delta


Young startups go full throttle

MIT News

From June to August each year, MIT delta v, hosted in the Martin Trust Center for MIT Entrepreneurship, provides a cohort of startups with the wherewithal to launch: office and lab space for prototyping, mentorship from veteran entrepreneurs, $20,000 in funding, and $2,000 in living expenses. The diverse range of ideas included robots that analyze sewerage to track opioid consumption in populations, portable weight-lifting equipment that adjusts resistance in real time, a "Netflix" service for autonomous-vehicle data, augmented reality for recording and sharing knowledge of frontline workers in hospitals and care facilities, an online market that helps indigenous people digitize and sell their art, a battery for soldiers that recharges with fuel, cooking classes that donate meals to the needy, and advanced filtration systems that better remove heavy metals from drinking water. These milestones include partnerships and agreements with big-name companies, pilot programs, working prototypes or early product iterations, launched websites or apps, earned revenue, and -- perhaps most importantly -- customers. This year also saw the launch of a pilot program, the MIT NYC Summer Startup Studio, in New York City, where seven additional startups were offered the same perks that delta v provides.


The airports of the future are here

Mashable

One reason airports tend to look and function remarkably alike is that they're designed to accommodate air travel infrastructure--security, passenger ticketing, baggage, ground transport--with the primary concerns being safety and minimal overhead for their tenant airlines. "It's like having a Super Bowl worth of people every single day." "It's like having a Super Bowl worth of people every single day." At Changi, concession revenues rose 5 percent last year to a record S$2.16 billion ($1.6 billion), while the world's busiest airport, Atlanta's Hartsfield-Jackson International, topped $1 billion in concession sales in 2016, also a record.


The Fundamental Statistics Theorem Revisited

@machinelearnbot

It turned out that putting more weight on close neighbors, and increasingly lower weight on far away neighbors (with weights slowly decaying to zero based on the distance to the neighbor in question) was the solution to the problem. For those interested in the theory, the fact that cases 1, 2 and 3 yield convergence to the Gaussian distribution is a consequence of the Central Limit Theorem under the Liapounov condition. More specifically, and because the samples produced here come from uniformly bounded distributions (we use a random number generator to simulate uniform deviates), all that is needed for convergence to the Gaussian distribution is that the sum of the squares of the weights -- and thus Stdev(S) as n tends to infinity -- must be infinite. More generally, we can work with more complex auto-regressive processes with a covariance matrix as general as possible, then compute S as a weighted sum of the X(k)'s, and find a relationship between the weights and the covariance matrix, to eventually identify conditions on the covariance matrix that guarantee convergence to the Gaussian destribution.


The Death of the Statistical Tests of Hypotheses

@machinelearnbot

It is part of a data science framework (see section 2 in this article), in which many statistical procedures have been revisited to make them simple, scalable, accurate enough without aiming for perfection but instead for speed, and usable by engineers, machine learning practitioners, computer scientists, software engineers, AI and IoT experts, big data practitioners, business analysts, lawyers, doctors, journalists, even in some cases by the layman, and even by machines and API's (as in machine-to-machine communications). Over years, I have designed a new, unified statistical framework for big data, data science, machine learning, and related disciplines. I have also written quite a bit on time series (detection of accidental high correlations in big data, change point detection, multiple periodicities), correlation and causation, clustering for big data, random numbers, simulation, ridge regression (approximate solutions) and synthetic metrics (new variances, bumpiness coefficient, robust correlation metric and robust R-squared non sensitive to outliers.) Vincent also manages his own self-funded research lab, focusing on simplifying, unifying, modernizing, automating, scaling, and dramatically optimizing statistical techniques.


What's wrong with this pic?

FOX News

The Atlanta-based airline has recently teamed up with Tinder to transform the exterior of Brooklyn building into a "dating wall" covered in worldly murals depicting nine different Delta destinations. According to a press release, the idea is for Brooklynites to snap photos near the murals, upload them to their dating profiles, and trick unsuspecting Tinder dates into thinking they're more well-traveled than they actually are. "So this summer, Delta and Tinder are offering New York singles an opportunity to snap profile pictures that will make you look like a jet-setter via a series of painted walls on display on Wythe Avenue in Williamsburg, Brooklyn." The airline has also placed another large mural -- the second in its Painted Wall Series -- a few blocks away at the site of Brooklyn's weekly Smorgasburg food festival.


Tinder and Delta want to help you pretend to be a world traveler on your dating profile

Mashable

If you are too busy or strapped for cash to actually go abroad, but you still want to look like a seasoned traveler, Delta and Tinder have a fix for you. Apparently if your profile photo makes you look like a world traveler, you're more likely to get that coveted swipe right on Tinder. There is now a wall featuring nine different scenes of popular destinations around the world, conveniently located in Williamsburg, Brooklyn. So happy we finally got to go to London -what a quick trip thanks to @delta's #deltadatingwall - just an hour overseas (over the Hudson sea that is) #nyc #brooklyn #williamsburg #tinder #london This wall is cute purely as art, but it's pretty clear if you look at the photos for more than two seconds that the image behind them is painted on brick.


The Fundamental Statistics Theorem Revisited

@machinelearnbot

It turned out that putting more weight on close neighbors, and increasingly lower weight on far away neighbors (with weights slowly decaying to zero based on the distance to the neighbor in question) was the solution to the problem. For those interested in the theory, the fact that cases 1, 2 and 3 yield convergence to the Gaussian distribution is a consequence of the Central Limit Theorem under the Liapounov condition. More specifically, and because the samples produced here come from uniformly bounded distributions (we use a random number generator to simulate uniform deviates), all that is needed for convergence to the Gaussian distribution is that the sum of the squares of the weights -- and thus Stdev(S) as n tends to infinity -- must be infinite. More generally, we can work with more complex auto-regressive processes with a covariance matrix as general as possible, then compute S as a weighted sum of the X(k)'s, and find a relationship between the weights and the covariance matrix, to eventually identify conditions on the covariance matrix that guarantee convergence to the Gaussian destribution.


A Step by Step Backpropagation Example

#artificialintelligence

For an interactive visualization showing a neural network as it learns, check out my Neural Network visualization. For this tutorial, we're going to use a neural network with two inputs, two hidden neurons, two output neurons. We figure out the total net input to each hidden layer neuron, squash the total net input using an activation function (here we use the logistic function), then repeat the process with the output layer neurons. We repeat this process for the output layer neurons, using the output from the hidden layer neurons as inputs.


Why Risk Detection Is Always One Step Behind

#artificialintelligence

Various studies, including research from Ponemon Institute, have found that detecting incidents can sometimes extend to months or even years. There's also evidence that 911 emergency systems are at risk and that hackers have broken into election systems in Arizona and Illinois. "The vector and specific threat changes and, as a result, the underlying risks detection methods are always one step behind," Samide says. Ponemon Institute research indicates that nearly two-thirds of breaches are caused by human error or system glitches.


An interview with Arthur H. Walker – Identity Extensive Technology and "Going Delta"…

#artificialintelligence

AW: In the books I refer to it identity extensive technologies. It is what I expect will eventually arise from current cognitive technologies like IBM's Watson. There are cognitive technologies (IBM's Watson) and data holds (the Internet) that could give rise to such in the future. Arthur H. Walker likes to write about identity extensive technologies, fiscal/economic collapse, Intelligent Agents and A.I.s, Compliance Implants, and genetic engineering.