @@
 But while ChatGPT, due to its stunningly simple user interface, is accessible to young students who let AI write their homework, or practically to everyone, getting stable and high quality results from various sources without constant user intervention is another level. Sometimes, minimal changes to the training data can make a rather big difference. Contradictions between rules and examples are a common mistake when editing and testing the training data, and they tend to bring the AI on thin ice. In some cases, the output can completely fail, because the AI is not prepared to something in the data, does not see how it could apply the rules, and it needs some testing to find out what it is and how to avoid it.
 
+AI behaviour is less predictable than the result of an algorithm, it's not even reproducible. If you ask the untrained chat application on the openAI website for instructions how to, for example, reset a specific smartphone to factory settings, you will get different results each time, but (hopefully) all of them correct. It finds one of the local maxima in the solution space - not necessarily the best possible solution. Training data with examples and clear rules to follow increase consistency and quality and define the certain format and structure of the output. With a certain amount of training, tasks that have not too many degrees of freedom can be brought to yield predictable results. A task like "write song lyrics about a horse" will - by design, because here, creativity is the job description - yield different results every time, and that's what the user wants in this case.
+
+When I started working with AI applications, I had the - let's call it //prejudice// - saying "Is //Prompt Engineer// really worth a job title on its own?". Now, I believe, yes, Prompt Engineering is probably best described as a category of Software Engineering. Prompt Engineering follows very similar targets as the mother science (namely, explain to a computer how to solve a problem) and both use text input to do that. The term //Natural Language Processing// seems to imply, that at least the ways the instructions are written, are totally different. But not even that. In AI training, it is quite helpful to use natural sentences first to define a sort of syntax and rules how the AI should behave in command-like language. There are some differences though. The syntax is mostly user-defined and liberal. Training also uses examples and anti-examples, a concept rather unknown in classical computing. But even with that in mind: experience in Software Development helps a lot when designing training data for an AI application.
+
 I hope, this article gave you an impression that Prompt Engineering is more complex than typing something like "Hey, I got some text here, can you write it more clearly?" (even though this alone can yield surprisingly good results). Still, it's neither alchemy nor rocket science. It follows certain rules and best practices and can be learned. And many people are just doing so in this very minute. I'm almost certain, within the next twelve months, I'll be surprised not too rarely about a new application no-one has ever expected.