This article about the best artificial intelligence logistics startups is part of the "Logistics of the Future" series looking at the top logistics startups today. We are officially living in the age of Artificial Intelligence. It's everywhere we look, from AI-powered personal assistants to predictive analytics to making medical diagnoses, Artificial Intelligence is making incredible advances across all industries. In fact, a recent report on the state of Artificial Intelligence for enterprises found that supply chain and operations are some of the top areas where businesses are driving revenue from AI investment. Why is AI making such a big difference in the logistics and supply chain, particularly?

AI could help health professionals cope with the gigantic quantities of data – Discover why healthcare facilities increasingly realize that AI could help achieve significant impacts with digital pathology. Thanks to approvals from the Food and Drug Administration (FDA) for applications such as primary disease diagnosis, digital pathology is rapidly becoming the new standard of care. However, this advancement creates challenges that artificial intelligence could help solve. Digital pathology enables capturing pathology information, such as whole slide images (WSI), and working with it digitally using a specialized scanner. Acquiring, studying and managing data in this way allows sharing between parties on a computer or mobile device.

Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns. 1 1. We define artificial intelligence (AI) as the ability of a machine to perform cognitive functions that we associate with human minds (such as perceiving, reasoning, learning, and problem solving) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI 2 2. We define AI use in standard business processes as embedded AI in at least one product or business process for at least one function or business unit. The online survey was in the field from March 26 to April 5, 2019, and garnered responses from 2,360 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of these respondents, 1,872 work at companies they say have piloted AI in at least one function or business unit, embedded at least one AI capability in at least one product or business process for at least one function or business unit, or embedded at least one AI capability in products or business processes across multiple functions or business units.

Lying has never looked so good, literally. Concern over increasingly sophisticated technology able to create convincingly faked videos and audio, so-called'deepfakes', is rising around the world. But at the same time they're being developed, technologists are also fighting back against the falsehoods. "The concern is that there will be a growing movement globally to undermine the quality of the information sphere and undermine the quality of discourse necessary in a democracy," Eileen Donahoe, a member of the Transatlantic Commission on Election Integrity, told CNBC in December 2018. She said deepfakes are potentially the next generation of disinformation.

Dr Catriona Wallace, an entrepreneur in the Artificial Intelligence field, and Founder & Executive Director of Flamingo AI dropped into Speakers Corner towers to share her expertise on Artificial Intelligence, Ethics & Human Rights in technology and Women in Leadership. Needless to say, we were blown away by her visit and decided to learn more. Find out how Catriona became the second female-led business on the ASX, the importance of neurodiversity in the workplace, and what the future of AI has in store for ethics and the world at large. I only ever wanted to be a farmer! After a couple of years studying agriculture at University, I realised most of my peers were becoming investment bankers and not farmers.

The recent progress in neural architectures search (NAS) has allowed scaling the automated design of neural architectures to real-world domains such as object detection and semantic segmentation. However, one prerequisite for the application of NAS are large amounts of labeled data and compute resources. This renders its application challenging in few-shot learning scenarios, where many related tasks need to be learned, each with limited amounts of data and compute time. Thus, few-shot learning is typically done with a fixed neural architecture. To improve upon this, we propose MetaNAS, the first method which fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a meta-architecture along with the meta-weights during meta-training. During meta-testing, architectures can be adapted to a novel task with a few steps of the task optimizer, that is: task adaptation becomes computationally cheap and requires only little data per task. Moreover, MetaNAS is agnostic in that it can be used with arbitrary model-agnostic meta-learning algorithms and arbitrary gradient-based NAS methods. Empirical results on standard few-shot classification benchmarks show that MetaNAS with a combination of DARTS and REPTILE yields state-of-the-art results.

This paper proposes a methodology for host-based anomaly detection using a semi-supervised algorithm namely one-class classifier combined with a PCA-based feature extraction technique called Eigentraces on system call trace data. The one-class classification is based on generating a set of artificial data using a reference distribution and combining the target class probability function with artificial class density function to estimate the target class density function through the Bayes formulation. The benchmark dataset, ADFA-LD, is employed for the simulation study. ADFA-LD dataset contains thousands of system call traces collected during various normal and attack processes for the Linux operating system environment. In order to pre-process and to extract features, windowing on the system call trace data followed by the principal component analysis which is named as Eigentraces is implemented. The target class probability function is modeled separately by Radial Basis Function neural network and Random Forest machine learners for performance comparison purposes. The simulation study showed that the proposed intrusion detection system offers high performance for detecting anomalies and normal activities with respect to a set of well-accepted metrics including detection rate, accuracy, and missed and false alarm rates.

Sparse neural networks have been shown to be more parameter and compute efficient compared to dense networks and in some cases are used to decrease wall clock inference times. There is a large body of work on training dense networks to yield sparse networks for inference (Molchanov et al., 2017; Zhu & Gupta, 2018; Narang et al., 2017; Li et al., 2016; Guo et al., 2016). This limits the size of the largest trainable sparse model to that of the largest trainable dense model. In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods. We show that this approach requires fewer floating-point operations (FLOPs) to achieve a given level of accuracy compared to prior techniques. Importantly,by adjusting the topology it can start from any initialization - not just "lucky" ones. We demonstrate state-of-the-art sparse training results with ResNet-50, MobileNet v1 and MobileNet v2 on the ImageNet-2012 dataset, WideResNets on the CIFAR-10 dataset and RNNs on the WikiText-103 dataset. Finally, we provide some insights into why allowing the topology to change during the optimization can overcome local minima encountered when the topology remains static. 1 Introduction The parameter and floating point operation (FLOP) efficiency of sparse neural networks is now well demonstrated on a variety of problems (Han et al., 2015; Srinivas et al., 2017). Multiple works have shown inference time speedups are possible using sparsity for both Recurrent Neural Networks (RNNs) (Kalchbrenner et al., 2018) and Convolutional Neural Networks (ConvNets) (Park et al., 2016; Elsen et al., 2019). Currently, the most accurate sparse models are obtained with techniques that require, at a minimum, the cost of training a dense model in terms of memory and FLOPs (Zhu & Gupta, 2018; Guo et al., 2016), and sometimes significantly more (Molchanov et al., 2017). This paradigm has two main limitations: 1. The maximum size of sparse models is limited to the largest dense model that can be trained. Even if sparse models are more parameter efficient, we can't use pruning to train models that are larger and more accurate than the largest possible dense models. Large amounts of computation must be performed for parameters that are zero valued or that will be zero during inference. Gale et al. (2019) found that three different dense-to-sparse training algorithms all achieve about the same sparsity / accuracy tradeoff.

This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles -- in multiple system scales -- illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates. 1 Introduction Starting with the seminal work of Mochales Palau and Moens (2009), argument mining has mainly focused on the following tasks - identifying argumentative text segments within a given document; labeling these text segments according to the type of argument and its stance; and elucidating the discourse relations among the detected arguments. Typically, the considered documents were argumentative in nature, taken from a well defined domain, such as legal documents or student essays. More recently, some attention had been given to the corresponding retrieval task - given a controversial topic, retrieve arguments with a clear stance towards this topic. This is usually done by first retrieving - manually or automatically - documents relevant to the topic, and then using argument mining techniques to identify relevant argumentative segments therein. This documents-based approach was originally explored over Wikipedia (Levy et al. 2014; Rinott et al. 2015), and more recently over the entire Web (Stab et al. 2018). For an argument retrieval system to be of practical use requires: (1) high precision, and (2) wide coverage.