Waste no Time Waiting for e-Waste Data

The town where I currently reside is planning to change its e-Waste collection policy starting next year. As it is today, town people can go downtown once a month and drop their old computers, laptops, monitors and the rest. This will now be reduced to one day per year. Missing that date will entail people having to go to some other place out of town to take care of business. Or one could try to go to a nearby and more affluent village where one can drop the stuff at any time. Probably not kosher, though.

I am not sure if this change is the result of budget cuts or lower demand for such service  – or both. I am not really following town decision-making processes. But I do know that e-Waste collection is a state law, and all towns must thus take care of business. Note that appliances such

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ICOs: Endangered Species

As expected, ICOs are finally cooling down. There are several reasons for this. First, ICO oversight by regulators in many countries has substantially increased. Regulators are poking not so much into new ICOs. Instead, they are doing deep dives into those that have already been completed and going after those who look fraudulent. Second, the token market is in a massive downswing. Some tokens have lost at least 90 percent of their value thus leading to substantial loses for ICO investors. As a result, crypto tokens have become much less attractive.

Third, many of the successfully completed ICOs are having a hard time showing or delivering on the ground results in spite of massive infusions of capital. While lack of maturity and technology constraints might play a role here, it may also

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Uncertainty and Artificial Intelligence

In a world where perfect information supposedly rules across the board, uncertainty certainly poses a challenge to mainstream economists. While some of the tenets of such assumption have been already addressed – via the theory of information asymmetries and the development of the rational expectations school, for example, uncertainty still poses critical questions.

For starters, uncertainty should not be confused with risk. The latter in a nutshell can be quantified using probability theory. Based on existing data and previous behavior, we could say predict there is a 75 percent chance investments in the stock market can yield a 25 percent reward in say 5 years. This is not the case for uncertainty as here the outcome is entirely unknown. In other words, we have no idea what is going to

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Nein Uber in Berlin

Arriving in Berlin from Africa via Frankfurt proved to be a nightmare this time around. While the flight and connections were almost perfect, the same cannot be said about my luggage. I checked in one bag at the point of origin and asked the airline agent to confirm my bag will indeed show up in Berlin while ensuring the bag tag had the TXL symbol (for Berlin’s Tegel airport) and the right flight number printed on it. Both checks yielded positive results.

Twelve hours later I found myself waiting for my smallish suitcase at Tegel. Bags are unloaded in batches. I oversaw all three of them. The conveyor belt then stopped, telling me my bag did not board the plane in Frankfurt. Or maybe at the point of origin. No idea.

As far as I know, there are at least two lost and found windows in Tegel.

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Visa on Arrival

My passport seems to profess a deep love for visa stamps. Every time the possibility of travel to another country arises, I can hear its excitement of filling yet another passport page with a brand new and (maybe) shiny visa stamp. The more, the merrier – although blank pages to host additional stamps are becoming scarce, yet again.

Finding an index for all sort of things is one of the traits of our data age. Yes, there is a passport-power index that ranks countries according to visa-free travel. For 2018, Japan and Singapore shared top honors followed by Germany and Denmark. The usual suspects sat comfortably in the basement: Pakistan, Somalia, Syria, Iraq, and Afghanistan. My passport is part of the middle-class having recently risen in the ranks thanks to the addition of the Schengen

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Learning about Machine Learning

A few months ago, as I was finishing a paper on blockchain technology, I received an unexpected comment on Artificial Intelligence (AI from here on in) from one of the peer reviewers. While addressing the overall topic of innovation in the 21st Century, I mentioned in passing the revival of both AI and Machine Learning (ML, not be confused with Marxism-Leninism) as a good example. The reviewer requested the deletion of one of the two terms as, in his book, they were exactly the same. Not so fast, was my prompt reply. In the end, both survived the overall peer review.

Looking at the history of AI helps shed some light on these concepts. While the AI term was coined in the 1950s, the work of Alan Turing, limited by the use of analog/mechanical computers, can be seen as its launching pad. Digital

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The Scale of the Blockchain Scalability Constraints

According to latest estimates, global Internet penetration was close to 54 percent by the end of 2017. That is roughly 4 billion people. Figures for the number of unique cell phone users show that 5 billion people have access to the technology.1 BTW, this means that 1 billion people have a cell phone but are not connected to the Internet. But that is another story.

Armed with this numbers, I asked a business acquaintance who is a blockchain enthusiast and practitioner if the most popular blockchain platforms could effectively cater to all those users. Answer: “Not at this moment. But do not worry, we are working on it.”

The reason for this stems from the scalability constraints the most reputed blockchain platforms face. As I see, the scalability issue is related to three factors: 1.

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Endnotes   [ + ]

1. BTW, this means that 1 billion people have a cell phone but are not connected to the Internet. But that is another story.

Have You Ever Seen a Blockchain?

A silent but intense competition seems to be taking place when it comes to defining blockchain technology. A Google search for the question “What is blockchain” yields over 120 million possible results. This number includes thousands of guides, videos, FAQs and other “educational” material on the subject. A shining example is a video depicting a blockchain expert trying to explain the technology to a 5-year-old kid. Really?

One common trait of all these resources is the lack of agreement on a single and straightforward definition of a blockchain. So take your pick. But, as mentioned in a previous post, this is probably not that relevant. After all, many people use mobile phones on an hourly basis and have no idea how they work. They do not need to, nor do they seem to care about it. The

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Bitcoin Inequality

In the short and medium term, technology and inequality seemed to be positively correlated. In the long term, however, things are not as clear-cut. With the right policies and democratic institutions in place, technology could become a catalyst to reduce income and wealth inequality. Historical evidence from last century clearly supports this claim. Will digital technologies of the 21st Century follow the same path?

The long-term is still quite a few years away for digital technologies such as AI and blockchains. In this post, I will look at the world of Bitcoin and explore its links to income and wealth inequality. I will assume the Bitcoin network is a country on its own with defined financial ties to the rest of the world mostly via crypto exchanges and miners.

Last May, the total

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Blockchain Mining Costs and Revenues

In a previous post, I pushed the idea that mining is part of the real sector of the blockchain economy. Unlike financial speculation, mining requires investment in hardware, electricity, space, human resources, etc. This also applies to small miners who undoubtedly will have to defray a lower investment amount but who can join a mining pool to share mining revenues. Also, miners face intense competition which in turn is a reflection of the high level of profitability in the sector.

Mining calculators seem to proliferate in the web. Such sites offer potential mining investors a rough idea of how much they can make on a daily and/or monthly basis given the current price of the crypto being mined and the hashing the investors is willing to purchase. For example, I am told that if I buy Bitcoin

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