From flatulent dinosaurs to ones that can pee, a new dinosaur exhibit in Canada is causing quite a stir. It includes twenty dinosaur skeletons and robots, up to more than 60 feet (18 meters) in length. The new exhibit at the Manitoba Museum presents a touring collection of some of the world's largest dinosaurs. The World's Giant Dinosaurs exhibit opened at the Manitoba Museum on Thursday, featuring a robotic velociraptor named Vicky the Velociraptor The World's Giant Dinosaurs exhibit opened on Thursday morning, with the exhibit's creator'Dino' Don Lessem in attendance. 'We want to give the impression of every aspect of dinosaur life,' Lessem told CBC News, also mentioning that there's discussion in the science community that dinosaur flatulence may have led to global warming.

In the previous post, I talked about networks which can take datasets rather than data points as input, using pooling over the data to assemble a representation of the task which is then used to classify the test set. This satisfies some of our goals of making networks which learn how to learn continually – as the size of the training dataset is increased, the variance of the network's representation of the task will tend to decrease and the network's performance on the task will improve. However, there still seems to be a gap between this kind of behavior and the sort of generality possessed by algorithms like backprop (which is broad enough to include things like learning how to learn). The issue seems to be that once the networks have learned to parameterize the ensemble of tasks they're given, what's left is just a fixed-dimension parametric inference problem. The ultimate complexity of what the network can do is pretty strongly constrained by the complexity of the representation layer and the task ensemble during training via gradient descent.

Organizations like Insight Data science founded by Jake Klamka is specifically designed for helping PhD's transition into industry. At the other end of the spectrum, aspiring data scientists, who have enough domain expertise and are keen to pursue this art can take umbrage from the example of Clare Corthell who has embarked on a self crafted journey to embrace the art of data science purely on online learning MOOCs. In Fact she has herself come out with a curriculum for data science with the Open Source Data Science Masters--OSDSM- program. These courses can help you to bridge the gap in your learning and practicing the craft. The OSDSM is a collection of open source resources that will help you to acquire skills necessary to be a competent entry level data scientist. You can access the curriculum here . You have to be adept at learning and upgrading on the job and on the fly. Kunal Punera the Co founder / CTO at Bento labs talks about this aspect when he says.. I spent two years at RelateIQ. I worked on building the data mining system from scratch -- and by the time I left I had built most of the data products deployed in RelateIQ.

Private car ownership in the United States is still holding strong as the dominant form of transportation, but the base of that paradigm is starting to crack. Americans are beginning to trade in their private cars and depending on ride hailing apps to get around, according to a new Reuters/Ipsos opinion poll. Nearly a quarter of US adults sold or traded in a car over the last year, and almost 10 percent of that group didn't replace their vehicle with a new one to get around. Instead, they opted to depend on ride hailing apps like Uber and Lyft as their go-to mode of car transportation, bucking the norm of private car ownership. The poll was conducted online, with 584 respondents who said they got rid of a vehicle within the last 12 months and 566 people who claimed they're planning to do so in the next 12 months.

Networks of people can coordinate better with bots in the mix. But a new study shows that computers that behave randomly can push us to better coordinate our actions with others and accomplish tasks more quickly. The approach could ease traffic flow, improve corporate strategy, and possibly even tighten marriages. If you want a project to do well, it's not enough to have its members get along; they need to share a game plan. One solution is top-down control: A leader or governing body tells everyone what to do.

China's biggest online retailer, JD.com Inc., announced plans Monday to develop drone aircraft capable of carrying a ton or more for long-distance deliveries. The company said it will test the drones on a network it is developing to cover the northern Chinese province of Shaanxi. It said they will carry consumer goods to remote areas and farm produce to cities. China's biggest online retailer, JD.com Inc., announced plans Monday to develop drone aircraft capable of carrying a ton or more for long-distance deliveries. China's biggest online retailer, JD.com Inc., has announced plans to develop drone aircraft capable of carrying a ton or more for long-distance deliveries. The craft will be tested on a network it is developing to cover the northern Chinese province of Shaanxi.

"Model" means different things to different people and different things at different times. As I briefly explain in A Model's Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we'll have a closer look at the last of these, statistical models. First, it's critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they're all wrong but some of them are useful. So why do we use statistical models?

Could sailing be the next desk job? Some sailors of tomorrow will never have to get their sea legs. These sailors will be seated in office buildings on land, hundreds of miles from their ship, which won't have any humans on board. Automation is storming the transportation sector, eliminating some jobs and turning others into desk jobs. Sailors, pilots and drivers are slowly shifting from the high seas, blue skies and open roads to staid office buildings where they monitor vessels from afar.

AlphaGo has again defeated Ke Jie, the world's number one Go player, in their second game, meaning the AI has secured victory in the three-part match. The win over Ke, universally considered the best Go player in the world, essentially confirms that AlphaGo has surpassed human Go ability a little over a year after the AI first beat Lee Se-dol. Ke played "perfectly" for about the first 50 moves, according to AlphaGo's analysis, and evolved his strategy to engage in a series of complex battles across the board. But AlphaGo handled the multi-angled assault well to simplify the engrossing game and eventually forced Ke to resign. "For the first 100 moves it was the closest we've ever seen anyone play against the Master version of AlphaGo," DeepMind CEO Demis Hassabis said in the post-game press conference.