Solar Superstorms and IT BC/DR

Very interesting research paper with a scary title “Solar Superstorms: Planning for an Internet Apocalypse“. It is about a Black Swan event which has actually already happened in 1859, a major solar Corona Mass Ejection (CME) which has some chance to happen in the next future. Without entering in any detail (the research paper is quite readable) the main point is that if a CME of 1859’s magnitude would hit earth today, the consequences would be catastrophic.  Apart from the impacts on the electric grid, and in particular to the long distance power distribution (but power operator should be aware of this threat), the research paper points out that there would be severe damages to satellites, in particular low-orbit ones, with possible total failure of satellite communication including GPS, television broadcasting and data (internet) transmission. But equivalently at risk are long distance communication cables, more noticeable submarine optical fibre cables. Actually, optical fibres per se would not be affected, but optical repeaters along the fibres at distances of 50 – 150 km at the bottom of the oceans would burn out and stop almost all communication between continents.

I remember years ago discussing a similar scenario with some physicist friends and wondering if it could have been a threat or not. It seems that it can be, but is the cost of mitigating this threat worth it?  Should we act today?

Mixed Results on AI/ML from Google

Artificial Intelligence, or better Machine Learning, is increasingly becoming part of everyday IT, but it is still unclear (at least to me) which are its real potentials, limits, risks etc.

For example, very recently there have been 2 somehow contradictory news from Google/Alphabet funded research in AI/ML:

  • the paper “Underspecification Presents Challenges for Credibility in Modern Machine Learning” (here the full paper) studies some possible reasons why “ML models often exhibit unexpectedly poor behaviour when they are deployed in real-world domains“, but it is unclear (at least to me) how much these Challenges can be overcome;
  • CASP has announced (here the announcement) that DeepMind AlphaFold has practically solved the problem of predicting how proteins fold, which is fundamental to find cures to almost all diseases, including cancer, dementia and even infectious diseases such as COVID-19.

Information, Science, Common Sense and Fake News

These days we drown in COVID-19 news, some reliable, some less, some … unclear.

One striking point is the difficulty of communicating scientific facts, whenever they are available, and how they get twisted, misinterpreted and even manipulated by almost everyone: from top leaders, politicians, journalists, communicators, “evangelists”, scientists down and down to even (hopefully not too much) myself.

Having a scientific background, I have been struggling to make sense of the numbers we can read on the news or social media, hear in television or on internet streaming. A few times I tried to make sense of it scientifically, always ending up in “not enough data” or “source and meaning of data unclear”. Numbers do not lie, but only if you know what they mean.

I tried to make sense of the information submerging us using “common sense” (whatever that means) and understanding even less. Actually I realised that “common sense” often leads us closer to give more credibility to Fake News than to authentic information.

This made me think back to the time when I was at the university doing physics. It is well known that in the scientific research world there are at least two major kinds of communicating issues:

  1. communicating with fellow researchers
  2. communicating with the public

and the second is a much bigger issue than the first.

Communication among fellow researchers has to be extremely clear and detailed, all data must be presented and clearly defined otherwise misunderstanding and misinterpretations are the inevitable consequence.  It is possible that a great number of different “opinions” of the experts we hear every day, are indeed based on this type of incomplete communication.

But these days this gets worse since these “opinions” are rushed to the public who understands something else entirely by using common sense and not the appropriate scientific approach. Of this I had personal experiences when I was asked by friends to explain some concepts of elementary particle physics: describing these concepts by means of analogies and not well defined ideas, often led my friends to conclusions which had nothing to do with physics, were wrong or plainly absurd. Obviously it was not my friends’ fault but mine: all of us interpret the information we receive with the logical tools we have, even if none is really appropriate.

So where does this bring us concerning COVID-19? Scientific information should be clearly and precisely presented to the public and in particular to our leaders and politicians, in a form in which data can be logically understood by the recipient. This is difficult but particularly important for information presented to the public by the news and by all information channels in times of crisis such as the one we are experiencing right now. Otherwise misunderstanding, misinterpretation and manipulation of information become too easy and common. Unfortunately recently this seems to have happened too often.

A New Theoretical Result on the Learnability of Machine Learning (AI)

Theoretical mathematical results have often little immediate practical application and in some cases initially can seem obvious. Still they usually are not obvious as such since it is quite different to imagine that a result holds true, and to prove it mathematically in a rigorous way. Moreover such a proof often helps explaining the reasons of the result and its possible applications.

Very recently a theoretical (mathematical) results in Machine Learning (the current main version of Artificial Intelligence) has been announced: the paper can be found in Nature here and a comment here .

Learnability can be defined as the ability to make predictions about a large data set by sampling a small number of data points. This is what usually Machine Learning does. The mathematical result is that, in general, this problem is ‘undecidable’, that is it is impossible to prove that it always exists a limited sampling set which allows to ‘learn’ (for example to always recognise a cat in an image from a sample of a limited number of cat’s images). Mathematicians have proven that Learnability is related to fundamental mathematical problems going back to Cantor’s set theory, the work of Gödel and Alan Turing, and it is related to the theory of compressibility of information.

This result poses some theoretical limits on what Machine Learning can ever achieve, even if it does not seem to have any immediate practical consequence.

A New Approach to Quantum Random Number Generators and news on Quantum Cryptography

I am still interested in developements in the area of Quantum phenomena which can be used in ICT and in particular in ICT Security. Recently there have been quite a few announcements of interest. Here are a some of them:

  • A scientific paper proposes on a new way of generating Quantum Random Number, that is ‘real random numbers’ (whatever that means) by using every day technology like the camera of our smart phone; this does not mean that the smart phone camera is enough to produce real random numbers (for the moment you still need a computer to process the data produced by it) but it is a sign that the technology is providing us with tools of unprecedented power, and soon our smart phone will be enough for a good many things;
  • New developments in Quantum Cryptography (se here and here for details) would make it easier to implement Quantum Cryptography in practice; this is nice, even if it does not changes dramatically the current status and relevance of Quantum Cryptography;
  • Another article (see here for a comment) leaves me instead quite puzzled: either I don’t understand it or there is something fundamentally flawed in the argument otherwise it will look like it is possible to obtain quantum effects in classical physics, which is just what it is not.

On D-Wave and Quantum Computing

I have been following at a distance since a few years the development of Quantum Computers. One of the more controversial approaches to Quantum Computing is the one proposed by D-Wave. D-Wave is also the only company which claims to have a specialized version of Quantum Computer ready to sell, and actually they did sell at least one Quantum Computer to a consortium made by Google, NASA, and the Universities Space Research Association.

What it is not yet clear is if it is really a Quantum computer, and even if it is, if it gives any advantages with respect to traditional computers. There are quite some different opinions about this, and this IEEE Spectrum article tries to understand where we stand now.


More Evidence for the Higgs Boson

At the 2013 Moriond Conference, CERN has released more data indicating that the particle discovered last year is really a Higgs boson, and it looks more like the Standard Model particle we studied in text books, see here for CERN announcement.

The ATLAS experiment has also published here some very nice animated plots which show how the measured events slowly build up statistics which give the above mentioned results.