Do We Need AI Government? – Part 2

(read Part 1 here)

I have seen it many times while watching chess commentators (typically, Grandmasters of the highest level) performing game analysis in real time.  These GMs will be considering different possibilities for both sides and, infrequently, when the situation becomes too complex and unclear, say something like, “Hey, let’s check with the chess engine now…. Oh, it gives a strong advantage to White, but I don’t see why…. It says to do… WHAT?!  And then… WHAT?! No…. these are not “human-like moves”, the players will not do that. This is too deep and machine-like…”.

The truth is that even the strongest Grandmasters often feel like little children when comparing their own analysis to that of a machine. But this is exactly why they are using machine analysis!

Lucky for chess, nobody suspects that “Stockfish” or “AlfaZero” have some ulterior motives, biases, don’t like some of the players, or wants to take advantage of somebody.  Chess engines are considered to be fast, powerful, accurate, and objective analysis and decision-making tools capable of finding the best solution for any situation and being useful to us by simply being better than us.  And nothing else.

And this is exactly how the future AI governments should look like: fast, powerful, accurate, and objective analysis and decision-making TOOLS capable of finding the best solution for any situation and being useful to us by being better than us. And nothing else.

Machine learning (ML) might already offer a possible approach needed to build and test such an “AI governance engine” and create the entire democratic election process using ML’s normal training and testing approach and steps:

  • Provide the “governance engine” with a training dataset of historical or other examples that are of high value to us and explain how to classify them (for example, “bad” or “good”).  Cover important social, economic, judicial, cultural, and educational fields.  For example, imagine thousands upon thousands of statements or questions along with their classifiers/answers presented like this:
    • “Rosa Parks rejected bus driver James F. Blake’s order to relinquish her seat in the “colored section” to a white passenger.  Was she right or should she have stayed in the colored section?”. The answer: Rosa Parks was right. The driver was wrong.
    • Or, “greater investments in children education” are good. Cutting these investments is bad.
    • Cutting forest in Amazon delta is bad.  Reducing industrial water and air pollution is good.

We have tons of examples like this from our past and present.

  • Keep another dataset of examples with answers for testing. We will use it later to verify that the engine works well.

(Comment: The general population should take part in creating the above list of Q&A.  Millions of people can contribute to it. This will allow the people to have a very direct impact on the training and selection of their own government instead of choosing the best available but imperfect candidate) Continue reading

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2019 Meetings and Conferences on AI, Analytics, Big Data, Data Mining, Data Science, Machine Learning, AR/VR/XR/MR

If you plan to attend a meeting or a conference on AI, Big Data, Machine Learning, or a related subject in 2019 or want to read or publish a paper on these subjects, below are some resources for you to use.

Conferences:

I personally doubt that the world needs that many events on this (or any other) subject in one year (and the list will get longer over time, I am sure), but, at least, there are lots of options to chose  from:

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International Diplomacy and Google Translate

Recently, I purchased (online) a coin (number 3920) from The White House Gift Shop that commemorated the meeting between Donald Trump and Vladimir Putin that took place in Helsinki, Finland. Let’s say I did it for sentimental reasons.

After checking the impressive front of this coin, I switched my attention to the back.  And was shocked…

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The Beginning of Good Days (An Optimist’s View)

I found a summary that tells us a story of technology, the development of weapons, human hopes, and human fears.  The “doomsday clock” records.

As you may see in the picture below, a group of US scientists working in the field of nuclear technology have created this “doomsday clock” after the invention of the atomic bomb to alert the world of how close we are to its end.

doomsday-clock

Recently, the same folks have updated the clock to “2.5 minutes till the end,”  which is the closest time to “midnight” since 1953, when two superpowers first tested their H-bombs.  The message of this recent announcement is pretty gloomy and pessimistic and I decided to add my own interpretation to it− an optimistic view on this very subject.

I want to add here that there is little science in my interpretation of this data.  However, the data itself is not based on science either, but rather on a mix of facts, opinions, emotions, agendas, etc.  So, I think I can be forgiven.

Anyway, my view of the same data is different and very positive:

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Weekly Global Tech News, May 6, 2019

This is a guest post from Mike Montemorra, who is a technology guru with long and successful career in computer industry. Mike keeps an eye on the latest developments in computer and storage technology and publishes his observations weekly. Below is his brief summary of what was important in the past week.


What did we learn last week?

Hi Folks,

Here are some interesting tech bits from the weeks’ news.  I hope you find these both interesting and useful.  Enjoy!

Our friends at Backblaze have published another update to their HDD reliability stats, this one through CQ119.   It looks like Toshiba’s 9 disk MG07 drives are performing quite well (they did a very nice job of engineering this 9-disk platform), but Backblaze is currently a heavy Seagate drive user and those drives play reasonably well given the number of drive-days in their data set.  They also noted that they are testing a handful of both HAMR and MAMR drives… we can’t wait to see what they publish on these drives.

This 3 foot high stack represents a scale model of a small section of a 96 layer flash device!    Jim Handy was lucky enough to be offered a bit of a tutorial on what 3D flash looks like at the nano-scale.  Watch the video in here and then ponder the roadmaps touting 128 and perhaps even 256 layer 3D flash!  That layer cake might just tip over…

It’s pretty cool to watch MRAM technology maturing, but for those of us awaiting its use as a DRAM replacement, it is a painfully slow maturation taking place.  However, it looks like good progress is being made on spin-orbit torque (SOT) MRAM and that CY19 may just be the year we see eMRAM emerging as a non-volatile DRAM replacement technology in some applications. Note that thie work shown here was done in the lab on standalone devices, not sitting on top of topography where some of these effects might be a lot harder to control, but it is great progress nonetheless.

Here’s an interesting spin on host-managed SMR system architecture with a proposal that should improve the data efficiency of these systems.  As these types of server architectures proliferate, we can expect some of the best ideas proposed by these and other authors will become more widely used.

We’ve talked about the potential for bismuth ferrite (BiFeO3) in the past but it now looks like this material has some very interesting properties if only they could be harnessed into a working storage system.  Research into how to make this happen has yet to happen, so don’t be looking for those 250TB hard drives any time soon…

As hybrid data centers proliferate, it more important than ever to reduce latency between sometimes widely spaced cloud nodes.  The photonics industry is doing a good job of attempting to keep up with the massive datarate transmission requirements over long distances using the technologies like WDM (Wavelength Division Multiplexing) along multimode fiber optic cable at speeds up to 400Gb/s (400GbE) at distances of up to 100km (~62 miles).   Cool!  These modems have adaptable data speed/fidelity algorithms and are capable of even higher datarates at shorter distances… 

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Weekly Global Tech News, April 29, 2019

This is a guest post from Mike Montemorra, who is a technology guru with long and successful career in computer industry. Mike keeps an eye on the latest developments in computer and storage technology and publishes his observations weekly. Below is his brief summary of what was important in the past week.


What did we learn last week?

This is shaping up to be an eventful spring, both in technology as well as outside where everthing is growing and greening up nicely…  (for those of you in California, I suppose your green hills are giving way to brown as the rainy season ends, but hey, at least the resevoirs are full)…   I hope you find some of these new items from the week provocative.

As expected, there is continued growth in the server segment which will include AFA’s as well as hybrid server systems, plus more revolutionary systems that include QLC flash instead of HDD’s.  This business is growing and changing!

… and to underscore the potential for growth in this area, at least one AFA supplier (Pure Storage) is investing in hybrid cloud and object storage (Compuverde) to hedge his bets across this rapidly expanding product space.

…but as we all know, the solid state memory industry is in a slump at least through the first half of this year.  IC Insights has been tracking this cyclicity and CY19 does not look good if you believe their predictions.  Let’s hope the dip is brief and followed rapidly by another boom.

Perhaps client products like this Intel Optane H10 will help us move rapidly out of the current semi slump… there are plenty of memory chips on this device.  This performance review of the H10 shows it will help improve ultrabook performance, but it is not cheap and only works with compatible Intel processors.

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Deep Learning, Part 1: Not as Deep as You Think

Learn about Deep Learning from one of the leading analytics company…

The Official Blog of BigML.com

Gary Marcus has emerged as one of deep learning’s chief skeptics. In a recent interview, and a slightly less recent medium post, he discusses his feud with deep learning pioneer Yann LeCun and some of his views on how deep learning is overhyped.

I find the whole thing entertaining, but at many times LeCun and Marcus are talking past each other more than with each other. Marcus seems to me to be either unaware of or ignoring certain truths about machine learning and LeCun seems to basically agree with Marcus’ ideas in a way that’s unsatisfying for Marcus.

The temptation for me to brush 10 years of dust off of my professor hat is too much to ignore. Outside observers could benefit greatly from some additional context in this discussion and in this series of posts I’ll be happy to provide some. Most important here, in my opinion…

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