Archive for October, 2014

How to Install the Fire and Ice LED Light Bar

How to Install the Fire and Ice LED Light Bar
The Fire and Ice LED light bar is a clever aftermarket light bar that installs on the rear of a truck or SUV in the space between the bottom of the rear hatch and the bumper. It contains both red and white LED lights that provide for a solid white light bar when you engage the truck in reverse, and a solid red glowing bar that signals turns, hazards and stopping. Installation is simple since the light bar plugs into your towing harness and thus receives the power and signals already incorporated into the factory towing harness.

Instructions
1
Park your truck or SUV in a well-lit area that is dry and cool. Use an alcohol wipe to thoroughly clean the space between your bumper and rear hatch to remove all wax, tar or debris. If there’s a large amount of dirt, you may want to thoroughly wash the back area first, then use alcohol wipes as a finishing step.

2
Measure the length of the rear hatch area either using a measuring tape, or holding up the LED bar against the hatch without removing the adhesive backing. There is a rigid 60-inch version for full-sized trucks, or a flexible version which can be trimmed.

 

Trim the inside lip of your rear bumper if needed to insure that the light bar does not come into contact with the tailgate when lowered if your vehicle is a pickup. Use some painters tape to temporarily hold the bar in place, then very slowly and gently lower your tailgate, checking to see if it makes contact with the LED light bar. If there is contact, trim the bumper trim with a utility knife until you have adequate clearance.

4
Remove the adhesive backing on the light bar, then place it onto the slot between the tailgate and the bumper, holding it in place for at least a minute.

5
Run the led bar light plug down behind the rear bumper and connect it into your towing hitch wiring harness. Use a zip tie to tie up any loose wiring and use duct tape to secure it against the inside of the rear bumper.

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How to Install 12V LED Rope Lights on Boat

How to Install 12V LED Rope Lights on Boat

Rope lights have made the process of “dressing ship,” that is, decorating the ship with flags and lights for special occasions, a little bit easier. The lights can be bent to wrap around handrails or wound around masts, and even cut into varying lengths, to allow your nautical creativity free rein. The lights are readily available in colors or clear strips, come in 12-volt varieties to facilitate their use aboard and have their own specialized accessories so you can delight your boat’s visitors with a light display that isn’t limited to the Christmas tree.

 

Instructions
1
Use your measuring tape to measure how much rope light you will need. Two-wire rope lights, the most common, can be cut into segments about 18 inches in length.

2
Find the scissor icons on the rope light. You may cut the rope light safely at these points. Make a vertical cut with your scissors to sever the light, then bend the rope light left and right to expose any excess wire. Cut off the exposed excess wire.
Connect the power cord to the connector and tighten the connector by turning the fastener clockwise. Slip the fastener onto the rope light where you want to make the connection and push the connector into the wires protruding from the butt of the light–the end opposite the end you cut.

4
Put a terminal cap on the cut end of the rope light. Insert the plug on the power cord into a 12VDC outlet on your boat and place the lights as desired.

 

Tips & Warnings
For use on a boat, the junction between the light and the power cord should be wrapped with vinyl tape or sealed with liquid vinyl electrical coating after the connections are made and tested.

This project involves work with sharp tools. Appropriate caution is advised.

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How to Install 12V LED Rope Lights on Boat

How to Install 12V LED Rope Lights on Boat

Rope lights have made the process of “dressing ship,” that is, decorating the ship with flags and lights for special occasions, a little bit easier. The lights can be bent to wrap around handrails or wound around masts, and even cut into varying lengths, to allow your nautical creativity free rein. The lights are readily available in colors or clear strips, come in 12-volt varieties to facilitate their use aboard and have their own specialized accessories so you can delight your boat’s visitors with a light display that isn’t limited to the Christmas tree.

 

Instructions
1
Use your measuring tape to measure how much rope light you will need. Two-wire rope lights, the most common, can be cut into segments about 18 inches in length.

2
Find the scissor icons on the rope light. You may cut the rope light safely at these points. Make a vertical cut with your scissors to sever the light, then bend the rope light left and right to expose any excess wire. Cut off the exposed excess wire.
Connect the power cord to the connector and tighten the connector by turning the fastener clockwise. Slip the fastener onto the rope light where you want to make the connection and push the connector into the wires protruding from the butt of the light–the end opposite the end you cut.

4
Put a terminal cap on the cut end of the rope light. Insert the plug on the power cord into a 12VDC outlet on your boat and place the lights as desired.

 

Tips & Warnings
For use on a boat, the junction between the light and the power cord should be wrapped with vinyl tape or sealed with liquid vinyl electrical coating after the connections are made and tested.

This project involves work with sharp tools. Appropriate caution is advised.

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How to Repair Solar LED Lanterns

How to Repair Solar LED Lanterns

There are a number of problems that can besiege your solar LED lantern, none of them serious and all of them pretty simple to fix relatively inexpensively. The most important thing to remember is that an LED is not an incandescent light bulb, and does not operate the same way. As such, you don’t have to worry about bulbs “blowing out” or causing damage to the rest of the lantern. Solar LEDs have their own special set of problems you’ll need to diagnose.

Instructions
1
Ascertain which bulb in your lantern has blown by powering the device and looking for the dark bulb (or more than one). Shut off the power and unplug the lantern from its light source, and use extra LEDs to replace the burned-out ones. LEDs have a “polarity,” that is, they have a positive leg and a negative leg. Observe the proper polarity when replacing an LED, as it will not work if it is installed backwards.

2
Check the fuses of your lantern if all its LEDs are out simultaneously. Unplug your lantern from its power source, replace one fuse, and turn the lantern back on. Repeat until you find the blown fuse. Alternatively, you could use a volt/ohmmeter to check each fuse without replacing them.

 

3.Check your lanterns wiring for accidentally pinched, severed, stapled or otherwise damaged wiring. If you find any, cut out the damage area, strip the ends of the undamaged wires, and reconnect them. Cover the connected area with electrical tape.

4
Re-solder any burned-out or oxidized terminals on your solar panel. Locate bad solder joints by noting which are shiny, and which are not. Unsolder the non-shiny ones, clean the area of any leftover bad solder, and solder the connection afresh with 2 percent silver solder.
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How to Repair Solar LED Lanterns

 

There are a number of problems that can besiege your solar LED lantern, none of them serious and all of them pretty simple to fix relatively inexpensively. The most important thing to remember is that an LED is not an incandescent light bulb, and does not operate the same way. As such, you don’t have to worry about bulbs “blowing out” or causing damage to the rest of the lantern. Solar LEDs have their own special set of problems you’ll need to diagnose.

Instructions
1
Ascertain which bulb in your lantern has blown by powering the device and looking for the dark bulb (or more than one). Shut off the power and unplug the lantern from its light source, and use extra LEDs to replace the burned-out ones. LEDs have a “polarity,” that is, they have a positive leg and a negative leg. Observe the proper polarity when replacing an LED, as it will not work if it is installed backwards.

2
Check the fuses of your lantern if all its LEDs are out simultaneously. Unplug your lantern from its power source, replace one fuse, and turn the lantern back on. Repeat until you find the blown fuse. Alternatively, you could use a volt/ohmmeter to check each fuse without replacing them.

 

3.Check your lanterns wiring for accidentally pinched, severed, stapled or otherwise damaged wiring. If you find any, cut out the damage area, strip the ends of the undamaged wires, and reconnect them. Cover the connected area with electrical tape.

4
Re-solder any burned-out or oxidized terminals on your solar panel. Locate bad solder joints by noting which are shiny, and which are not. Unsolder the non-shiny ones, clean the area of any leftover bad solder, and solder the connection afresh with 2 percent silver solder.

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Interview with AI expert Michael Jordan on big data, machine learning, and our current limits

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report “Frontiers in Massive Data Analysis.” San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014.

Michael Jordan on…

  1. Why We Should Stop Using Brain Metaphors When We Talk About Computing
  2. Our Foggy Vision About Machine Vision
  3. Why Big Data Could Be a Big Fail
  4. What He’d Do With US $1 Billion
  5. How Not to Talk About the Singularity
  6. What He Cares About More Than Whether P = NP
  7. What the Turing Test Really Means
  1. Why We Should Stop Using Brain Metaphors When We Talk About Computing

    IEEE Spectrum: I infer from your writing that you believe there’s a lot of misinformation out there about deep learning, big data, computer vision, and the like.

    Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are. Specifically on the topic of deep learning, it’s largely a rebranding of neural networks, which go back to the 1980s. They actually go back to the 1960s; it seems like every 20 years there is a new wave that involves them. In the current wave, the main success story is the convolutional neural network, but that idea was already present in the previous wave. And one of the problems with both the previous wave, that has unfortunately persisted in the current wave, is that people continue to infer that something involving neuroscience is behind it, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.

    Spectrum: As a member of the media, I take exception to what you just said, because it’s very often the case that academics are desperate for people to write stories about them.

    Michael Jordan: Yes, it’s a partnership.

    Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists. You called these “cartoon models” of the brain.

    Michael Jordan: I wouldn’t want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

    Spectrum: In addition to criticizing cartoon models of the brain, you actually go further and criticize the whole idea of “neural realism”—the belief that just because a particular hardware or software system shares some putative characteristic of the brain, it’s going to be more intelligent. What do you think of computer scientists who say, for example, “My system is brainlike because it is massively parallel.”

    Michael Jordan: Well, these are metaphors, which can be useful. Flows and pipelines are metaphors that come out of circuits of various kinds. I think in the early 1980s, computer science was dominated by sequential architectures, by the von Neumann paradigm of a stored program that was executed sequentially, and as a consequence, there was a need to try to break out of that. And so people looked for metaphors of the highly parallel brain. And that was a useful thing.

    But as the topic evolved, it was not neural realism that led to most of the progress. The algorithm that has proved the most successful for deep learning is based on a technique called back propagation. You have these layers of processing units, and you get an output from the end of the layers, and you propagate a signal backwards through the layers to change all the parameters. It’s pretty clear the brain doesn’t do something like that. This was definitely a step away from neural realism, but it led to significant progress. But people tend to lump that particular success story together with all the other attempts to build brainlike systems that haven’t been nearly as successful.

    Spectrum: Another point you’ve made regarding the failure of neural realism is that there is nothing very neural about neural networks.

    Michael Jordan: There are no spikes in deep-learning systems. There are no dendrites. And they have bidirectional signals that the brain doesn’t have.

    We don’t know how neurons learn. Is it actually just a small change in the synaptic weight that’s responsible for learning? That’s what these artificial neural networks are doing. In the brain, we have precious little idea how learning is actually taking place.

    Spectrum: I read all the time about engineers describing their new chip designs in what seems to me to be an incredible abuse of language. They talk about the “neurons” or the “synapses” on their chips. But that can’t possibly be the case; a neuron is a living, breathing cell of unbelievable complexity. Aren’t engineers appropriating the language of biology to describe structures that have nothing remotely close to the complexity of biological systems?

    Michael Jordan: Well, I want to be a little careful here. I think it’s important to distinguish two areas where the word neural is currently being used.

    One of them is in deep learning. And there, each “neuron” is really a cartoon. It’s a linear-weighted sum that’s passed through a nonlinearity. Anyone in electrical engineering would recognize those kinds of nonlinear systems. Calling that a neuron is clearly, at best, a shorthand. It’s really a cartoon. There is a procedure called logistic regression in statistics that dates from the 1950s, which had nothing to do with neurons but which is exactly the same little piece of architecture.

    A second area involves what you were describing and is aiming to get closer to a simulation of an actual brain, or at least to a simplified model of actual neural circuitry, if I understand correctly. But the problem I see is that the research is not coupled with any understanding of what algorithmically this system might do. It’s not coupled with a learning system that takes in data and solves problems, like in vision. It’s really just a piece of architecture with the hope that someday people will discover algorithms that are useful for it. And there’s no clear reason that hope should be borne out. It is based, I believe, on faith, that if you build something like the brain, that it will become clear what it can do.

    Spectrum: If you could, would you declare a ban on using the biology of the brain as a model in computation?

    Michael Jordan: No. You should get inspiration from wherever you can get it. As I alluded to before, back in the 1980s, it was actually helpful to say, “Let’s move out of the sequential, von Neumann paradigm and think more about highly parallel systems.” But in this current era, where it’s clear that the detailed processing the brain is doing is not informing algorithmic process, I think it’s inappropriate to use the brain to make claims about what we’ve achieved. We don’t know how the brain processes visual information.

    Back to top

  2. Our Foggy Vision About Machine Vision

    Spectrum: You’ve used the word hype in talking about vision system research. Lately there seems to be an epidemic of stories about how computers have tackled the vision problem, and that computers have become just as good as people at vision. Do you think that’s even close to being true?

    Michael Jordan: Well, humans are able to deal with cluttered scenes. They are able to deal with huge numbers of categories. They can deal with inferences about the scene: “What if I sit down on that?” “What if I put something on top of something?” These are far beyond the capability of today’s machines. Deep learning is good at certain kinds of image classification. “What object is in this scene?”

    But the computational vision problem is vast. It’s like saying when that apple fell out of the tree, we understood all of physics. Yeah, we understood something more about forces and acceleration. That was important. In vision, we now have a tool that solves a certain class of problems. But to say it solves all problems is foolish.

    Spectrum: How big of a class of problems in vision are we able to solve now, compared with the totality of what humans can do?

    Michael Jordan: With face recognition, it’s been clear for a while now that it can be solved. Beyond faces, you can also talk about other categories of objects: “There’s a cup in the scene.” “There’s a dog in the scene.” But it’s still a hard problem to talk about many kinds of different objects in the same scene and how they relate to each other, or how a person or a robot would interact with that scene. There are many, many hard problems that are far from solved.

    Spectrum: Even in facial recognition, my impression is that it still only works if you’ve got pretty clean images to begin with.

    Michael Jordan: Again, it’s an engineering problem to make it better. As you will see over time, it will get better. But this business about “revolutionary” is overwrought.

    Back to top

  3. Why Big Data Could Be a Big Fail

    Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.

    Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.

    Spectrum: How so?

    Michael Jordan: In a classical database, you have maybe a few thousand people in them. You can think of those as the rows of the database. And the columns would be the features of those people: their age, height, weight, income, et cetera.

    Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns—and we do in modern databases—you’ll get up into millions and millions of attributes for each person.

    Now, if I start allowing myself to look at all of the combinations of these features—if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age—what’s the probability you will have a certain disease or you will like my advertisement? Now I’m getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe.

    Those are the hypotheses that I’m willing to consider. And for any particular database, I will find some combination of columns that will predict perfectly any outcome, just by chance alone. If I just look at all the people who have a heart attack and compare them to all the people that don’t have a heart attack, and I’m looking for combinations of the columns that predict heart attacks, I will find all kinds of spurious combinations of columns, because there are huge numbers of them.

    So it’s like having billions of monkeys typing. One of them will write Shakespeare.

    Spectrum:Do you think this aspect of big data is currently underappreciated?

    Michael Jordan: Definitely.

    Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?

    Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.

    Spectrum: What will happen if people working with data don’t heed your advice?

    Michael Jordan: I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.

    Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.

    And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up. So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.

    Spectrum: Do we currently have the tools to provide those error bars?

    Michael Jordan: We are just getting this engineering science assembled. We have many ideas that come from hundreds of years of statistics and computer science. And we’re working on putting them together, making them scalable. A lot of the ideas for controlling what are called familywise errors, where I have many hypotheses and want to know my error rate, have emerged over the last 30 years. But many of them haven’t been studied computationally. It’s hard mathematics and engineering to work all this out, and it will take time.

    It’s not a year or two. It will take decades to get right. We are still learning how to do big data well.

    Spectrum: When you read about big data and health care, every third story seems to be about all the amazing clinical insights we’ll get almost automatically, merely by collecting data from everyone, especially in the cloud.

    Michael Jordan: You can’t be completely a skeptic or completely an optimist about this. It is somewhere in the middle. But if you list all the hypotheses that come out of some analysis of data, some fraction of them will be useful. You just won’t know which fraction. So if you just grab a few of them—say, if you eat oat bran you won’t have stomach cancer or something, because the data seem to suggest that—there’s some chance you will get lucky. The data will provide some support.

    But unless you’re actually doing the full-scale engineering statistical analysis to provide some error bars and quantify the errors, it’s gambling. It’s better than just gambling without data. That’s pure roulette. This is kind of partial roulette.

    Spectrum: What adverse consequences might await the big-data field if we remain on the trajectory you’re describing?

    Michael Jordan: The main one will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress. And then there will be a period during which it will be very hard to get resources to do data analysis. The field will continue to go forward, because it’s real, and it’s needed. But the backlash will hurt a large number of important projects.

    Back to top

  4. What He’d Do With $1 Billion

    Spectrum: Considering the amount of money that is spent on it, the science behind serving up ads still seems incredibly primitive. I have a hobby of searching for information about silly Kickstarter projects, mostly to see how preposterous they are, and I end up getting served ads from the same companies for many months.

    Michael Jordan: Well, again, it’s a spectrum. It depends on how a system has been engineered and what domain we’re talking about. In certain narrow domains, it can be very good, and in very broad domains, where the semantics are much murkier, it can be very poor. I personally find Amazon’srecommendation system for books and music to be very, very good. That’s because they have large amounts of data, and the domain is rather circumscribed. With domains like shirts or shoes, it’s murkier semantically, and they have less data, and so it’s much poorer.

    There are still many problems, but the people who build these systems are hard at work on them. What we’re getting into at this point is semantics and human preferences. If I buy a refrigerator, that doesn’t show that I am interested in refrigerators in general. I’ve already bought my refrigerator, and I’m probably not likely to still be interested in them. Whereas if I buy a song by Taylor Swift, I’m more likely to buy more songs by her. That has to do with the specific semantics of singers and products and items. To get that right across the wide spectrum of human interests requires a large amount of data and a large amount of engineering.

    Spectrum: You’ve said that if you had an unrestricted $1 billion grant, you would work on natural language processing. What would you do that Google isn’t doing with Google Translate?

    Michael Jordan: I am sure that Google is doing everything I would do. But I don’t think Google Translate, which involves machine translation, is the only language problem. Another example of a good language problem is question answering, like “What’s the second-biggest city in California that is not near a river?” If I typed that sentence into Google currently, I’m not likely to get a useful response.

    Spectrum:So are you saying that for a billion dollars, you could, at least as far as natural language is concerned, solve the problem of generalized knowledge and end up with the big enchilada of AI: machines that think like people?

    Michael Jordan: So you’d want to carve off a smaller problem that is not about everything, but which nonetheless allows you to make progress. That’s what we do in research. I might take a specific domain. In fact, we worked on question-answering in geography. That would allow me to focus on certain kinds of relationships and certain kinds of data, but not everything in the world.

    Spectrum: So to make advances in question answering, will you need to constrain them to a specific domain?

    Michael Jordan: It’s an empirical question about how much progress you could make. It has to do with how much data is available in these domains. How much you could pay people to actually start to write down some of those things they knew about these domains. How many labels you have.

    Spectrum: It seems disappointing that even with a billion dollars, we still might end up with a system that isn’t generalized, but that only works in just one domain.

    Michael Jordan: That’s typically how each of these technologies has evolved. We talked about vision earlier. The earliest vision systems were face-recognition systems. That’s domain bound. But that’s where we started to see some early progress and had a sense that things might work. Similarly with speech, the earliest progress was on single detached words. And then slowly, it started to get to be where you could do whole sentences. It’s always that kind of progression, from something circumscribed to something less and less so.

    Spectrum: Why do we even need better question-answering? Doesn’t Google work well enough as it is?

    Michael Jordan: Google has a very strong natural language group working on exactly this, because they recognize that they are very poor at certain kinds of queries. For example, using the word not. Humans want to use the word not.For example, “Give me a city that is not near a river.” In the current Google search engine, that’s not treated very well.

    Back to top

  5. How Not to Talk About the Singularity

    Spectrum: Turning now to some other topics, if you were talking to someone in Silicon Valley, and they said to you, “You know, Professor Jordan, I’m a really big believer in the singularity,” would your opinion of them go up or down?

    Michael Jordan: I luckily never run into such people.

    Spectrum: Oh, come on.

    Michael Jordan: I really don’t. I live in an intellectual shell of engineers and mathematicians.

    Spectrum: But if you did encounter someone like that, what would you do?

    Michael Jordan: I would take off my academic hat, and I would just act like a human being thinking about what’s going to happen in a few decades, and I would be entertained just like when I read science fiction. It doesn’t inform anything I do academically.

    Spectrum: Okay, but knowing what you do academically, what do you think about it?

    Michael Jordan: My understanding is that it’s not an academic discipline. Rather, it’s partly philosophy about how society changes, how individuals change, and it’s partly literature, like science fiction, thinking through the consequences of a technology change. But they don’t produce algorithmic ideas as far as I can tell, because I don’t ever see them, that inform us about how to make technological progress.

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CurrentC Is The Big Retailers’ Clunky Attempt To Kill Apple Pay And Credit Card Fees

Long before Apple Pay, big brick-and-mortar retail chains were conspiring to sidestep the typical 2% to 3% fees they’re charged by credit card companies when consumers pay with credit. A company called MCX (Merchant Customer Exchange), spearheaded by Walmart, was started to build a mobile payment solution that would become an app called CurrentC that’s preparing to launch, but is already in the app stores.

Rather than NFC, CurrentC uses QR codes displayed on a cashier’s screen and scanned by the consumer’s phone or vice versa to initiate and verify the transaction. The system is also designed to automatically apply discounts, use loyalty programs, and charge purchases to a variety of payment methods without passing sensitive financial data to the merchant.

CurrentC Signup Loyalty Scan

Retailers including CVS and Rite-Aid were planned partners for CurrentC. Now those businesses have pulled unofficial support for Apple Pay through their existing NFC readers, according to a report from MacRumors and a memo obtained by SlashGear. This implies they’ve established exclusive deals with MCX to use CurrentC as their mobile payment option.

Thanks to research shared with TechCrunch by Stanford student and developer sleuthAndrew Aude, we have more details on MCX’s plan and a closer look at the CurrentC app.

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A Multi-Year Plot To Ditch Credit Card Fees

Originally incorporated in 2011, MCX spent years in a sort of stealth mode working on the payments user experience. The company is run by merchants including Walmart, Target, Best Buy, CVS, Shell Oil, Darden Restaurants (Olive Garden), HMSHost (airport restaurants), Hy-Vee (supermarkets), Lowes, Michaels, Publix Super Markets and Sears. Wal-Mart VP and Assistant Treasurer Mike Cook is considered the MCX group’s de facto CEO, with some joking that MCX stands for Mike Cook Exchange, as FierceRetail reported.

Together, the companies operate over 110,000 retail locations and process over $1 trillion in payments annually, with a significant chunk coming in the form of credit card payments that cost the retailers fees.

CurrentC Retailers Logo

Walmart has long voiced its disdain for credit card processing fees that drain its slim margins, and even filed an anti-trust lawsuit against Visa and MasterCard over them back in 2003, but rejected the settlement they offered because it wanted more.

The idea behind MCX was that if enough retailers teamed up, they could convince consumers to adopt their mobile payment system that would let retailers avoid paying credit card fees in the 2 percent to 3 percent range by processing payments through Automatic Clearing House transactions through bank accounts that have much smaller fees. MCX’s app could also help retailers by encouraging loyalty to participating merchants and possibly provide them additional intelligence on their customers.

If MCX’s app caught on, partner retailers could escape tons of fees, which could directly increase their profits. Alternatively, they could use the leverage of MCX and the threat of sidestepping the processing fees to negotiate lower fees with the credit card companies. Former Walmart CEO Lee Scott reportedly once said “I don’t know that MCX will succeed, and I don’t care. As long as Visa suffers.”

 

To speed up development, MCX struck a deal to use Paydiant’s white-labeled mobile wallet system on the backend that works with ACH to reduce fees, which was announced in February 2012. Paydiant has raised around $35 million for its payment solution’s development.

In January 2013, Fierce Retail reported MCX had been asking retailers in 2012 to pay a big upfront fee from $250,000 to $500,000 to get on board, and sign three-year mobile payment app exclusivity deals with MCX. Retailers who signed up may have had a one-year grace period from the start of their exclusivity contract to bail out of the deal. If Apple Pay gains steam early, some retailers might look to take advantage of this option to ditch MCX. However, if deals were signed in 2012, that grace period is long gone but retailers may be coming up on the end of their exclusivity agreements even though CurrentC hasn’t launched yet,

PaydiantThose exclusivity deals may be why CVS and Rite-Aid are reportedly pulling unofficial support for Apple Pay on their NFC readers. In a memo to employees, obtained by SlashGear, Rite Aid wrote:

“Please note that we do not accept Apple Pay at this time. However we are currently working with a group of large retailers to develop a mobile wallet that allows for mobile payments attached to credit cards and bank accounts directly from a smart phone. We expect to have this feature available in the first half of 2015.

If customers attempt to pay for a transaction with Apple Pay, a message will prompt both customer and cashier for a different form of payment. Please instruct cashiers to apologize to the customer and explain that we do not currently accept Apple Pay, but will have our own mobile wallet next year.”

Until then, Apple Pay may gain steam with more graceful NFC payments, which could make CurrentC’s QR code method seems clunky and undesirable when it finally launches.

The CurrentC App

CurrentC’s app is now in the iOS and Android app stores, but can only be used by those with an invite code. Luckily, Aude was able to attain these screenshots and information.

When you sign up for CurrentC, you’re supposed to add your bank account. This lets CurrentC process payments for you without retailers having to pay the steep credit card processing fee. You can also add retailers’ loyalty credit cards or gift cards as payment methods. It’s possible that if you already have your bank account connected to a partner retailer’s loyalty or credit card, you may be able to automatically link that bank account to your CurrentC account rather than going through the clumsy standard process.

Pay WIth CurrentC

When it’s time for a user to check out, they request to pay with CurrentC. The consumer then unlocks their phone, opens the CurrentC app, opens the code scanner, and scans the QR code shown on the cashier’s screen. In some case, the reverse may happen where the consumer’s CurrentC app displays a payment code and the cashier scans it. If a QR code can’t be generated, a manually entered numeric code may be offered.

Rather than sending the customer’s financial data over the air, transactions trigger the transmission of a token placeholder. This is then securely converted by the financial institution to process the ACH payment and charge the user.

CurrentC also has a method in place for paying at gas station pumps. It shows the consumer a code on their phone that can then be entered on the pump keypad to initiate a CurrentC payment.

CurrentC Pump

CurrentC includes a merchant map for finding participating retailers. Discounts and coupons will be automatically applied to the purchase, and any loyalty program points will be automatically pegged to the customer’s account. CurrentC users will also be able to check their receipts in the app. These loyalty and discount programs may be the main selling point retailers use to try to convince customers to sign up for CurrentC.

Merchant Map
One refreshing inclusion in the app is a visual breakdown of what data CurrentC receives from users, who it can be shared with, and what data sharing is optional.
CurrentC Data collected

CurrentC notes it may share info with your device maker, app store, or developer tool makers. Oddly, it will collect health data. Precise location information is used to verify you’re at the retailer where you’re making a transaction, and if you opt in it can be used for marketing or advertising. CurrentC notes that you can opt in to be able to capture and store photos in the app for a hypothetical visual shopping list or other features down the road.

CurrentC Policies

After his investigation of the app, Aude told me “CurrentC borders on the creepy line” due to it pulling health info. He also that found that its Terms Of Service leaves high liability for fraud to the user if someone else is able to get access to a user’s phone and make CurrentC payments.

Will Anyone Want CurrentC? Probably Not

CurrentC is now being tested at some retail locations in Minnesota. Before CurrentC can rolled out, point of sale systems at retailers need to be modified, which can take time and explains the early 2015 launch date cited in the internal Rite Aid memo obtained by SlashGear.

CurrentC doesn’t rely on new technology like NFC or Bluetooth LE, so it will likely be compatible with older iPhones and Androids, unlike the iPhone 6 and 6 Plus-only Apple Pay. That could give it some broad appeal. MCX will also tout the automatic discount and loyalty programs that could appeal to bargain hunters.

The problem with the CurrentC system, as John Gruber points out, is that it’s based more around solving the retailers’ credit card fee problems than the consumers’ payment friction problems. Users have to open their phone, open CurrentC, open the scanner, scan the code from the cashier, and wait for the transaction to be confirmed. That may present more friction than simply paying with a credit card, and it’s certainly harder than a quick Touch ID verification and tap of Apple Pay.

518408669_c_853_480

The only way CurrentC has a real chance is if it can organize some big discount for all CurrentC payments across retailers. For example, if it said you’d get 5 percent off for paying with CurrentC, some people might be willing to use it. In the short-term, this would eradicate any savings on credit card fees for the merchants. But eventually, if the app gains a loyal user base it could scale back those fees to start reaping the benefits of sidestepping credit cards.

If CurrentC doesn’t offer a vivid value proposition to consumers, it’s likely to go the way of the dinosaur while Apple Pay pushes the evolution of the rest of the mobile payments industry.

 

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CVS and Rite Aid reportedly disabling NFC in stores to shut down Apple Pay and Google Wallet, as they prepare to roll out MCX’s rival payment system

Earlier this week, pharmacy chain Rite Aid shut down unofficial support for the Apple Pay and Google Wallet mobile payments systems, resulting in an outcry from users who have been testing out Apple’s new system since its launch on Monday. Rite Aid was not an official Apple Pay partner, but the payments system generally works with existing near field communications (NFC) payment terminals anyway, and many users had had success using Apple Pay at Rite Aid stores early in the week.

It now appears that fellow major pharmacy chain CVS is following suit and as of today is shutting down the NFC functionality of its payment terminals entirely, a move presumably intended to thwart Apple Pay. Google Wallet services are obviously also being affected by the move.

Multiple reports on Twitter and the MacRumors forums have indicated that CVS has sent an email to its stores indicating that NFC support is to be turned off. It is still relatively early in the day in the U.S., but we are now starting to see reports of NFC indeed being turned off at CVS stores.

The reason behind Rite Aid’s and CVS’s moves to disable unofficial Apple Pay support in their stores is presumably related to their participation in Merchant Customer Exchange (MCX), a retailer group developing its own mobile payments system known as CurrentC. A claimed internal Rite Aid message shared with SlashGear supports this notion, instructing cashiers to explain to customers that Apple Pay is not supported but that MCX’s solution will be available next year.

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How to Make LED Spotlights

 

Light-emitting diodes — LEDs — are useful electrical components that allow you to make a low-power light easily. The only things required are some light soldering and careful wiring of the power supply. You can replace inefficient incandescent light bulbs with LED designs to reduce your overall carbon footprint. Start with a spotlight design that runs off a home power outlet and provides inexpensive lighting for any situation.
Instructions
Preparation
1
Open the spotlight casing. Unscrew the lens to expose the raw power cord inside.

2
Cut the circuit board down to a circle that can fit inside the spotlight casing. Confirm it will fit by slipping it inside the casing. If it does not fit, cut it down a little more each time until it does.
Slip the LEDs into one side of the circuit board. Each wire from an individual diode should only be in a hole with a wire from a neighboring diode.

Assembly
4
Put on your safety goggles to protect your eyes from soldering fumes. Solder the diodes together into rows with your soldering iron. Solder each wire to a neighboring diode wire. The diodes at the end of each row will have one wire free.

5
Wire the free wires to the row below. Leave the first and last diode in this circuit unsoldered for attachment to the power supply.

6
Cut wire long enough to reach from the power supply wire to the circuit board. Strip the ends of the wires, no more than an inch at each end, to ensure a good connection.

7
Solder one wire to the first diode’s free wire. Solder the other wire to the last diode’s free wire. Solder the wires to the power supply wire, which you may have to strip if it did not come already stripped.

8
Wrap the soldered wire connections in electrical tape. Push the circuit board into the casing, LED side out.

9
Screw on the spotlight casing lens. Plug the spotlight into an electrical outlet and test its operation.
Tips & Warnings

Spotlight casing can be purchased at a hardware or electronics store.
Wear protective eyewear when soldering. While soldering is not exceptionally dangerous, it still poses a danger to the eyes, if only from the fumes.

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How to Make Lights Flash to Music

 

Whether you’re a DJ or just a fan of Christmas with a string of lights, making lightbulbs flash to the beat of your music is a fun thing to do. Believe it or not, synchronizing lights and music is actually extremely easy to accomplish, and helps you add that visual pulse to the groove of the tune. With only a few simple materials and half an hour of work, you’ll have a homemade music-to-light adapter that you can plug any bulb into, creating your own audio-visual show.

Instructions
1
Unscrew the casing on the primary speaker. This is the speaker to which the power cord is connected. Heat the wire connections to the actual speaker part; inside, until you can pull the speaker loose from them. Solder these two loose wires to the two left-most connections on the relay.

2
Cut a hole in the back of the main speaker’s case and run the non-plug end of your power cable through it. You may have to cut off the end and strip the wires to do this. Cut two small notches in the top of the case and stick the plug ends of the outlet adapter through it. Secure the adapter with glue or solder.

 

3.Solder the left plug end of the adapter to the right-most relay connection. You may need to use extra wire in between the two to connect them. Solder one of the two wires from the power cable to the right plug end, and the other power cable wire to the last connection on the relay.

4
Unscrew and remove any speaker arts left in the case and screw the case shut again. Plug a light of your choice into the outlet adapter. You can use a string of Christmas lights, an ordinary bulb, or even a small light inside a jack-o-lantern for a spooky effect.

5
Plug in both the speaker’s power cord and the extra power cord. Plug the speaker’s audio cable into a source of music, and press play!

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