On the Impact of Artificial Intelligence on Economy

Rapid advancements in artificial intelligence (AI) will have a dramatic impact on the global economy. This paper provides a systematic review of the economic impact of AI, focusing on the promotion of AI on productivity and economic growth; the impact of AI on labor employment; and the question of whether AI will increase income inequality. On this basis, a summary of how to implement public policies to reduce the potential negative impacts of AI on the employment structure and income inequality is provided. Finally, a summary and prospective research directions are provided

Task Scheduling in Networks

Scheduling a set of tasks on a set of machines so as to yield an efficient schedule is a basic problem in computer science and operations research. Most of the research on this problem incorporates the potentially unrealistic assumption that communication between the different machines is instantaneous. In this paper we remove this assumption and study the problem of network scheduling, where each job originates at some node of a network, and in order to be processed at another node must take the time to travel through the network to that node. Our main contribution is to give approximation algorithms and hardness proofs for fully general forms of the fundamental problems in network scheduling. We consider two basic scheduling objectives: minimizing the makespan and minimizing the average completion time. For the makespan, we prove small constant factor hardness-to-approximate and approximation results. For the average completion time, we give a log-squared approximation algorithm for the most general form of the problem. The techniques used in this approximation are fairly general and have several other applications. For example, we give the first nontrivial approximation algorithm to minimize the average weighted completion time of a set of jobs on related or unrelated machines, with or without a network

Improved Bounds on Relaxations of a Parallel Machine Scheduling Problem

We consider the problem of scheduling n jobs with release dates on m identical parallel machines to minimize the average completion time of the jobs. We prove that the ratio of the average completion time of the optimal nonpreemptive schedule to that of the optimal preemptive schedule is at most 7}{3}, improving a bound of (3- 1}{m}) due to Phillips, Stein and Wein. We then use our technique to give an improved bound on the quality of a linear programming relaxation of the problem considered by Hall, Schulz, Shmoys and Wein

Approximating the Minimum-Cost Maximum Flow is P-Complete

We show that it is impossible, in NC, to approximate the value of the minimumcost maximum flow unless P = NC. Keywords: Theory of computation, P-complete, minimum-cost flow, maximum flow. 1 Introduction Once a problem is proved to be P-complete, it is generally believed that there exists no NC or RNC algorithm to solve it exactly 1 . Therefore, the next important question becomes how well can it be approximated in NC or RNC? In this note we establish an interesting contrast between the parallel complexity of two related P-Complete problems, the maximum-flow problem and the minimum-cost maximum flow problem. We show that despite the fact that one can approximate the value of a maximum flow arbitrarily closely in RNC, approximating the value of the minimum-cost maximum flow within a factor of C, the maximum cost in the network, is P-Complete. Our proof also shows that this is true for networks with C polynomial in the size of the network, when the costs of the network are expressed ..

On the Massively Parallel Solution of The Assignment Problem

In this paper we discuss the design, implementation and effectiveness of massively parallel algorithms for the solution of large-scale assignment problems. In particular, we study the auction algorithm of Bertsekas, an algorithm based on the method of multipliers of Hestenes and Powell, and an algorithm based on the alternating direction method of multipliers of Eckstein. We discuss alternative approaches to the massively parallel implementation of the auction algorithm, including Jacobi, Gauss-Seidel and a hybrid scheme. The hybrid scheme, in particular, exploits two different levels of parallelism and an efficient way of communicating the data between them without the need to perform general router operations across the hypercube network. We then study the performance of massively parallel implementations of the two methods of multipliers. Implementations are carried out on the Connection Machine CM-2, and the algorithms are evaluated empirically with the solution of large scale problems. The hybrid scheme significantly outperforms all of the other methods and gives the best computational results to date for a massively parallel solution to this problem

On the Existence of Schedules that are Near-Optimal for both Makespan and Total Weighted Completion Time

We give a simple proof that, for any instance of a very general class of scheduling problems, there exists a schedule of makespan at most twice that of the optimal possible and of total weighted completion time at most twice that of the optimal possible. We then refine the analysis, yielding variants of this theorem with improved constants, and give some algorithmic consequences of the technique

Minimizing average completion time in the presence of release dates

Abstract A natural and basic problem in scheduling theory is to provide good average quality of service to a stream of jobs that arrive over time. In this paper we consider the problem of scheduling n jobs that are released over time in order to minimize the average completion time of the set of jobs. In contrast to the problem of minimizing average completion time when all jobs are available at time 0, all the problems that we consider are N P-hard, and essentially nothing was known about constructing good approximations in polynomial time. We give the first constant-factor approximation algorithms for several variants of the single and parallel machine model. Many of the algorithms are based on interesting algorithmic and structural relationships between preemptive and nonpreemptive schedules and linear programming relaxations of both. Many of the algorithms generalize to the minimization of average weighted completion time as well

Task Scheduling in Networks

Scheduling a set of tasks on a set of machines so as to yield an efficient schedule is a basic problem in computer science and operations research. Most of the research on this problem incorporates the potentially unrealistic assumption that communication between the different machines is instantaneous. In this paper we remove this assumption and study the problem of network scheduling, where each job originates at some node of a network, and in order to be processed at another node must take the time to travel through the network to that node. Our main contribution is to give approximation algorithms and hardness proofs for fully general forms of the fundamental problems in network scheduling. We consider two basic scheduling objectives: minimizing the makespan, and minimizing the average completion time. For the makespan we prove small constant factor hardness-to-approximate and approximation results. For the average completion time, we give a log-squared approximation algorithm for..