Have you ever been worried on a tour, if you've switched off a light bulb back home? We've all had this confusion at least once, for there was no way you could ensure that everything was perfect. Now, with AI, we don't need to leave anything to chance nor assumptions. AI provides perfect ways to ensure that use of appliances is optimized perfectly when in use and when not in use. Let's imagine some of the ways through which AI and Smart Home Automation will impact and change the way we live: Amazon's Alexa, Google Home, Apple's Siri and Microsoft's Cortana have all optimized and automated living inside a home to a great extent. You can ask Alexa to play a song, while you can also ask Alexa to switch off or switch on the lights in your home.

Whether we know it or not, artificial intelligence (AI) is already steeped into everyday life. It's present in the way social media feeds are organised; the way predictive searches show up on Google; and how music services such as Spotify make song suggestions. The technology is also helping transform the way enterprises do business. Commonwealth Bank of Australia, for instance, has applied AI to analyse 200 billion data points to free up more time so its customer service officers can focus on doing exactly what their title suggests: servicing customers. As a result, the bank has seen a 400% uplift in customer engagement.

Conversational assistants are here to stay, making everything from boiling an egg to making a payment that much easier. And consumers expect more of them day by day. If they meet these growing expectations, conversational assistants are in a position to transform the customer experience landscape. But do organizations have the customer centricity and organizational capabilities necessary to deploy these technologies successfully? In the new report from the Capgemini Research Institute, Smart Talk: How organizations and consumers are embracing voice and chat assistants,we talked to over 12,000 consumers who've used and continue to use voice and/or chat assistants and to 1,000 executives from consumer products and retail, financial services, and automotive, including pure-play digital players.

Today we want to go a step further and implement product recommendation as well. Product recommendation are widely used and are implemented using so called Recommender Systems. There are different ways of implementing recommendations like those we can see on Amazon or Netflix for example. In our case, we will use a multi-class classifier that depending on the answer provided by the user, it will select the product with the highest probability. Using a classifier allows us to avoid having to store past customer behaviour to train the model.

Retail Industry is looking at Artificial intelligence (AI) and machine learning (ML) as a solution to take their organization to the next level of productivity and customer experience. To explain this better, let us look at it this way – Retail companies have access to a massive amount of data about their customers and their shopping preferences. It is difficult for the companies to drill down into these huge mines of useful data and analyze it properly and derive actionable insights in real time. Therefore, massive amounts of this useful information could go waste which would otherwise have helped in increasing sales conversion rates or enhancing the customer satisfaction. With the help of AI and ML, the huge amount of big data could be used in creating web shops that take customer information and turn it into targeted shopping experiences or online chatbots that can easily answer questions and assist customers, or in-store intelligence to make the customer experience even more interactive.

Virtual Assistants have a huge potential in transforming business processes, like cutting down costs, training employees etc. However, it brings some challenges, along with its benefits. Will the CIOs able to overcome it? FREMONT, CA: A virtual wireless assistant is basically an engineered entity embedded in software that communicates with humans in the natural human language. This technology embodies components of communicative voice response, and other advanced artificial intelligence (AI) projects to offer skillful "virtual identities" that interact with users. The list of modern and popular virtual assistants (VA) include Apple's Siri and Microsoft's Cortana among many others.

Cascading bandit (CB) is a variant of both the multi-armed bandit (MAB) and the cascade model (CM), where a learning agent aims to maximize the total reward by recommending $K$ out of $L$ items to a user. We focus on a common real-world scenario where the user's preference can change in a piecewise-stationary manner. Two efficient algorithms, \texttt{GLRT-CascadeUCB} and \texttt{GLRT-CascadeKL-UCB}, are developed. The key idea behind the proposed algorithms is incorporating an almost parameter-free change-point detector, the Generalized Likelihood Ratio Test (GLRT), within classical upper confidence bound (UCB) based algorithms. Gap-dependent regret upper bounds of the proposed algorithms are derived and both match the lower bound $\Omega(\sqrt{T})$ up to a poly-logarithmic factor $\sqrt{\log{T}}$ in the number of time steps $T$. We also present numerical experiments on both synthetic and real-world datasets to show that \texttt{GLRT-CascadeUCB} and \texttt{GLRT-CascadeKL-UCB} outperform state-of-the-art algorithms in the literature.

Beyond sharing datasets or simulations, we believe the Recommender Systems (RS) community should share Task Environments. In this work, we propose a high-level logical architecture that will help to reason about the core components of a RS Task Environment, identify the differences between Environments, datasets and simulations; and most importantly, understand what needs to be shared about Environments to achieve reproducible experiments. The work presents itself as valuable initial groundwork, open to discussion and extensions.

It seems like artificial intelligence is taking over the world, leaving many of us non-techies feeling terrified. Yet when you stop to think about it, we all use artificial intelligence (AI) every day. When we Google something, use Siri on our smartphones or ask Alexa a question, we are using AI. Hollywood has certainly featured AI in many movies from "The Terminator" series to "Robocop" and "I, Robot." In "Minority Report," algorithms predict who is going to commit a crime, and the person is arrested before the crime can be committed.

I'm an undergraduate data scientist, about to start work on my dissertation project. I thought I'd create a system that, given someone's career history and education, predicts what job they're likely to get, and at what company. Essentially this is to help focus the efforts of job seekers, and help them get to where they belong. Originally I planned to do this by scraping data from LinkedIn profiles. From the LinkedIn profile, you can obtain information about someone's current job and employer, as well as their career history and education. Therefore you can see what education and career history (the input) resulted in their current job (the output - the thing I'm trying to predict).