Another fast-growing Chinese startup, Beijing Bytedance Technology Co., in recent weeks issued about $300 million in convertible bonds to private-equity firm KKR KKR 1.12% & Co., according to other people familiar with the matter. The company, which owns a popular Chinese news-aggregation app called Jinri Toutiao, was valued at $22 billion in an equity fundraising round in late 2017, according to an individual familiar with that transaction. Spokespeople for Didi, Bytedance and KKR declined to comment. The emerging trend is a result of technology companies' increasing need for cash to fund their expansion at home and abroad, which has involved heavy spending on marketing and other costs to acquire customers and fend off rivals. Selling convertible securities allows companies to raise capital with less dilution to existing shareholders, while giving buyers of the instruments an opportunity to reap additional gains if and when the debt converts into stock down the road.

The development of Artificial General Intelligence (AGI) promises to be a major event. Along with its many potential benefits, it also raises serious safety concerns (Bostrom, 2014). The intention of this paper is to provide an easily accessible and up-to-date collection of references for the emerging field of AGI safety. A significant number of safety problems for AGI have been identified. We list these, and survey recent research on solving them. We also cover works on how best to think of AGI from the limited knowledge we have today, predictions for when AGI will first be created, and what will happen after its creation. Finally, we review the current public policy on AGI.

This paper presents a novel approach to kernel tuning. The method presented borrows techniques from reproducing kernel Banach space (RKBS) theory and tensor kernels and leverages them to convert (re-weight in feature space) existing kernel functions into new, problem-specific kernels using auxiliary data. The proposed method is applied to accelerating Bayesian optimisation via covariance (kernel) function pre-tuning for short-polymer fibre manufacture and alloy design.

However, despite much recent interest in IRL, little work has been done to understand of the minimum set of demonstrations needed to teach a specific sequential decision-making task. We formalize the problem of finding optimal demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing an equivalence to the set cover problem, and use this equivalence to develop an efficient algorithm for determining the set of maximally-informative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: benchmarking active learning IRL algorithms and developing an IRL algorithm that, rather than assuming demonstrations are i.i.d., uses counterfactual reasoning over informative demonstrations to learn more efficiently.

Customer requirements are evolving Data variety and data volumes are increasing rapidly Customers want to democratize access to data in a governed way Security and cost remain key decision factors Analytic needs are evolving beyond batch reports to real-time and predictive Customers are looking to incorporate voice, image recognition, and IoT use cases into applications 3. 2018, Amazon Web Services, Inc. or its Affiliates. Traditionally, analytics used to look like this 4. 2018, Amazon Web Services, Inc. or its Affiliates. Exchange Data • 12 equities markets • 4 options markets SIP Data • SIP trades • SIP NBBO • OPRA Broker Dealer data • 4000 plus firms Third Party Data • Bloomberg • Thompson Reuters • DTCC • OCC Management Amazon S3 Amazon Glacier Intake Linkage Normalization Validation Analytics Amazon Redshift Amazon EMR Machine Learning API API RDS IAM KMS Usage Stats • 33k • 20Pb Structured and unstructured data Millions of documents 25K data checks daily Normalization 33,000 servers daily Centralized data Normalized data Integrated data Discoverable Direct data query ML/AI platforms Applications/ Visualizations 19. Serverless Analytics Deliver cost-effective analytic solutions faster $ 24. Video Amazon Rekognition Video 1. Video is uploaded and stored to S3 2. Rekognition Video creates metadata for people and objects with time segments for search 4. Lambda also pushes the metadata and confidence scores into Elasticsearch 3. The output is persisted as metadata into DynamoDB to ensure durability Amazon Rekognition Video AWS LambdaAmazon S3 Amazon Elasticsearch Amazon DynamoDB 26.

We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic imagery by separating and recombining image content and style. This process of using CNN to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. This review aims to provide an overview of the current progress towards NST, as well as discussing its various applications and open problems for future research.

In the corporate world, the only thing worse than a lack of information is an abundance of inaccurate or useless information. While the lack of data can prompt one to action, having access to numerous low-quality pieces of information can lull businesses into a false sense of security. Once disaster strikes, this unintegrated data proves to be just as "effective" as the non-existent data was. The key role in preventing this scenario is the organization's ability to ensure comprehensive data integration across its many departments. It is essential to make all of them work together in unison, as this can oil the cogs of your business machinery.

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named SASS which is based on the notion of similarities in the agent's state-action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single speedup method with minimal human designer effort overhead.

Over the past years, distributed representations have proven effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey is focused on semantic representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their main limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and provides an analysis of five important aspects: interpretability, sense granularity, adaptability to different domains, compositionality and integration into downstream applications.