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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AI Humor

Source: Dilbert.com

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Weekly Global Tech News, December 10, 2018

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?

The solid state memory market is dominated by a few players, so their moves are well tracked.  This interview with the Yole Group discusses current trends and why we may not see a large downturn as we enter a period of oversupply.

This week, WDC held their annual Investor Day with transcripts here.  They highlight the market opportunities and the potential exabyte growth and try to make it all look good in the face of the current downturn in flash and enterprise HDD businesses.  Long term, IoT and 5G should again exponentially expand the storage universe and when this happens, WDC’s vision should pay off (assuming they deliver to their vision).

This new carbide might have useful tribological properties.  Variants of this have been tried commercially, but this looks one to be more stable as well as more durable.  I don’t know if you can sputter this and while still retaining those nice properties, but it is worthy of further development.

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Principal Component Analysis (PCA): Dimensionality Reduction!

PCA is a statistical technique that transforms a dataset defined by possibly correlated variables (whose noise negatively affects the performance of your model) into a set of uncorrelated variables, called principal components. Read more below.

The Official Blog of BigML.com

The new BigML release is here! Join us on Thursday, December 20, 2018, at 10:00 AM PST (Portland, Oregon. GMT -08:00) / 07:00 PM CET (Valencia, Spain. GMT +01:00) for a FREE live webinar to discover the latest addition to the BigML platform. We will be showcasing Principal Component Analysis (PCA), a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. PCA is most commonly applied in fields with high dimensional data including bioinformatics, quantitative finance, and signal processing, among others.

Principal Component Analysis (PCA), available on the BigML Dashboard, API and WhizzML for automation as of December 20, 2018, is a statistical technique that transforms a dataset defined by possibly correlated variables (whose noise negatively affects the performance of your model) into a set of uncorrelated variables, called principal components. This technique is used as…

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Weekly Global Tech News, December 3, 2018

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?

If you’ve ever wondered how flash cells are programmed, wonder no more and read this from YeeTech and the Memory Guy.  The illustrative videos show why flash cell writes take so much longer to complete and highlights some of the ways bits can be transposed, causing errors that are sometimes tough for even LDPC to correct.

Source: Yeestor Microelectronics Co, Ltd.


…and if you think programming an individual cell to its correct level is difficult, consider programming an array of cells at various levels.  The reprogramming of a single bit (similar to what can be done with HDD) is the holy grail of solid state storage and is difficult to accomplish.  This article from Jim Handy (The Memory Guy) discusses the critical role of bit selectors in storage class memories.  They are a must have!


It looks like Samsung is on track to spend over $22.6B in CY18 on CapEx, much of this for their 7nm silicon fabs as well as for 3D flash.  This is pretty amazing given the dire predictions of oversupply in the industry recenntly, but it looks like these guys are getting ready for the next semi boom!  Something to ponder as we enter CY19, the first half of which may indeed be slow.


SMG QLC drive reviews show that this technology is marginally acceptable for client applications and probably not yet the ‘HDD killer’ technology promised for enterprise cold storage.  Also, right now, it is a lot more expensive than HDD’s but it does play well right up to the point that the SLC saturates and QLC writes begin.  Both Tom’s Hardware and Anandtech published reviews this week.

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Do We Need AI Government?

The question sounds intentionally provocative, but it is only so if you think this question pertains to the current world.  But if you think about life in the future, say, 50 to 100 years from now, this question becomes as real as the concept of singularity, machine intelligence exceeding that of a human, or transhumanism and humans merging with machines.

All of this might look like fiction for now, but not as something entirely impossible or contradictory to the fundamental laws of nature. So, let’s imagine that we will eventually manage to create an AI that is general enough to address questions of our society, economics, ecology, culture, and can interface with us in a human-like way – speak, listen, write.  Basically, imagine that we develop an artificial intelligence that matches ours in every possible way, and perhaps even exceeds our computational and analytical powers. Imagine that this is possible.

Now, think of the significant number of problems humans always have with the various ideas of governance we have experienced thus far:  

  • Authoritarianism (including absolute monarchy, aristocracy, oligarchy, and dictatorship) can work for some time, but depends too much on the personal characteristics of a leader. Eventually, power corrupts the individual, leading to great abuses, nepotism, neglect of the existing laws, disappearance of basic freedoms, police state, societal poverty and general suffering.
  • Democracy, as Churchill said, “is the worst form of government, except for all those other forms that have been tried from time to time.” This doesn’t sound like a compliment.  Democracy can be very inefficient at times and is often undermined by corruption of government and elected officials, lobbyism, one-sided control of mass-media, excessive bureaucracy, political nepotism (yes, even for democracy), and could “deform” over time into Potemkin’s Villages with great-looking facades but truly rotten guts.

Some people desire “anarchy” as a form of governance (where the centralized government is unnecessary) but this approach has never, to my knowledge, led to anything successful at any significant scale.

What is wrong with the above forms of governance and why are even the best of them still “the worst”?  

Sorry to say – the weakest link is always us.  The people.

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