Deliveroo and EduMe today announced an exclusive new global partnership that will drive the success of the food delivery giant with effective onboarding, training and continuous learning by using EduMe's platform. The initiative is being rolled out to Deliveroo's entire global network of riders. It will take advantage of EduMe's experience as the training provider of choice by other leading technology companies. This will help facilitate effective onboarding at scale for new riders. Furthermore, an integration with hiring platform Fountain will be leveraged to present a seamless engagement and onboarding experience for new riders.
Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable constituent in urban logistics distribution system. In the last decade, numerous methods for MVRPSTW have sprung up, but most of them are based on heuristic rules which require huge computation time. With the rapid increasing of logistics demand, traditional methods incur the dilemma of computation efficiency. To efficiently solve the problem, we propose a novel reinforcement learning algorithm named Multi-Agent Attention Model in this paper. Specifically, the vehicle routing problem is regarded as a vehicle tour generation process, and an encoder-decoder framework with attention layers is proposed to generate tours of multiple vehicles iteratively. Furthermore, a multi-agent reinforcement learning method with an unsupervised auxiliary network is developed for model training. By evaluated on three synthetic networks with different scale, the results demonstrate that the proposed method consistently outperforms traditional methods with little computation time. In addition, we validate the extensibility of the well-trained model by varying the number of customers and capacity of vehicles. Finally, the impact of parameters settings on the algorithmic performance are investigated.
Uber Eats has been one of the fastest-growing food delivery services since the initial launch in Toronto in December 2015. Currently, it's available in over 600 cities worldwide, serving more than 220,000 restaurant partners and has reached 8 billion gross bookings in 2018. The ability to accurately predict delivery times is paramount to customer satisfaction and retention. Additionally, time predictions are important on the supply side as we calculate the time to dispatch delivery partners. My recent talk covered how Uber Eats has leveraged machine learning to address these challenges. With the mission "Make eating well effortless, every day, for everyone" one of our top priorities is ensuring reliability.
Kawasaki Kisen Kaisha., Ltd. (hereinafter, "K" Line) has reached an agreement with Hiroshima University, the National Institute of Maritime, Port and Aviation Technology (hereinafter, "MPAT") and Marubeni Corporation (hereinafter, "Marubeni") to jointly work on research and analysis on maritime logistics and shipping market conditions using AI (hereinafter "The Research"). In recent years, it has become possible to use comprehensive, chronologically ordered ship movement and static data, such as position (coordinate information), speed, direction, port of call and drafts, for ships with over 300 gross tonnage traveling internationally. This data is being applied in a variety of ways. Additionally, AI is making remarkable progress with improving machine learning and deep learning technology, and there is much research and practical application of this technology that is being used to find patterns hidden in big data and to make predictions. The purpose of The Research is to estimate maritime logistics by combining data and technology, and to explore the possibility of developing predictive models with high accuracy.
Although many companies talk about artificial intelligence, it's likely that the majority of their employees aren't actually using machine-learning technologies in the workplace. One big reason for that is while executives may be excited about A.I., employees may feel threatened or even insulted that managers would force them to use tools that that they fear will one day replace them. As FedEx senior data scientist Clayton Clouse said during an A.I. conference in San Francisco last week, "We shouldn't expect that people will jump up and down and be excited when we say, 'Hey, we're going to be augmenting your job with A.I.'" Citing a survey about A.I. from McKinsey, Clouse said that while the majority of companies polled by the consulting firm said they were implementing A.I. either in their business or through pilot projects, "only 6% reported that their employees were actually using the system the way they should be used." The employees, it turns out, are skeptical about A.I., especially machine-learning tools intended to automate decision-making in some way, Clouse said. If workers don't trust the A.I. tools to do as good of a job as them, they simply aren't going to use them, he explained.
The rapid growth of artificial intelligence and automation presents threats -- and opportunities -- for workers and businesses in the Miami Valley. More than 31,600 people in the Dayton metro area work in the five largest occupations at high risk of automation, according to data the Brookings Institution prepared exclusively for the Dayton Daily News. Those jobs include food preparation, waiters, stock clerks, tractor-trailer truck drivers and accounting clerks. But about 34,600 people in the region that includes Montgomery, Greene and Miami counties work in the largest low-risk occupations. Those include registered nurses, freight and stock movers, janitors, customer service representatives and general managers, according to the Brookings data.
Improved performance is of prime concern for any business or enterprise. Together, AI/Machine learning technologies are viewed as the most impactful technology given its wide applicability and promise of addressing complex business problems across the value chain. Logistics, initially, was one aspect of management but in this era of the profound transformation, it is becoming one of the most disruptive fields across the globe. Leading companies have already started using the Artificial Intelligence and machine learning to fine-tune core strategies such as warehouse locations, as well as to enhance real-time decision making related to issues like availability, costs, inventories, carriers, vehicles and personnel. The potential of AI and Machine learning is not only enhancing everyday business activities and strategies but also is streamlining the logistics on a global scale.
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba. The CDP aims to assign a set of couriers to serve pickup requests with stochastic spatial and temporal arrival rate among urban regions. The objective is to maximize the revenue of served requests given a limited number of couriers over a period of time. Many online algorithms such as dynamic matching and vehicle routing strategy from existing literature could be applied to tackle this problem. However, these methods rely on appropriately predefined optimization objectives at each decision point, which is hard in dynamic situations. This paper formulates the CDP as a Markov decision process (MDP) and proposes a data-driven approach to derive the optimal dispatching rule-set under different scenarios. Our method stacks multi-layer images of the spatial-and-temporal map and apply multi-agent reinforcement learning (MARL) techniques to evolve dispatching models. This method solves the learning inefficiency caused by traditional centralized MDP modeling. Through comprehensive experiments on both artificial dataset and real-world dataset, we show: 1) By utilizing historical data and considering long-term revenue gains, MARL achieves better performance than myopic online algorithms; 2) MARL is able to construct the mapping between complex scenarios to sophisticated decisions such as the dispatching rule. 3) MARL has the scalability to adopt in large-scale real-world scenarios.
We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.
These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.