Employers can now practice laying off an ultra-realistic, AI-powered virtual employee in order to develop their soft skills before they have to fire someone in real life. Capable of realistically engaging trainees in conversation and displaying appropriate emotions, poor virtual employee Barry Thompson gets the sack over and over again. However, his reaction -- which can range from calm acceptance to angry and defensive shouting -- varies depending on the user's handing of the scenario. The firm who created Barry have also developed a number of other virtual training scenarios, from negotiation and making sales to giving feedback to subordinates. Barry is a virtual employee created by Talespin Studios.

The collaborative ranking problem has been an important open research question as most recommendation problems can be naturally formulated as ranking problems. While much of collaborative ranking methodology assumes static ranking data, the importance of temporal information to improving ranking performance is increasingly apparent. Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with. In particular, the SASRec model, inspired by the popular Transformer model in natural languages processing, has achieved state-of-art results in the temporal collaborative ranking problem and enjoyed more than 10x speed-up when compared to earlier CNN/RNN-based methods. However, SASRec is inherently an un-personalized model and does not include personalized user embeddings. To overcome this limitation, we propose a Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of NDCG@10 on 5 real-world datasets. Furthermore, after examining some random users' engagement history and corresponding attention heat maps used during the inference stage, we find our model is not only more interpretable but also able to focus on recent engagement patterns for each user. Moreover, our SSE-PT model with a slight modification, which we call SSE-PT++, can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements. Code and data are open sourced at https://github.com/wuliwei9278/SSE-PT.

Facebook Inc. has been paying hundreds of outside contractors to transcribe clips of audio from users of its services, according to people with knowledge of the work. Facebook has reportedly been paying contractors to transcribe the audio chats of users on its various platforms. According to Bloomberg, which cites people with knowledge of the work, contractors were given audio clips with no context of where they were obtained and told to transcribe them. Facebook told Bloomberg the users affected by this agreed to have voice chats transcribed through its Messenger app. "Much like Apple and Google, we paused human review of audio more than a week ago," said Facebook in a statement to USA TODAY.

Alexa can read you the latest headlines, give you the weather forecast, and help you find a recipe to make for dinner, but did you know that your Echo device can also assist your student with homework? If you have an Amazon Echo smart speaker at home, like our favorite Echo device, the Echo (2nd Generation), your student has access to a wealth of brain-sharpening, knowledge-enhancing activities to help tackle homework assignments. While kids probably don't need more time interacting with electronics, Alexa offers a variety of skills that can help students with their homework when parents aren't available. To enable a skill on your Echo device say, "Alexa, enable [exact name of skill]." Or, open the Alexa app, tap the menu button, and select "Skills."

LAS VEGAS – The Tencent Blade Team of researchers demonstrated several ways they have developed to hack and run remote code on Google Home smart speakers. The hacks center around what is known as a Magellan vulnerability, which can be used to exploit the massively popular SQLite database engine. Here at a session at DEF CON on Thursday, the researchers shed light on their work "breaking" Google Home. What made the talk unique wasn't necessarily that Google Home smart speakers could be compromised using Megellan – that was public news in Dec. 2018 – rather it was how the hack was pulled off. On stage Tencent researchers Wenxiang Qian, YuXiang Li and HuiYu Wu laid out the evolution of their research.

Facebook has become the latest company to admit that human contractors listened to recordings of users without their knowledge, a practice the company now says has been "paused". Citing contractors who worked on the project, Bloomberg News reported on Tuesday that the company hired people to listen to audio conversations carried out on Facebook Messenger. The practice involved users who had opted in Messenger to have their voice chats transcribed, the company said. The contractors were tasked with re-transcribing the conversations in order to gauge the accuracy of the automatic transcription tool. "Much like Apple and Google, we paused human review of audio more than a week ago," a Facebook spokesperson told the Guardian.

The two companies have butted heads for years, and it's likely they'll continue to do so--Spotify's protest web page (in which Spotify details accusations that Apple engages in anticompetitive behavior) is just one example of hurt feelings. But despite the mutual dislike, Apple and Spotify are reportedly in talks to integrate Spotify more tightly with Siri, Apple's digital assistant. The companies are "discussing a plan" that would let iPhone users ask Siri to play music with Spotify, instead of requiring them to manually navigate to whatever song, album, or playlist they want to hear via the third-party app. The Information's report on this handy potential change cites three anonymous sources who are "familiar with the discussions." Neither company confirmed the report when contacted by Fast Company.

You'd expect Microsoft's offices to be wired with some snappy technology. When it comes to booking a meeting room, Microsoft employees can tell an artificial intelligence-powered (AI) bot how many people need to sit down at what time and the bot will return ideas for the best spaces based on the headcount plus availability. The American software giant envisions that sort of AI making its way into medical exam rooms, PowerPoint slides and other companies' administrative offices over the next five years, says Ken Sun, general manager of Microsoft Taiwan where a lot of the research is being done. Artificial intelligence technology, better known as AI, refers to making machines react and process information, often based on giant data reserves, much as people do. "It's super exciting," Sun says in an interview.

Today, machine learning has become an integral part of almost every industry and the intersection of product management with machine learning is widespread. However, business managers and IT leaders sometimes find it difficult to decide whether they really need machine learning for a problem or not. The most important thing to consider when developing a product using ML is to focus on user needs. Instead, machine learning is a tool to solve users' need, which should be applied for solving problems and enhancing experiences. There are plenty of startups in the industry that focus only on the technology front and not on solving a user problem.

Machine Learning is a buzzword in the technology world right now and for good reason, it represents a major step forward in how computers can learn. The need for Machine Learning Engineers are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. On an Average, an ML Engineer can expect a salary of ₹719,646 (IND) or $111,490 (US). So, let's discuss some of the Applications of Machine Learning. I'll be discussing the following Applications of Machine Learning one by one: Now, Google Maps is probably THE app we use whenever we go out and require assistance in directions and traffic.