The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.

Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.

In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.

Sentiment Analysis is one of the most obvious things Data Analysts with unlabelled Text data (with no score or no rating) end up doing in an attempt to extract some insights out of it and the same Sentiment analysis is also one of the potential research areas for any NLP (Natural Language Processing) enthusiasts. For an analyst, the same sentiment analysis is a pain in the neck because most of the primitive packages/libraries handling sentiment analysis perform a simple dictionary lookup and calculate a final composite score based on the number of occurrences of positive and negative words. But that often ends up in a lot of false positives, with a very obvious case being'happy' vs'not happy' – Negations, in general Valence Shifters. Consider this sentence: 'I am not very happy'. Any Primitive Sentiment Analysis Algorithm would just flag this sentence positive because of the word'happy' that apparently would appear in the positive dictionary.

I know that this looks like chat-bot problem, but as I think it must be a lot easier, because in a games there should be some boundaries of information that would be interesting for a gamer. So NPC should have an understanding of game important stuff only and just don't talk about any other things. This looks like very interesting application of machine learning but I never heard about something similar to it. The only thing I found is a Spirit AI with no information about how their technology works or any public demonstration. So, where could I find any research or projects similar to this topic?

Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the fit of an NMF-based topic model based on the duality of the KL divergence and Poisson maximum likelihood estimation. The test correctly identifies whether a topic model based on an NMF approach yields reliable results in simulated and real data.

Algorithmia started out as an online marketplace for -- can you guess it? Many of these algorithms that developers offered on the service focused on machine learning (think face detection, sentiment analysis, etc.). Today, with the boom in ML/AI, that's obviously a big draw and Algorithmia is now taking its next step in this direction with the launch of a new… Read More

When you think of artificial intelligence (AI), the word "emotion" doesn't typically come to mind. But there's an entire field of research using AI to understand emotional responses to news, product experiences, movies, restaurants, and more. It's known as sentiment analysis, or emotion AI, and it involves analyzing views – positive, negative, or neutral – from written text to understand and gauge reactions. Sentiment analysis can be used for survey research, social media analyses, and tracking psychological trends. Picture software that scans articles, reviews, ratings, and social media posts to determine sentiment changes for hotel guests.

Update 10/25/17 3:53 PM: A Google spokesperson responded to Motherboard's request for comment and issued the following statement: "We dedicate a lot of efforts to making sure the NLP API avoids bias, but we don't always get it right. This is an example of one of those times, and we are sorry. We take this seriously and are working on improving our models. We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone." John Giannandrea, Google's head of artificial intelligence, told a conference audience earlier this year that his main concern with AI isn't deadly super-intelligent robots, but ones that discriminate.

Google developed its Cloud Natural Language API to give customers a language analyzer that could, the internet giant claimed, "reveal the structure and meaning of your text." Part of this gauges sentiment, deeming some words positive and others negative. When Motherboard took a closer look, they found that Google's analyzer interpreted some words like "homosexual" to be negative. Which is evidence enough that the API, which judges based on the information fed to it, now spits out biased analysis. The tool, which you can sample here, is designed to give companies a preview of how their language will be received.