This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Re's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks.

Organizations that have enabled AI at the enterprise level are increasing operational efficiency, making faster more informed decisions and innovating new products and services. However, challenges remain for those who aren't deploying AI in this capacity. Enterprises that lack a clear AI strategy, support from the c-suite and a specific set of metrics, struggle to make progress. At the EmTech Digital Conference, produced by MIT Technology Review Insights, EY conducted a brief survey of conference attendees and revealed how AI is being applied and enabled at their organizations. This first question asked respondents for their views on the impact of AI on jobs – and a clear majority said that AI will transform the workplace and that more jobs will be created than lost.

We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages. Based on these insights, we introduce a new graph-based intermediate representation (IR) which specifically aims to efficiently support fully-general AD for array programming. Unlike existing dataflow programming representations in ML frameworks, our IR naturally supports function calls, higher-order functions and recursion, making ML models easier to implement. The ability to represent closures allows us to perform AD using ST without a tape, making the resulting derivative (adjoint) program amenable to ahead-of-time optimization using tools from functional language compilers, and enabling higher-order derivatives. Lastly, we introduce a proof of concept compiler toolchain called Myia which uses a subset of Python as a front end.

SINGAPORE--(BUSINESS WIRE)--Oct 25, 2018--DXC Technology (NYSE: DXC), the world's leading independent, end-to-end IT services company, today announced the opening of the DXC Digital Innovation Lab in Singapore. Developed with the support of the Singapore Economic Development Board (EDB), the DXC Digital Innovation Lab Singapore is an advanced environment for the incubation of ideas, learning and innovative technology solutions developed by data scientists and enterprise solution experts. The lab will benefit DXC employees, clients and partners, as well as the technology and business communities of Singapore, the region and beyond. The DXC Digital Innovation Lab Singapore is an extension of DXC Labs, whose goal is to ensure that DXC masters the emerging technologies it needs in order to lead clients through accelerating digital transformation. At the innovation lab, digital specialists will explore novel technologies, develop prototypes and create reference architectures for rapid business deployment.

Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Tensor factorization approaches have proved promising for such link prediction problems. Proposed in 1927, Canonical Polyadic (CP) decomposition is among the first tensor factorization approaches. CP generally performs poorly for link prediction as it learns two independent embedding vectors for each entity, whereas they are really tied. We present a simple enhancement of CP (which we call SimplE) to allow the two embeddings of each entity to be learned dependently. The complexity of SimplE grows linearly with the size of embeddings. The embeddings learned through SimplE are interpretable, and certain types of background knowledge can be incorporated into these embeddings through weight tying. We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity. We show empirically that, despite its simplicity, SimplE outperforms several state-of-the-art tensor factorization techniques. SimplE's code is available on GitHub at https://github.com/Mehran-k/SimplE.

Those expectations are now hitting speed bumps, according to interviews with eight current and former GM and Cruise employees and executives, along with nine autonomous vehicle technology experts familiar with Cruise. These sources say that some unexpected technical challenges - including the difficulty that Cruise cars have identifying whether objects are in motion - mean putting GM's driverless cars on the road in a large scale way in 2019 is looking highly unlikely.

The first panel of the symposium began at 11:05 a.m. and reached a broad range of topics during the discussion entitled "The good, the bad, and the ugly of AI and robotics." The speakers of the panel included Jason Millar, assistant professor in the School of Electrical Engineering and Computer Science, Cindy Grimm, associate professor of mechanical engineering, Geoffrey Hollinger, assistant professor in the Collaborative Robotics and Intelligent Systems Institute at OSU and Stephanie Jenkins, assistant professor in the School of History, Philosophy and Religion at OSU. The panel then allowed each speaker to give a brief opinion of what the greatest risk and the greatest benefit of the widespread adoption of AI and robotics are. Grimm began by explaining that a large benefit of AI will be its ability to complete simple tasks, allowing people more time to tackle larger issues. Grimm went on to explain that the flip side of this is as AI becomes more common in daily, simple tasks, the public may become too trusting of these systems and allow them to make decisions that may be beyond their capability.

The first panel of the symposium began at 11:05 a.m. and reached a broad range of topics during the discussion entitled "The good, the bad, and the ugly of AI and robotics." The speakers of the panel included Jason Millar, assistant professor in the School of Electrical Engineering and Computer Science, Cindy Grimm, associate professor of mechanical engineering, Geoffrey Hollinger, assistant professor in the Collaborative Robotics and Intelligent Systems Institute at OSU and Stephanie Jenkins, assistant professor in the School of History, Philosophy and Religion at OSU. The panel then allowed each speaker to give a brief opinion of what the greatest risk and the greatest benefit of the widespread adoption of AI and robotics are. Grimm began by explaining that a large benefit of AI will be its ability to complete simple tasks, allowing people more time to tackle larger issues. Grimm went on to explain that the flip side of this is as AI becomes more common in daily, simple tasks, the public may become too trusting of these systems and allow them to make decisions that may be beyond their capability.

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of this paradigm. The list of current and envisioned applications of deep RL ranges from autonomous navigation and robotics to control applications in the critical infrastructure, air traffic control, defense technologies, and cybersecurity. While the landscape of opportunities and the advantages of deep RL algorithms are justifiably vast, the security risks and issues in such algorithms remain largely unexplored. To facilitate and motivate further research on these critical challenges, this paper presents a foundational treatment of the security problem in DRL. We formulate the security requirements of DRL, and provide a high-level threat model through the classification and identification of vulnerabilities, attack vectors, and adversarial capabilities. Furthermore, we present a review of current literature on security of deep RL from both offensive and defensive perspectives. Lastly, we enumerate critical research venues and open problems in mitigation and prevention of intentional attacks against deep RL as a roadmap for further research in this area.