This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions. Our framework caters to a wide range of expertise levels by supporting interoperability between two popular approaches, state machine and information state, to dialogue management. Our Natural Language Expression package allows seamless integration of pattern matching, custom NLP modules, and database querying, that makes the workflows much more efficient. As a user study, we adopt this framework to an interdisciplinary undergraduate course where students with both technical and non-technical backgrounds are able to develop creative dialogue managers in a short period of time.

Microsoft News has shed dozens of editorial workers this past week as it moves to an AI-driven system of picking news and away from human editors for MSN.com, one of the world's biggest news destinations. People close to the situation said the layoffs impacted all its contractors in the US, numbering around 50, all of whom are employed by staffing agencies Aquent and MAQ Consulting. Calls and emails to those agencies seeking comment weren't returned. A Microsoft spokesperson said: "Like all companies, we evaluate our business on a regular basis. This can result in increased investment in some places and, from time to time, re-deployment in others. These decisions are not the result of the current pandemic."

At the end of May 2020, Microsoft took the decision to sack dozens of journalists in favor of replacing them with artificial intelligence (AI). The journalists and editors fired ran the MSN News website, the automatic homepage of the Microsoft Edge browser. The site doesn't write news itself, but draws news from other sources and splits advertising revenue with the original publishers. It used to be curated by humans, who would select stories adhering to their editorial guidelines and edit articles, photos, and headlines wherever necessary. Now, just a few weeks after replacing those humans with software, robots at MSN News have been accused of racism by UK band Little Mix member Jade Thirlwall, after MSN posted a story of her opening up about the racism she experienced at school, accompanied by an image of fellow Little Mix bandmate Leigh-Anne Pinnock.

Microsoft's decision to replace human journalists with robots has backfired, after the tech company's artificial intelligence software illustrated a news story about racism with a photo of the wrong mixed-race member of the band Little Mix. A week after the Guardian revealed plans to fire the human editors who run MSN.com and replace them with Microsoft's artificial intelligence code, an early rollout of the software resulted in a story about the singer Jade Thirlwall's personal reflections on racism being illustrated with a picture of her fellow band member Leigh-Anne Pinnock. Thirlwall, who attended a recent Black Lives Matter protest in London, criticised MSN on Friday, saying she was sick of "ignorant" media making such mistakes. She posted on Instagram: "@MSN If you're going to copy and paste articles from other accurate media outlets, you might want to make sure you're using an image of the correct mixed race member of the group." "This shit happens to @leighannepinnock and I ALL THE TIME that it's become a running joke," she said.

Dozens of scientists funded by Mark Zuckerberg have protested against his decision to leave inflammatory Donald Trump posts on the site. Mr Zuckerberg is allowing the president to use the social network to "spread both misinformation and incendiary statements", the researchers warn. Scientists, including 60 professors at leading US research institutions, wrote to the Facebook boss asking Mr Zuckerberg to "consider stricter policies on misinformation and incendiary language that harms people," especially during the current turmoil over racial injustice. The letter calls the spread of "deliberate misinformation and divisive language" contrary to the researchers' goals of using technology to prevent and eradicate disease, improve childhood education and reform the criminal justice system. Their mission "is antithetical to some of the stances that Facebook has been taking, so we're encouraging them to be more on the side of truth and on the right side of history as we've said in the letter," said Debora Marks of Harvard Medical School, one of three professors who organised it.

Abstract: Advanced text-to-speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs during training and use predicted values during inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inference than FastSpeech.

The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. Moreover, the trade-off between equity and precision makes it difficult to obtain recommendations that meet both criteria. Here we propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users. Experimental results show that it is possible to make fair recommendations without losing a significant proportion of accuracy.

The '19 Impact on Global Artificial Intelligence (AI) in Construction market' research added by Market Study Report, LLC, offers a comprehensive …

The report also presents forecasts for Commerce Artificial Intelligence investments from 2020 till 2026.