AI assistants have started carrying out tasks on a user's behalf by interacting directly with the web. However, training an interface that maps natural language (NL) commands to web actions is challenging for existing semantic parsing approaches due to the variable and unknown set of actions that characterize websites. We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions rather than low-level UI interactions, thus being able to flexibly adapt to different websites and handle their transient nature. We frame this as a ranking problem where, given a user command and a webpage, FLIN learns to score the most appropriate navigation instruction (involving action and parameter values). To train and evaluate FLIN, we collect a dataset using nine popular websites from three different domains. Quantitative results show that FLIN is capable of adapting to new websites in a given domain.

Only 10 years ago, artificial intelligence (AI) was just a lofty concept for consumers, appearing in pop culture references or fleeting news stories. Today, it pervades every corner of life, from Siri on our iPhones, to smart home security systems, to the recommended products in our Amazon feed. Everywhere we look, AI has become part of our daily processes -- and as we live, learn and work from home amidst the pandemic, this has only accelerated. It's safe to say that AI is no longer just a novel concept; it's a convenience we've come to expect in day-to-day life. What's interesting to me is that, in customer care, the benefits of AI are not quite so widely welcomed.

Amazon's smallest Echo has evolved quite a bit over the years. The first Amazon Echo Dot was small and puck-like but didn't have very good audio. In 2018, the company upgraded the Dot's speakers to a new 1.6-inch driver that gave it a lot more bass and overall better performance, plus it had a much more stylish fabric-clad exterior. Last year, Amazon added a new model called the Echo Dot with Clock, which is basically the same thing but with a digital clock on the front. In 2020, however, the company has decided to go… round.

Amazon users in the UK can now try and answer questions that Alexa doesn't know. The US tech company has announced the general availability of Alexa Answers in the UK – a crowd-sourced method of making its Alexa digital assistant more intelligent. The online hub offers users the chance to answer questions that Amazon's smart assistant Alexa didn't know the answer to. Users just need to sign in to their Amazon account at the Alexa Answers webpage and start browsing unanswered questions that they think they can answer. The UK launch will help Alexa get smart on topics specific to the UK, including the Spice Girls and the two-pound coin, Amazon hopes. In return for their knowledge, Alexa Answers users can earn points and get onto leaderboards on the hub.

The smart speaker that got things started is back, and it looks a little different now. Nathan Ingraham reviewed the new spherical Amazon Echo, and the good news is that no matter what you think of its looks, it sounds better than ever. Adding an extra tweeter -- not to mention the built-in Zigbee home hub -- seems to have made all the difference. That odd shape does mean its indicator light is a bit hidden, but when the sound is good enough that buying two to create a stereo setup seems like a reasonable option, maybe we can get over it… maybe. Garmin is offering Twitch broadcasters and other game streamers a way to layer their heart rate and other metrics into their streams, with an Esports Edition of its Instinct GPS smartwatch.

This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, randomly picked from a population of $m$ users, requests a recommendation. The decision-maker observes the user and selects an item from a catalogue of $n$ items. Importantly, an item cannot be recommended twice to the same user. The probabilities that a user likes each item are unknown. The performance of the recommendation algorithm is captured through its regret, considering as a reference an Oracle algorithm aware of these probabilities. We investigate various structural assumptions on these probabilities: we derive for each structure regret lower bounds, and devise algorithms achieving these limits. Interestingly, our analysis reveals the relative weights of the different components of regret: the component due to the constraint of not presenting the same item twice to the same user, that due to learning the chances users like items, and finally that arising when learning the underlying structure.

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.