Today machine learning is centre stage in the world of technology, and thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains. However, making effective use of machine learning in practice can be daunting, especially for newcomers to the field. Over the last five decades, researchers have created literally thousands of machine learning algorithms. Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, often constrained those algorithms they happen to be familiar with, or by the availability of software implementations. In this talk we view machine learning from a fresh perspective which we call'model-based machine learning', in which a bespoke solution is formulated for each new application.

Following the death by bomb-armed-robot of the suspect in last week's cop killings in Dallas, which apparently involved an improvised setup that the local police department's robot wasn't built to be used for, a public outcry over police access to high tech weaponry has erupted. "The Dallas Police Department's unprecedented use of an explosive-laden robot to kill an armed suspect ushers in a new phase in the militarization of U.S. police departments," reports the Los Angeles Times. The article goes on to point out that there have been similar uses of modified machines originally built as anti-bomb robots, particularly in the military. But Dallas is certainly the most high profile domestic policing use so far, and experts say it could have lasting implications. "If lethally equipped robots can be used in this situation, when else can they be used?"

While everyone keeps saying that robots are not a job security threat, it is also true that robots are steadily getting better at tasks. Enter this little bad boy, the robot that won Amazon's Picking Challenge. A team of engineers from Netherland's TU Del have won this year's challenge, both in the picking and stowing finals. They dubbed their creation "Delft." The cool thing is their robot is no ordinary warehouse bot; it relies on a suction cup, a "two-fingered" gripper, and the combination of deep learning artificial intelligence and depth-sensing cameras to get the job done.

Years ago, I was working on a project that needed to be adaptive. Essentially, the software needed to learn and get better at a frequently repeated task over time. I'd read about neural networks and some early success people had achieved with them, so I decided to try it out myself. That marked the beginning of a life-long fascination with AI. AI is a really big deal.

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.