More than a few of us were surprised in 2018 when Google demonstrated how their artificial intelligence personal assistant can now ring unknowing hairdressers and make a booking. It's perhaps a little uncomfortable to think that soon we may not be able to tell the difference between a machine and a person. That, though, is beside the point. Google is the unprecedented world leader of internet search. Each time you type a sentence into Google, and click on one of the links that comes back, you provide the AI system with a data point.

Are you ready for artificial intelligence in schools? You may already know that researchers believe AI is likely to predict the onset of diseases in future and that you're already using AI every day when you search online, use voice commands on your phone or use Google Translate. Maybe you heard the Canadian government has invested millions of dollars in AI research during the past few years and is emerging as one of the global leaders in AI research. But did you know that some companies are developing AI for use in schools, for example in forms such as AI tutoring systems? Such systems can engage students in dialogue and provide feedback in subjects where they need extra help.

Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate \textit{induction of explainable rules from knowledge graph} with \textit{construction of a rule-guided neural recommendation model}. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments\footnote{Code and data can be found at: \url{https://github.com/THUIR/RuleRec}} show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over "noisy" item knowledge graphs, generated by linking item names to related entities.

There are at least two clear trends that show a demand-supply mismatch in tech jobs in cutting-edge IT fields such as Artificial Intelligence and Machine Learning. One is via industry predictions that estimate growth in the AI market from USD 21.46 billion to USD 190.61 billion between 2018 and 2025. Year on year growth is projected to be an impressive 36.62% during the same period. The second trend is more subtle. Big Indian IT firms in the US are reportedly'hoarding' employees in these 2 fields as they foresee a shortage in skilled experts.

Jefferson Graham takes a look at two products that bring Alexa and voice-activated controls to the auto, Garmin Speak and Roav Viv. MANHATTAN BEACH, Calif.--"Alexa, find the nearest gas station!" It's a request you'd probably love to get the answer to, especially if you're low on the gas and behind the wheel on a dark, deserted highway. Or, you're hungry and determined to go to Chipotle. Specific music that's not going to come up on your radio.

Where have you seen Machine Learning in your everyday life? November 29, 2017 1 – Google's AI-Powered Predictions Using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work. 2 – Ridesharing Apps Like Uber and Lyft How do they determine the price of your ride? How do they minimize the wait time once you hail a car?

The world, in both a business and personal setting, continues to be disrupted by digital trends that are driving innovation across industries and sectors. Digital trends continue to create richer, more personal customer relationships, while enhancing operations and processes across the board. These trends are ultimately helping organisations reimagine their capabilities and stave off elimination in the era of disruption. Information Age asked ten experts for their views on what are some of the key current digital trends, and how they will shape future endeavours. Chris Dixon, technical consultant at Axians UK, explains the benefits that 5G will bring in an increasingly connected era.

Apple transformed the cell phone industry with the release of the first-generation iPhone in 2007. At the time, Steve Jobs described the first-generation iPhone as an "iPod, a phone and an Internet communicator." The first-generation iPhone included the basic functions of calling and text messaging, browsing the internet, and listening to music. But it did not have a video recording function, front-facing camera, or GPS, and at the time in 2007, the most popular smartphone applications today had not yet been created, such as Instagram and Uber. Following the first-generation iPhone, Apple and its competitors have continued to offer innovative and helpful features and applications on each new smartphone model.

As part of Firebrand Talent's #Digitalks event series, Lisa Bouari, Executive Director at OutThought, recently spoke to audiences on the topic'AI Assistants: How to take your business to the next level'. The key areas Lisa focused on are detailed below. I'm not sure about you, but I wasn't surprised to hear the latest stats from Gartner suggesting that 25% of organisations will use Chatbots in their customer service by 2020. It's a large number when we consider the thousands of organisations that are yet to adopt this technology – but the take-up rate and activity in the marketplace would suggest we are well on our way. IBM states that 70% of consumers also prefer to message over calling, for customer support.

So many people were asking Alexa what song they just heard and who sang it that Amazon responded with a new Echo feature. Rolling out first to Amazon Music Unlimited subscribers and Prime Music listeners in the US, Song ID will tell you the title and artist of each song before it plays on your Echo device. That should cut down on the "hundreds of thousands" of music questions Alexa hears daily. Don't worry, Song ID is optional, and you'll be able to turn it on and off with a simple voice command. When you use the feature, Amazon hopes it will "aid in music discovery" and make the speaker more of a personal DJ.