We investigate contextual bandits in the presence of side-observations across arms in order to design recommendation algorithms for users connected via social networks. Users in social networks respond to their friends' activity, and hence provide information about each other's preferences. In our model, when a learning algorithm recommends an article to a user, not only does it observe his/her response (e.g. an ad click), but also the side-observations, i.e., the response of his neighbors if they were presented with the same article. We model these observation dependencies by a graph $\mathcal{G}$ in which nodes correspond to users, and edges correspond to social links. We derive a problem/instance-dependent lower-bound on the regret of any consistent algorithm. We propose an optimization (linear programming) based data-driven learning algorithm that utilizes the structure of $\mathcal{G}$ in order to make recommendations to users and show that it is asymptotically optimal, in the sense that its regret matches the lower-bound as the number of rounds $T\to\infty$. We show that this asymptotically optimal regret is upper-bounded as $O\left(|\chi(\mathcal{G})|\log T\right)$, where $|\chi(\mathcal{G})|$ is the domination number of $\mathcal{G}$. In contrast, a naive application of the existing learning algorithms results in $O\left(N\log T\right)$ regret, where $N$ is the number of users.

The applications of artificial intelligence have grown over the past decade. Here are examples of artificial intelligence that we use in our everyday lives. The words artificial intelligence may seem like a far-off concept that has nothing to do with us. But the truth is that we encounter several examples of artificial intelligence in our daily lives. From Netflix's movie recommendation to Amazon's Alexa, we now rely on various AI models without knowing it.

Machine learning is a sub-field of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning algorithms are usually categorized as supervised or unsupervised. Artificial Intelligence is a branch of computer science that endeavors to replicate or simulate human intelligence in a machine, so machines can perform tasks that typically require human intelligence. Some programmable functions of AI systems include planning, learning, reasoning, problem-solving, and decision making. My social, promotional, and primary mails might be different than what you have in your mailbox.

The Bose Home 300's sleek design fits in well with most decor. We weren't sure what to expect upon opening the Bose Home 300 for testing, but we were pleasantly surprised on almost every level. While the sound quality can't quite compete with the (much larger) Echo Studio, the Bose Home 300 allows users to choose between Alexa or Google Assistant; it has handy preset buttons on the top of the speaker; and it can stream audio over Bluetooth, AirPlay, WiFi, or via an old-school auxiliary cable. Through its app and smart assistants, the Bose Home 300 can play music from a large number of streaming services, such as Spotify, TuneIn, Amazon Music, Tidal, Pandora, and even Apple Music via Airplay or Bluetooth. The compatible music and podcast sources will vary a bit depending on which smart assistant you choose (you can only use one assistant at a time, however it is very easy to switch in the Bose app). Though not any larger, this speaker is much louder than most of the other smart speakers we included in this roundup.

Google is said to be testing a smart display feature that would activate Google Assistant by proximity instead of using the "Hey Google" or "OK Google" wake words. Leaker Jan Boromeusz posted a YouTube video (via Android Central) that shows the feature in an apparent internal Nest Hub Max firmware build. Boromeusz is able to use the voice assistant without first saying one of the wake words. The Assistant UI disappears whenever Boromeusz stays still or stops talking, but you can see it pop back up when he moves a little closer to the smart display. It's unclear whether Google has plans to bring the feature, which is codenamed Blue Steel, to the public version of the firmware. It merely appears to be in testing for now.

Amazon has new Echo speakers to sell you. And if you're wondering whether or not to ditch the old ones for these, the answer comes down to two key questions for Alexa, the personal assistant. Do I prefer the looks of a round speaker over a cylinder? Do I crave better sound? This year Amazon is all about being spherical, in the shape of its "The Spheres" corporate headquarters in Seattle.

It's been over five years since the first Amazon Echo arrived, showing people how useful a virtual assistant in your home could be. Amazon has added tons of new features to Alexa, its virtual assistant, over those years -- and as such, new Echo hardware isn't quite as exciting. Of course, that hasn't stopped Amazon from updating its devices on a more-or-less annual basis, as well as launching tons of Echo variants. Last year's Echo was one of the best smart speakers we'd used, adding the improved speakers first found in 2018's $150 Echo Plus at a lower $100 price point. This year, however, Amazon has made some of the most significant hardware updates to the Echo yet, including a fresh design and some high-end features brought over from the more expensive Echo Plus. Even so, Amazon's pedigree in the category might give it the advantage here.

How does the latest Echo compare to other top smart speakers? The newest thing about the 2020 Echo is its round design. In the box, we find the ball speaker and a power adapter. As with all Echo speakers, you don't need any substantial directions to get it up and running. Just plug it in, open the Alexa app, and follow the prompts when, after a few seconds, the new speaker is detected and a pop-up appears asking whether to set the speaker up.

The fourth generation of Amazon's Echo smart speaker marks a radical departure in industrial design, ditching the familiar columnar form factor of previous iterations for something that looks for all the world like a child's bowling ball. Two things haven't changed: The Echo (and Alexa) remain our favorite tools for smart home control, and the company still trails Sonos in terms of building smart speakers that sound great. The recent introduction of the Nest Audio leaves Amazon in third place behind Google in terms of audio performance (don't forget the powerful Google Home Max). Amazon continues to make strides in terms of audio quality, and the fourth-gen Echo sounds very good, especially if you like your tunes leavened with bass. But in a three-way comparison with the Nest Audio and the Sonos One (which, I should note, costs twice as much as either of its competitors), the Echo comes up just a wee bit short, as I'll discuss a bit later.

Technology is ever-changing and has had a huge impact on our ability to adapt to the ever-changing world around us. As humans, we use technology in almost every aspect of our lives from smartphones to laptops to washing machines – these gadgets have not only become essential, we expect them to do more and more for us over time. The advent of innovation in Artificial Intelligence (AI) and Machine (ML) Learning are enabling brands to transform the customer journey into a more personalised and productive experience and substantively differentiate themselves from competitors. But unlike buying the latest manufacturing equipment or the newest laptops for their employees, AI and ML technologies deliver compounded value with every interaction, creating layers of insights and improvements, over time. Enterprises that have embarked on this journey will build an insurmountable advantage over their competition that is yet to get started.