Please stand in front of Walklake for your examination. This health checking robot takes just 3 seconds to diagnose a variety of ailments in children, including conjunctivitis, and hand, foot and mouth disease. Over 2000 preschools in China, with children aged between 2 and 6, are using Walklake every morning to check the health status of their students. Walklake has a boxy body and smiling cartoony face.

As a digital artist and experimental games designer, I was one of the first in line to dive into Dreams – a PlayStation 4 game that aims to give everyone the ability to unlock the potential artist within – when the developer Media Molecule opened up limited early access in April. From the breadth of its artistic toolset to the community of creators it is enabling, Dreams feels like the start of a genuine revolution in accessible, creative play. The promise was that Dreams would represent a space where almost anything is possible, and Media Molecule has somehow got closer than I ever imagined. Dreams is hard to sum up succinctly, but it sits somewhere at the intersection of art studio, game engine and vibrant creative community hub. Almost the first thing new players see is a fun video of the development staff, smiling together in their office and holding up handmade "Welcome" signs.

When women are over-represented in the workforce, it tends be in industries of assistance – cleaning, nursing, secretarial work and, now, the world of virtual assistants. Research by Unesco has shown that using default female voices in AI – as Microsoft has done with Cortana, Amazon with Alexa, Google with Google Assistant and Apple with Siri – is furthering the belief that women exist merely to help men to get on with more important things. There is no real reason for AI technologies to be gendered at all, but we are at the mercy of tech companies "staffed by overwhelmingly male engineering teams", fixated on living out a Captain Kirk fantasy and delegating to the subservient, silky-voiced computers of Star Trek. These systems are unapologetically built by men, for men. They can even struggle to understand the "breathy" voices of women as software is often developed with male voice samples.

A growing backlash against face recognition suggests the technology has a reached a crucial tipping point, as battles over its use are erupting on numerous fronts. Face-tracking cameras have been trialled in public by at least three UK police forces in the last four years. A court case against one force, South Wales Police, began earlier this week, backed by human rights group Liberty. Ed Bridges, an office worker from Cardiff whose image was captured during a test in 2017, says the technology is an unlawful violation of privacy, an accusation the police force denies. Avoiding the camera's gaze has got others in trouble.

Enterprises are increasingly adopting Spark for tasks ranging from ingestion, ETL, and data processing to advanced analytics and machine learning. But despite its growing popularity, Apache Spark is complex and the learning curve is steep. Data-driven enterprises can now rely on a low-code solution that provides an alternative to time-consuming and tedious manual programming. Find out how StreamAnalytix provides a practical and viable alternative to the complexities of building enterprise-grade Spark applications.

Regardless of the metric you decide to optimize, it's necessary to establish a baseline measure of performance. This baseline provides a point of comparison that enables you to track your progress. It also allows you to judge the rate of return you'll get by increasing the complexity of your modeling solution. Suppose you work for a real estate firm and are asked to build a model to predict the price of a house. You decide to optimize for RMSE and build a linear regression model with features including the square footage of the house, the number of bedrooms and bathrooms, and other information.

Artificial Intelligence or AI has already reached citizen scale making its mark in everyday applications such as healthcare, e-commerce, automobiles, financial services, defense etcetera. In layman terms, it is yet another zone of computer science that concentrates on the creation of intelligent machines that do the work of humans and indeed act like them. Prameya DS holds the best artificial intelligence training in Hyderabad with a complete collection of advanced cognitive science, various level of mathematical concepts such as probability, calculus, algorithms, statistics, and multiple programming languages and coding. Since Artificial Intelligence is to perform the logical tasks without the presence of a human, a lucrative Artificial Intelligence Course in Hyderabad must be needed to learn the nitty-gritty details and Prameya DS produced it for you. Our program starts with the essential languages - Python and R followed by a detailed survey of Mathematical and Statistical concepts.

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I get asked many times "How can I do a good Exploratory Data Analysis (EDA) so that I get the necessary information for feature engineering and building machine learning model?" In this and the next post, I hope to get the question answered. I will NOT claim my process is the best but I hope as more people come into the field, they can use my process as a basis for better EDA and build better models. There are two main benefits of doing EDA and these benefits will reap benefits through the model building process. I will discuss EDA in two posts, non-visual (mainly through simple calculations) and visual.

Machine learning researchers have produced a system that can recreate lifelike motion from just a single frame of a person's face, opening up the possibility of animating not just photos but also paintings. It's not perfect, but when it works, it is -- like much AI work these days -- eerie and fascinating. The model is documented in a paper published by Samsung AI Center, which you can read here on Arxiv. It's a new method of applying facial landmarks on a source face -- any talking head will do -- to the facial data of a target face, making the target face do what the source face does. This in itself isn't new -- it's part of the whole synthetic imagery issue confronting the AI world right now (we had an interesting discussion about this recently at our Robotics AI event in Berkeley).