Inside AI: Computer Algorithms & NLP
What We Do Know About AI
What is a computer algorithm? With today’s reliance on technology and automation, consumers hear the words “computer algorithm” thrown around a lot. Anyone who went through high school math has probably come across an algorithm in some form, but what is a computer algorithm? It’s often used to describe some type of application that performs a task. This definition is different than the standard algebraic algorithm we learn in high school.
Application Algorithms Versus Math Algorithms. If you recall from your basic math classes, an algorithm solves a problem. The “problem” is a generic variable that is undefined, but you can solve it using values of other variables.
A computer algorithm is similar, but it’s somewhat more complex. Engineers design algorithms to solve a problem that might use several factors to solve several problems. Assume, for example, that you have a social media application. You could have these three problems to solve:
User must sign up to become a member.
The information entered by the user is used to suggest groups and contacts.
Groups that each user joins should be used to generate future suggestions.
These three requirements could be set by the app owner or the developer. The requirements are a separate set of specifications that determine the functionality of the computer algorithm.
For the social media application, a computer algorithm would be created to take the user’s information, perform a lookup of stored data on other user information, and create a list of suggestions based on input.
Computer algorithms perform with much more functionality than a math problem, and they often have multiple results based on input. You will usually hear developers describe multiple algorithms for each section of an application.
When you think of a computer algorithm, it’s not just a calculation like a simple math problem. Computer algorithms solve multiple problems and create output based on user input.
Algorithms are leveraged to stimulate human learning in AI and machine learning.
Computer Algorithms and Artificial Intelligence
Modern technology has gone beyond basic computer algorithms that just take input and display output. Machine learning and artificial intelligence (AI) are the new tools used to create software.
Developers who create machine learning and AI use algorithms to simulate human learning. The algorithms take data from large data warehouses and determine output based on previous input. The “learning” is used to solve problems in modern society and reduce the time it takes to come up with a solution compared to humans.
For instance, AI can determine the best pharmaceutical solution for a given disease without a doctor experimenting on a living patient with different standard drugs. It saves the patient from the frustration of finding the right drug combination, and helps the physician give the best care to his patients.
Artificial intelligence is also in basic applications such as Apple’s Siri. Siri provides each user with a unique response based on previous preferences. Siri learns from the user’s choice in restaurants, bars, television shows, and vacation favorites.
Computer algorithms are used in any software you find on the Internet, on a smartphone or tablet, or on your desktop. These algorithms continue to improve in medicine, technology, business, and entertainment.
What is Natural Language Processing and Why You Need to Know It? With advancements in computer processing capabilities, the move toward interactive artificial intelligence has grown. In order to engage knowledgeably in the field of interactive technology, you must have a working understanding of the natural language processors that form the foundation of speech recognition and search algorithms.
Putting aside the question of whether our current version of AI, like Apple’s Siri, Google’s computer learning, Amazon’s Alexa, or IBM’s Watson is actually artificial intelligence or not, interactive technology is dependent upon natural language processing. Naturally Language Processing (NLP) is a computational model enabling computers to derive meaning from human language. NLP is what allows Siri to show me movie times when I say, “Show me Avengers Endgame,” but to respond with a joke when I say, “Show me the money.”
What are syntax and semantics? Many natural language processors incorporate treebanks to parse and annotate syntax and semantic language structure. Syntax is just a fancy way of describing the order of words and phrases in a sentence. Syntax changes based on the language you are using. German usually places the subject first, the verb second, and any other elements last; the verb is always the second element in the sentence. In English, the verb floats, meaning it can appear in different places. For example:
English: On the way home, the man ran.
German: Der Mann rannte auf dem Heimweg.
Semantics focus on the actual meaning of the sentence, more than sentence construction or ordering of the words. For NLPs, the ability to parse and annotate both of these linguistic elements is vital for accurate translation, interpretation, and most importantly appropriate action-response.
The Penn Wall Street Treebank model was one of the earliest computational syntax and semantic annotating programs. The model typically achieves accuracy of around 87.7%. More recent developments in NLPs attempt to overcome ambiguity error by extending the parser beyond the headwords of a text to the complement/adjunct distinction.
The process of determining the visual representation of a parse tree is called parameterization. A parse tree is merely a series of decisions, similar to the Choose Your Own Adventure books. Every choice a reader makes results in a different outcome. Similarly, every decision the NLP algorithm makes takes it further away from some interpretive options, even eliminating some branch variations, and closer to other decisions.
Related Content : The Stanford Natural Language Parser