British researchers have trained an artificial intelligence to recognize keystrokes by sound. A smartphone placed near a laptop served as the microphone. The researchers combined the sound of each key with the corresponding letter for the training. They then typed a password into the laptop and had the AI calculate which word was heard based on the sound. The AI recognized the password with an accuracy of 95 percent.

Bing recently hit 100M daily users (and 100M chats)! Today, we're excited to share new AI-powered experiences that extend these capabilities to millions of additional people across devices and around the globe! In recent weeks, we've added a variety of new ways to access and interact with the new Bing. Today, we are announcing yet another, with powerful updates to SwiftKey that put the Bing AI experience one touch away across any iOS or Android mobile experience that supports a third-party keyboard. An updated SwiftKey represents a growing set of access points and improvements to Bing experiences, including new updates to existing app integrations spanning Bing, Skype, Microsoft Start, and Microsoft Edge apps.

In today's announcement, Microsoft seems to be stressing the social and creative aspects of Bing much more. This isn't a bad thing, as these functions are better suited to the limitations of AI language models. In the Skype blog post, for example, the company highlights Bing as "a great tool for generating ideas and inspiration," with screenshots of the chatbot writing poetry. There's only a passing mention of using Bing to find out about the news; a task for which the chatbot is ill-suited, making frequent factual errors.

Microsoft's Skype Desktop app is getting an upgrade, as it will have a new feature that would augment its voice and video call experience for its users. Microsoft has announced that it will be integrating an artificial intelligence or AI-enabled noise cancellation feature into Skype's Desktop app for both Mac and Windows OS. This feature is not yet available on the mobile and web versions of Skype. In order to activate the background noise cancellation feature, users can click on "Settings" and select the audio tab. According to Gadgets Now, the noise cancellation feature will then pop up, with the options auto, low and high to choose from. Also Read: Skype For Web Is Now Just As Good As The App, But There's A Huge Catch In a blog post, Skype has mentioned that the noise cancellation technology was made for Microsoft Teams.

Microsoft has introduced AI-enabled Noise Cancellation functionality to Skype. You will be able to locate the Noise Cancellation feature under the Settings option. There will be three options like Auto, Low and High. Microsoft is constantly updating Skype with new features to enable you to establish connectivity with other users easily. The voice conferencing and the messaging platform have played a pivotal role not only for enterprise companies but also among individuals during the COVID-19 lockdown.

Back in July, Microsoft added a feature called Together Mode to its Teams workplace messaging app. The tool uses an AI-powered segmentation technology to put everyone in a video call in the same virtual space. Even if you don't use Microsoft Teams, you've probably seen Together Mode in action. The NBA turned to the feature when it tried to recreate the atmosphere of a packed arena without any fans physically present. It's quickly become one of the app's marquee features, and, as The Verge points out, it's now making its way to Skype.

If you browse the internet, there's a good chance you've come into contact with a chatbot. These conversational programs have proved a popular application of advanced tech, such as machine learning and natural language processing (NLP). And all the signs are there that chatbots will continue to play a role in business for years to come – so learning as much as possible about them means you're well-placed to benefit in the long-term. In this essential chatbot beginner's guide, you'll learn: Chatbots are computer programs with a persona – that of a robot (often a square-headed one with antennas). These robots' primary purpose is to communicate with humans via text, voice, and touch.

Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to replace constants with learned contextual functions for Skype - a widely used real-time communication (RTC) application. We present Resonance, a system based on contextual bandits (CB). We describe experiences from three real-world experiments: applying it to the audio, video, and transport components in Skype. We surface a unique and practical challenge of performing machine learning (ML) inference in large software systems written using encapsulation principles. Finally, we open-source FeatureBroker, a library to reduce the friction in adopting ML models in such development environments

Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained in a supervised setting with a collection of textual utterance and intent label pairs. Gathering a substantial and wide coverage of training data for different intent is a bottleneck in the bot building process. Moreover, the cost of labeling a hundred to thousands of conversations with intent is a time consuming and laborious job. In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations. We have introduced a novel density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Subject Matter Expert (Annotators with domain expertise) manually looks into the clustered user utterances and provides an intent label for discovery. We conducted user studies to validate the effectiveness of the trained intent model generated in terms of coverage of intents, accuracy and time saving concerning manual annotation. Although the system is developed for building an intent model for the conversational system, this framework can also be used for a short text clustering or as a labeling framework.

A specific use case worth exploring in this regard is MT for User Generated Content (UGC). Because of the speed with which UGC (comments, feedback, reviews) is being created and the corresponding costs of its professional translation, many organizations turn to MT. Popular examples of such companies are Skype (in addition to text translation, Microsoft developed the Automatic Speech Recognition (ASR) for audio speech translation in Skype) and Facebook. The social network is aiming to solve the challenge of fine-tuning each system relating to a specific language pair, using neural machine translation (NMT) and benefiting from various contexts for translations. One solution that tackles this issue is the technology developed by Language I/O. It takes into account the client's glossaries and TMs, selects the best MT engine output and then improves on the results using cultural intelligence and/or human linguists who compare machine translations post-facto to ensure that their MT Optimizer engine learns over time.