Societal growth depends on IT

Read an interesting article the other day in SciencDaily (IT played a key role in growth of ancient civilizations) and a Phys.Org article (Information drove development of early states) both of which were reporting on a Nature article (Scale and information processing thresholds in Holocene social evolution) which discussed how the growth of society during ancient times was directly correlated to the information processing capabilities they possessed. In these articles IT meant writing, accounting, currency, etc., relatively primitive forms of IT but IT nonetheless.

Seshat: Global History Databank

What the researchers were able to do was to use the Seshat: Global History Databank which “systematically collects what is currently known about the social and political organization of human societies and how civilizations have evolved over time” and use the data to analyze the use of IT by societies.

We have talked about Seschat before (See our Data Analysis of History post)

The Seshat databank holds information on 30 (natural) geographical areas (NGA), ~400 societies and, their history from 4000 BCE to 1900CE.

Seschat has a ~100 page Code Book that identifies what kinds of information to collect on each society, how it is to be estimated, identified, listed, etc. to normalize the data in their databank. Their Code Book provides essential guidelines on how to gather the ~1500 variables collected on societies.

IT drives society growth

The researchers used the Seshat DB and ran a statistical principal component analysis (PCA) of the data to try to ascertain what drove society’s growth.

PCA (see wikipedia Principal Component Analysis article) essentially produces a list of variables and their inter-relationships. Their combined inter-relationships is essentially a percentage (%Var) of explanatory power in how much those variables explains the variance of all variables. PCA can be one, two, three or N-dimensional.

The researchers took Seshat 51 society variables and combined them into 9 (societal) complexity characteristics (CC)s and did a PCA of those variables across all the (285) society’s information available at the time.

Fig, 2 says that the average PC1 component of all societies is driven by the changes (increases and decreases) in PC2 components. Decreases of PC2 depend on those elements of PC2 which are negative and increases in PC2 depend on those elements of PC2 which are negative.

The elements in PC2 that provide the largest positive impacts are writing (.31), texts (.24), money (.28), infrastructure (.12) and gvrnmnt (.06). The elements in PC2 that provide the largest negative impacts are PolTerr (polity area, -0.35), CapPop (capital population, -0.27), PolPop (polity population, -0.25) and levels (?, -0.15). Below is another way to look at this data.

The positive PC2 CC’s are tracked with the red line and the negative PC2 CC’s are tracked with the blue line. The black line is the summation of the blue and red lines and is effectively equal to the blue line in Fig 2 above.

The researchers suggest that the inflection points in Fig 2 and the black line in Fig 3),represent societal information processing thresholds. Once these IT thresholds have passed they change the direction that PC2 takes on after that point

In Fig4 they have disaggregated the information averaged in Fig. 2 & 3 and show PC2 and PC1 trajectories for all 285 societies tracked in the Seshat DB. Over time as PC1 goes more positive, societie, start to converge on effectively the same level of PC2 . At earlier times, societies tend to be more heterogeneous with varying PC2 (and PC1) values.

Essentially, societies IT processing characteristics tend to start out highly differentiated but over time as societies grow, IT processing capabilities tend to converge and lead to the same levels of societal growth

Classifying societies by I

The Kadashev scale (see wikipedia Kardashev scale article) identifes levels or types of civilizations using their energy consumption. For example, The Kardashev scale lists the types of civilizations as follows:

  • Type I Civilization can use and control all the energy available on its planet,
  • Type II Civilization can use and control all the energy available in its planetary system (its star and all the planets/other objects in orbit around it).
  • Type III Civilization can use and control all the energy available in its galaxy

I can’t help but think that a more accurate scale for civilization, society or a polity’s level would a scale based on its information processing power.

We could call this the Shin scale (named after the primary author of the Nature paper or the Shin-Price-Wolpert-Shimao-Tracy-Kohler scale). The Shin scale would list societies based on their IT levels.

  • Type A Societies have non-existant IT (writing, money, texts, money & infrastructure) which severely limits their population and territorial size
  • Type B Societies have primitive forms of IT (writing, money, texts, money & infrastructure, ~MB (10**6) of data) which allows these societies to expand to their natural boundaries (with a pop of ~10M).
  • Type C Societies have normal (2020) levels of IT (world wide Internet with billions of connected smart phones, millions of servers, ZB (10**21) of data, etc.) which allows societies to expand beyond their natural boundaries across the whole planet (pop of ~10B).
  • Type D Societies have high levels of IT (speculation here but quintillion connected smart dust devices, trillion (10**12) servers, 10**36 bytes of data) which allows societies to expand beyond their home planet (pop of ~10T).
  • Type E Societies have high levels of IT (more speculation here, 10**36 smart molecules, quintillion (10**18) servers, 10**51 bytes of data ) which allows societies to expand beyond their home planetary system (pop of ~10Q).

I’d list Type F societies here but a can’t think of anything smaller than a molecule that could potentially be smart — perhaps this signifies a lack of imagination on my part.

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OFA DNNs, cutting the carbon out of AI

Read an article (Reducing the carbon footprint of AI… in Science Daily) the other day about a new approach to reducing the energy demands for AI deep neural net (DNN) training and inferencing. The article was reporting on a similar piece in MIT News but both were discussing a technique original outlined in a ICLR 2020 (Int. Conf. on Learning Representations) paper, Once-for-all: Train one network & specialize it for efficient deployment.

The problem stems from the amount of energy it takes to train a DNN and use it for inferencing. In most cases, training and (more importantly) inferencing can take place on many different computational environments, from IOT devices, to cars, to HPC super clusters and everything in between. In order to create DNN inferencing algorithms for use in all these environments, one would have to train a different DNN for each. Moreover, if you’re doing image recognition applications, resolution levels matter. Resolution levels would represent a whole set of more required DNNs that would need to be trained.

The authors of the paper suggest there’s a better approach. Train one large OFA (once-for-all) DNN, that covers the finest resolution and largest neural net required in such a way that smaller, sub-nets could be extracted and deployed for less weighty computational and lower resolution deployments.

The authors contend the OFA approach takes less overall computation (and energy) to create and deploy than training multiple times for each possible resolution and deployment environment. It does take more energy to train than training a few (4-7 judging by the chart) DNNs, but that can be amortized over a vastly larger set of deployments.

OFA DNN explained

Essentially the approach is to train one large (OFA) DNN, with sub-nets that can be used by themselves. The OFA DNN sub-nets have been optimized for different deployment dimensions such as DNN model width, depth and kernel size as well as resolution levels.

While DNN width is purely the number of numeric weights in each layer, and DNN depth is the number of layers, Kernel size is not as well known. Kernels were introduced in convolutional neural networks (CovNets) to identify the number of features that are to be recognized. For example, in human faces these could be mouths, noses, eyes, etc. All these dimensions + resolution levels are used to identify all possible deployment options for an OFA DNN.

OFA secrets

One key to the OFA success is that any model (sub-network) selected actually shares the weights of all of its larger brethren. That way all the (sub-network) models can be represented by the same DNN and just selecting the dimensions of interest for your application. If you were to create each and every DNN, the number would be on the order of 10**19 DNNs for the example cited in the paper with depth using {2,3,4) layers, width using {3,4,6} and kernel sizes over 25 different resolution levels.

In order to do something like OFA, one would need to train for different objectives (once for each different resolution, depth, width and kernel size). But rather than doing that, OFA uses an approach which attempts to shrink all dimensions at the same time and then fine tunes that subsets NN weights for accuracy. They call this approach progressive shrinking.

Progressive shrinking, training for different dimensions

Essentially they train first with the largest value for each dimension (the complete DNN) and then in subsequent training epochs reduce one or more dimensions required for the various deployments and just train that subset. But these subsequent training passes always use the pre-trained larger DNN weights. As they gradually pick off and train for every possible deployment dimension, the process modifies just those weights in that configuration. This way the weights of the largest DNN are optimized for all the smaller dimensions required. And as a result, one can extract a (defined) subnet with the dimensions needed for your inferencing deployments.

They use a couple of tricks when training the subsets. For example, when training for smaller kernel sizes, they use the center most kernels and transform their weights using a transformation matrix to improve accuracy with less kernels. When training for smaller depths, they use the first layers in the DNN and ignore any layers lower in the model. Training for smaller widths, they sort each layer for the highest weights, thus ensuring they retain those parameters that provide the most sensitivity.

It’s sort of like multiple video encodings in a single file. Rather than having a separate file for every video encoding format (Mpeg 2, Mpeg 4, HVEC, etc.), you have one file, with all encoding formats embedded within it. If for example you needed Mpeg-4, one could just extract those elements of the video file representing that encoding level

OFA DNN results

In order to do OFA, one must identify, ahead of time, all the potential inferencing deployments (depth, width, kernel sizes) and resolution levels to support. But in the end, you have a one size fits all trained DNN whose sub-nets can be selected and deployed for any of the pre-specified deployments.

The authors have shown (see table and figure above) that OFA beats (in energy consumed and accuracy level) other State of the Art (SOTA) and Neural (network) Architectural Search (NAS) approaches to training multiple DNNs.

The report goes on to discuss how OFA could be optimized to support different latency (inferencing response time) requirements as well as diverse hardware architectures (CPU, GPU, FPGA, etc.).

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When I first heard of OFA DNN, I thought we were on the road to artificial general intelligence but this is much more specialized than that. It’s unclear to me how many AI DNNs have enough different deployment environments to warrant the use of OFA but with the proliferation of AI DNNs for IoT, automobiles, robots, etc. their will come a time soon where OFA DNNs and its competition will become much more important.

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Breaking optical data transmission speed records

Read an article this week about records being made in optical transmission speeds (see IEEE Spectrum, Optical labs set terabit records). Although these are all lab based records, the (data center) single mode optical transmission speed shown below is not far behind the single mode fibre speed commercially available today. But the multi-mode long haul (undersea transmission) speed record below will probably take a while longer until it’s ready for prime time.

First up, data center optical transmission speeds

Not sure what your data center transmission rates are but it seems pretty typical to see 100Gbps these days and inter switch at 200Gbps are commercially available. Last year at their annual Optical Fiber Communications (OFC) conference, the industry was releasing commercial availability of 400Gbps and pushing to achieve 800Gbps soon.

Since then, the researchers at Nokia Bell Labs have been able to transmit 1.52Tbps through a single mode fiber over 80 km distance. (Unclear, why a data center needs an 80km single mode fibre link but maybe this is more for a metro area than just a datacenter.

Diagram of a single mode (SM) optical fiber: 1.- Core 8-10 µm; 2.- Cladding 125 µm; 3.- Buffer 250 µm; & 4.- Jacket 400 µm

The key to transmitting data faster across single mode fibre, is how quickly one can encode/decode data (symbols) both on the digital to analog encoding (transmitting) end and the analog to digital decoding (receiving) end.

The team at Nokia used a new generation silicon-germanium chip (55nm CMOS process) able to generate 128 gigabaud symbol transmission (encoding/decoding) with 6.2 bits per symbol across single mode fiber.

Using optical erbium amplifiers, the team at Nokia was able to achieve 1.4Tbps over 240km of single mode fibre.

A wall-mount cabinet containing optical fiber interconnects. The yellow cables are single mode fibers; the orange and aqua cables are multi-mode fibers: 50/125 µm OM2 and 50/125 µm OM3 fibers respectively.

Used to be that transmitting data across single mode fibre was all about how quickly one could turn laser/light on and off. These days, with coherent transmission, data is being encoded/decoded in amplitude modulation, phase modulation and polarization (see Coherent data transmission defined article).

Nokia Lab’s is attempting to double the current 800Gbps data transmission speed or reach 1.6Tbps. At 1.52Tbps, they’re not far off that mark.

It’s somewhat surprising that optical single mode fibre technology is advancing so rapidly and yet, at the same time, commercially available technology is not that far behind.

Long haul optical transmission speed

Undersea or long haul optical transmission uses multi-core/mode fibre to transmit data across continents or an ocean. With multi-core/multi-mode fibre researchers and the Japan National Institute for Communications Technology (NICT) have demonstrated a 3 core, 125 micrometer wide long haul optical fibre transmission system that is able to transmit 172Tbps.

The new technology utilizes close-coupled multi-core fibre where signals in each individual core end up intentionally coupled with one another creating a sort of optical MIMO (Multi-input/Multi-output) transmission mechanism which can be disentangled with less complex electronics.

Although the technology is not ready for prime time, the closest competing technology is a 6-core fiber transmission cable which can transmit 144Tbps. Deployments of that cable are said to be starting soon.

Shouldn’t there be a Moore’s law for optical transmission speeds

Ran across this chart in a LightTalk Blog discussing how Moore’s law and optical transmission speeds are tracking one another. It seems to me that there’s a need for a Moore’s law for optical cable bandwidth. The blog post suggests that there’s a high correlation between Moore’s law and optical fiber bandwidth.

Indeed, any digital to analog optical encoding/decoding would involve TTL, by definition so there’s at least a high correlation between speed of electronic switching/processing and bandwidth. But number of transistors (as the chart shows) and optical bandwidth doesn’t seem to make as much sense probably makes the correlation evident. With the possible exception that processing speed is highly correlated with transistor counts these days.

But seeing the chart above shows that optical bandwidth and transistor counts are following each very closely.

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So, we all thought 100Gbps was great, 200Gbps was extraordinary and anything over that was wishful thinking. With, 400Gbps, 800 Gbps and 1.6Tbps all rolling out soon, data center transmission bottlenecks will become a thing in the past.

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DNA storage using nicks

Read an article the other day in Scientific American (“Punch card” DNA …) which was reporting on a Nature Magazine Article (DNA punch cards for storing data… ). The articles discussed a new approach to storing (and encoding) data into DNA sequences.

We have talked about DNA storage over the years (most recently, see our Random access DNA object storage post) so it’s been under study for almost a decade.

In prior research on DNA storage, scientists encoded data directly into the nucleotides used to store genetic information. As you may recall, there are two complementary nucleotides A-T (adenine-thymine) and G-C (guanine-cytosine) that constitute the genetic code in a DNA strand. One could use one of these pairs to encode a 1 bit and the other for a 0 bit and just lay them out along a DNA strand.

The main problem with nucleotide encoding of data in DNA is that it’s slow to write and read and very error prone (storing data in DNA separate nucleotides is a lossy data storage). Researchers have now come up with a better way.

Using DNA nicks to store bits

One could encode information in DNA by utilizing the topology of a DNA strand. Each DNA strand is actually made up of a sugar phosphate back bone with a nucleotide (A, C, G or T) hanging off of it, and then a hydrogen bond to its nucleotide complement (T, G, C or A, respectively) which is attached to another sugar phosphate backbone.

It appears that one can deform the sugar phosphate back bone at certain positions and retain an intact DNA strand. It’s in this deformation that the researchers are encoding bits and they call this a “DNA nick”.

Writing DNA nick data

The researchers have taken a standard DNA strand (E-coli), and identified unique sites on it that they can nick to encode data. They have identified multiple (mostly unique) sites for nick data along this DNA, the scientists call “registers” but we would call sectors or segments. Each DNA sector can contain a certain amount of nick data, say 5 to 10 bits. The selected DNA strand has enough unique sectors to record 80 bits (10 bytes) of data. Not quite a punch card (80 bytes of data) but it’s early yet.

Each register or sector is made up of 450 base (nucleotide) pairs. As DNA has two separate strands connected together, the researchers can increase DNA nick storage density by writing both strands creating a sort of two sided punch card. They use this other or alternate (“anti-sense”) side of the DNA strand nicks for the value “2”. We would have thought they would have used the absent of a nick in this alternate strand as being “3” but they seem to just use it as another way to indicate “0” .

The researchers found an enzyme they could use to nick a specific position on a DNA strand called the PfAgo (Pyrococcus furiosus Argonaute) enzyme. The enzyme can de designed to nick distinct locations and register (sectors) along the DNA strand. They designed 1024 (2**10) versions of this enzyme to create all possible 10 bit data patterns for each sector on the DNA strand.

Writing DNA nick data is done via adding the proper enzyme combinations to a solution with the DNA strand. All sector writes are done in parallel and it takes about 40 minutes.

Also the same PfAgo enzyme sequence is able to write (nick) multiple DNA strands without additional effort. So we can replicate the data as many times as there are DNA strands in the solution, or replicating the DNA nick data for disaster recovery.

Reading DNA nick data

Reading the DNA nick data is a bit more complicated.

In Figure 1 the read process starts by by denaturing (splitting dual strands into single strands dsDNA) and then splitting the single strands (ssDNA) up based on register or sector length which are then sequenced. The specific register (sector) sequences are identified in the sequence data and can then be read/decoded and placed in the 80 bit string. The current read process is destructive of the DNA strand (read once).

There was no information on the read time but my guess is it takes hours to perform. Another (faster) approach uses a “two-dimensional (2D) solid-state nanopore membrane” that can read the nick information directly from a DNA string without dsDNA-ssDNA steps. Also this approach is non-destructive, so the same DNA strand could be read multiple times.

Other storage characteristics of nicked DNA

Given the register nature of the nicked DNA data organization, it appears that data can be read and written randomly, rather than sequentially. So nicked DNA storage is by definition, a random access device.

Although not discussed in the paper, it appears as if the DNA nicked data can be modified. That is the same DNA string could have its data be modified (written multiple times).

The researcher claim that nicked DNA storage is so reliable that there is no need for error correction. I’m skeptical but it does appear to be more reliable than previous generations of DNA storage encoding. However, there is a possibility that during destructive read out we could lose a register or two. Yes one would know that the register bits are lost which is good. But some level of ECC could be used to reconstruct any lost register bits, with some reduction in data density.

The one significant advantage of DNA storage has always been its exceptional data density or bits stored per volume. Nicked storage reduces this volumetric density significantly, i.e, 10 bits per 450 (+ some additional DNA base pairs required for register spacing) base pairs or so nicked DNA storage reduces DNA storage volumetric density by at least a factor of 45X. Current DNA storage is capable of storing 215M GB per gram or 215 PB/gram. Reducing this by let’s say 100X, would still be a significant storage density at ~2PB/gram.

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New science used to combat COVID-19 disease

Read an article last week in Science Magazine (A completely new culture on doing research… ) on how the way science is done to combat disease has changed the last few years.

In the olden days (~3-5 years ago), disease outbreaks would generate a slew of research papers to be written, submitted for publication and there they would sit, until peer-reviewed, after which they might get published for the world to see for the first time. Estimates I’ve seen say that the scientific research publishing process takes anywhere from one month (very fast) to 4-8 months, assuming no major revisions are required.

With the emergence of the Zika virus and recent Ebola outbreaks, more and more biological research papers have become available through pre-print servers. These are web-sites which accept any research before publication (pre-print), posting the research for all to see, comment and understand.

Open science via pre-print

Most of these pre-print servers focus on specific areas of science. For example bioRxiv is a pre-print server focused on Biology and medRxiv is for health sciences. On the other hand, arXiv is a pre-print server for “physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.” These are just a sampling of what’s available today.

In the past, scientific journals would not accept research that had been published before. But this slowly change as well. Now most scientific journals have policies gol pre-print publication and will also publish them if they deem it worthwhile, (see wikipedia article List of academic journals by pre-print policies).

As of today (9 March 2020) ,on biorXiv there are 423 papers with keyword=”coronavirus” and 52 papers with the keyword COVID-19, some of these may be the same. The newest (Substrate specificity profiling of SARS-CoV-2 Mpro protease provides basis for anti-COVID-19 drug design) was published on 3/7/2020. The last sentence in their abstract says “The results of our work provide a structural framework for the design of inhibitors as antiviral agents or diagnostic tests.” The oldest on bioRxiv is dated 23 January 2020. Similarly, there are 326 papers on medRxiv with the keyword “coronavirus”, the newest published 5 March 2020.

Pre-print research is getting out in the open much sooner than ever before. But the downside, is that pre-print papers may have serious mistakes or omissions in them as they are not peer-reviewed. So the cost of rapid openness is the possibility that some research may be outright wrong, badly done, or lead researchers down blind alleys.

However, the upside is any bad research can be vetted sooner, if it’s open to the world. We see similar problems with open source software, some of it can be buggy or outright failure prone. But having it be open, and if it’s popular, many people will see the problems (or bugs) and fixes will be rapidly created to solve them. With pre-print research, the comment list associated with a pre-print can be long and often will identify problems in the research.

Open science through open journals

In addition to pre-print servers , we are also starting to see the increasing use of open scientific journals such as PLOS to publish formal research.

PLOS has a number of open journals focused on specific arenas of research, such as PLOS Biology, PLOS Pathogyns, PLOS Medicine, etc.

Researchers or their institutions have to pay a nominal fee to publish in PLOS. But all PLOS publications are fully expert, peer-reviewed. But unlike research from say Nature, IEEE or other scientific journals, PLOS papers are free to anyone, and are widely available. (However, I just saw that SpringerNature is making all their coronavirus research free).

Open science via open data(sets)

Another aspect of scientific research that has undergone change of late is the sharing and publication of data used in the research.

Nature has a list of recommended data repositories. All these data repositories seem to be hosted by FAIRsharing at the University of Oxford and run by their Data Readiness Group. They list 1349 databases of which the vast majority (1250) are for the natural sciences with over 1380 standards used for data to be registered with FAIRsharing.

We’ve discussed similar data repositories in the past (please see Data banks, data deposits and data withdrawals, UK BioBank, Big open data leads to citizen science, etc). Having a place to store data used in research papers makes it easier to understand and replicate science.

Collaboration software

The other change to research activities is the use of collaborative software such as Slack. Researchers at UW Madison were already using Slack to collaborate on research but when Coronavirus went public, they Slack could help here too. So they created a group (or channel) under their Slack site called “Wu-han Clan” and invited 69 researchers from around the world. The day after they created it they held their first teleconference.

Other collaboration software exists today but Slack seems most popular. We use Slack for communications in our robotics club, blogging group, a couple of companies we work with, etc. Each has a number of invite-only channels, where channel members can post text, (data) files, links and just about anything else of interest to the channel.

Although I have not been invited to participate in Wu-han Clan (yet), I assume they usee Slack to discuss and vet (pre-print) research, discuss research needs, and other ways to avert the pandemic.

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So there you have it. Coronavirus scientific research is happening at warp speed compared to diseases of yore. Technologies to support this sped up research have all emerged over the last five to 10 years but are now being put to use more than ever before. Such technological advancement should lead to faster diagnosis, lower worldwide infection/mortality rates and a quicker medical solution.

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Breaking IoT security

Earth globe within a locked cage

Read an article the other day (Researchers exploit low entropy of IoT devices to break RSA certificates) about researchers cracking IoT device security and breaking their public key encryption keys. The report focused on PKI and RSA certificates and IoT devices. The article mentioned the research paper describing the attack in more detail.

safe 'n green by Robert S. Donovan (cc) (from flickr)
safe ‘n green by Robert S. Donovan (cc) (from flickr)

RSA certificates publish a public key and the digital signature of the certificate and identify the device that owns the certificate.

What the researchers were able to show was that ~250K keys in IoT device RSA certificates were insecure. They were able to compromise the 250K RSA certificates using a single Microsoft Azure VM and about $3K of computer time.

It turns out that if two RSA certificate public keys share the same factor, it’s much easier to determine the greatest common devisor GCD) of the two public keys than it is to factor any one of them. And once you have the GCD of the two keys, it’s relatively trivial to determine the other factor in a public key. And that’s just what they did.

Public key infrastructure (PKI) encryption depends on asymmetric cryptography using a “public” key to encrypt messages (or to encrypt a one time key to be used in later encryption of messages) and the use of a “private” key to decrypt the message (or keys) and sign digital certificates. There are certificate authorities and a number of other elements used in PKI but the asymmetric cryptography at its heart, rests on the foundation of the difficulty in factoring large numbers but those large numbers need to be random and prime.

True randomness is hard

Just some of the recently donated seeds that are being added to the Reading Food Growing Network seed swap boxes, including some Polish gherkin seeds.

The problem starts with generating truly random numbers in a digital computer. Digital algorithms typically depend on a computer to perform the some set of instructions, in the same way and sequence so as to get the same answer every time we run the algorithm.

But if you want random numbers this predictability of always coming up with the same answer each time results in non-random numbers (or rather random numbers that are the same each time you run the algorithm). So to get around this, most random number generators can make use of a (random) seed which is used as an input to the algorithm to generate random numbers.

However, this seed needs to be a random number. But to create a random number it needs to be generated not with instructions but using something outside the digital computer. One approach noted above is to use a human typing keys to generate a random number to be used as a seed.

The researchers exploited the fact that most IoT devices don’t use a random (enough) seed for their PKI key generation. And they were able to use the GCD trick to figure out the factors to the PKI.

But the lack of true randomness (or entropy) is the real problem. Somehow, these devices need to have a cheap and effective way to generate a random seed. Until this can be found, they will be subject to these sorts of attacks.

… but not impossible to obtain

I remember in times past when tasked to create a public key-private key pair I had to type some random characters. The Public key encryption algorithm used the inter-character time interval of my typing to generate a random seed that was then used to generate the key pair used in the public key. I believe the two keys also need to be prime numbers.

Earth globe within a locked cage

Perhaps a better approach would be to assign them keys from a centralized key distributor. That way the randomness could be controlled by the (key) distributor.

There are other approaches that depend on the sensors available to an IoT device. If the device has a camera or mic, taking raw data from the camera or sound sensor and doing a numerical transform on them may suffice. Strain gauges, liquid levels, temperature, humidity, wind speed, etc. all of these devices have something which senses the world around them and many of these are, at their base, analog sensors. Reading and converting some portion of these analog signals from raw analog to a digital random seed could be very effective way to generate true(r) randomness.

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The paper has much more information about the attack and their results if your interested. They said that ~50% of the compromised devices were from a large network supplier. Such suppliers probably also have a vast majority of devices deployed. Still it’s troubling, nonetheless.

Until changes are made to IoT devices, they will continue to be insecure. Not as much of a problem when they are read only sensors but when the information they sense is used by robots or other automation to make decisions about actions, then having insecure IoT becomes a safety issue.

This is not the first time such an attack was attempted and each time, it’s been very successful. That alone should be cause for alarm. But IoT and similar devices are hard to patch in the field and their continuing insecurity may be more of a result of the difficulty of updating a large install base than anything else.

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Internet of Tires

Read an article a couple of weeks back (An internet of tires?… IEEE Spectrum) and can’t seem to get it out of my head. Pirelli, a European tire manufacturer was demonstrating a smart tire or as they call it, their new Cyber Tyre.

The Cyber Tyre includes accelerometer(s) in its rubber, that can be used to sense the pavement/road surface conditions. Cyber Tyre can communicate surface conditions to the car and using the car’s 5G, to other cars (of same make) to tell them of problems with surface adhesion (hydroplaning, ice, other traction issues).

Presumably the accelerometers in the Cyber Tyre measure acceleration changes of individual tires as they rotate. Any rapid acceleration change, could potentially be used to determine whether the car has lost traction due and why.

They tested the new tires out at a (1/3rd mile) test track on top of a Fiat factory, using Audi A8 automobiles and 5G. Unclear why this had to wait for 5G but it’s possible that using 5G, the Cyber Tyre and the car could possibly log and transmit such information back to the manufacturer of the car or tire.

Accelerometers have become dirt cheap over the last decade as smart phones have taken off. So, it was only a matter of time before they found use in new and interesting applications and the Cyber Tyre is just the latest.

Internet of Vehicles

Presumably the car, with Cyber Tyres on it, communicates road hazard information to other cars using 5G and vehicle to vehicle (V2V) communication protocols or perhaps to municipal or state authorities. This way highway signage could display hazardous conditions ahead.

Audi has a website devoted to Car to X communications which has embedded certain Audi vehicles (A4, A5 & Q7), with cellular communications, cameras and other sensors used to identify (recognize) signage, hazards, and other information and communicate this data to other Audi vehicles. This way owning an Audi, would plug you into this information flow.

Pirelli’s Cyber Car Concept

Prior to the Cyber Tyre, Pirelli introduced a Cyber Car concept that is supposedly rolling out this year. This version has tyres with real time pressure, temperature, (static) vertical load and a Tyre ID. Pirelli has been working with car manufacturers to roll out Cyber Car functionality.

The Tyre ID seems to be a file that can include anything that the tyre or automobile manufacturer wants. It sort of reminds me of a blockchain data blocks that could be used to validate tyre manufacturing provenance.

The vertical load sensor seems more important to car and tire manufacturers than consumers. But for electrical car owners, knowing car weight could help determine current battery load and thereby more precisely know how much charge is left in a battery.

Pirelli uses a proprietary algorithm to determine tread wear. This makes use of the other tyre sensors to predict wear and perhaps uses an AI DL algorithm to do this.

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ABS has been around for decades now and tire pressure sensors for over 10 years or so. My latest car has enough sensors to pretty much drive itself on the highway but not quite park itself as of yet. So it was only a matter of time before something like smart tires would show up.

But given their integration with car electronics systems, it would seem that this would only make sense for new cars that included a full set of Cyber Tyres. That is until all tire AND car manufacturers agreed to come up with a standard protocol to communicate such information. When that happens, consumers could chose any tire manufacturer and obtain have similar if not the same functionality from them.

I suppose someone had to be first to identify just what could be done with the electronics available today. Pirelli just happens to be it for now in the tire industry.

I just don’t want to have to upgrade tires every 24 months. And, if I have to wait a long time for my car to boot up and establish communications with my tires, I may just take a (dumb) bike.

Photo Credit(s):

Data analysis of history

Read an article the other day in The Guardian (History as a giant data set: how analyzing the past could save the future), which talks about this new discipline called cliodynamics (see wikipedia cliodynamics article). There was a Nature article (in 2012), Human Cycles: History as Science, which described cliodynamics in a bit more detail.

Cliodynamics uses mathematical systems theory on historical data to predict what will happen in the future for society. According to The Guardian and Nature articles, the originator of cliodynamics, Peter Turchin, predicted in 2010 that the world would change dramatically for the worse over the coming decade, with violence peaking in 2020.

What is cliodynamics

Cliodynamics depends on vast databases of historical data that has been amassed over the last decade or so. For instance, the Seshat Global History Databank (started in 2011, has 3 datasets: moralizing gods, axial age history [8th to 3rd cent. BCE], & social complexity), International Institute of Social History (est. 1935, in 2013 re-organized their collection to focus on data, has 33 dataverses ranging from data on apprenticeships, prices and wage history, strike history of various countries and time periods, etc. ), and Google NGRAM viewer (started in 2010, provides keyword statistics on Google BOOKs).

Cliodynamics uses the information from databases like the above to devise a mathematical model of the history of the world. From their mathematical model, cliodynamics researchers have discerned patterns or cycles in human endeavors that have persisted over centuries.

Cliodynamic cycles

Two of cycles of interest come to mind:

  • Secular cycle – this plays out over 2-3 centuries and starts out with a new egalitarian society that has low levels of inequality where the supply and demand for labor are roughly equal. Over time as population grows, the supply of labor outstrips demand and inequality increases. Elites then start to battle one another, war and political instability results in a new more equal society, re-starting the cycle .
  • Fathers and sons cycle – this plays out over 50 years and starts when the (fathers) generation responds violently to social injustice and the next (sons) generation resigns itself to injustice (or hopefully resolves it) until the next (fathers) generation sees injustice again and erupts violently re-starting the cycle over again. .

It’s this last cycle that Turchin predicted to peak again in 2020, the last one peaking in 1970 and the ones before that peaking in 1920 and 1870.

We’ve seen such theories before. In the 19th and 20th centuries there were plenty of historical theorist. Probably the most prominent was Marx but there were others as well.

The problem with cliodynamics, good data

Sparsity and accuracy of data has always been a problem with historical study. Much information is lost through natural or manmade disasters and much of what’s left is biased. Nonetheless, more and more data is being amassed of a historical nature every day, most of it quantitative and suitable to analysis.

Historical data, where available, can be assessed scientifically, and analyzed by using current tools such as data analytics, machine learning, & deep learning to ascertain trends and make predictions. And the more data available, the more accurate these analyses and predictions can become. Cliodynamics pre-dates much of these tools. but that’s no excuse for not to taking advantage of them.

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As for 2020, AI, automation and globalization has led and will lead to more job disruption. Inequality is also on the rise, at least throughout much of the west. And then there’s Brexit, USA elections and general mid-east turmoil that seems to all be on the horizon.

Stay tuned, 2020 seems only months away.

Photo Credits:

From Key Historic Figures of WW1 article, Mansell/Ghetty Images, (c) ThoughtCo

Anti War March (1968 Chicago) By David Wilson , CC BY 2.0, Link

Eleven times Americans have marched on Washington, (1920, Washington DC) (c) Smithsonian Magazine