AI algorithms
The Artificial Intelligence (AI) algorithms are trained to read text and look for a specific concept. They read novel language, learn from experience, and improve over time. The algorithms use two types of artificial intelligence: machine learning (ML) and natural language processing (NLP)
AI eyes to look over your shoulder.
Algorithm reviews your document to find an important concept that demands attention or is missing entirely.
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always learning
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natural language processing (NLP)
NLP teaches computers to interpret human language as written, regardless of style. Our algorithms use NLP to convert text into meaning
machine learning (ML)
We train our algorithms with machine learning to identify a specific concept like a consequential damages waiver. Specialist lawyers find hundreds or thousands of examples of a specific concept. A Data Science team uses machine learning to find the intangible thread that binds the writing of lots of lawyers, all with their own grammatical flair.
Rule-Based Automation
Refers to a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence.
Repetitive, rules-based processes have excellent potential for automation. Some examples include searching, cutting and pasting, updating the same data in multiple places, moving data around, collating, and making simple choices
Is rule-based considered AI?
Broadly speaking, the field of AI distinguishes between rule-based techniques and machine learning techniques. ... A computer system that achieves AI through a rule-based technique is called rule-based system. A computer system that achieves AI through a machine learning technique is called a learning system.
Due to early use in the fields, rule-based systems are commonly confused with artificial intelligence and machine learning. However, they are not AI, and they are not machine learning.
It’s easy to confuse the two as they can look very similar. Both involve machines completing tasks, seemingly on their own. The difference is that AI can determine the action to take itself; it can learn and adapt. Meanwhile, rule-based systems do exactly as instructed by a human.
In other words, unlike artificial intelligence and machine learning, the actions carried out by rule-based systems (the rules that they follow) are determined by a human.
The system doesn’t work it out for itself, or intelligently make decisions.
How does a rule-based system work?
Rule-based systems, unsurprisingly, work based on rules. These rules outline triggers and the actions that should follow (or are triggered). For example, a trigger might be an email containing the word “invoice”. An action might then be to forward the email to the finance team.
These rules most often take the form of if statements. ‘IF’ outlines the trigger, ‘THEN’ specifies the action to complete. So, if you want to create a rule-based system capable of handling 100 different actions, you’d have to write 100 different rules. If you want to then update the system and add actions, then you would need to write new rules.
In short, you use rules to tell a machine what to do, and the machine will do exactly as you tell it. From there, rule-based systems will execute the actions until you tell it to stop.
But remember: if you tell it to do something incorrectly, it will do it incorrectly.
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