27 Words About Search Algorithms, Every AI-Savvy Leader Must Know

Author(s): Yannique Hecht

Originally published on Towards AI.

Artificial Intelligence

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

The number of jobs artificial intelligence will ultimately transform is everyone’s guess. Most predictions, including Deloitte’s, are in the range of 60 to 80 percent within the next five to ten years.

For stats like this, the World Economic Forum considers artificial intelligence and machine learning as the Fourth Industrial Evolution. Even if you’re still skeptical, it’s clear that AI’s impact on future work is significant and coming sooner rather than later. Reason enough to prepare and build your knowledge!

To stay relevant and effective, even non-Tech leaders must learn to manage not only quants but also algorithms.

Learning about AI’s language and concepts is only the first step, but the most essential. To get you going, this article briefly defines the main concepts and terms around Search. This vast field includes practical problem-solving, for example, route navigation and playing games like maze runner or chess.

I will cover other AI-related topics, including Knowledge, Uncertainty, Optimization, Machine Learning, Neural Networks, and Language in future articles.

Search

algorithm: a defined sequence of steps, designed to solve a specific problem

agent: an entity that perceives its environment and acts upon that environment

state: a configuration of the agent and its environment

initial state: the state in which the agent begins

action: choices that can be made in a state

transition model: a description of what state results from performing any applicable action in any state

state-space: the set of all states reachable from the initial state by any sequence of actions

goal test: way to determine whether a given state is a goal state

path cost: the numerical cost associated with a given path

solution: a sequence of actions that leads from the initial state to a goal state

optimal solution: a solution that has the lowest path cost among all solutions

node: a data structure that keeps track of a state, a parent (node that generated this node), an action (action applied to the parent to get node), and a path cost (from the initial state to node)

frontier: a set of paths from a start node, containing the initial state

depth-first search: search algorithm that always expands the deepest node in the frontier

breadth-first search: search algorithm that always expands the shallowest node in the frontier

stack: last-in-first-out data type

queue: first-in-first-out data type

uninformed search: search strategy that uses no problem-specific knowledge

informed search: search strategy that uses problem-specific knowledge to find solutions more efficiently

greedy best-first search: search algorithm that expands the node that is closest to the goal, as estimated by a heuristic function

heuristic function: a way to provide an informed guess about the direction (neighboring node) to a goal

a* search: search algorithm that expands node with the lowest value of

a(n) + e(g), where a(G) = cost to reach node, and 
 e(n) = estimated cost to goal

adversarial search: a search, where other agents plan against you, for example in games

utility: final numerical value for a terminal state

minimax: an algorithm (sometimes called MinMax, MM or saddle point) providing the optimal move to maximize/minimize potential outcomes; used for example in decision theory, game theory, or statistics

depth-limited minimax: a constrained version of the traditionally unlimited minimax

evaluation function: a function that estimates the expected utility of the game from a given state

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

However, you cannot complete your journey to becoming a fully-fledged AI-savvy leader without exploring the other remaining key topics, including Knowledge, Uncertainty, Optimization, Learning, Neural Networks, and Language.

I will cover all these areas in future articles.

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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