Artificial intelligence (AI) is the most disruptive innovation of our lifetime. Its adoption has grown 60 percent in the last year, according to an April 2020 report by Narrative Science. The report's authors say the technology is having a "significant and imminent impact on everything from company strategy, to business operations, to job functions." So what are some of AI's implications in the new normal, one in which American entrepreneurs find themselves saving cash, working from home and wearing masks everywhere they go? Currently, for entrepreneurs, the most popular AI-powered solutions deal with predictive analytics (24 percent), machine learning (21 percent), language processing (14 percent) and voice recognition and response (14 percent), according to the same Narrative Science report.

Voice tech is another application of Natural Language Processing and voice seems to be getting adopted quicker than any other major technology. Led by Amazon's Alexa, smart speakers' install base is expected to reach 200 million units worldwide by 2020. I have compiled a list of voice tech startups which are innovative in nature and are already in the market. This list is a starting point and probably far from exhaustive and is not sorted in any order. I will come up with another post with more startups added, in future. SoundHound develops voice-enabled AI and conversational intelligence technologies.

When robots were first discovered in the year 1495 by Leonardo Da Vinci, the world would not have that they would become an integral part of human life. However, with the advancement in technology, robotics has seen innovation and intervention that was earlier not possible. At present, Humanoid robots present a vast range of scope to the daily mundane task. Alexa, Chatbots in Financial spaces, and Siri are some of the examples where robots exhibited assistance to humans in a mundane task. Be it playing a song or assuring emotional support, especially during COVID 19, the humanoid robots halved the task conducted by humans.

We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to out-of-the-box Surprise library, Auto-Surprise performs better when evaluated with MovieLens, Book Crossing and Jester Datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.

As IoT smart home devices such as Amazon Alexas continue to grow in popularity, they provide us the opportunity to connect and interact with the internet in a variety of ways. Despite their popularity and the benefits, they bring to the user, they also pose a significant security risk if not properly secured. This is because hackers are acutely aware about the lack of basic security measures some .... [Read More ]

Career advisor systems are essentially recommender systems in the space of job searching and career advice. They provide recommendations to candidates with possible career paths and to employers with possible candidates for a job opening. In this post we will outline the required capabilities of such systems, and highlight the challenges that need to be overcome in order to construct a working system. Questions like "What is a career advisor system?", "What is it capable of doing?", "Why do we need them?" etc are answered in this article. We also discuss our recent work (presented at AAAI-IAAI [1]) which describes how we proposed to solve this problem.

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.

Recently, conversational recommender system (CRS) has become With the rapid development of intelligent agents in e-commerce an emerging and practical research topic. Most of the existing CRS platforms, conversational recommender system (CRS) [5, 6, 9] has methods focus on learning effective preference representations for become an emerging research topic in seeking to provide highquality users from conversation data alone. While, we take a new perspective recommendations to users through conversations. Generally, to leverage historical interaction data for improving CRS. a CRS consists of a conversation module and a recommendation For this purpose, we propose a novel pre-training approach to module. The conversation module focuses on acquiring users' preference integrating both item-based preference sequence (from historical via multi-turn interaction, and the recommendation module interaction data) and attribute-based preference sequence (from conversation focuses on how to utilize the inferred preference information to data) via pre-training methods. We carefully design two recommend suitable items for users.

Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In realistic settings, this is often not true---the dynamics of an RS ecosystem couple the long-term utility of all agents. In this work, we explore settings in which content providers cannot remain viable unless they receive a certain level of user engagement. We formulate the recommendation problem in this setting as one of equilibrium selection in the induced dynamical system, and show that it can be solved as an optimal constrained matching problem. Our model ensures the system reaches an equilibrium with maximal social welfare supported by a sufficiently diverse set of viable providers. We demonstrate that even in a simple, stylized dynamical RS model, the standard myopic approach to recommendation---always matching a user to the best provider---performs poorly. We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.

Matrix factorization is one of the most commonly used technologies in recommendation system. With the promotion of recommendation system in e-commerce shopping, online video and other aspects, distributed recommendation system has been widely promoted, and the privacy problem of multi-source data becomes more and more important. Based on Federated learning technology, this paper proposes a shared matrix factorization scheme called SharedMF. Firstly, a distributed recommendation system is built, and then secret sharing technology is used to protect the privacy of local data. Experimental results show that compared with the existing homomorphic encryption methods, our method can have faster execution speed without privacy disclosure, and can better adapt to recommendation scenarios with large amount of data.