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.

Photo Credit(s):

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.

Photo Credit(s):

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

Cambrian Explosion of AI DL app’s in industry and the world

I was at the NetApp Insight conference last week and recorded a podcast (see: GreyBeards Podcast) on what NetApp is doing in the AI DL (Deep Learning) space. On the podcast, we talked about a number of verticals that were deploying AI DL right now and using it to improve outcomes.

It was only is 2012 that AI DL broke out and pretty much conquered the speech recognition contest by improving recognition accuracy by leaps and bounds. Prior to that improvements had been very small and incremental at best. Here we are, just 7 years later and AI DL models are proliferating across industry and every other sector of the world economy.

DL applications in the real world

At the show. we talked about AI DL models being used in healthcare (radiological image analysis, cell counts for infection assessments), automotive (self driving cars), financial services (fraud detection), and retail (predicting how make up would look on someone).

And early this year, at HPE Discover, they discussed a new technique to share training data but still keep it private. In this case, they use block chain technology to publish and share a DL neural network model weights and other hyper parameters trained for some real world purpose.

Customers download and use the model in their day to day activities but record the data that their model analyzes and its predictions. They use this data to update (re-train) their DL neural net. They then publish their new neural net model weights and other parameters to all the other customers. Each customer of the model do the same, updating (re-training) their DL neural net.

At some point an owner or global model arbitrator takes all these individual model updates and aggregates the neural net weights, into a new neural net model and publishes the new model. And then the process starts over again. In this way, training data is never revealed, kept secure and private but DL model updates that result from re-training the model with secured private data would be available to any customer.

Recently, there’s been a slew of articles across many different organizations that show how AI DL is being adopted to work in different areas:

And that’s just a sample of the last few weeks of papers of AI DL activity.

Next Steps

All it takes is data, that can be quantified and classified. With data and classifications in hand, anyone can train a DL model that performs that classification. It doesn’t require GPU farms, decent CPUs are up to the task for TB of data.

But if you want better prediction/classificatoin accuracy, you will need more data which means longer AI DL training runs. So at some point, maybe at >100TB of data, or use AI DL training a lot, you may want that GPU farm.

The Deep Learning with Python book (my favorite) has a number of examples such as, sentiment analysis of text, median real estate pricing predictions, generating text that looks like an authors work, with maybe a dozen more that one can use to understand AI DL technology. But it’s not rocket science, I believe any qualified programmer could do it, with some serious study.

So the real question is what are you doing with your data to make use of AI DLmodels now?

I suppose the other question ought to be, how can you collect more data and classification information, to train more AI DL models?

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It’s great to be in the storage business.

Photo Credit(s):

Where should IoT data be processed – part 1

I was at FlashMemorySummit 2019 (FMS2019) this week and there was a lot of talk about computational storage (see our GBoS podcast with Scott Shadley, NGD Systems). There was also a lot of discussion about IoT and the need for data processing done at the edge (or in near-edge computing centers/edge clouds).

At the show, I was talking with Tom Leyden of Excelero and he mentioned there was a real need for some insight on how to determine where IoT data should be processed.

For our discussion let’s assume a multi-layered IoT architecture, with 1000s of sensors at the edge, 100s of near-edge processing/multiplexing stations, and 1 to 3 core data center or cloud regions. Data comes in from the sensors, is sent to near-edge processing/multiplexing and then to the core data center/cloud.

Data size

Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)
Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)

When deciding where to process data one key aspect is the size of the data. Tin GB or TB but given today’s world, can be PB as well. This lone parameter has multiple impacts and can affect many other considerations, such as the cost and time to transfer the data, cost of data storage, amount of time to process the data, etc. All of these sub-factors include the size of the data to be processed.

Data size can be the largest single determinant of where to process the data. If we are talking about GB of data, it could probably be processed anywhere from the sensor edge, to near-edge station, to core. But if we are talking about TB the processing requirements and time go up substantially and are unlikely to be available at the sensor edge, and may not be available at the near-edge station. And PB take this up to a whole other level and may require processing only at the core due to the infrastructure requirements.

Processing criticality

Human or machine safety may depend on quick processing of sensor data, e. g. in a self-driving car or a factory floor, flood guages, etc.. In these cases, some amount of data (sufficient to insure human/machinge safety) needs to be done at the lowest point in the hierarchy, with the processing power to perform this activity.

This could be in the self-driving car or factory automation that controls a mechanism. Similar situations would probably apply for any robots and auto pilots. Anywhere some IoT sensor array was used to control an entity, that could jeopardize the life of human(s) or the safety of machines would need to do safety level processing at the lowest level in the hierarchy.

If processing doesn’t involve safety, then it could potentially be done at the near-edge stations or at the core. .

Processing time and infrastructure requirements

Although we talked about this in data size above, infrastructure requirements must also play a part in where data is processed. Yes sensors are getting more intelligent and the same goes for near-edge stations. But if you’re processing the data multiple times, say for deep learning, it’s probably better to do this where there’s a bunch of GPUs and some way of keeping the data pipeline running efficiently. The same applies to any data analytics that distributes workloads and data across a gaggle of CPU cores, storage devices, network nodes, etc.

There’s also an efficiency component to this. Computational storage is all about how some workloads can better be accomplished at the storage layer. But the concept applies throughout the hierarchy. Given the infrastructure requirements to process the data, there’s probably one place where it makes the most sense to do this. If it takes a 100 CPU cores to process the data in a timely fashion, it’s probably not going to be done at the sensor level.

Data information funnel

We make the assumption that raw data comes in through sensors, and more processed data is sent to higher layers. This would mean at a minimum, some sort of data compression/compaction would need to be done at each layer below the core.

We were at a conference a while back where they talked about updating deep learning neural networks. It’s possible that each near-edge station could perform a mini-deep learning training cycle and share their learning with the core periodicals, which could then send this information back down to the lowest level to be used, (see our Swarm Intelligence @ #HPEDiscover post).

All this means that there’s a minimal level of processing of the data that needs to go on throughout the hierarchy between access point connections.

Pipe availability

binary data flow

The availability of a networking access point may also have some bearing on where data is processed. For example, a self driving car could generate TB of data a day, but access to a high speed, inexpensive data pipe to send that data may be limited to a service bay and/or a garage connection.

So some processing may need to be done between access point connections. This will need to take place at lower levels. That way, there would be no need to send the data while the car is out on the road but rather it could be sent whenever it’s attached to an access point.

Compliance/archive requirements

Any sensor data probably needs to be stored for a long time and as such will need access to a long term archive. Depending on the extent of this data, it may help dictate where processing is done. That is, if all the raw data needs to be held, then maybe the processing of that data can be deferred until it’s already at the core and on it’s way to archive.

However, any safety oriented data processing needs to be done at the lowest level and may need to be reprocessed higher up in the hierachy. This would be done to insure proper safety decisions were made. And needless the say all this data would need to be held.

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I started this post with 40 or more factors but that was overkill. In the above, I tried to summarize the 6 critical factors which I would use to determine where IoT data should be processed.

My intent is in a part 2 to this post to work through some examples. If there’s anyone example that you feel may be instructive, please let me know.

Also, if there’s other factors that you would use to determine where to process IoT data let me know.

Improving floating point

Read a post this week in Reddit pointing to an article that was from The Next Platform (New approach could sink floating point computation). It was all about changing IEEE floating point format to something better called posits, which was designed by noted computer architect, John Gustafson, et al, (see their paper Beating floating point at its own game: Posit arithmetic, for more info).

The problems with standard floating point have been known since they were first defined, in 1985 by the IEEE. As you may recall, an IEEE 754 floating point number has three parts a sign, an exponent and a mantissa (fraction or significand part). Both the exponent and mantissa can be negative.

IEEE defined floating point numbers

The IEEE 754 standard defines the following formats (see Floating point Floating -point arithmetic, for more info)

  • Half precision floating point, (added in 2008), has 1 sign bit (for the significand or mantissa), 5 exponent bits (indicating 2**-62 to 2**+64) and 10 significand bits for a total of 16 bits.
  • Single precision floating point, has 1 sign bit, 8 exponent bits (indicating 2**-126 to 2**+128) and 23 significand bits for a total of32 bits.
  • Double precision floating point, has 1 sign bit, 11 exponent bits (2**-1022 to 2**+1024) and 52 significand bits.
  • Quadrouple precision floating point, has 1 sign bit, 15 exponent bits (2**-16,382 to 2**+16,384) and 112 significand bits.

I believe Half precision was introduced to help speed up AI deep learning training and inferencing.

Some problems with the IEEE standard include, it supports -0 and +0 which have different representations and -∞ and +∞ as well as can be used to represent a number of unique, Not-a-Numbers or NaNs which are illegal floating point numbers. So when performing IEEE standard floating point arithmetic, one needs to check to see if a result is a NaN which would make it an illegal result, and must be wary when comparing numbers such as -0, +0 and -∞ , +∞. because, sigh, they are not equal.

Posits to the rescue

It’s all a bit technical (read the paper to find out) but posits don’t support -0 and +0, just 0 and there’s no -∞ or +∞ in posits either, just ∞. Posits also allow for a variable number of exponent bits (which are encoded into Regime scale factor bits [whose value is determined by a useed factor] and Exponent scale factor bits) which means that the number of significand bits can also vary.

So, with a 32 bit, single precision Posit, the number range represented can be quite a bit larger than single precision floating point. Indeed, with the approach put forward by Gustafson, a single 32 bit posit has more numeric range than a single precision IEEE 754 float and about as 1/2 as much range as double precision IEEE floating point number but only uses 32 bits.

Presently, there’s no commercial hardware implementations of posits, but there’s a lot of interest. Mostly because, the same number of bits can represent a lot more numeric range than equivalently sized IEEE 754 floats. And for HPC environments, AI deep learning applications, scientific computing, etc. having more numeric range (or precision), in less space, means they can jam more data in the same storage, transfer more data over the same networking bandwidth and save more numbers in limited amounts of DRAM.

Although, commercial implementations do not exist, there’s been some FPGA simulations of posit floating point arithmetic. Those simulations have shown it to be more energy efficient than IEEE 754 floating point arithmetic for the same number of bits. So, you need to add better energy efficiency to the advantages of posit arithmetic.

Is it any wonder that HPC/big science (weather prediction, Square Kilometer Array, energy simulations, etc.) and many AI hardware accelerator chip designers are examining posits as a potential way to boost precision, reduce storage/memory footprint and reduce energy consumption.

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Yet, standards have a way of persisting. Just look at how long the QWERTY keyboard has lasted. It was originally designed in the 1870’s to slow down typing and reduce jamming, when typewriters were mechanical devices. But ever since 1934, when the DVORAK keyboard was patented, there’s been much better layouts for keyboards. And there’s no arguing that the DVORAK keyboard is better for typing on non-mechanical typewriters. Yet today, I know of no computer vendor that ships DVORAK labeled keyboards. Once a standard becomes set, it’s very hard to dislodge.

Comments?

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All that AI DL training data comes from us

Read a couple of articles the past few weeks that highlighted something that not many of us are aware of, most of the data used to train AI deep learning (DL) models comes from us.

That is through our ignorance or tacit acceptation of licenses for apps that we use every day and for just walking around/interacting with the world.

The article in Atlantic, The AI supply chain runs on ignorance, talks about Ever, a picture sharing app (like Flickr), where users opted in to its facial recognition software to tag people in pictures. Ever also used that (tagged by machine or person) data to train its facial recognition software which it sells to government agencies throughout the world.

The second article, in Engadget , Colorado College students were secretly used to train AI facial recognition (software), talks about a group using a telephoto security camera than was pointed at a high traffic area on campus. The data obtained was used to help train an AI DL model to identify facial characteristics from far away.

The article went on to say that gathering photos from people in public places is not against the law. The study was also cleared by the school. The database was not released until after the students graduated but it did have information about the time and date the photos were taken.

But that’s nothing…

The same thing applies to video sharing and photo animation models, podcasting and text speaking models, blogging and written word generation models, etc. All this data is just lying around the web, freely available for any AI DL data engineer to grab and use to train their models. The article which included the image below talks about a new dataset of millions of webpages.

From an OpenAI paper on better language models showing the accuracy of some AI DL models “trained on a new dataset of millions of webpages called WebText.”

,Google photo search is scanning the web and has access to any photo posted to use for training data. Facebook, IG, and others have millions of photos that people are posting online every day, many of which are tagged, with information identifying people in the photos. I’m sure some where there’s a clause in a license agreement that says your photos, when posted on our app, no longer belong to you alone.

As security cameras become more pervasive, camera data will readily be used to train even more advanced facial recognition models without your say so, approval or even appreciation that it is happening. And this is in the first world, with data privacy and identity security protections paramount, imagine how the rest of the world’s data will be used.

With AI DL models, it’s all about the data. Yes much of it is messy and has to be cleaned up, massaged and sometimes annotated to be useful for DL training. But the origins of that training data are typically not disclosed to the AI data engineers nor the people that created it.

We all thought China would have a lead in AI DL because of their unfettered access to data, but the west has its own way to gain unconstrained access to vast amounts of data. And we are living through it today.

Yes AI DL models have the potential to drastically help the world, humanity and government do good things better. But a dark side to AI DL models also exist to help bad actors, organizations and even some government agencies do evil.

Caveat usor (May the user beware)

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Photo Credit(s): “Still Watching You” by jhcrow is licensed under CC BY-NC 2.0 

Computational Photography Homework 1 Results.” by kscottz is licensed under CC BY-NC 2.0 

From Language models are unsupervised multi-task learners OpenAI research paper