The Sapir-Whorf hypothesis claims that: The structure of a language shapes what people think and do. The critical tradition is concerned with the way that language can be used to perpetuate power imbalances. The Sapir-Whorf hypothesis suggests that reality shapes our language.

Insight. sudden awareness of the relationships among various elements that had previously appeared to be independent of one another.

Linguistic determinism is the concept that language and its structures limit and determine human knowledge or thought, as well as thought processes such as categorization, memory, and perception.

You could say that a heuristic exploits problem-dependent information to find a ‘good enough’ solution to a specific problem, while metaheuristics are, like design patterns, general algorithmic ideas that can be applied to a broad range of problems.

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or …

Classical metaheuristics, such as Iterated Local Search, Hill Climbing, Genetic Algorithms, Simulated Annealing, TabuSearch and Ant Colony Optimization, have shown their suitability to solve complex scheduling problems, space allocation problems, and clustering problems, among others.

There is no algorithm that can solve this problem for every possible program-input pair (at least for Turing machines). This is called the Halting problem , and it’s a common example of an undecidable problem in computer science.

Problems that have no algorithm are called unsolvable.

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics and combinatorial optimization. As results compared with the exactly optimal solutions, the AI search techniques can provide very satisfactory solutions for all TSP problems.

In fact, TSP belongs to the class of combinatorial optimization problems known as NP-complete. This means that TSP is classified as NP-hard because it has no “quick” solution and the complexity of calculating the best route will increase when you add more destinations to the problem.

The oldest logistic problem in the transport history is the Travelling Salesman Problem (TSP), which can be solved by various mathematical models [29] [30] [31]. One of the most common methods for solving TSP is represented by the Ant Colony Optimization (ACO) [30,32,33]. …

How can these 7 AI problem characteristics help me decide on an approach to a problem?

• Decomposable to smaller or easier problems.
• Solution steps can be ignored or undone.
• Predictable problem universe.
• Good solutions are obvious.
• Uses internally consistent knowledge base.

An algorithm must have five properties:

• Input specified.
• Output specified.
• Definiteness.
• Effectiveness.
• Finiteness.

Well, an algorithm is a sequence of steps that solves a problem. With that definition (and in fact most definitions of algorithm) any computer program is also an algorithm. Every Euler problem can be solved with a computer program, so the answer is yes.

Be Strategic, Think First

1. Analyze the problem.
2. Restate the problem.
3. Write out examples of input and output.
4. Break the problem into its component parts.
5. Outline a solution in psuedo-code.
6. Step through your example data with your psuedo-code.

Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a certain order to get the desired output. Algorithms are generally created independent of underlying languages, i.e. an algorithm can be implemented in more than one programming language.

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos.

EVERY day, algorithms make decisions that affect your life, whether it is social media firms determining the news you read or mapping apps choosing the route you will take. As machine learning advances, we are conceding more control to the algorithms, with potentially dire consequences.