Representation & Reasoning

Amazon Echo Show Launching With Alexa Support, Touchscreen, Smart Camera Support June 28

International Business Times

Amazon announced its first touchscreen smart speaker, the Amazon Echo Show in May. The device will cost $229.99 Just like other products from the Echo range, the Echo Show will have artificial–intelligence based voice command support from the company's Alexa voice assistant. This feature makes it capable of multiple functionalities, including letting it function as an intercom and even letting users make hands-free calls to others by simply giving a voice command. Smart camera connectivity: The speaker can be connected to other smart cameras and show you a feed from them.

Half of smart household gadgets vulnerable to hackers

Daily Mail

From devices that order our groceries to smart toys that speak to our children, high-tech home gadgets are no longer the stuff of science fiction. But even as they transform our lives, they put families at risk from criminal hackers taking advantage of security flaws to gain virtual access to homes, a report warns. The team then used the toy pets, which cost as little as £5.99 each, to send commands to the Amazon Echo home hub, using its'voice purchasing' system to order cat food from the online retailer.

Amazon Fire 7 tablet review: still a lot of tablet for just £50

The Guardian

It looks quite different to the traditional Android experience from Google, lacks Google apps and only has access to the Amazon App Store, not the Google Play Store. Navigating it is easy with clearly marked panes filled with either apps, games, books, video, music, magazines, audio books etc. The jewel in the crown for Fire OS 5.4 is Alexa – Amazon's voice-enabled smart digital assistant. It's the same Alexa that's found in the company's Fire TV and Echo smart speaker devices, and has access to the same information.

Google's research chief questions value of 'Explainable AI'


Despite being used to make life-altering decisions from medical diagnoses to loan limits, the inner workings of various machine learning architectures – including deep learning, neural networks and probabilistic graphical models – are incredibly complex and increasingly opaque. Just as humans worked to make sense and explain their actions after the fact, a similar method could be adopted in AI, Norvig explained. "So we might end up being in the same place with machine learning where we train one system to get an answer and then we train another system to say – given the input of this first system, now it's your job to generate an explanation." Besides, Norvig added yesterday: "Explanations alone aren't enough, we need other ways of monitoring the decision making process."

Numbers war: How Bayesian vs frequentist statistics influence AI


In other words, infected people test positive 99 per cent of the time and healthy people test negative 99 per cent of the time. We also need a figure for the prevalence of the infection in the population; if we don't know it, we can start by guessing that half of the population is infected and half is healthy. But this line of reasoning ignores the fact that 1 per cent of the healthy people will test positive and, as the proportion of healthy people increases, the number of those healthy people who test as positive begins to overwhelm those who are infected and also test positive. In slightly more formal terms we would say that the number of false positives (healthy people being misdiagnosed) begins to overwhelm the true positives (infected people testing positive).

Machine Learning: A New Potential in Customer Service


Similar conveniences have made their way into customer service via machine learning. The difference is that while the aforementioned machine learning examples learn and adapt to your daily routines, AI tools for customer service focus more on the customer's journey and the workflows of support staffs. We've highlighted some of the potential innovations coming to customer service by way of machine learning: Effective machine learning tools could have the same impact as a personal assistant, one that's infallible when it comes to routine tasks. Most people think of chatbots when it comes to machine learning in customer service (as they should, since they're all the rage at the moment), but don't forget about your other support channels.

Digital Marketing Tips For Small Businesses 2015 - Booming


Today, Businesses Have More Ways – And Places – Than Ever To Market Themselves.Your Local Digital Marketing Strategy Should Specifically Target And Appeal To Potential Customers In Your Geographic Area. Many Local Companies Have Used Some Form Of Digital Marketing Online Even If They Are Not Aware Of It.This Is An Important Local Digital Marketing Tip For Any Business. It's Also Important That You Get Your Local Seo Strategy Right So Your Business Scores A Consistently High Rank On Local Search Engine Results Pages.So Make Sure You Include Your Location Information In Keywords. NOTE: Local Digital Marketing Strategy You Choose For Your Local Business, It's Important To Track Your Progress And Find Out What Is Working And What Isn't.Remember Creating Content That Is Relevant To Your Business And Making It Searchable Is Key.

How Does the Random Forest Algorithm Work in Machine Learning


In decision tree algorithm calculating these nodes and forming the rules will happen using the information gain and gini index calculations. In random forest algorithm, Instead of using information gain or gini index for calculating the root node, the process of finding the root node and splitting the feature nodes will happen randomly. In the above Mady trip planning, two main interesting algorithms decision tree algorithm and random forest algorithm used. First, let's begin with random forest creation pseudocode The beginning of random forest algorithm starts with randomly selecting "k" features out of total "m" features.

ABI Research Forecasts Almost One Million Businesses Worldwide Will Adopt AI Technologies by 2022 - AI Trends


"Many businesses will have to adapt their corporate governance policies to deal with the lack of a guaranteed outcome when implementing machine learning. While most enterprises start using machine learning to analyze their existing business for insights, the technologies have far-reaching application in specific industries, ranging from reduction of false positives in fraud detection to powering conversational interfaces for chatbots and virtual assistants." While some of the world's largest and innovative enterprises, such as Amazon, American Express, Citrix, Coca Cola, Facebook, Google, Netflix, PayPal, and Uber, already deploy projects powered by machine learning, ABI Research finds that not all will benefit. On the other hand, companies that focus only on ROI timetables will find emerging technologies, including machine learning, cybersecurity, and IoT, to be frustrating to implement and difficult to measure.

Agencies Are a Step Closer to Creating a Their Own Siri


The General Services Administration recently wrapped a pilot that walked federal agencies through the process of building chatbots and other intelligent personal assistants similar to Apple's Siri and Amazon's Alexa. But prototypes weren't the point of the pilot, GSA's Emerging Citizen Technology Office lead Justin Herman told Nextgov--instead, it was to help agencies understand what they'd need before they can fully deploy intelligent personal assistants. It's not yet clear where in each federal agency responsibility for creating intelligent personal assistants falls, Herman told Nextgov. In the hackathon culminating the workshop, out of which agencies' prototypes emerged, federal employees worked alongside representatives from Google, Amazon, Microsoft and Oracle.