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21 Words About Knowledge, Every AI-Savvy Leader Must Know
Latest   Machine Learning

21 Words About Knowledge, Every AI-Savvy Leader Must Know

Last Updated on July 24, 2023 by Editorial Team

Author(s): Yannique Hecht

Originally published on Towards AI.

Artificial Intelligence

Think you can explain these? Put your knowledge to the test!

21 Words About Knowledge, Every AI-Savvy Leader Must Know

[This is the 2nd part of a series. Make sure you read about Search Algorithms before continuing. Future topics include Uncertainty, Optimization, Machine Learning, Neural Networks, and Language.]

A single second of the internet produces 2,902,666 emails, 81,649 videos viewed on Youtube, 8,936 tweets, 4,581 Skype calls, 1,686 Tumblr posts, and another 90,992 gigabytes of web traffic.

By the time you read these numbers, they’re already outdated, with us setting new records every single day. And that’s before COVID-19 changed the way we use the internet!

We create more than 2.5 quintillion bytes of data each day.

How can artificial intelligence make sense out of this sheer volume of data? How does it store and build information?

The answer to this is Knowledge representation and reasoning. This article briefly defines the main concepts and terms related to knowledge and shows how information is represented and inferred from simple sentences in a variety of different logical languages.

Knowledge

knowledge-based agents: agents that reason by operating on internal representations of knowledge

sentence: an assertion about the world in a knowledge representation language

logical connections: knowledge representations of an agent’s world, its goals, and the current situation by sentences in logic; for example:

¬ not
∧ and
∨ or
→ implication
↔ biconditional

model: assignment of a truth value to every propositional symbol

knowledge base: a set of sentences known by a knowledge-based agent

entailment: a relationship between sentences, where “in every model in which sentence α is true, sentence β is also true.”

α ⊨ β

inference: the process of deriving new sentences from old ones

model checking: a method for checking whether a finite-state model of a system meets a given specification (also called property checking)

knowledge engineering: the process of emulating the judgment and behavior of a human with expertise in a specific situation

modus ponens: including elimination, double negation elimination, implication elimination, biconditional elimination, De morgan’s law, distributive property

De Morgan’s law: a pair of transformation rules that are valid rules of inference:

¬(P ∨ Q) = (¬ P ∧ ¬ Q)

distributive property: two valid rules of replacement:

P ∨ (Q ∧ R) = (P ∨ Q) ∧ (P ∨ R)

commutative law: another transformation rule:

P ∧ Q = Q ∧ P

associative law: and another transformation rule:

(P ∧ Q) ∧ R = P ∧ (Q ∧ R)

contrapositive law: and the final transformation rule, promise:

P — Q = ¬ P — ¬ Q

theorem proving: a formal method of providing proof in symbolic logic, using deductive inference

clause: a disjunction of literals

conjunctive normal form (CNF): a logical sentence that is a conjunction of clauses, e.g.:

(A ∨ B ∨ C) ∧ (D ∨ ¬E) ∧ (F ∨ G)

first-order logic: a collection of formal systems, using quantified variables over non-logical objects and allowing the use of sentences that contain variables

universal quantification: a type of quantifier, a logical constant which is interpreted as “given any” or “for all,” notated with:

∀x.

existential quantification: a type of quantifier, a logical constant which is interpreted as “there is at least one” or “for some,” notated with:

∃x.

Now that you’re able to explain the most essential Knowledge related terms, you’re hopefully more comfortable exploring these concepts further on your own.

This puts you on the second stage of your journey to becoming a fully-fledged AI-savvy leader. Continue exploring other vital topics, including Search, Uncertainty, Optimization, Machine Learning, Neural Networks, and Language.

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About the author:
Yannique Hecht works in the fields of combining strategy, customer insights, data, and innovation. While his career has been in the aviation, travel, finance, and technology industry, he is passionate about management. Yannique specializes in developing strategies for commercializing AI & machine learning products.

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Published via Towards AI

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