The James Webb telescope will be the world biggest and most powerful telescope when it launches in 2018. Nasa describes it as a'time machine' that can peer back 200 million years after the Big Bang. This week, Nasa engineers in Maryland got a little closer to launch with the completion of testing on its science cameras and the installation of the final flight mirrors. NASA's James Webb Space Telescope completed primary mirror sits in the cleanroom at NASA Goddard Space Flight Center, and supported over it on the tripod is the secondary mirror After over a year of planning, nearly four months of final cold testing and monitoring, the testing on the science instruments module of the observatory was completed. They were removed from a giant thermal vacuum chamber at Nasa Goddard Space Flight Center in Greenbelt, Maryland called the Space Environment Simulator.

Global technology leaders and senior executives from around the world spoke on a range of topics, including Digital India, Smart Cities, Make in India, Skill India and cutting-edge technologies like artificial intelligence, machine learning, 3D printing, drones, robotics, robotic surgeries and genomics, at the two-day EmTech India 2016 event, held in New Delhi on 18 and 19 March. The event was organized by Mint and MIT Technology Review, published by the Massachusetts Institute of Technology (MIT). The speakers included R.S. Sharma, chairman of the Telecom Regulatory Authority of India; John Chambers, executive chairman of Cisco Systems Inc. and chairman of the US-India Business Council; Una-May O'Reilly, principal research scientist, AnyScale Learning For All Group, MIT Computer Science and Artificial Intelligence Laboratory; and Harsh Mariwala, chairman of Marico Ltd. The full list can be accessed here. Here are edited excerpts from their speeches and discussions that followed. John Chambers, executive chairman of Cisco Systems Inc and Chairman of US-India Business Council (USIBC), reiterated the reason for his bullishness on India in a chat with Mint's R. Sukumar, on the first day of EmTech India 2016. When most of us here read the India narrative, it is not uniformly positive. Yet, you are amazingly bullish on the country. What do you see that others don't? Sometimes when you see what is happening in other countries and other businesses around the world from the outside, you are able to gather data very quickly, and then you can connect the dots on the market transitions. I am very bullish on the country for that very simple reason--follow and connect the dots on transitions. The transition to digitization will be the biggest technology change ever. I don't go into a country unless the leader, he or she, really understands this. Second, I don't go to a country that does not have sustainable differentiation capabilities.

The ability to access and use financial services is critical to managing day-to-day life, weathering unexpected events, and capturing opportunities. Yet, some 46 percent of working-age adults in developing countries remain excluded from the formal financial system. It means they use the age-old informal mechanisms such as the moneylender, the pawnbroker, or the rotating savings club that can be unreliable and very expensive. In developed countries, working families are more likely to be under- or badly served rather than outright excluded. In the US, for example, every year some 25 million households use alternative services such as payday lenders or check cashers.

How will Practo cope up with network connectivity when Internet penetration in tier 3 cities is not high? How can telecom operators play a role in aiding remote healthcare? Today, it is true that mobile broadband has not been fully covered in our country, but we believe that India will get there soon. Today, we have around 30 percent of our traffic coming from tier 2 and 3 towns, so we can say that the Internet has reached some parts of these cities. From the B2B side, our product Practo Tab runs our entire Practo Ray software in offline mode; hence doctors and other healthcare providers can use it in this mode and then synchronize it back with the cloud once they get connectivity.

Awesome post, Ray! It's always encouraging to learn that one is not alone in thinking there's a Tsumani of change ahead. And that we need to try and tame the beast, or the beast will tame us. I was yesterday at a (fascinating) breakfast seminar with a very small group of business leaders here in Argentina. One of the founders of the Singularity University was leading the debate. And one of the topics we discussed at large was the consequence of AI automating jobs… everywhere, anywhere.

I don't know about you, but I haven't seen any dinosaurs lately. I mean, I've seen lots of birds, some lizards, and the occasional crocodile, but none of those massive Jurassic Park-style dinos. For paleontologists who want to know how a 60- to 70-ton dinosaur got around, this lack of subjects to study is a bit of an obstacle. At Drexel University, researchers are 3D printing small scale robotic models of the legs of one of the largest dinosaurs ever found to figure out how it was able to keep itself moving. Fossils of Dreadnoughtus schrani were discovered in Argentina in 2005.

Most commuters who travel by road will know the frustration of being caught in traffic jams that can double and even triple the journey to work. But a group of scientists claims to have found a way to ease congestion during the busiest periods, and cut commuting times by a third. However, not everyone will be happy with their solution as it involves some drivers agreeing to endure longer journeys. Scientists analysed billions of journeys made in five cities around the world during morning rush hours record on mobile phones. They found when drivers made selfish, uncoordinated choices, they made congestion worse (stock picture).

Digital expertise will become increasingly critical in the insurance sector as digitization and machine learning leads to more highly'automatable' insurance according to management consultants McKinsey & Company. Meanwhile a separate piece of research by Accenture found that insurance companies are accelerating the shift to a radically different distribution model, where they say digital will play an increasingly important role in most interactions, and were agents' efforts are being refocused to add more value. And analysis by research outfit Ovum suggests strong investment in digital channels also. According to Ovum, " When it comes to investment, digital channels remains the top area for insurers. However, the significant majority of insurers will be increasing budgets across a broad range of functional areas with no single activity completely dominating spend. This reflects the complex set of priorities that IT groups are being asked to meet by the wider business, simultaneously addressing revenue growth, operational efficiency and regulatory compliance."

We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data. Similar to Recurrent Neural Networks (RNNs), RGPs can have different formulations for their internal states, distinct inference methods and be extended with deep structures. In such context, we propose a novel deep RGP model whose autoregressive states are latent, thereby performing representation and dynamical learning simultaneously. To fully exploit the Bayesian nature of the RGP model we develop the Recurrent Variational Bayes (REVARB) framework, which enables efficient inference and strong regularization through coherent propagation of uncertainty across the RGP layers and states. We also introduce a RGP extension where variational parameters are greatly reduced by being reparametrized through RNN-based sequential recognition models. We apply our model to the tasks of nonlinear system identification and human motion modeling. The promising obtained results indicate that our RGP model maintains its highly flexibility while being able to avoid overfitting and being applicable even when larger datasets are not available.

Vector autoregression (VAR) is a fundamental tool for modeling the joint dynamics of multivariate time series. However, as the number of component series is increased, the VAR model quickly becomes overparameterized, making reliable estimation difficult and impeding its adoption as a forecasting tool in high dimensional settings. A number of authors have sought to address this issue by incorporating regularized approaches, such as the lasso, that impose sparse or low-rank structures on the estimated coefficient parameters of the VAR. More traditional approaches attempt to address overparameterization by selecting a low lag order, based on the assumption that dynamic dependence among components is short-range. However, these methods typically assume a single, universal lag order that applies across all components, unnecessarily constraining the dynamic relationship between the components and impeding forecast performance. The lasso-based approaches are more flexible but do not incorporate the notion of lag order selection. We propose a new class of regularized VAR models, called hierarchical vector autoregression (HVAR), that embed the notion of lag selection into a convex regularizer. The key convex modeling tool is a group lasso with nested groups which ensure the sparsity pattern of autoregressive lag coefficients honors the ordered structure inherent to VAR. We provide computationally efficient algorithms for solving HVAR problems that can be parallelized across the components. A simulation study shows the improved performance in forecasting and lag order selection over previous approaches, and a macroeconomic application further highlights forecasting improvements as well as the convenient, interpretable output of a HVAR model.