If you haven't hopped aboard the smart speaker train, now is as good a time as any, as there are two relatively inexpensive ones on the market today--the Google Home Mini and the Amazon Echo Dot. If you're looking for a device that can play music, give you the weather, and operate your smart gadgets, you may have been eyeballing these small-but-mighty smart speakers. The question is, though, which one is better? How do the features compare? There are distinct differences in how each speaker--the Echo Dot, left, and Google Home Mini--lights up when given a command.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Think of all the things a microphone can pick up: voices, noises, whispers, conversations, arguments, confessions – even people alone, in a room, mumbling to themselves. Think of all the things you say in a private space, all the weird things you do. Once those sounds have been saved, that data can be stored, edited, and manipulated and shared.

Machine Learning (ML), the subset of artificial intelligence, is gaining fresh momentum. Powerful and affordable computational processing, growing volumes of huge data sets, affordable data storage options and the ability to automatically apply complex mathematical calculations to big data faster than before are the factors responsible for the resurging interest. ML is basically the idea of training machines to recognize patterns in data and apply them to particular problems. The iterative aspect of machine learning is important because when models are exposed to new data, they are able to independently adapt. They learn from previous computations and predictions to produce reliable, repeatable decisions and results with minimal or no human intervention.

In the age of a big data and breathtaking advances of artificial intelligence, social infrastructure promotes digital engagement and active presence. Digital democracy propagates the participation of a growing number of users to interact with institutions and services, ensuring that decisions made by AI-powered digital tools reflect human values. Immersed in automation, many choices we make include some form of a computationally-modeled process. This transformation from manual to programmed behavior has started with the introduction of recommendation systems to find similar products according to users' preferences. However, today's AI systems go beyond imposing suggestions and know pretty well what we do and what we want.

The robot showed up at Kenneth Williams' doorstep when he needed it most. Williams had just been laid off from his job when he plugged in Jibo, a social home robot, on November 1st, 2017. "For that year [that I didn't have a job], it was a presence in my life every single day that I talked to," he says. Jibo sat in Williams' bedroom, on his desk, where every day, it greeted him in the morning and ran through the weather and his calendar. Williams, 44, asked Jibo questions, requested music, and played its games. Jibo couldn't do much, really, but its most redeeming feature, the one that cemented it as a robot darling in its owner's heart, was its facial recognition.

The research was led by Justin Chan, a PhD student in the department of computer science and engineering. Almost 500,000 Americans die each year from a cardiac arrest, the researchers wrote in the journal npj Digital Medicine. And the condition kills 100,000 Britons annually, according to Arrhythmia Alliance. Study author Dr Jacob Sunshine, assistant professor of anesthesiology and pain medicine, said: 'Cardiac arrests are a very common way for people to die and right now many of them can go unwitnessed. 'Part of what makes this technology so compelling is that it could help us catch more patients in time for them to be treated.'

Artificial intelligence (AI), in the simplest terms, refers to systems or machines that mimic human intelligence to perform tasks and can interactively improve themselves, based on the information they collect. Today, when asked about AI, many people still hink that it is a concept, something that is going to happen tomorrow. But organisations in every sector are already embracing and investing in AI tools and techniques to boost their business. In reality, AI is already part of our lives. We use AI every day. It is not only on smartphones, laptops and cars, it is everywhere.

However, they soon find out they have neither the time nor stamina for such a feat. Faced with this realization, in 2017 most of them turn to the idea of hiring a virtual assistant to help them out. In most cases, we are talking about a self-employed remote worker who assumes the role of your personal assistant. The advantages of this are numerous. First of all, you save money by not hiring a full-time employee. Second, they don't need an office.

What do Russian trolls, Facebook, and US elections have to do with machine learning? Recommendation engines are at the heart of the central feedback loop of social networks and the user-generated content (UGC) they create. Users join the network and are recommended users and content with which to engage. Recommendation engines can be gamed because they amplify the effects of thought bubbles. The 2016 US presidential election showed how important it is to understand how recommendation engines work and the limitations and strengths they offer.

Here's a great talk from Build 2019 about the importance of design in creating Voice and chat virtual assistants. Designing a natural language interface can be difficult, is the interface supposed to be able to interpret every single nuance of speech? Or should we aim more towards forced language and make our users learn how to interact with simple commands? All the big companies are making huge investments in AI personal assistants. Amazon has Alexa, Google has Google assistant, Apple has Siri and Microsoft has Cortana to name a few.