Despite the tremendous progress we've made in the fields of robotics and artificial intelligence, building a robot that can convincingly emulate normal human behavior remains merely a fantasy at this stage. Some of them can speak and maybe even hold a conversation, but no robot comes close to being a real life'Ex Machina' yet. For the most part, the humanoid robots we've built so far come across as overly mechanical, unnervingly awkward and threateningly soulless… but somehow all of this creepiness makes them irresistibly fascinating. Tara the Android is perhaps the godmother of the robotic creepfest. While the video of this eerie singing mannequin was first uploaded back in 2009 and has since received over seven million views, little information is available about either Tara or her creator.

Time is the most valuable and finite asset in the world. Our weekly dose of "Monday Morsels" centre's on optimising our time to impact our lives and business. We have no intent on the theme, just purely lessons that can consistently be used. This week we cover quantum computing, how reality is not what it seems, what's on the other side of fear, and which artificial intelligence platforms will reign. Richard Feynman is undoubtedly the most famous physicist since Einstein and the man that first embedded my intrigue for science & technology.

This is the fifth in a series of reports to document development of a generalized method to create artificial neural networks (ANNs) via a genetic algorithm (GA). This report will be divided into several main sections. The goal of this report is to demonstrate the ability of an ANN to detect edges in images and use those edges to recognize letters. Reports #1-3 showed the development of a genetic algorithm that could evolve an artificial neural network given a set of input images. Report #3 focused on training an ANN to recognize patterns, specifically letters in an image.

Amazon is always on the lookout for new robotic technologies to improve efficiency in its warehouses, and this year deep learning appears to be leading the way. That's according to the results of the second annual Amazon Picking Challenge, which has been won by a joint team from the TU Delft Robotics Institute of the Netherlands and the company Delft Robotics. Amazon's 2016 event was held in conjunction with Robocup 2016 in Leipzig, Germany. Two parallel competitions took place: a Pick Task much like last year's, in which a mix of items has to be lifted from warehouse shelves and packed into a container; and a new "Stow Task," which involves taking items out of a tote and putting them onto the shelves. The Pick Task asked contestants to pick up and safely deposit 12 items from a mixed shelf into a container in the shortest possible time.

One million anonymised eye scans from Moorfields Eye Hospital will be used to train an artificial intelligence (AI) system from Google. Machine learning algorithms will scour the images for signs of diseases such as macular degeneration and diabetes-related sight loss. Moorfields is teaming up with Google's AI division DeepMind during the scheme. Previously, DeepMind faced criticism over a little-known data sharing agreement with three London hospitals. An agreement to share patient data from the Royal Free, Barnet and Chase Farm hospitals over the past five years and continuing until 2017 was revealed by the New Scientist in May. In that case, Google said it was analysing kidney data in the hope of developing an app for medical staff.

A scan of a human eye. SAN FRANCISCO -- Google plans to use more than one million anonymized eye scans to teach computers how to diagnose ocular disease. The Menlo Park, Calif.-based company has signed a deal with a British eye hospital to use artificial intelligence to learn from the medical records of 1.6 million patients in London hospitals. The goal is to teach a computer program to recognize the signs of two common types of eye disease, diabetic retinopathy and age-related macular degeneration. That's something humans are surprisingly imperfect at.

In June on /r/MachineLearning, there were free videos, free books, free courseware, and a quality curriculum made up of free offerings. The word of the month for June is clearly a four letter word starting with'F'. This lot of videos covers a wide range of topics, from general AI, to design issues, to cloud computing, to a variety of machine learning topics and beyond. Microsoft Research has added heavily to these offerings on what seems to be a daily basis since this Reddit post as well. Free knowledge from a top research institute in the field is always welcome.

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in? An intriguing post by MonkeyLearn attempts to answer this question by analyzing tweets and, as a bonus, provides tools that you might well find useful for similar exercises.

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code. Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.

To some, this sounds like the beginning of humanity's end: Scientists race to create innovative robots while researchers build out artificial-intelligence platforms and complex algorithms that many fear could soon make our jobs obsolete. But some experts believe our forthcoming high-tech offspring could actually be a golden ticket for the good life. Just imagine island hopping in Croatia while a robot, equipped with image-recognition software and natural language-processing abilities, fills in for you at the office -- with periodic check-ins through your pair of virtual reality glasses. Sure, we might be getting ahead of ourselves here, though experts predict that high-tech gains across corporate America will make seemingly mundane jobs more interesting, while enriching our near future and making some jobs safer by getting into dangerous spots that humans just shouldn't be entering. A wave of technological advancement that let many of us do things faster -- and more safely -- with fewer workers assisted in these gains, and Sprague says this kind of productivity surge is "the economic factor that has the potential to lead to improved living standards for an economy."