These days, it seems that nearly everything is a smart product. We have smart boards in K-12 classrooms, smart watches, and even smart refrigerators. You can immediately tell that manufacturers love to use this misguided descriptor whenever they integrate modern technology, like touch screens or internet connectivity, to a previously existing product. Do products like these deserve this term? What exactly makes them smart?

Automation and algorithms already play a big role in the media industry. Programmatic advertising, algorithm-operated front pages and machine translation are some of the ways automation has infiltrated the news desk. So-called "robot journalists" are another blooming example of automation in the newsroom. The name is a bit disingenuous, since it's not really a robot, nor really a journalist, but an algorithm. By the use of Natural Language Generation (NLG), the algorithm generates text from a pool of data, and writes the text based on a set of predefined rules and templates.

The wildebeest, hippos, gazelles and warthogs of Southern Kenya have a little less to worry about. Wildlife advocates began testing an advanced system this spring to better identify and apprehend poachers. They say the technology has led to dozens of arrests that wouldn't have happened otherwise. Brian Heath, chief executive of the Mara Conservancy in Kenya, believes expanding the use of the technology throughout Africa could significantly improve anti-poaching efforts. "Our rangers now feel completely disadvantaged and blind without it," Heath said. "They get a huge amount of reassurance by having it and the ability to [better] see and identify people and animals."

The many and old systems of record: Some banks are making major investments (in the billions) in modernizing and federating systems of record to unlock the extraordinary customer data value – giving us a glimpse of what leaders are doing (and what fast followers and others will need to contemplate) to respond to customer dynamics. New systems of insight: Some banks are making early investments in artificial intelligence and machine learning to intelligently harvest and employ the treasure trove of customer transactional and behavioral data. These banks realize gains now and put in place systems that continuously learn and improve experiences. New systems of engagement: Banks are faced with a diminishing relationship equity that threatens growth, but the answer is not more human touchpoints for customers increasingly seeking digital engagement. Rather, leveraging investments in systems of record and systems of insight, banks will implement intelligent, predictive services that can deliver just-in-time value to customers – whether that is video chat, conversational interfaces, or intelligent agents – that exploit technology to help form more valuable, stickier relationships.

A few weeks ago I posted a map of the Artificial Intelligence landscape in Ireland. The map is a visual representation of resident Irish companies in the A.I. space. I decided to go a step further and put together an overview of the Irish Artificial Intelligence industry. I used the companies on the map to form the basis of the findings covered in this blog. I use the phrase'Artificial Intelligence' as an umbrella term to categorise the industry.

Computer algorithms can automatically interpret echocardiographic images and distinguish between pathological hypertrophic cardiomyopathy (HCM) and physiological changes in athletes' hearts, according to research from the Icahn School of Medicine at Mount Sinai (ISMMS), published online yesterday in the Journal of the American College of Cardiology. HCM is a disease in which a portion of the myocardium enlarges, creating functional impairment of the heart. It is the leading cause of sudden death in young athletes. Diagnosing HCM is challenging since athletes can present with physiological hypertrophy, in which their hearts appear large, but do not feature the pathological abnormality of HCM. The current standard of care requires precise phenotyping of the two similar conditions by a highly trained cardiologist.

The Amazon Echo is remarkably useful. Alexa, the digital personal assistant within the cylindrical black gadget, plays music, helps with recipes, and orders stuff online. One thing it cannot do, however, is speak Chinese. The name may sound funny to you, but this gadget is no joke. It could introduce millions of people to the power of a voice-activated, cloud-based smart home speaker.

To protect your devices, security experts often recommend having a backup method of authentication – but, they probably didn't mean your pet hedgehog. In an adorable new video, a hedgehog named Sashimi can be seen using her tiny paw to unlock an iPhone, successfully passing through the TouchID sensor. It works just like it would for a person, requiring the print be pressed multiple times onto the home button in order to capture its unique design. The Instagram-star Sashimi, of course, does this with some assistance from a human. Though adorable, the video also raises questions on the security of this feature.

In our previous blog posts of this series, we have introduced Topic Models, BigML's latest resource that helps you find thematically related terms in your unstructured text data, explained how to use it through the BigML Dashboard and the API, and lastly showed how to apply Topic Models in a real-life use case. This post will focus on automating LDA workflows by using WhizzML, a DSL for Machine Learning that provides programmatic support for all the resources you work with in our platform. Let's dive in by creating a Topic Model and making a prediction with it. In BigML, you can perform single instance predictions (referred to as a Topic Distribution) or in batch mode, which is called Batch Topic Distribution. Firstly, we will create a Topic Model without specifying any particular configuration option, that is, relying on default settings.

Food delivery marketplace Just Eat has continued its innovation push this week with a showcase of some of latest foodtech innovations at an event in Shoreditch. The London-based business held its'The Future Now - Redefining Food Discovery' event at Village Underground in Shoreditch today (Tuesday), showing off a raft of innovations including virtual and augmented reality innovations, AI chatbots and self-driving delivery robots. Technology on display included harnessing VR to feed into analytics and research for Just Eat's restaurant partners to utilising Microsoft's Hololens technology to display menu options as a buffet from which customers are able to choose their order. Elsewhere, AI also made its presence felt with Just Eat's customer care and Facebook chatbots providing a conversational approach to customer support and recommending restaurant choices depending on a user's mood and preferences. Just Eat has been on a serious innovation push this year, launching its own foodtech accelerator at the beginning of August and trialling Starship Technologies' self-driving delivery robots, which were also on show at the event.