Samsung has just acquired Korean artificial intelligence startup Fluenty, and the acquisition has already sparked rumors that the tech giant could be planning an upgrade for its AI assistant Bixby. The acquisition comes in the wake of the announcement that the company has named a new chief to lead its software development department. Samsung revealed this week that it has acquired Fluenty, a startup that has developed a machine learning-based chatbot. The startup is also known for being founded by researchers from top tech firms in South Korea including Naver, LG Electronics and Kakao, according to The Investor. The acquisition comes a little more than a year since Fluenty joined Samsung's startup accelerating program that provided funding worth 100 million won or US$18,000.

This post was done in partnership with Wirecutter, reviews for the real world. When readers choose to buy Wirecutter's independently chosen editorial picks, they may earn affiliate commissions that support their work. Read their continuously updated list of deals here. We already consider these a great value at $70, this additional $20 off makes this pair of earbuds a fantastic value. The Marshall Mode earbuds are the top pick in our guide to earbuds under $100.

Artificial Intelligence (AI)/machine learning has been one of the hottest topics among the tech community in recent years. Unlike natural intelligence utilized by humans, artificial intelligence is applied when a machine mimics cognitive functions, such as problem-solving. But how is AI helping consumers with their real estate investment goals, especially as prices rise, inventory dwindles and competition intensifies? "We are constantly utilizing AI/machine learning to give investors a competitive edge and to improve the home buying, selling and renting experience," explains Ramin Vatanparast, VP of Product and Data for HomeUnion. Recommendation Engine Customizes Your Properties "To help our users, we developed a recommendation engine that determines the best investment portfolio based on their customized preferences. This'decision engine' uses machine learning to recommend property portfolios that achieve their specific, personalized investment goals," he says.

Medical transcription is often seen as one of the more mundane tasks that need to be done in the doctor's office. Yet, it's vitally important for making sure that medical records are accurate, and that all of the physician's observations, orders, and conversations with patients is properly documented. Google wanted to see if the voice recognition technologies already available in Google Assistant, Google Home, and Google Translate could be used to automate the transcription process and help doctors, as well as medical scribes, take notes more quickly. In a recent proof of concept study, Google developed a system that utilized two automatic speech recognition models, a Connectionist Temporal Classification (CTC) phoneme-based model and a Listen Attend and Spell (LAS) grapheme-based model, and trained them with over 14,000 hours of recorded speech. The result was a pretty respectable word error rate of 20.1% for the CTC model and 18.9% for the LAS model, although the CTC model required the researchers to clean up noise in the recordings before processing it. Based on the favorable results, Google will be soon start working with physicians and researchers at Stanford University to investigate what types of clinically relevant information can be automatically extracted from medical conversations to reduce the amount time doing documentation and increase productive time with patients.

In the world we live in today, Artificial Intelligence (AI) is everywhere. Some of the places we experience it are very obvious, but sometimes AI is being used in ways we may not even realize. The question we face isn't when AI will begin to play a role in our everyday lives because the answer is that it already is. Rather, we should be asking whether or not we are using it to its full capacity. I had the opportunity to talk to Robert Weideman, Executive Vice President and General Manager of Nuance Enterprise.

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.

Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each user as a Gaussian mixture model (GMM). Recommendations can be obtained directly by sampling from a user's generative model. Using Latent semantic analysis (LSA) as comparison, we compute and explore document representations on the Delicious bookmarks dataset, a standard benchmark for recommender systems. We then perform density estimation in both spaces and show that learned representations outperform LSA in terms of predictive performance.

The global recorded music market grew by 5.9 percent last year. It was the fastest rate of growth since 1997 and was as a result of the shift from traditional CDs and portable devices to the ability to stream content anywhere, at any time. Yet despite an estimated 498 online music streaming services available in over 40 countries in 2007, many of these companies cease to exist today. This emphasizes the importance of building a clear growth strategy that can continuously appeal to a demographic that is yearning for instant and tailored music on demand. Today, brands across the globe are continually searching for fresh ways to connect and resonate with their audiences while striving to stand out from the competition in order to grow their business.

Your message has been sent. There was an error emailing this page. From smart light bulbs and thermostats that think for themselves to Bluetooth door locks, wireless security cameras, and all manner of sensors, today's home technology can sound awfully sophisticated while actually being a messy hodgepodge of gizmos and apps. Whether you call it home automation or the connected home, installing all this stuff in your house is one thing. Getting it to work together smoothly and with a single user interface can be something entirely different.

Maybe there's an audiophile on your list, or maybe you're shopping for someone who recently acquired a new phone and could use something better than the pack-in headphones. Either way, we have a slew of recommendations in the audio gear section of our holiday gift guide. On our list you'll find smart speakers from Google and Amazon alike, along with Sonos, whose new "One" speaker includes Alexa built in, with Google Assistant support coming soon. When it comes to headphones, our selections run the gamut from the affordable (Jabra's Move headset) to the high end (Bragi's Dash Pro wireless earbuds and these noise cancelling headphones from Sony), with a couple mid-range options in between. Rounding out the list, we have a soundbar, drum machine, synth app, the Amazon Echo Show and one of our favorite portable Bluetooth speakers.