Welcome to the Techstars application! We are now accepting applications for 6 programs running 2018 Q4. Check out our Program Pages (www.techstars.com/programs/) and Apply Page (www.techstars.com/apply/) to learn more about each program and find the right one for you. SAP.iO Foundry, Powered by Techstars Accelerator SAP.iO Foundry, Powered by Techstars Accelerator is focused on B2B & enterprise SaaS companies, specifically on machine learning & artificial intelligence. Techstars Adelaide Accelerator Techstars Adelaide supports startups advancing innovative applications in IoT, big data, sensors and robotics, with potential to develop and commercialize technologies connected to the defense and security sectors.

Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.

The exploration vs. exploitation dilemma is one of the fundamental balances in deep reinforcement learning applications. How much resources to devote to acquire knowledge that can improve future actions versus performing specific actions? This is one of the main heuristics that rule the behavior of reinforcement learning systems. In theory, optimal exploration should always conduce to more efficient knowledge but this is far from true in the real world. Developing techniques to improve the exploration of an environment is one of the pivotal challenge of the current generation of deep reinforcement learning models.

This is the final installment of a three-part series exploring the impact of artificial intelligence (AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The initial articles offered a primer on the AI technologies that are relevant to investment professionals and explored the potential threat AI posed to human portfolio managers. Not all is lost, investment professionals. Despite artificial intelligence (AI)'s significant and rapidly increasing "brain" power, the investment management business is not going away tomorrow.

From a couple of years, IoT has created buzz worldwide and is considered second major digital revolution. This new technology wave is going beyond smartphones, offering innovation that has made life more simplified and comfortable with the advanced level of connectivity. The future of connected cars, smart homes, connected wearables, smart cities and connected healthcare has become reality. Moreover, the growth of IoT is drastically changing how consumers interact with their cars, homes, and appliances. When we take into account, the machine-learning, machine-to-machine communication or artificial intelligence, the industrial internet of things (IIoT) leverages this innovative technology for industrial concerns to improves efficiency and productivity.

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

Over the past two decades, technological innovations have brought significant change for organisations throughout Ireland. Through the development of the internet, the explosion in the number of mobile devices, and the emergence of the sharing economy, technology is now central to all our lives – from how we share information, access services and interact with others. And that change is accelerating. We are on the verge of a new era of digital transformation. Emerging technologies ranging from Artificial Intelligence (AI), Virtual Reality (VR) Augmented Reality (AR), cloud computing and robotics, to machine learning will revolutionise how we work, live, play, and learn.

The insurance industry has come under real pressure over the past decade or so. The fintech revolution has meant that smaller and more agile startups are able to offer a variety of new services to consumers and businesses. These services are not only more interactive and based on the latest technologies, but they are also services that bigger insurance firms cannot easily offer. This increased competition from newer market entrants is a growing problem for more established insurance providers. But with more data available to insurance firms than ever before, there is an opportunity to embrace the technological changes that are taking place.

When we were invited to write a retrospective article about our AAAI-99 paper on mutual bootstrapping (Riloff and Jones 1999), our first reaction was hesitation because, well, that algorithm seems old and clunky now. But upon reflection, it shaped a great deal of subsequent work on bootstrapped learning for natural language processing, both by ourselves and others. So our second reaction was enthusiasm, for the opportunity to think about the path from 1999 to 2017 and to share the lessons that we learned about bootstrapped learning along the way. This article begins with a brief history of related research that preceded and inspired the mutual bootstrapping work, to position it with respect to that period of time. We then describe the general ideas and approach behind the mutual bootstrapping algorithm. Next, we overview several types of research that have followed and shared similar themes: multi-view learning, bootstrapped lexicon induction, and bootstrapped pattern learning. Finally, we discuss some of the general lessons that we have learned about bootstrapping techniques for NLP to offer guidance to researchers and practitioners who may be interested in exploring these types of techniques in their own work.

Japanese brokerages are launching a consortium dedicated to driving the adoption of blockchain and other innovative technologies in the industry, with the goal of boosting efficiency and providing more convenient services to their customers. The group's 18 founding members include online players like SBI Securities and Rakuten Securities, as well as conventional brokerages like Nomura Securities and Daiwa Securities. SBI Holdings unit SBI Ripple Asia will be the lead organizer for the group. It will hold one or two working meetings a month, as well as carry out proof-of-concept tests on cutting-edge technologies. The group aims to cut costs through industry-wide cooperation. Specifically, the consortium is looking into a shared log-in mechanism for brokerage accounts for customers that uses biometrics and other personal identification information, as well as using artificial intelligence to screen trading activity.