In this paper, we investigate the impact of diverse user preference on learning under the stochastic multi-armed bandit (MAB) framework. We aim to show that when the user preferences are sufficiently diverse and each arm can be optimal for certain users, the O(log T) regret incurred by exploring the sub-optimal arms under the standard stochastic MAB setting can be reduced to a constant. Our intuition is that to achieve sub-linear regret, the number of times an optimal arm being pulled should scale linearly in time; when all arms are optimal for certain users and pulled frequently, the estimated arm statistics can quickly converge to their true values, thus reducing the need of exploration dramatically. We cast the problem into a stochastic linear bandits model, where both the users preferences and the state of arms are modeled as {independent and identical distributed (i.i.d)} d-dimensional random vectors. After receiving the user preference vector at the beginning of each time slot, the learner pulls an arm and receives a reward as the linear product of the preference vector and the arm state vector. We also assume that the state of the pulled arm is revealed to the learner once its pulled. We propose a Weighted Upper Confidence Bound (W-UCB) algorithm and show that it can achieve a constant regret when the user preferences are sufficiently diverse. The performance of W-UCB under general setups is also completely characterized and validated with synthetic data.

Check out the Most AMAZING Examples Of Artificial Intelligence (AI)! From deep learning sophisticated robots to machine learning computers, this top 10 list of incredible technology will amaze you! Watch our "REAL Mermaid Sightings Around The World!" video here: https://youtu.be/ChM0CBRmVsM Watch our "10 Sea Monsters ATTACKING A Boat!" video here: https://youtu.be/0XROvoPCDNc Watch our "STRANGEST Animals People Keep As Pets!" video here: https://youtu.be/OMa96nPqz-Y 8. Siri Most people don't realize that there are several "levels" of artificial intelligence, and an example of a "lower tier AI" is that of Siri, the "digital assistant" to Apple device users.

"He helped us build our core tech and helped us recruit talent," said D'Souza. The ProNavigator team, based in the Velocity Garage in the Tannery building in downtown Kitchener, used Google Home and Amazon's Alexa when building the smart software. Both devices transcribe spoken words into printed text. Software programmers spoke into the devices as if they were consumers looking for quotes or asking questions about their policy. The text was used to build the platform's understanding of spoken language.

Contrary to popular Anglocentric belief, English isn't the world's most-spoken language by the total number of native speakers -- nor is it the second. In fact, the West Germanic tongues rank third on the list, followed by Hindi, Arabic, Portuguese, Bengali, and Russian. Surprisingly, Google Assistant, Apple's Siri, Amazon's Alexa, and Microsoft's Cortana recognize a relatively narrow slice of those. It wasn't until this fall that Samsung's Bixby gained support for German, French, Italian, and Spanish -- dialects collectively spoken by 616 million people worldwide. And it took years for Cortana to become conversant in Spanish, French, and Portuguese.

Artificial Intelligence (AI) may look like something out of the pages of a sci-fi book, yet you'd be surprised how often you use it daily. As the technology continues to improve, AI will become even more common with more widespread utilization among diverse industries. To start with, let's begin with the basic definition of Artificial Intelligence (AI) and what it includes. Seeking Alpha gives a very apt description of the same in their article- At a basic level, artificial intelligence is the concept of machines accomplishing tasks which have historically required human intelligence. Applied AI: Machines designed to complete very specifics tasks like navigating a vehicle, trading stocks, or playing chess – as IBM's Deep Blue demonstrated in 1996 when it defeated chess grand master Gerry Kasparov. General AI: Machines designed to complete any task which would normally require human intervention. The broad nature of General AI requires machines to "learn" as they encounter new tasks or ...

In this paper, we study a multi-step interactive recommendation problem, where the item recommended at current step may affect the quality of future recommendations. To address the problem, we develop a novel and effective approach, named CFRL, which seamlessly integrates the ideas of both collaborative filtering (CF) and reinforcement learning (RL). More specifically, we first model the recommender-user interactive recommendation problem as an agent-environment RL task, which is mathematically described by a Markov decision process (MDP). Further, to achieve collaborative recommendations for the entire user community, we propose a novel CF-based MDP by encoding the states of all users into a shared latent vector space. Finally, we propose an effective Q-network learning method to learn the agent's optimal policy based on the CF-based MDP. The capability of CFRL is demonstrated by comparing its performance against a variety of existing methods on real-world datasets.

Tech Review (TR 2019-02-02)--Domain Mondo's weekly review of tech investing news: Features • 1) Giving Up On Bitcoin & Cryptocurrency, 2) Amazon Alexa And Google Assistant Becoming Omnipresent, 3) Investing: The Week & Notes: WEF, EU, Brexit, US, 4)ICYMI Tech News: Facebook, Nvidia, Twitter, Google, Oracle, Huawei, China VC, Microsoft, Amazon, Startup Starbreeze Implodes, Personal Tech: Chromebooks vs Windows? 1) Giving Up On Bitcoin & Cryptocurrency Bitcoin in $US Bitcoin Volatility Problematic For Analysts: Crypto enthusiasts such as Mike Novogratz made wild predictions on Bitcoin's price in 2018, most of which proved wrong. Tom Lee of Fundstrat said Bitcoin would hit $25,000 by end of 2018. Now, Lee, and others, are not making timing calls at all. However, Ripple CEO says decentralized payment systems are likely to win: Ripple's Brad Garlinghouse discusses blockchain technology and cryptocurrencies at a CNBC-hosted panel in Paris. CNBC International TV video above published Jan 31, 2019.

Europe is expected to have more than 84 million smart homes by the end of 2022, experts claim. This prediction, if it comes true, will see the amount of so-called'smart homes' on the continent quadruple from the 22.5million recorded at the end of 2017. It is expected that a typical family home could contain more than 500 individual smart objects by 2022, with the most popular of these being smart speakers and assistants such as Amazon's Alexa and Google Assistant. Reports suggest that the number of smart homes in Europe will jump from 22.5 million at the end of 2017 to 84 million by 2022. Mr Giulio Salvadori, from the Polytechnic University of Milan, said the use of smart devices for heating could lead to energy savings of up of 28 per cent for a typical family. He said: 'Widespread adoption of smart solutions for heating, in Milan, would reduce carbon dioxide emissions by over 54,000 tons per year, with an annual saving of €70million (£61million) for citizens.'

Box subscription companies are reinventing retail using personalization and artificial intelligence. In recent years, amid a lackluster shopping environment, an unheralded retail phenomenon has taken off quite dramatically -- with growth rates exceeding 1,000%. The startups representing this evolving segment are collectively grouped under the label of subscription e-commerce. With such evocative names as BarkBox, Birchbox, Blue Apron, Harry's, OwlCrate, Trunk Club, and Winc, these online companies mail monthly boxes containing specially curated items in beauty, fashion, food, personal grooming, and pet products priced between $10 and $80 per box right to their subscribers' doorsteps. Attention was brought to this category when consumer giant Unilever snapped up one of the best-known startups -- Dollar Shave Club -- in July 2016 for an eye-popping $1 billion -- five times its annual revenue.

Authorities say a 13-year-old northwestern Indiana boy is charged with intimidation after telling Apple's digital assistant Siri he planned a school shooting and posting an iPhone screenshot of the response on social media as an apparent joke.