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Wayne Radinsky
Attended University Of Colorado At Boulder
Lives in Denver
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Wayne Radinsky

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Robotics and AI is prominent in MIT’s annual 35 Innovators Under 35. "Yunji Chen, Inventor, 32: designing dedicated deep-learning processors, optimized 'to compute the basic blocks of machine learning.'" "Adam Coates, Visionary, 34: designing artificial intelligence for Chinese search company Baidu." "Jaime Shotton, Inventor, 34: computer visionary at Microsoft Research. Giving computers a new way to see the world." "Ilya Sutskever, Visionary, 29: pushing the frontiers of deep-learning for the Google Brain." "Benjamin Tee, Inventor, 33: developing medical diagnostic and treatment technologies using a synthetic sense of touch." "Conor Walsh, Inventor, 33: working on robots that are soft, lightweight, and flexible so people can wear them to enhance their abilities."
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cool article. found out about some stuff i didnt even know was in development. 
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"If Amazon has humans carrying out machine-like work in its fulfillment centers, it’s simply because they haven’t yet figured out how to automate those particular jobs. In this sense, human labor provides a flexibility that fills in the edges of where our automation hasn’t yet reached. As machines gain scale and efficiency, the system becomes increasingly productive. That accelerating flow of work then adds more and more pressure on the inefficient human bottlenecks in the system."

This inefficient human bottleneck pressure affects blue-collar and white-collar workers differently. For blue-collar workers, it means minute-by-minute performance monitoring with reprimands for failing to use the bathroom closest to their workstation, and that sort of thing, while for white-collar workers it means eighty-hour workweeks and harvesting time from vacations, "capturing maximum share of mind."
 
What's Really Happening at Amazon?

The recent reports about Amazon's work conditions are disturbing, but I wanted to take a deeper dive into what I think is really happening at the company. At the heart of the problem is what I'm starting to now see as my new mantra on the effects of robotics, artificial intelligence and automation more generally on human work: 

Today, the role of human labor is to do the work machines have not yet learned.

This article started off as just a post here on Google+, but then I started going deeper and deeper down the rabbit hole, and fell into some conclusions that I think are pretty important about the relatively near-term future of human work. So this article is about Amazon because it's at the cutting edge of something bigger.

It's not a particularly optimistic take on where things are going, and I do think there are alternatives to the picture painted here, which I'd be happy to get into in comments. 

This one's hot off the press, and, I think, one of my better ones. 

#amazon   #automation   #technologicalunemployment   #robotics   #artificialintelligence  
Amazon's "Amabot" - humans doing the work machines haven't yet learned. The results can be dehumanizing.
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Ah, you're right, +fil smyth. Good catch. Thank you. Just fixed it. 
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The 21 best water-based myths tested on Mythbusters [infographic]. A person who falls into water from high enough will sustain the same injuries as if he/she had landed on pavement, dipping a sleeping person's hand in a bowl of warm water will cause him or her to wet the bed, a water heater can explode like a rocket and shoot through the roof of a house, a motorcycle moving at freeway speed can ride across the surface of a lake, a rowing eight can pull a water skiier at sufficient speed for the skiier to stay upright, and a bunch of others.
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I really like the graphic for #12.  I've never seen a water skier in front of a boat before.
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A watch with a second display added to the band has been made by Lenovo. "The second display isn't an LCD though, it's a viewfinder that you have to hold up to your eye to see. The viewfinder reminds us a lot of Google Glass, and it seems to be based on the same technology: light is projected into a prism that serves as the display surface. Google Glass used this to make a see-through display, but on the Magic View, the prism is encased in the watch, so there is a solid black background. And just like Google Glass, the display was so pixel dense and the lenses focused it so  that holding it up to your face made me think I was looking at a much larger display."
This Lenovo smartwatch has two displays—a round LCD and a viewfinder.
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Xiaomi and OnePlus aim to eliminate warehousing in their supply chain and pull extra value from the future forward into the present. People who complain that the company’s manufacturing proves too slow for both its customers and partners are missing the point -- they are manufacturing cutting-edge technology and selling it as it comes off the assembly line, in whatever minimum order quantity is available, with no wait until a product launch. They optimize the supply chain to deliver the new as quickly as possible.
Xiaomi and OnePlus (a convincing rebrand of Chinese firm Oppo / 广东欧珀移动通信有限公司) are two darlings of the technology industry. Their handset
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Queso, don't think I didn't pick up on your little hint there, Orims. I've got your resume here on file...

B-)
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IBM claims to have an algorithm that can predict air pollution in Beijing 72 hours to 10 days ahead. The system combines large quantities of data from several different models using "adaptive machine learning." "The models (and accompanying predictions), which have a resolution close to a kilometer, are likely the most precise predictive pollution models in existence."

That's great, but why not just have less pollution?
IBM is developing machine learning and AI tools for modeling Beijing’s air quality.
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"Haskell is ideally suited to fighting Facebook spam because it's so adept at executing many different tasks at the same time -- and because it gives engineers the tools they need to code all these tasks on the fly. Facebook's social network is so large and spammers are changing their techniques so quickly that the company needs a way of both building and operating its anti-spam engine at speed."
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/calls bullshit

R is as functional as Haskell and far more readable.
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10 Secrets Only Billionaires Know, from +Alex Lightman.
Billionaires know that there is a market for secret or hidden money, just like there is a market for so many other things.
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Only Billionaire knows these things... That's why we read it on a news article!
I think the Billionaire was playing with the guy, and told him what he wanted to hear.
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If you have amblyopia, also known as lazy eye, you can volunteer to spend 5 or 10 days in total darkness to see if it will work as a factory-reset for the visual system, bringing the visual cortices back to the changeable "plastic" condition it has in childhood.
The email from a professor offered an unusual spring break adventure: Come spend five days in complete darkness. To Morgan Williams,…
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You can estimate the lean angle of a motorcycle from the coefficient of friction. Based on this, you can do it in reverse and, from looking at how far racing motorcycles lean, calculate the coefficient of friction. From this you can calculate "racing motorcycle tires are just awesome."
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The Algorithmist is a wiki just for algorithms. You're probably wondering, why not just put all the algorithms on Wikipedia? Well, Wikipedia doesn't give you problem sets.
The Algorithmist. The Algorithmist is a resource dedicated to anything algorithms - from the practical realm, to the theoretical realm. There are also links and explanation to problemsets. Quick Table of Contents. Sorting · Exhaustive Search · Graph Theory · Dynamic Programming · Greedy ...
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Was just going to say, works for me.
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Physicists have constructed logic gates from single particles of pure light. "The Toronto group's setup consisted of a collection of rubidium atoms cooled to just a millionth of a degree above absolute zero. Single photons are fired through this medium, which experiences a phase shift and a small change in its refractive index. This change is observed using a second "probe" beam, and the result is a coupling between the individual photons and the probe beam via the rubidium 'atomic vapor.' This is accomplished via what's known as electromagnetically induced transparency (EIT), where the degree of interaction between light and some material (rubidium atoms) can be manually tweaked or tuned."
This could be big news for quantum computing.
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I don't know whether the researchers think of it in terms of specific heat or the kinetic energy of individual particles, but I know quantum computers are always super-cooled near absolute zero. They don't work otherwise because the entanglement of the particles that make up the qubits gets lost due to the thermal noise.
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Software Design Engineer
Introduction
I'm a software engineer specializing in great design of software -- every successful large software project ever made started out as a small software project that got larger. The key to a successful large project is knowing how to design software when it is small so it is capable of growing. Poor design in the early stages leads to high-entropy software that is difficult to maintain and add new features to years down the line. Good design in the initial stages allows new software features to be added easily. Good design doesn't take any more time than poor design, but you have to know how to do it.

Certain keys are very essential to good design. The beginning is the program's data structures, which form the foundation for any software project. The key to good data structure design is to make sure that the relationships between bits of data in your data structures are the same as the relationships between the objects or ideas that those data structures represent in the minds of your users. Any time these get out of sync, you are in for trouble -- but the trouble does not usually arrive immediately -- it can arrive months or years down the line. This delayed feedback cycle is one reason many software projects run late or fail. Any time the data structures are out of sync with the minds of users, there is the temptation to "patch" the problem by adding more data structures, that form a bridge between the existing data structures, and what you want to do. These "patches" are, unfortunately, "dirty hacks", that down the road will add complexity to your software. It is this complexity -- and more to the point, *unnecessary* complexity, that makes it more difficult to maintain or extend your software with new features in the future.

It is also extremely important to design the code structure correctly. It is very common to make basic errors like using global variables. Globals are very powerful, but should be used with care -- they connect separate components of the software with each other. (And be aware that many variables are global even when they are not called "global" in your particular programming language -- they can have other names). When you *want* something to apply "everywhere", globals are the right choice, because you change them in one place and the change is applied everywhere. But more often than not, globals are used when they shouldn't be, causing a change in one part of a program to cause another part of the program, that seems unrelated, to break.

Another minefield is object oriented programming. Objects are an extremely powerful and flexible programming metaphor -- and that's the problem. They are so flexible that they can mean almost anything, and they can make it easy for you to shoot yourself in the foot with excessive complexity. In reality, there is nothing wrong with non-object-oriented programming -- proper and thoughtful use of functions and libraries of functions -- so it is not necessary to use objects everywhere or make "everything" an object in your program. In particular, there is no advantage in doing "object-relational mapping" -- if you're doing this, it means you have designed all your data structures *twice* (once in the relational data model, and again in an object-oriented model), wasting effort. Furthermore, objects should only be used when they add *clarity* to a program, when they make it easier to understand how the program works, rather than more difficult. In certain situations, such as when polymorphism is needed to solve whatever problem your software needs to solve for the user, objects are a clear benefit, simplifying the design and adding clarity to the code. In many other situations, however, excess use of objects creates obfuscation, leading to maintainability problems and difficulty adding features to your software in the future.

And it is these complexity issues that impose limitations on how big your software can get, how many features it can have, and ultimately how well your business can grow and how well you can serve your customers.
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  • University Of Colorado At Boulder
    Computer Science
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