Over the past couple of years, YouTube has come under fire for its recommender system, with the media suggesting that it is promoting violent content, or banning LGBT content for violating its terms of service. Seemingly in response to all of this, Google has finally released a paper explaining YouTube's recommender system, including how it makes recommendations and the information it gathers in doing so. The paper, by Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi, discusses some of the problems that common/"normal" recommender systems face, some of the specific ones that a platform as big as YouTube faces, and the architecture they used to create their system. One of the biggest issues the program had to tackle was that of scalability. Basically, no other recommender system has to work with such a large user platform, or with so many individual pieces of content.
It's a question most of us have asked at one point in our lives -is Santa real? Today's children aren't looking to their parents for an answer, but are turning to Google and the search engine is shattering the shattering the illusion. A report found that 1.1 million children learn online that Saint Nick is a fictitious character, as the first article in the search says'as adults we know Santa Claus isn't real.' When searching'Is Santa real' the first article that is displayed comes from Quartz, which provides parents with advice on how to answer the question . And the introductory sentence of the article reads: 'As adults we know Santa Claus isn't real.'
TV shows and fiction aside, the present day examples of basic AI are Google Voice, Cortana, Alexa, Siri and chatbots. However, AI isn't just limited to voice assistants, it's turning tables in other domains and industries as well. Several restaurants for instance have bots for room service, serving food and carrying luggage. Let's take a look at some interesting and mind-bending stats and facts on this prevalent technology to give you a better idea of the direction the market is headed. He said that at a town hall event in San Francisco in January last year and he couldn't be more right.
The ADAPT Centre at Trinity College Dublin has received funding to hire a start-up founder to spin-out the business start-up Darwin & Goliath http://darwingoliath.com in the field of recommendations-as-a-service. The position is to be filled with a product manager, machine-learning engineer, software engineer, or software architect. The person is expected to work together very closely with the project lead and Business Administrator. Both will be responsible for developing a recommender-system as-a-service that uses unique machine-learning technology, which is based on the research of Professor Joeran Beel who is the project lead. This position is flexible in the responsibilities and open to focus more on either software engineering / machine-learning or on the architectural and product management part.
Conversational Intelligence for smart customer care Be sure to fully care of your customers 24/7 with HelloMyBot, the first platform based on Conversational Intelligence hellomybot.io Reach out to your audience on any kind of platform and take advantage of its singular features. In a nutshell, hellomybot is one single platform, to create, manage and enhance the conversation with your audience on the platform they prefer!
Instead, it relies primarily on a technique called federated learning, Apple's head of privacy, Julien Freudiger, told an audience at the Neural Processing Information Systems conference on December 8. Federated learning is a privacy-preserving machine-learning method that was first introduced by Google in 2017. It allows Apple to train different copies of a speaker recognition model across all its users' devices, using only the audio data available locally. It then sends just the updated models back to a central server to be combined into a master model. In this way, raw audio of users' Siri requests never leaves their iPhones and iPads, but the assistant continuously gets better at identifying the right speaker.
Many AI vendor companies offer AI-enabled products and services for pushing more and more products in front of customers. That said, it is not always clear how these solutions determine which products to advertise to which customers. Retailers and other businesses should consider what they need to do to prepare their enterprise for one of these solutions and familiarize themselves with how AI recommendations are built and trained. In this article, we will explain how AI-enabled product recommendations work. We begin our explanation by outlining the data requirements of a company looking to adopt a product recommendation solution.
What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.
Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.
For starters, Amazon says it's been using neural networks to make Alexa's voice sound more human when it translates text (like your text messages) into speech. Rohit Prasad, who heads up Alexa machine learning and artificial intelligence, told me that this technology has allowed Amazon to take a totally different approach to generating speech. In the past, Alexa's algorithms broke down language into word parts or vocal sounds, then tried to string them together as smoothly as possible. But it always sounded somewhat choppy and robotic. Now, Amazon is using neural networks that can generate whole sentences of text in real time, says Prasad.