Generative artificial intelligence is artificial intelligence capable of generating text, images or other data using generative models, often in response to prompts. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language.

how large language models work

 

• Tokenizer, text to numbers: Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk.
• Predicting output tokens: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence.
• Selection process, probability distribution: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature.

In the upcoming article, we will engage in practical demonstrations.

Thanks

InterSystems FAQ rubric

Here, we will introduce a sample code for registering and referencing task schedules.

 ①Sample of task schedule registration

*Create a task to execute do ^TEST every day at 1:00 am. 

 set task=##class(%SYS.Task).%New()
 set task.Name="MyTask1"
 set taskDescription="Execute ^xxx every day at 1:00 AM" // Optional
 set task.NameSpace="USER"
 set task.TimePeriod=0
 set task.DailyFrequency=0
 set task.DailyFrequencyTime=""
 set task.DailyIncrement=""
 set task.DailyStartTime=$ZTimeh("01:00:00")
 set task.DailyEndTime=""
 set task.TaskClass="%SYS.Task.RunLegacyTask"
 set task.Settings=$LB("ExecuteCode","do ^TEST") // Set ExecuteCode for RunLegacyTask
 write task.%Save()

② Sample of task schedule reference

*The contents registered in the task schedule are obtained programmatically.

USER>set task=##class(%SYS.Task).%OpenId(1) USER>zwrite tasktask=<object reference="">[14@%SYS.Task]
：
+----------------- attribute values ------------------
| %Concurrency = 1
| DailyEndTime = 0
| DailyFrequency = 0
| DailyFrequencyTime = ""
| DailyIncrement = ""
| DailyStartTime = 0
| DayNextScheduled = 63877
| DeleteAfterRun = 0
| Description = "Journal files are switched at midnight every day."
：
// If you want to refer to individual items, do the following:
USER>write $ZDT(task.DayNextScheduled)
11/21/2015
USER>write task.Name
Journal switching
USER>