Artificial intelligence took center stage at Google's annual user conference, Cloud Next 2018. The company made several announcements that make machine learning and artificial intelligence accessible to both developers and businesses. One of the first announcements came in the form of Cloud AutoML, a managed service that lets developers build machine learning models without requiring any specialized knowledge in machine learning or coding. AutoML Vision, along with other automated ML services became publicly available. According to Google, it is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google's state-of-the-art transfer learning, and Neural Architecture Search technology.

We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.

Companies like to flaunt their use of artificial intelligence to the point where it's virtually meaningless, but the truth is that AI as we know it is still quite dumb. While it can generate useful results, it can't explain why it produced those results in meaningful terms, or adapt to ever-evolving situations. DARPA thinks it can move AI forward, thoug. It's launching an Artificial Intelligence Exploration program that will invest in new AI concepts, including "third wave" AI with contextual adaptation and an ability to explain its decisions in ways that make sense. If it identified a cat, for instance, it could explain that it detected fur, paws and whiskers in a familiar cat shape. Importantly, DARPA also hopes to step up the pace.

We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting patient mortality. Compared to rule sets built from single-valued rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-valued rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments show that our model was able to achieve better performance than baseline method including the current system used by the hospital.

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowl\-edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.

Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rockt{\"{a}}schel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.

Developers know a whole lot about the machine learning (ML) systems that they produce and manage, that is a given. But, there's a demand for non-developers to have a higher level understanding of the kinds of systems. Expert systems and artificial neural networks would be the classical two important classes. With the advancements in computing functionality, softwares capacities, algorithm complexity and analytical algorithm could be said to have combined both of them. This article is a summary of the three different types of systems.

The user uses the control panel (A) to specify the detail information to visualize (e.g., level of detail, rule filters). The rule-based explanatory representation is visualized as a matrix (B), where each row represents a rule, and each column is a feature used in the rules. The user can also filter the data or use a customized input in the data filter (C) and navigate the filtered dataset in the data table (D). Abstract--With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little ...

Artificial Intelligence (A.I.) will soon be at the heart of every major technological system in the world including: cyber and homeland security, payments, financial markets, biotech, healthcare, marketing, natural language processing, computer vision, electrical grids, nuclear power plants, air traffic control, and Internet of Things (IoT). While A.I. seems to have only recently captured the attention of humanity, the reality is that A.I. has been around for over 60 years as a technological discipline. In the late 1950's, Arthur Samuel wrote a checkers playing program that could learn from its mistakes and thus, over time, became better at playing the game. MYCIN, the first rule-based expert system, was developed in the early 1970's and was capable of diagnosing blood infections based on the results of various medical tests. The MYCIN system was able to perform better than non-specialist doctors. While Artificial Intelligence is becoming a major staple of technology, few people understand the benefits and shortcomings of A.I. and Machine Learning technologies. Machine learning is the science of getting computers to act without being explicitly programmed.