Is high-speed Internet pointless​? No.

A contributor to Dave Farber’s IP (“Important People” list) recently stated that 1 Megabit per second  (Mbps) is adequate bandwidth for consumers. This compares to “high speed Internet” which in the US is 20 Mbps or higher, and Korea where speeds over 50 Mbps are common.

My response: 1 Mbps is woefully low for any estimate of “useful bandwidth” to an individual, much less to a home. It’s risky to give regulators an any excuse to further ignore consumer desires for faster connections.  1 Mbps is too low by at least one order of magnitude, quite likely by three orders of magnitude, and conceivably by even more. I have written this note in an effort to squash the 1Mbps idea in case it gets “out into the world.”

The  claim that 1 Megabit per second is adequate:

>From: Brett Glass <brett@lariat.net>
>Date: Sun, Dec 31, 2017 at 2:14 PM

> The fact is that, according to neurophysiologists, the entire bandwidth of
> all of the human senses combined is about 1 Mbps. (Some place it slightly
> higher, at 1.25 Mbps.) Thus, to completely saturate all inputs to the human
> nervous system, one does not even need a T1 line – much less tens of megabits.
> And therefore, a typical household needs nowhere near 25 Mbps – even if they
> were all simultaneously immersed in high quality virtual reality. Even the

My response:

First, I don’t know where the 1Mbps number comes from, but a common number is the bandwidth of the optic nerve, which is generally assessed at around 10Mbps. See references.

 

retina-diagram

An American Scientist article on “How the Retina Works” is available here.

 

Second, a considerable amount of pre-processing occurs in the retina and the layer under the retina, before reaching the optic nerve. These serve as the first layers of a neural network, and handle issues like edge detection.

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Irrational tax cuts won’t raise long term economic growth

I recently received the following on a Dave Farber’s “Interesting People” list, a collection of techies with interest in Internet policy issues. Why discuss it now, since the tax bill has been passed? It is important for all to realize how much the Republicans in Washington no longer believe in basing their decisions on  reality  (“facts”). It is very hard to believe this, but the evidence is now overwhelming, and the consequences will continue to be grave. I wrote the following quick response.
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It seems completely reasonable and even desirable to take actions such as lowering corp taxes,  lowering taxes on productivity and reducing regulation to get the economy growing at the 3-4%  range.

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Some of my harder to find papers

Accelerated Learning by Experimentation

Abstract

In most technologies and most industries, experiments play a central role in organizational learning as a source of knowledge and as a check before changes are implemented. There are four primary types of experiments: controlled, natural, ad-hoc, and evolutionary operation. This paper discusses factors that affect learning by experimentation and how they influence learning rates. In some cases, new ways of experimenting can create an order of magnitude improvement in the rate of learning. On the other hand, some situations are inherently hard to run experiments on, and therefore learning remains slow until basic obstacles are solved. Examples of experimentation are discussed in four domains: product development, manufacturing, consumer marketing, and medical trials.

Keywords: Learning, Experimentation

Full published version:    Bohn Accelerated Learning by Experimentation.    in Learning Curves: Theory, Models, and Applications  edited by Mohamad Y. Jaber, CRC Press, 2011.
Preprint version, through SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640767.

This is a rewritten version of my 1987 paper about manufacturing. Although I never published that paper, it was very influential in the 1990s, including becoming the (plagiarized) framework of a book and several articles by Stefan Thomke.  This book chapter expands and extends the original concepts, including new material from Michael Lapré.

Fraudulent academic journals are growing

Gina Kolata in the NY Times has been running a good series of articles on fraudulent academic publishing. The basic business model is an unholy alliance between academics looking to enhance their resumes, and quick-buck internet sites. Initially, I thought these sites were enticing naive academics. But many academics are apparently willing participants, suggesting that it’s  easy to fool many promotion and award committees.

All but one academic in 10 who won a School of Business and Economics award had published papers in these journals. One had 10 such articles.

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What snakes are growing in the Gardens of Technological Eden?

Two emerging technologies are revolutionizing industries, and will soon have big impacts on our health, jobs, entertainment, and entire lives. They are Artificial Intelligence, and Big Data. Of course, these have already had big effects in certain applications, but I expect that they will become even more important as they improve. My colleague Dr. James Short is putting together a conference called Data West at the San Diego Supercomputer Center, and I came up with a list of fears that might disrupt their emergence.

1) If we continue to learn that ALL large data repositories will be hacked from time to time (Experian; National Security Agency), what blowback will that create against data collection? Perhaps none in the US, but in some other countries, it will cause less willingness to allow companies to collect consumer data.

2) Consensual reality is unraveling, mainly as a result of deliberate, sophisticated, distributed, attacks. That should concern all of us as citizens. Should it also worry us as data users, or will chaos in public venues not leak over into formal data? For example, if information portals (YouTube, Facebook, etc.) are forced to take a more active role in censoring content, will advertisers care? Again, Europe may be very different. We can presume that any countermeasures will only be partly effective – the problem probably does not have a good technical solution.

3) Malware, extortion, etc. aimed at companies. Will this “poison the well” in general?

4) Malware, extortion, doxing, etc. aimed at Internet of Things users, such as household thermostats, security cameras, cars. Will this cause a backlash against sellers of these systems, or will people accept it as the “new normal.” So far, people have seemed willing to bet that it won’t affect them personally, but will that change. For example, what will happen when auto accidents are caused by deliberate but unknown parties who advertise their success? When someone records all conversations within reach of the Alexa box in the living room?

Each of these scenarios has at least a 20% chance of becoming common. At a minimum, they will require more spending on defenses. Will any become large enough to suppress entire applications of these new technologies?

I have not said anything about employment and income distribution. They may change for the worse over the next 20 years, but the causes and solutions won’t be simple, and I doubt that political pressure will become strong enough to alter technology evolution.

Continuing problems with Tesla 3: Musk doesn’t understand manufacturing

It has always been clear that Musk does not understand high-volume manufacturing. Building rockets is very hard, but building 100,000 cars is very hard for a different reason! His predicted ramp rate was absurd. In the last 6 months, I think he has started to realize this.

Tesla has little chance of hitting its 5,000 weekly output during the fourth quarter. The chief reason: Its current production line can’t build vehicles at that rate unless it runs two 10-hour shifts seven days a week, which is unlikely. impossible.

Source: Tesla | Hiccups Threaten to Slow Model 3 Launch | Industry content from WardsAuto

According to the article, Tesla has also deliberately ignored much of the accumulated wisdom about how to ramp in auto production. That might be OK for his second high-volume vehicle.

More details on Tesla’s ramp plans:

Manufacturing expert says Tesla Model 3 plan to skip beta testing is risky

Tesla now has 2 choices, both bad:

  1. Go ahead and start building and shipping as fast as possible. The result will be multiple problems that require expensive hardware recalls.
  2. Add another 6? months to the schedule to run the as a pilot line for learning, rather than for volume. Expect zero salable output during that period. (As one of the comments said, they can give/sell those cars to employees.)

Added December 27: Tesla “still in manufacturing hell.”

Latest of many articles about Tesla manufacturing problems.

Here is a comment on that article: Musk  needs to face up to having made a MAJOR mistake when he skipped some steps in the original manufacturing ramp-up.
He is probably also making another major mistake at present: adding new machines to the manufacturing process, before he has the existing machines working perfectly. This seems logical to people with no manufacturing experience, but it does not work. For one thing, it diverts his key resource, which right now is manufacturing engineers.