When we covered Leena AI as a member of the Y Combinator Summer 2018 cohort, the young startup was firmly focused on building HR chatbots, but in the intervening years it has expanded the vision to a broader HR policy platform. Today, the company announced an $8 million Series A led by Greycroft with help from several individual industry investors. Company CEO and co-founder Adit Jain says that in 2018 the company was concentrating on building an intelligent virtual assistant for HR-related questions. It allowed employees to ask the bot questions like how many vacation days they have left or what holidays they have off this year. Over the last couple of years since leaving Y Combinator, the company has moved into broader HR service delivery.

Researchers are studying whether artificial-intelligence tools that analyze things like typing speed, sleep patterns and speech can be used to help clinicians better identify patients with early-stage dementia. Huge quantities of data reflecting our ability to think and process information are now widely available, thanks to watches and phones that track movement and heart rate, as well as tablets, computers and virtual assistants such as Amazon Echo that can record the way we type, search the internet and pay bills.

The time has come for Artificial Intelligence (AI)-driven teaching assistants to help ease a human teacher's workload in the age of online learning, however, such virtual machines have to be effective and communicate well to be accepted by the society in a broad way, argue researchers. The increase in online education has allowed a new type of teacher to emerge -- an artificial one. But just how accepting students are of an artificial instructor remains to be seen, said researchers at the University of Central Florida's Nicholson School of Communication and Media who are working to examine student perceptions of AI-based teachers. Some of their findings, published in the'International Journal of Human-Computer Interaction', indicated that for students to accept an AI teaching assistant, it needs to be effective and easy to talk to. "The hope is that by understanding how students relate to AI-teachers, engineers and computer scientists can design them to easily integrate into the education experience," said Jihyun Kim, an associate professor in the school and lead author of the study.

How to Do It is Slate's sex advice column. Send it to Stoya and Rich here. Every week, the crew responds to a bonus question in chat form. I recently met a guy on Tinder, where I usually don't have much luck because I'm not conventionally attractive and want to date, not just hook up. But after talking to this guy for a few days we seem practically perfect for each other!

Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems. Personalization and recommender systems have gone hand-in-hand to help customers fulfill their shopping needs and improve their experiences in the process. With the growing adoption of conversational platforms for shopping, it has become important to build personalized models at scale to handle the large influx of data and perform inference in real-time. In this work, we present an end-to-end machine learning system for personalized conversational voice commerce. We include components for implicit feedback to the model, model training, evaluation on update, and a real-time inference engine. Our system personalizes voice shopping for Walmart Grocery customers and is currently available via Google Assistant, Siri and Google Home devices.

Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective. However, producing samples from the generator is very time-consuming, and our empirical study shows that the discriminator performs poor in top-k item recommendation. To this end, a theoretical analysis is made for the GAN-style algorithms, showing that the generator of limit capacity is diverged from the optimal generator. This may interpret the limitation of discriminator's performance. Based on these findings, we propose a Sampling-Decomposable Generative Adversarial Recommender (SD-GAR). In the framework, the divergence between some generator and the optimum is compensated by self-normalized importance sampling; the efficiency of sample generation is improved with a sampling-decomposable generator, such that each sample can be generated in O(1) with the Vose-Alias method. Interestingly, due to decomposability of sampling, the generator can be optimized with the closed-form solutions in an alternating manner, being different from policy gradient in the GAN-style algorithms. We extensively evaluate the proposed algorithm with five real-world recommendation datasets. The results show that SD-GAR outperforms IRGAN by 12.4% and the SOTA recommender by 10% on average. Moreover, discriminator training can be 20x faster on the dataset with more than 120K items.

Every day, Every Hour, Every Minute, we are saying few simple words like "Hey Google" or "Hey Alexa" or "Hey Siri" to know something or to get our works done. "Hey Google, How do you work?" or "Hey Alexa, Why are you so smart?" or "Hey Siri, What's behind your success?" It's simply because as a client we never bother about these things. In any application clients are the ones who needs to be satisfied and in today's world these assistants are so much smarter that there is no reason to think or ask these things as a client. But as a Tech Enthusiast, or as a guy from CS background it's always too much fascinating to know about behind the scene technologies, these popular companies are using to make these smart assistants capable of extraordinary performance.

Amazon's Echo devices are a runaway success. Tap or click here for a features comparison lineup of the various Amazon Echo devices [2020 Chart]. If you already own an Echo, prepare to be shocked. Several of my conversations Alexa recorded had nothing to do with playing music, getting news, or ordering items on Amazon. I talked about a real estate transaction, college courses online, and when the pandemic might end.

After the stock market closed today, Five9 announced the acquisition of intelligent virtual agent (IVA) company Inference Solutions. The purchase price is $172 million, $148 million in cash and $24 million when certain bookings targets are met. Inference brings 550 customers, among them several joint Five9 customers -- including Chick-fil-A and Wyndham Hotels. Inference was founded in 2005, spun out from Telstra Research Labs -- think of it as the Australian version of Bell Labs. Headquartered in San Francisco, the company has additional offices in Austin, TX and Melbourne, Australia.

The Arlo Pro 4 Spotlight camera performed beautifully in all of our tests. Touting a 160-degree viewing angle and 2K video with high-def resolution, Arlo's Pro 4 offers a wider range of view and higher video quality than the Nest Cam Outdoor, our previous No. 1 pick, and is our new recommendation for the best outdoor smart security camera. Arlo's video quality is just as clear at night as it is during the day thanks to the camera's color night vision output. It also has a built-in spotlight that illuminates when motion is detected and a smart siren that can be triggered remotely or automatically. We found the installation process to be fairly uncomplicated, though a 2.4 GHz WiFi connection is required for setup. There are no wires to fiddle with, as this camera includes a mount that can easily be screwed in on the exterior of your home. Before you place it, we suggest giving it a good charge right out of the box using the included magnetic charging cable.