Everywhere I turn, I seem to encounter discussions on Machine Learning and Artificial Intelligence (AI). The Nasscom conference on Big Data and Analytics in June was heavily AI focused. The cover of last week's issue of The Economist reads "March of the Machines" with a special report on AI. Analytics websites are full of it. So why is the analytics community so upbeat about this technology?

Minecraft is one of those rare tech phenomenons that came out of the blue (a single person and a Java compiler in this case) and went on to somehow revolutionize its field. For the blocky building-survival simulator that field is definitely gaming, but by effectively redefining the sandbox genre, Minecraft has managed to affect an expected number of other fields as well. Ever since its humble beginnings and especially after Microsoft took over and allowed it to really take off in popularity, the Minecraft world has been a canvas for incredible creativity with projects ranging from epic 1:1 scale reconstruction of buildings to working PC emulators inside a Minecraft map and even a functioning phone. But it is perhaps Microsoft itself that has managed to find the most exciting use of the sandbox to date. It is called Project Malmo (formerly known an Project AIX) and its purpose is to experiment with and train artificial intelligence and advance cutting-edge technologies like machine learning and neural networks.

Despite being a good few decades old, the basis of the Turing test -- developed by the computer science genius Alan Turning -- remains the exact same to this day. It asks whether a computer can trick a human into believing they are speaking with another fellow human. In one part of the test, a human judge is asked to interact with two hidden objects -- one is a human and the other a machine -- to determine if they can distinguish between the two. And if not, that AI has passed the Turing test. There has been a lot of challengers who have claimed that AI has actually passed the test.

The race to build the world's first true quantum computer is on, with huge potential payoffs for businesses that harness the technology before their competitors. The computers we use today represent information in binary bits – on/off, 0/1 – while a quantum computer's qubit can, in simple terms, be both on and off the same time. That means many computations can be performed in parallel; a quality that, when fully realised, will give quantum computers a huge speed advantage over'classical' computers in solving certain problems. Microsoft and IBM are ploughing significant sums into related research. Google, NASA and Lockheed Martin have invested in a D-Wave 2X -- described by its maker as the "world's first commercially available quantum computer", although debate rages over its capabilities.

In this benchmark, I've used a Windows 10 Pro 64 Bit computer with Intel Core i7 6700HQ 2.60 GHz with 32 Gb RAM and NVIDIA GeForce GTX 960M. As a programming environment, I've used Python 2.7 (Anaconda distribution) and Jupyter. The code I've written is this (without matplotlib functions and float32 numbers, in order to use the GPU): So, Numpy is on average 300% slower than Theano (with GPU support). The spikes should be due to CPU overload, multitasking or memory swapping. However, it's absolutely clear that Theano (I'm going to test also Tensorflow) should be the best choice if you want to implement deep learning algorithms (in particular if you have a good GPU).

At the conference last week, there were tons of startups trying to get the attention of reporters and Venture Capitalists (VC's.) From what I saw, most software products centered around one five things, Communication Tools, Bots, Artificial Intelligence (AI), the "Gig" Economy, and Virtual Reality (VR). There used to be a hard line between tools that we used for our professional lives and tools that we used for our personal lives. That line is more blurred than ever. There is a crossover that allows us to use one device for all of our professional and personal needs.

June was kind to macro hedge funds. They were up more than 3 per cent, in what was their best month since 2010, according to data from Hedge Fund Research, buoyed by long positions on the yen and -- for the most fortunate -- long on both the yen and the dollar, and short sterling. Currencies and currency-related trades have been among the few bright spots for hedge funds recently (along with gold, which has risen more than 30 per cent since the end of 2015, according to Credit Suisse). Riding the yen and dollar up and taking on derivative trades, such as shorting Chinese companies that have lots of US dollar debt and dollar costs, have been among the few clear trends to follow. But generally, these are challenging times for the industry both in the short and longer term.

In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post.

Ed Felten, the deputy U.S. chief technology officer for the White House Office of Science and Technology Policy, says humans have two major responsibilities when it comes to the development and advancement of artificial intelligence. The first, he says, is "to make the benefits of A.I. a reality." The second: "to address the risks of A.I." Felten was speaking to a roomful of people at New York University's Skirball Center for the Performing Arts at AI Now -- a summer lecture series co-sponsored by the White House that sought to examine and discuss key issues related to the future of A.I. technology. A.I. is at a crossroads, AI Now co-chairs Kate Crawford (a researcher at Microsoft Research) and Meredith Whittaker (the founder and lead for Google Open Research), pointed out. Private and public sectors need to work together to create some sort of feasible A.I. policy.

I had a recent meeting with a person who was introduced to Machine Learning for the first time. It was interesting to know how someone totally new to the field would interpret what Machine Learning would be. He could instantly connect the term learning with what most Data Scientists would call Reinforcement Learning. A machine could observe phenomena and refine itself by itself is what he thought.It is weird that the one branch of Artificial Intelligence a layman could best connect to is the one least studied. I hope that Google Deepmind's recent work in Deep Reinforcement learning would change this.