Hmm not sure but I do know that Charles Babbage and Ada Lovelace created the first big computer that could add and subtract and many other things. Ada Lovelace wrote the first computer code I think but that doesn't really answer your question.
problem solving in computer sciences is used to divide a large problem into atomic steps and solving all steps hierarchically.
A problem is a situation that needs to be solved, while an algorithm is a step-by-step procedure for solving a problem. In problem-solving, the problem is the challenge to be addressed, while the algorithm is the specific method used to find a solution to the problem.
Yes, solving the knapsack problem is considered NP-complete.
The expected backtracking runtime for solving this problem is O(2n), where n is the number of decision points in the problem.
The key challenges in solving the job shop scheduling problem efficiently include the complexity of the problem, the large number of possible solutions to consider, and the need to balance multiple conflicting objectives such as minimizing makespan and maximizing machine utilization. Additionally, the problem is NP-hard, meaning that finding the optimal solution can be computationally intensive and time-consuming.
There is no such thing as a machine "capable of solving any problem".
davros
alan turning
who nose unless u were born in them times
That sounds like the description of a Turing machine, which was a theoretical machine described by Alan Turing.
Alan Turing proved that a machine capable of processing a stream of symbols, known as the Turing machine, could theoretically solve any problem that is computable. His work laid the foundation for modern computer science and established the concept of algorithmic computation. Turing's findings demonstrated that, given sufficient time and resources, such a machine could perform any calculation that can be algorithmically defined.
The concept that a machine capable of processing a stream of 1s and 0s can solve any problem was primarily established by Alan Turing in the 1930s. His formulation of the Turing machine provided a theoretical framework for understanding computation and the limits of what can be computed. This concept laid the foundation for modern computer science and the idea of universality in computation.
1942
Alan Turing proved that a machine capable of processing a stream of 1s and 0s, known as a Turing machine, could solve any problem that can be algorithmically defined. This concept is foundational to the field of computer science and establishes the basis for the theory of computation. Turing's work demonstrated that such machines could simulate any algorithm, thus laying the groundwork for modern computing.
Parallel processing
parallel
Distributed processing