We consider the following algorithmic question: given a list of jobs, each of which requires a certain number of time steps to be completed while incurring a random cost in every time step until finished, learn the relative priorities of jobs and make scheduling decisions of which job to process in each time step, with the objective of minimizing the expected total cumulative cost. Here, we need an algorithm that seamlessly integrates learning and scheduling. This question is motivated by several applications. Modern data processing platforms handle complex jobs whose characteristics are often unknown in advance, in which case, it is difficult to judge which jobs have higher priorities than others before accumulating enough information about the jobs [11]. Here, there is significant uncertainty in determining the relative importance of jobs or tasks, and moreover, the population of jobs may be highly dynamic, e.g., they may have all distinct features. Nevertheless, these platforms need to start processing jobs in a sequence based on partial information, which potentially results in undesired delays. However, as a system learns more about the jobs' features, it may flexibly adjust scheduling decisions to serve the jobs with high priority first.

With the increased interest in machine learning and big data problems, the need for large amounts of labelled data has also grown. However, it is often infeasible to get experts to label all of this data, which leads many practitioners to crowdsourcing solutions. In this paper, we present new techniques to improve the quality of the labels while attempting to reduce the cost. The naive approach to assigning labels is to adopt a majority vote method, however, in the context of data labelling, this is not always ideal as data labellers are not equally reliable. One might, instead, give higher priority to certain labellers through some kind of weighted vote based on past performance. This paper investigates the use of more sophisticated methods, such as Bayesian inference, to measure the performance of the labellers as well as the confidence of each label. The methods we propose follow an iterative improvement algorithm which attempts to use the least amount of workers necessary to achieve the desired confidence in the inferred label. This paper explores simulated binary classification problems with simulated workers and questions to test the proposed methods. Our methods outperform the standard voting methods in both cost and accuracy while maintaining higher reliability when there is disagreement within the crowd.

And then I found Professor Bitcoin in tubing at the Max Planck Institute for biological cybernetics. And he had their junior research group they are back then and I was doing very very exciting research in, computational neuroscience and said that this is exactly what I wanted to do, so I wrote him an email and explain what I did before and what I want to do now and I was asking him for a for a PhD position and luckily he already did you just had a PhD position open for somebody with my my track record. So Started talking to him and me and then we decided okay sounds like a good match so I went to tune him and yeah started my academic career then into being.

Military drones may have autonomously attacked humans for the first time ever last year, according to a United Nations report. While the full details of the incident, which took place in Libya, haven't been released and it is unclear if there were any casualties, the event suggests that international efforts to ban lethal autonomous weapons before they are used may already be too late. The robot in question is a Kargu-2 quadcopter produced by STM, a Turkish firm.

The importance of data in modern tech can hardly be over-emphasized; because there are so many services and products, there have become so many reasons and channels for collecting user or enterprise data. Companies leverage data to improve the user experience of customers, while in-house, there is a need for effective data accumulation for record-keeping and effective operations. As we clamor and advocate for more frictionless operations in our businesses and everyday activities, we simultaneously create a channel for more data to be collected and used in order to automate processes. In fact, the entire reason why we say companies and organizations should'upgrade' is so that our services or operations are faster. However, this increase in speed or quality in service that we call for can only be achievable when operations are automated i.e. there is a digital record of that operation happening before, then when it wants to happen again, it happens with less human efforts because the existing data are enough to implement the operations automatically. At times, there might not be any need for a previous occurrence of the event in order to automate it, we just have to program whatever digital platform or channel we are using to carry out the operation seamlessly without human effort or with the aid of minimum human effort as the case may be.

Arabshahi, Singh, and Anandkumar (2018) propose a method for creating a dataset of symbolic mathematical equations for the tasks of symbolic equation verification and equation completion. Unfortunately, a dataset constructed using the method they propose will suffer from two serious flaws. First, the class of true equations that the procedure can generate will be very limited. Second, because true and false equations are generated in completely different ways, there are likely to be artifactual features that allow easy discrimination. Moreover, over the class of equations they consider, there is an extremely simple probabilistic procedure that solves the problem of equation verification with extremely high reliability. The usefulness of this problem in general as a testbed for AI systems is therefore doubtful.

Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image. In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index. Maria consists of three flexible components, i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator. The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image. Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response. Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

They're called the Sampo Generation: South Koreans in their 20s and 30s who have given up (po) three (sam) of life's conventional rites of passage -- dating, marrying and having children. They've made these choices because of economic constraints and in the process have worsened South Korea's demographic imbalances. Last year, when the country registered more deaths than births for the first time in recent history, then-Vice Finance Minister Kim Yong-beom pronounced the milestone a "death cross." "I Live Alone" is one of South Korea's most popular reality TV shows. It follows the single lives of movie actors and K-pop singers engaging in mundane activities such as feeding their pets or eating ramen in the middle of the night -- all alone.

Many plants become limp or droop through heat, loss of water, or disease. This is also known as wilting. In this paper, we examine plant wilting caused by bacterial infection. In particular, we want to design a metric for wilting based on images acquired of the plant. A quantifiable wilting metric will be useful in studying bacterial wilt and identifying resistance genes. Since there is no standard way to estimate wilting, it is common to use ad hoc visual scores. This is very subjective and requires expert knowledge of the plants and the disease mechanism. Our solution consists of using various wilting metrics acquired from RGB images of the plants. We also designed several experiments to demonstrate that our metrics are effective at estimating wilting in plants.