sent2null space

So the last few years has seen impressive performance in machine learning models leveraging deep model processes involving multiple layers of neural networks emerging an ability to highly characterize a target image in the "style" of a given input image to produce an output image that appears as if it were created in an artistic way by the algorithm. The apps. and filters leveraging these neural networks (convolutional being the ones most effective at this proto creative action) are quickly appearing in various apps. However, for creating art....particularly creating novel art that is not just the result of a complex mathematical process against a single source and a single target image....such approaches are an utter failure....for example, as an illustrator I can be given two or 3 input images of a given character from different perspectives and on the basis of that small set of input create a wide variety of new images ....of that same character with

The last 5 years have been a great awakening in the space of machine learning and the subset discipline that up to this point had been called AI. When Hinton et al discovered the power of GPU's toward improving the training rate of their neural network models they allowed radical improvements in experimentation using those models while also enabling them to train on vastly larger data sets then were practical (read: cost effective) in the past. The later innovation of drop out as a means of reducing over fitting in the trained results of such networks allowed older models to be significantly more flexible in avoiding over focus on features in a given data set that made the final trained network too specific to the original trained set. The extension of neural network models to incorporate GPU acceleration , drop out and multiple layers then enabled the exploration of neural network approaches of all manner of real world training scenarios that model more closely than ever

The last few years as the layman media has cottoned on to the previously silent revolution happening in self driving car technology since 2003 and DARPA's "grand challenges", we've seen lots of introduction of arguments expressing the necessity of ethics and philosophy to help deal with supposedly dangerous ramifications of a cars that drive themselves. Namely issues like what is known as the trolly problem . I'll be blunt, there is no need to address any moral dilemma at all. Self Driving cars don't need to be that intelligent, all they need to do is know and relentlessly follow the law. The laws work to define what is legal *action* given possible scenarios with other cars and pedestrians...acting within those laws 100% means one is not subject to violating them....so knowing the laws and behaving to their letter ....*even if that means killing people* will get you free of at least the litigation. See China. In China a

It's pretty clear at this point that Uber's surge pricing model has been met with mixed reactions and in many cases outright derision by the customer base. The pricing model instituted in some large cities at the end of 2015 allows customers to pay more for the luxury of having an Uber driver arrive in a timely fashion when demand is high . At first this sounds like a very  good idea, Uber simply keys up the price of the fair percentage doled out to the driver until drivers swarm an area where demand is high, this gets the drivers a larger payout per fair but also ensures that the customers in high demand areas also get picked up faster ...so what's the problem? The problem is that surge pricing can't be accurately given a price estimate like non surge pricing calculations are given and often people being picked up in high demand areas are simply focused on one factor, getting picked up ....often under inebriated circumstances , when they sober up after the revelry

New tantalizing reports of gravitational waves being detected hit the web recently. So if it does detect them I'd imagine it would detect distal waves with high periodicity rates. The proximal waves are going to have very long wave length and I don't know how they'd disambiguate those without making very long observation windows. Proximal sources of such waves are: a)  the Sun itself ...very very tiny micro shedding as it gives up mass to energy. These are likely to be super super weak and likely not capturable by current generation technology. b) the Sun - Mercury transit , though Mercury is tiny compared to the sun both do distort space time and sit in mutual wells...which should create a very tiny wake (a GW) that has periodicity matched to the rotation rate of Mercury around the Sun. This being a longer wavelength it would require a long observation window and it is also abysmally tiny. So again ...unlikely to be captured with current tech. c) the Sun - V

"Uber has been investing heavily in China, and the service is growing there like crazy. Uber’s service is taking off in China much faster than it did in the United States. Nine months after launching in Chengdu, Uber had 479 times the trips it had in New York after the same amount of time. Uber is also putting a lot of money into its Chinese growth. Uber's China branch has closed a funding round that values it at $7 billion.  In total, Uber has raised more than $6 billion in several funding rounds, including a $600 million investment from Chinese search engine company Baidu." --- And yet , combined we are still talking about 4 major markets. US, Europe, China and India. South America , Africa , South East Asia are also all ripe for entry. This quote comes from this article. He with the most funding has the best chance of radically innovating fast enough in the space to grab market share from the others, so far on a global basis Uber has executed brilliantly.