Is AI the new HR?


Are your employees taking a flexible work policy too far? Anna O'Dea offers some tips for getting productivity back without taking away this desirable benefit The notion of AI often conjures up images of automation without human involvement. The modern conception of AI has its antecedents in world mythology, through figures such as Galatea or the Golem. Later medieval and enlightenment thinkers would expand on the concept, directly or indirectly contributing to the development of the modern computer in the process. The idea of AI has also flourished in science fiction, with one of the most famous examples found in Mary Shelley's Frankenstein.

The Church of Artificial Intelligence: A Religion in Need of a Responsible Theology


A decade ago, the prospect of a religion that worships Artificial Intelligence would have seemed absurd, a fringe delusion both socially unacceptable and technologically improbable. In the last several years, however, advances in machine learning, robotics, cognitive science, genetic editing, and other fields have given rise to the belief that the destiny of our species will be determined by technology--whether it saves us or destroys us. Although the machine-as-god theme has appeared in science fiction as far back as far back as Isaac Asimov's short stories "The Last Question" and "Reason," and more recently in films like The Matrix and iRobot, the divinization of AI is no longer merely a fancy of fiction. It has become a mainstream metaphor, as evidenced by the growing number of scientists who openly describe technological progress in religious terms, including Hans Peter Moravec, Allen Newell, Ray Kurzweil, and Hugo de Garis. But this drive to replace the old gods and old religions with the new ones of science and technology doesn't stop at metaphor.

Microsoft's Chinese chatbot inspired by images to write poetry


Microsoft's chatbot Xiaoice does a lot more than other bots. She has presented the weather on live TV and now even composed a book of poems. Xiaoice has written 12 million of poems in fact. In a paper on arXiv, researchers from Microsoft, National Taiwan University, and the University of Montreal explained that all text Xiaoice wrote is inspired by images. "Given an image, we first extract a few keywords representing objects and sentiments perceived from the image.

Text Mining Fedspeak · Len Kiefer


Textmining is an exciting topic. There is tremendous potential to gain insights from textual analysis. See for example Gentzko, Kelly and Taddy's Text as Data. While text mining may be quite advanced in other fields, in finance and economics the application of these techniques is still in its infancy. In order to take advantage of text as data, economists and financial analysts need tools to help them.

Comparison of the Most Useful Text Processing APIs


Nowadays, text processing is developing rapidly, and several big companies provide their products which help to deal successfully with diverse text processing tasks. In case you need to do some text processing there are 2 options available. The first one is to develop the entire system on your own from scratch. This way proves to be very time and resource consuming. On the other hand, you can use the already accessible solutions developed by well-known companies. This option is usually faster and simpler.

It Isn't Emotional AI. It's Psychopathic AI. – Jonathan Cook – Medium


This week, I'm writing a series of articles about sentiment analysis, which is often referred to as Emotional AI. Engineers of this new brand of technology claim to be able to detect and analyze emotion using electronic sensors and machine learning. To date, media coverage of this emerging field of has been rather credulous, accepting Silicon Valley's assertions about Emotional AI at face value. In this series, I'm attempting to balance that fawning coverage with critical questions, building toward suggestions for ways in which sentiment analysis can be more meaningfully employed by businesses that sincerely wish to enhance their emotional connection with the human beings they serve. This is the fourth article in the series, which also includes the following: Can AI Understand Your Emotions?

Announcing ML.NET 0.4


A few months ago we released ML.NET 0.1 at //Build 2018., ML.NET is a cross-platform, open source machine learning framework for .NET developers. We've gotten great feedback so far and would like to thank the community for your engagement as we continue to develop ML.NET together in the open. We are happy to announce the latest version: ML.NET 0.4. In this release we've improved support for natural language processing (NLP) scenarios by adding the Word Embedding Transform, improved the speed of linear learners like binary classification and linear regression by adding support for the SymSGD learner, made improvements to the F# API and samples for ML.NET, bug fixes and more. Additionally, we really want your feedback on making ML.NET really easy to use.

Multimodal Language Analysis with Recurrent Multistage Fusion Machine Learning

Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.

Emotion Recognition and Sentiment Analysis Market to Reach $3.8 Billion by 2025


Significant advances have been made during the past few years in the ability of artificial intelligence (AI) systems to recognize and analyze human emotion and sentiment, owing in large part to accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning capabilities combined with natural language processing (NLP) and computer vision. According to a new report from Tractica, these trends are beginning to drive growth in the market for sentiment and emotion analysis software. Tractica forecasts that worldwide revenue from sentiment and emotion analysis software will increase from $123 million in 2017 to $3.8 billion by 2025. The market intelligence firm anticipates that this growth will be driven by several key industries including retail, advertising, business services, healthcare, and gaming. According to Tractica's analysis, the top use case categories for sentiment and emotion analysis will be as follows: "A better understanding of human emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our needs," says principal analyst Mark Beccue.

The AI In Fintech Market Map: 100 Companies Using AI Algorithms To Improve The Fin Services Industry


Startups are using AI to improve and expand credit offerings, insurance options, personal finance services, and regulatory software. AI and deep learning have vast potential in financial services. Get the free report to see their applications and use cases. Using CB Insights, we expanded upon our AI 100 analysis to identify companies that use AI in financial services and mapped them according to the areas where they're operating. In broad terms, our analysis includes companies whose core offering includes the application of AI to serve the financial services industry, including commercial banking and credit offerings, insurance, asset management, accounting & personal finance, as well as regulatory & compliance services.