The technology industry is plagued with buzzword bingo in support of the fashion driven nature of the technology beast. Often confusing and occasionally downright ridiculous, we're never going to prevent smart ass marketers, ably supported by their anal-yst surrogates from making stuff up. The least some of us can do is make clear what is under discussion without mindlessly parroting what others say or conflating one concept with another. The latest in this stream of marketing laden garbage is AI or Artificial Intelligence, smeared with ML or Machine Learning and DL or Deep Learning. Add a soupçon of'robotics' just to amp the volume to something people can'get' and you have the potential for an exotic mix that both captivates the sentient mind but can also plant fear.

In much the same way children have to be told not only what they are saying is wrong, but why it is wrong, artificial intelligence (AI) systems need to be able to receive and act on similar feedback, according to Monash University Department of Electrical and Computer Systems Engineering Head Professor Tom Drummond. "I think fundamentally we need ways of giving machine learning systems more feedback than'yes' or'no'," Drummond told journalists at the GTCx Australia conference on Tuesday. "Rich feedback is important in human education, I think probably we're going to see the rise of machine teaching as an important field -- how do we design systems so that they can take rich feedback and we can have a dialogue about what the system has learnt?" To illustrate his point, Drummond used the example of a system that was able recognise and caption images of fire hydrants, and appeared to be working well until one of the images was modified. "You take Photoshop, and you colour it green, and it says: 'A red fire hydrant'."

Well this tutorial has been a long time coming. Neural Networks (NNs) are something that i'm interested in and also a technique that gets mentioned a lot in movies and by pseudo-geeks when referring to AI in general. They are made out to be these really intense and complicated systems when in fact they are nothing more than a simple input output machine (well at least for the standard Feed Forward Neural Networks (FFNN)). As with any field the more you delve into it the more technical it gets and NNs are the same, the more research you do into them the more complicated architectures, training techniques, activation functions become. For now this is just a simple primer into NNs.

The omnipotent mythology surrounding artificial intelligence and machine learning, particularly as it relates to investing and trading, is the real bubble that needs to be burst. Such are messages from two of the quantitative trading world's brightest minds. Speaking at the Bloomberg Markets Most Influential Summit Wednesday, David Siegel, founder of the 37 billion Two Sigma Investments, warned the audience that artificial intelligence lacks common sense. Seven days ago Ewan Kirk, founder of the 4.5 billion Cantab Capital Partners and an original managed futures CTA pioneer, penned an influential piece in Institutional Investor titled "Beneath the Sizzle of Artificial Intelligence." His advice to investors was clear: buy the steak, not the sizzle.

The excitement around artificial intelligence (AI) has created a dynamic where perception and reality are at odds: everyone assumes that everyone else is already using it, yet relatively few people have personal experience with it, and it's almost certain that no one is using it very well. This is AI's third cycle in a long history of hype – the first conference on AI took place 60 years ago this year – but what is better described as "machine learning" is still very young when it comes to how organisations implement it. While we all encounter machine learning whenever we use autocorrect, Siri, Spotify and Google, the vast majority of businesses are yet to grasp its promise, particularly when it comes to practically adding value in supporting internal decision making. Over the last few months I've been asking a wide range of leaders of large and small companies how and why they are using machine learning within their organisations. By exposing the areas of confusion, concerns and different approaches business leaders are taking, these conversations highlight five interesting lessons.

Artificial Intelligence (AI) has always been the subject of dreams and visions about the distant future of humankind. Even though we are nowhere near a conscious robotic system, nowadays, AI systems are ubiquitous and showing tremendous successes in various fields of our everyday life. We are using these on a daily basis, often without even noticing. Whether it is the Virtual Personal Assistants on our mobile phones (such as Siri, Google Now, and Cortana), self-driving cars, the ranking of the web pages given your search query, or the classical textbook examples such as spam filtering and recommendation systems of online media providers and marketplaces like Amazon. Various fields of AI have made a major leap forward in the recent years. As most AI systems are too complex to be defined manually, we have to resort to automatically learning rules and patterns from data using sophisticated Machine Learning (ML) techniques.

It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or, AI as it's commonly referred to), is an area of computer science where computers are "developed" to behave much the way humans do.

Despite its old fashion, Victorian era style the Frankenstein is still worth reading and studying at the light of today's progress and madness in artificial intelligence (AI). I read Frankenstein the same summer I discovered Asimov's Robots Cycle, and I can't help but relate the 2 centuries old novel to The Naked Sun. If you had to read only one of Asimov's Robots novel, pick up The Naked Sun. It revolves with a paradox in Asimov's 3 Laws of Robotics where a robot ends killing a human. Did you ever notice how we often call Frankenstein's creature by the name of its creator?

Google took on rivals Apple, Samsung and Amazon in a new push into hardware Tuesday, launching premium-priced Pixel smartphones and a slew of other devices showcasing artificial intelligence prowess. The unveiling of Google's in-house designed phone came as part of an expanded hardware move by the US company, which also revealed details about its new "home assistant" virtual reality headset and Wi-Fi router system. The San Francisco event marked a shift in strategy for Google, which is undertaking a major drive to make Google Assistant artificial intelligence a futuristic force spanning all kinds of internet-linked devices. "We are evolving from a mobile-first world to an AI-first world," Google chief executive Sundar Pichai said. "Our goal is to build a personal Google for each and every user."

This episode includes some amazing stories from some brilliant guests all talking about technology and its impact in our modern world. First you hear from Jim Hendler. Jim is an artificial intelligence researcher at Rensselaer Polytechnic Institute, United States, and one of the originators of the Semantic Web. He talks to us about AI and privacy issues on the web. He has a new book out soon so be sure to get it here.