Our platform archives the crypto assets historical data, absorb the news feed, generate trading signals in real-time and finally make the decision to buy, sell or hold. Our bots based on these signals trade automatically on third-party exchanges using your own accounts. The AI engine makes use of Technical Analysis, Sentiment Analysis along with our 10 unique strategies, to make the most accurate predictions of the market using Machine Learning.

Artificial Intelligence is one of the most innovative technological breakthroughs of the modern times. From daily lives to corporate cultures, everything is being impacted by the novel technology. Artificial Intelligence (AI) strategically improves business processes by giving managers the power to analyze a vast amount of valuable data derived from customers as well as employees. When it comes to human resources, AI is particularly solving one of the greatest issues managers have been facing for many years- to improve employee engagement and retention rates. AI has the potential to give managers the power to make a better workplace, where employees don't feel distracted or dissatisfied with their job roles.

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.

Dialogue state tracking, or estimating and keeping tabs on a person's goals throughout a multiturn conversation, is one of the ways Alexa figures out what users want. By combining conversation history with the most recent command, Amazon's intelligent assistant can better map slot names -- the price of a hotel or its star rating, for example -- to slot values, or entities mentioned in a dialogue. Alexa already performs dialogue state tracking pretty effectively, but a team of scientists at Amazon's R&D division think there's room for improvement. In a new paper ("Dialog State Tracking: A Neural Reading Comprehension Approach") scheduled to be presented at the International Speech Communication Association's Special Interest Group on Discourse and Dialogue, they propose an AI system that formulates dialogue state tracking as a classic question-answering problem. In other words, their machine learning model decides on the slot value for each slot name after reading a conversational passage. The team reports that their technique yielded a 6.5% improvement in slot tracking accuracy over the previous state of the art in qualitative tests and that it had an accuracy of up to 96% per slot on a data set of development data.

Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two computational models that automatically distinguish topics in politicians' social media content. Our models---one supervised classifier and one unsupervised topic model---provide different benefits. The supervised classifier reduces the labor required to classify content according to pre-determined topic list. However, tweets do more than communicate policy positions. Our unsupervised model uncovers both political topics and other Twitter uses (e.g., constituent service). These models are effective, inexpensive computational tools for political communication and social media research. We demonstrate their utility and discuss the different analyses they afford by applying both models to the tweets posted by members of the 115th U.S. Congress.

Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. One of the most popular frameworks in this field is the domain-invariant representation learning (DIRL) paradigm, which aims to learn a distribution-invariant feature representation across domains. However, in this work, we find out that applying DIRL may harm domain adaptation when the label distribution $\rm{P}(\rm{Y})$ changes across domains. To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework. We show that it is easy to transfer existing SOTA DIRL models to WDIRL. Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution.

There is an increasing demand for task-oriented dialogue systems which can assist users in various activities such as booking tickets and restaurant reservations. In order to complete dialogues effectively, dialogue policy plays a key role in task-oriented dialogue systems. As far as we know, the existing task-oriented dialogue systems obtain the dialogue policy through classification, which can assign either a dialogue act and its corresponding parameters or multiple dialogue acts without their corresponding parameters for a dialogue action. In fact, a good dialogue policy should construct multiple dialogue acts and their corresponding parameters at the same time. However, it's hard for existing classification-based methods to achieve this goal. Thus, to address the issue above, we propose a novel generative dialogue policy learning method. Specifically, the proposed method uses attention mechanism to find relevant segments of given dialogue context and input utterance and then constructs the dialogue policy by a seq2seq way for task-oriented dialogue systems. Extensive experiments on two benchmark datasets show that the proposed model significantly outperforms the state-of-the-art baselines. In addition, we have publicly released our codes.

The dialogue state tracker or just state tracker (ST) in a goal-oriented dialogue system has the primary job of preparing the state for the agent. As we discussed in the previous part the agent needs a useful state to be able to make a good choice on what action to take. The ST updates its internal history of the dialogue by collecting both user and agent actions as they are taken. It also keeps track of all inform slots that have been contained in any agent and user actions thus far in the current episode. The state used by the agent is a numpy array made of information from the current history and the current informs of the ST. In addition, whenever the agent wishes to inform a slot to the user the ST queries the database for a value that works given its current informs.

Episode 167 - Kevin Craine and Billee Howard discuss the use of nuero-powered technology to quantify, measure and understand human thought. Explore how to use artificial intelligence and sentiment analysis to connect customer emotion directly to improved business performance. Understand the convergence of'big emotion' and'big data' and how it is valuable from a strategic and marketing perspective. Stay tuned for three action items in the second half. Host, Kevin Craine Do you want to be a guest?

The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.