Modern day web app development is increasingly focused on building a customer-facing front-end presence with the use of Artificial Intelligence. In this post, we look at five key areas, illustrated by real-world examples, where web apps are employing Artificial intelligence to automate some part of their system. Curating content based on the user's context is one of the most widely used AI features in web apps. Amazon, for instance, uses item-based collaborative filtering for product classification. Amazon's recommendation system uses a combination of goods-based recommendation (users are recommended for those similar to what they liked in the past) and buddy-based recommendation (users are recommended things which their Facebook friends like.) Not just for their recommendation system, Amazon has been using AI for multiple tasks.

Some of us remember Will Robinson's loyal robotic pal in the "Lost in Space" series of the 1960s. Others will trace the sci-fi vision of intelligent autonomous machines to the day Skynet became self-aware and turned on humanity in the "Terminator" films. The term artificial intelligence (AI) and the official pursuit of intelligent machines in the scientific community actually dates to a 1956 conference of researchers from Dartmouth and IBM. Today's AI is invading our everyday lives, albeit in more subtle ways, such as digital assistants like Alexa and Siri. And now, Artificial Intelligence in dentistry has arrived! Consider a daily task that we as dentists view as routine and relatively simple: finding caries on X-rays.

If, like Rip Van Winkle, you've been asleep for the last decade and have just woken up, that flip phone you have has become super-popular among retro technologists and survivalists alike, and, oh yeah, Artificial Intelligence (AI) is either going to kill you or save you. AI is the latest in a long line of technology buzzwords that have gripped society, and if we are to believe the people at the respected technology analysts firm Gartner Inc., 2018 will be the year in which AI is truly integrated into our daily lives. As unnerving as the surreal robotics being cooked up at Boston Dynamics or the deployment of facial recognition AI in Chinese public schools may seem, this technology is a product of the human condition and as such, we are embedding our own culture within its coded DNA. Debates about AI currently focus on the notion of ethics. In the study of culture, ethics are embedded within values, and they've become an important part of the deliberations about how AI will integrate into our lives.

Wait a minute, cats already rule the'interwebs'. Wouldn't handing the growing Internet of Things over to them be the end of civilisation as we know it? If you have been keeping an eye on this radical development appearing on the pet community radar. You will probably agree that a cat would prefer a long nap. Let's find out why! We can't give you an Internet of Things tutorial, but we can give you an idea of what's coming your way. We found an Internet of Things definition that helps us show the IoT as it affects pet owners, and their fur families.

You can now buy a holographic'wife' in Japan The unit has been targeted at'single men who live alone'. Image Credit: YouTube / Gatebox Labs The Gatebox virtual assistant takes the form of a holographic anime girl who you can interact with. Voice assistants such as Amazon Alexa and Apple Siri have become increasingly popular in recent years, but now one Japanese company has taken things one step further by transitioning from a purely voice-based system to one that you can practically have a relationship with. Originally only available in a limited capacity to customers who placed a pre-order, the Gatebox virtual assistant appears as a holographic anime girl named Hikari Azuma. In addition to responding to voice commands, Azuma is capable of recognizing her owner's face, controlling various'smart' devices around the home and can even send text messages.

One important distinction to make is that chatbots are not full-blown virtual assistants like Alexa, Cortana, or Siri. The distinction between a text- or voice-based chat interface is less important than the scope of where digital assistants live and what they do for a user as opposed to the more narrow focus of a chatbot. Virtual assistants are omnipresent AI helpers embedded within our smartphones, Bluetooth speakers, operating systems (OSes), and other computing environments, offering predictive recommendations and performing a wide range of evolving functions. Chatbots, for the most part, live within a single app or messaging interface, and can be programmed more simply with a selection of automated actions for specific business tasks.

Conversational AI for banking – by its very nature – is a constantly-evolving technology. Based on simple human conversations, this amazing science continually refines and optimizes itself each time it interacts with a user. We call this "training the bot", an ongoing process that relies on plenty of data and user interaction to deliver consistently accurate responses from the Finn AI virtual assistant. With such cutting-edge technology, the question I get asked most often is: what's next? At Finn AI, we've recently partnered with Visa to explore what's next.

The goal of a recommender system is to show its users items that they will like. In forming its prediction, the recommender system tries to answer: "what would the rating be if we 'forced' the user to watch the movie?" This is a question about an intervention in the world, a causal question, and so traditional recommender systems are doing causal inference from observational data. This paper develops a causal inference approach to recommendation. Traditional recommenders are likely biased by unobserved confounders, variables that affect both the "treatment assignments" (which movies the users watch) and the "outcomes" (how they rate them). We develop the deconfounded recommender, a strategy to leverage classical recommendation models for causal predictions. The deconfounded recommender uses Poisson factorization on which movies users watched to infer latent confounders in the data; it then augments common recommendation models to correct for potential confounding bias. The deconfounded recommender improves recommendation and it enjoys stable performance against interventions on test sets.

Artificial intelligence or AI is encroaching on the sales process at an exciting rate, or alarming, depending on your side of the coin. Using machine learning, predictive analyses, and big data analytics, AI can now automate routine tasks, unearth insights off sales data, and assist sales reps in enhancing overall service. We see it integrated in CRM, salesforce automation, help desk solutions, and other B2B apps. Even as vendors are pushing the envelope for smarter solutions. The benefits of AI to sales efficiency are high, and the stakes, too.

Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.