With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense. One problem with drawing them as node maps: it doesn't really show how they're used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they "remember". I should add that this overview is in no way clarifying how each of the different node types work internally (but that's a topic for another day).

The decapitation of the robot named hitchBOT has offered greater insight into social robotics. More than 100 million robots with social skills will populate the planet by 2020, according to a research study by Tractica. While machine-to-machine communication is based on tested principles, the growing study of social interaction between humans and robots draws on developments in our understanding of empathy. What will our relationship be like with these domestic robots which are about to invade our day-to-day lives? "We can compare it to our relationship with pets, which is complex, emotional and relatively thankless," My trip must come to an end for now, but my love for humans will never fade.

There has been a dramatic increase in work in the last five or six years, coming from the exponential data explosion and an increase in audit regulation bureaucracy,

The issue of robotics, artificial intelligence, and their impact on companies, economies, and society as a whole is one in which S&P Global takes great interest. I attended a recent forum on the issue sponsored by the Council on Foreign Relations in New York, and published this overview on S&P Global's Global Credit Portal. Robots aren't here to take your job. Not yet, anyway, according to experts in the field of robotics and artificial intelligence (AI) who spoke at a conference sponsored by the Council on Foreign Relations in New York. Robotics and AI have tremendous disruptive potential and positive economic contributions to make, but the elimination of vast numbers of jobs as a result--in a relatively short time--may not be on the immediate horizon, speakers at the Nov. 14 session said. Inevitably, such a discussion turns quickly from the broad concepts of AI and robotics to a more specific application: autonomous cars.

Through machine learning, researchers have repeated the historic criminology experiment of telling criminals apart from law-abiding people using facial recognition. Physiognomy, the ability to judge a person's character from appearance alone, has been around since ancient Greece and was widely accepted by philosophers. Although the theory has generally been disbanded, studies still crop up now and again. Xiaolin Wu and Xi Zhang, Chinese researchers from Shanghai Jiao Tong University, released a controversial paper on arXiv, an online open-sourced pre-print journal – it has not been published officially. They have singled out three features that can supposedly tell if a person is more likely to be a delinquent or not by probing upper lip curvature, eye inner corner distance, and the angle from nose tip to two mouth corners (nose-mouth angle). It's bad news for those who have smaller mouths, curvier upper lips and closer-set eyes, as you look more like a crook, apparently.

Predicting the future is hard. It's nearly impossible to know what technological marvels await in the next few years, let alone the next eight decades. Undaunted, we've put together a list of 10 super-advanced technologies that should be around by the year 2100. Some of these technologies are rather "out there," but I'm reasonably confident in making these predictions. As radical as some of the items described here appear, most--if not all--should be around by the turn of the 22nd century.

This month, Portugal is descended upon not by tourists in search of sunshine but by some estimated 55,000 tech-savvy individuals to attend an event quoted by Bloomberg as'Davos for Geeks'. I am of course talking about Web-Summit. For those unknown about Web-Summit, it's Europe's largest Technology Marketplace and invites companies within the tech industry to share their knowledge, and inspire through talks on how to solve some of the world's most pressing issues through new cutting edge systems and intelligent innovation. Whilst watching these talks one message resonated louder and clearer than all others – the future of technology is accelerating at an alarming rate, leaving all others who do not keep up the pace by the wayside. This message didn't, however, alarm me but rather triggered hope that we are advancing towards a technological level where we can tackle vital, real world issues such as: Climate change – our planet is warming causing problems such as rising sea levels and the first ever climate refugees.

Machine learning is the new game changer in business technology. In a world where digital information volumes are doubling every two years on average, machine learning allows organizations to extract highly valuable information from enormous data stores at heretofore unimaginable speeds. Building and deploying machine learning solutions can be expensive, requiring investment in servers and storage, expanded networks, and data scientists. Alternatively, companies can invest in none of the above and turn to one of the many new machine learning as-a-service solutions. Getting started with machine learning in this way basically requires what virtually every organization is awash in today: data.

On 20 November, 1945 the Nuremberg trials began - the military tribunals called to prosecute Nazi war criminals closely involved in the Holocaust. Now, 71 years later, that work continues through the Bavarian State criminal office (LKA) in Munich, that has created a virtual reality version of the Auschwitz concentration camp to assist with the continued prosecutions. Digital imaging expert Ralf Breker is behind the project: "We spent five days in Auschwitz taking laser scans of the buildings and the whole project to complete took about six months." About 1.1 million people, mostly Jews, were killed at Auschwitz, most deceived into entering gas chambers where cyanide-based pesticide Zyklon B was released, killing those inside. Their bodies were then burned in the camp's many crematoria.

IBM launched a series of Watson technologies to incorporate machine learning, a data platform, conversational tools called Virtual agent, more integration with MobileFirst for iOS apps and education efforts to boost the cognitive computing ecosystem. The barrage of announcements comes as IBM hosts a Watson conference in Las Vegas. IBM CEO Ginny Rometty will use a keynote speech to outline the Watson portfolio, ecosystem and customer base. Here's what you need to know. There are some things that machines are simply better at doing than humans, but humans still have plenty going for them.