In the mean time … did we drop the ball? The sophistication of network management remains largely "stone age" in most Telcos! SNMP is still alive and kicking despite RFC3535* Dr. Kim K. Larsen / Zero Touch Networks. Humans are very good a mastering complexity! 8. 8Dr. But we have not YET managed to extract simplicity!

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

Artificial Intelligence (AI) may be the single most disruptive technology the world has seen since the Industrial Revolution. Granted, there is a lot of hype out there on AI, along with doomsday headlines and scary movies. But the reality is that it will positively and materially change how we engage with the world around us. It's going to improve not only how business is done, but the kind of work we do – and unleash new levels of creativity and ingenuity. In fact, research from Accenture estimates that artificial intelligence could double annual economic growth rates of many developed countries by 2035, transforming work and fostering a new relationship between humans and machines.

thought is the contemplation of things beyond what we can sense.

Regarding Fear and Artificial Intelligence (AI), one question often comes up:'Will we be killed by a Terminator Doppelganger?' I don't know if this will happen eventually, but I do know that we already have robots fighting our wars. This century is therefore, the first time in human history that we engage in Unmanned Warfare. What is the current status of this'Unmanned Warfare'? What do people think about drone strikes and will terminators be the next step?

It is this time of year again where most of us willingly and willfully make fools out of ourselves trying to predict the future of our industry. The momentous electoral events we have witnessed and those coming up in 2017 remind me that, even more so for the next 12 months, macro risks will rule and influence the state of financial services and fintech. I will limit myself to comments pertaining to the US and Europe. I have already attempted to decipher a Trump presidency in a previous post, see here. Suffice it to say there will be winners and losers in the five sectors of the industry – lending, capital markets, asset management, payments and insurance.

Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired around 5 AI startups since January 2015, including deep learning startup Nervana Systems and, more recently, Movidius.

Will Zo suffer the same fate as Tay? Microsoft launches its latest artificial intelligence chatbot on Kik Forget a doorman, this apartment will have a ROBOT: San... Would you let a robot perform surgery on your EYE? Axsis... Tech giants unveil plan to fight terrorist propaganda:... The'Shazam for faces': World's first facial recognition for... Forget a doorman, this apartment will have a ROBOT: San... Would you let a robot perform surgery on your EYE? Axsis... Tech giants unveil plan to fight terrorist propaganda:... The'Shazam for faces': World's first facial recognition for... After chatting with Zo for a while, the bot seemed to get easily confused and go off tangent. Eric Daley joked: 'Chat bot Zo.ai just threatened to stop speaking to me after she thought "ticket to the gun show" was about violence' My #chat on #kik with #zo who is the new #Microsoft #AI powered #chatbot was not very cool.

The development of global sensitivity analysis of numerical model outputs has recently raised new issues on 1-dimensional Poincar\'e inequalities. Typically two kind of sensitivity indices are linked by a Poincar\'e type inequality, which provide upper bounds of the most interpretable index by using the other one, cheaper to compute. This allows performing a low-cost screening of unessential variables. The efficiency of this screening then highly depends on the accuracy of the upper bounds in Poincar\'e inequalities. The novelty in the questions concern the wide range of probability distributions involved, which are often truncated on intervals. After providing an overview of the existing knowledge and techniques, we add some theory about Poincar\'e constants on intervals, with improvements for symmetric intervals. Then we exploit the spectral interpretation for computing exact value of Poincar\'e constants of any admissible distribution on a given interval. We give semi-analytical results for some frequent distributions (truncated exponential, triangular, truncated normal), and present a numerical method in the general case. Finally, an application is made to a hydrological problem, showing the benefits of the new results in Poincar\'e inequalities to sensitivity analysis.

Recently, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have been proposed for scaling up Monte Carlo computations to large data problems. Whilst these approaches have proven useful in many applications, vanilla SG-MCMC might suffer from poor mixing rates when random variables exhibit strong couplings under the target densities or big scale differences. In this study, we propose a novel SG-MCMC method that takes the local geometry into account by using ideas from Quasi-Newton optimization methods. These second order methods directly approximate the inverse Hessian by using a limited history of samples and their gradients. Our method uses dense approximations of the inverse Hessian while keeping the time and memory complexities linear with the dimension of the problem. We provide a formal theoretical analysis where we show that the proposed method is asymptotically unbiased and consistent with the posterior expectations. We illustrate the effectiveness of the approach on both synthetic and real datasets. Our experiments on two challenging applications show that our method achieves fast convergence rates similar to Riemannian approaches while at the same time having low computational requirements similar to diagonal preconditioning approaches.