Blockchain: Now What Is That Use Case?
February 7, 2020
The DarkCyber team invested some time in figuring out Amazon’s blockchain-related inventions. (A free executive summary is available at this link.) There were some interesting use cases explained in these public documents. But blockchain in Amazon is different in blockchain in the world of a specialist blockchain firm if the information in “Major Blockchain Developer ConsenSys Announces Job Losses” is accurate.
The write up states:
Major blockchain developer ConsenSys has laid off around 14% of its workforce, it said on Tuesday, a move that comes as companies around the world frantically search for applications for the much-hyped technology.
Blockchain in frantic search for applications? Yikes.
The issues blockchain faces range from “good enough”, better known alternatives to scaling.
The write up explains:
Companies from banks and oil traders to retailers and tech vendors, drawn to its promise of making cumbersome processes more efficient and secure, have invested billions as they look to find uses for the technology. Many have turned to blockchain development startups in the process for technical expertise. Yet there have so far there have been few major breakthroughs in the practical application of blockchain, despite the spate of tests and pilots.
Complexity, performance, cost, and security may be barriers. Just what catches Amazon’s attention?
Stephen E Arnold, February 7, 2020
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Buzzword Alert: Programmable Networks
February 5, 2020
DarkCyber noted “University Researchers Succeed in Boosting Computer Speeds by 2.5 Times.” The headline suggests zippy computers. Well, sort of. One bottleneck is accessing data written to a storage device. The innovation or insight, if it is economically and technically implementable, trims data access bottlenecks. DarkCyber noted:
Current data storage systems use only one storage server to process information, making them slow to retrieve information to display for the user. A backup server only becomes active if the main storage server fails. The new approach, called FLAIR, optimizes data storage systems by using all the servers within a given network. Therefore, when a user makes a data request, if the main server is full, another server automatically activates to fill it, the scientists state.
The approach exploits programmable networks. A network of servers is like a microprocessor. The shift is to meta-think about these components. Therefore, create a wrap up layer like the one described in the write up.
Popping up a level sometimes make sense. Marketing a meta-play may be even more beneficial.
Stephen E Arnold, February 3, 2020
A Solution to the Blockchain Trilemma?
February 3, 2020
Struggling to deliver a blockchain application which is decentralized, secure, and scalable? A solution may have been developed. Navigate to “Ex-Microsoft Researcher Says He’s Solved the Blockchain Scalability Problem.” Despite the hype about blockchain, there’s a problem mixed with the promise:
…It says that it’s easy to have a blockchain with two of three key attributes: decentralization, security, and scalability. What’s difficult is getting all three; so far, cranking up the volume has always meant sacrificing on another.
The alleged solution comes from Asensys, led by former Microsoft lead researcher JiaPing Wang. The alleged solution is avoiding “going off-chain or sharding transactions.” The idea is to eliminate duplicative processes:
instead spreading the workload across the entire network by creating multiple “zones” within it that work independently and asynchronously.
For now, this is a work in progress. And those marketing assurances about decentralization, security, and scalability? Yeah, right.
Stephen E Arnold, February 3, 2020
Amazon Blockchain: How Secure?
January 27, 2020
This write up does not address Amazon’s blockchain innovations. We have a summary of our Amazon blockchain technology which points out specific systems and methods, the online bookstore has “invented” to make blockchain more secure. (Keep in mind, Amazon is the inventor of S3 buckets, which in some circumstances, are somewhat leaky.) You can get a copy of the free DarkCyber Amazon Blockchain report using the information at the end of this blog post.
The article “Trust No One. Not Even a Blockchain” suggests that one of the most hyped data management technologies may have a weakness. Technology experts are not fond of weaknesses. Technology is a solution, and solutions must not have fatal flaws like mere humans working at a giant company or in the semi isolation of a coffee shop.
The write up points out:
Similarly, just because a person claims to have uploaded all of her photographs to a blockchain—like Mila’s mother in Parker’s story—does not mean there are no other pictures from her life. Omitted data, bad data, too much data: These dynamics rob a blockchain of the claim of being a source of truth. Garbage in, garbage out. This concept in computer science means that an input consisting of flawed data will generate a flawed output. So it is with blockchain technology. We can record false claims on a blockchain. We can omit data. Suddenly, that source of truth does not appear so honest.
The essay concludes with this observation:
Distortion of reality is a growing threat. Deepfakes, synthetic videos that replace an image of one person with that of another, may soon become indistinguishable from authentic videos. Today, deepfakes may largely be used in the making of memes, face-swapping celebrities, but their proliferation will undoubtedly have major implications on everything from political campaigns to policies around pornography. What makes the threat of deepfakes so profound is that they render a medium formerly viewed as reliable—namely video—undependable. We cannot trust the very thing that we are supposed to trust. This constitutes the most substantial danger to a society’s notion of reality. If we are supposed to trust whatever is on a blockchain, then we are in trouble indeed. After all, the blockchain is only as good as the data we put on it.
Amazon’s blockchain inventions address the “control” of the information placed in the blockchain. That may give Amazon an advantage in the policeware market.
If you want a copy of the DarkCyber executive summary for our 54 page report about Amazon’s blockchain and some of the implications of these inventions, send an email to darkcyber333 at yandex dot com. No charge for the summary. The full report, however, is not free.
Stephen E Arnold, January 27, 2020
Data Are a Problem? And the Solution Is?
January 8, 2020
I attended a conference about managing data last year. I sat in six sessions and listened as enthusiastic people explained that in order to tap the value of data, one has to have a process. Okay? A process is good.
Then in each of the sessions, the speakers explained the problem and outlined that knowing about the data and then putting it in a system is the way to derive value.
Neither Pros Nor Cons: Just Consulting Talk
This morning I read an article called “The Pros and Cons of Data Integration Architectures.” The write up concludes with this statement:
Much of the data owned and stored by businesses and government departments alike is constrained by the silos it’s stuck in, many of which have been built over the years as organizations grow. When you consider the consolidation of both legacy and new IT systems, the number of these data silos only increases. What’s more, the impact of this is significant. It has been widely reported that up to 80 per cent of a data scientist’s time is spent on collecting, labeling, cleaning and organizing data in order to get it into a usable form for analysis.
Now this is most true. However, the 80 percent figure is not backed up. An IDG expert whipped up some percentages about data and time, and these, I suspect, have become part of the received wisdom of those struggling with silos for decades. Most of a data scientist’s time is frittered away in meetings, struggling with budgets and other resources, and figuring out what data are “good” and what to do with the data identified by person or machine as “bad.”
The source of this statement is MarkLogic, a privately held company founded in 2001 and a magnet for $173 million from funding sources. That works out to an 18 years young start up if DarkCyber adopts a Silicon Valley T shirt.
A modern silo is made of metal and impervious to some pests and most types of weather.
One question the write up begs is, “After 18 years, why hasn’t the methodology of MarkLogic swept the checker board?” But the same question can be asked of other providers’ solutions, open source solutions, and the home grown solutions creaking in some government agencies in Europe and elsewhere.
Several reasons:
- The technical solution offered by MarkLogic-type companies can “work”; however, proprietary considerations linked with the issues inherent in “silos” have caused data management solutions to become consultantized; that is, process becomes the task, not delivering on the promise of data, elther dark or sunlit.
- Customers realize that the cost of dealing with the secrecy, legal, and technical problems of disparate, digital plastic trash bags of bits cannot be justified. Like odd duck knickknacks one of my failed publishers shoved into his lumber room, ignoring data is often a good solution.
- Individuals tasked with organizing data begin with gusto and quickly morph into bureaucrats who treasure meetings with consultants and companies pitching magic software and expensive wizards able to make the code mostly work.
DarkCyber recognizes that with boundaries like budgets, timetables, measurable objectives, federation can deliver some zip.
Silos: A Moment of Reflection
The article uses the word “silo” five times. That’s the same frequency of its use in the presentations to which I listened in mid December 2019.
So you want to break down this missile silo which is hardened and protected by autonomous weapons? That’s what happens when a data scientist pokes around a pharma company’s lab notebook for a high potential new drug.
Let’s pause a moment to consider what a silo is. A silo is a tower or a pit used to store core, wheat, or some other grain. Dust is silos can be exciting. Tip: Don’t light a match in a silo on a dry, hot day in a state where farms still operate. A silo can also be a structure used to house a ballistic missile, but one has to be a child of the Cold War to appreciate this connotation.
As applied to data, it seems that a silo is a storage device containing data. Unlike a silo used to house maize or a nuclear capable missile, the data silo contains information of value. How much value? No one knows. Are the data in a digital silo explosive? Who knows? Maybe some people should not know? What wants to flick a Bic and poke around?
Blockchain: A Loser in 2020?
December 31, 2019
I recently completed a report about Amazon’s R&D work in blockchain. If you want a free summary of the report, write darkcyber333 at yandex dot com. If not, no problem. You will want to read “Please Blockchain, Prove Me Wrong.” The author likes to use words on some online services stop list, but that’s okay. The writer is passionate about the perceived failings of blockchain.
Blockchain is, according to the write up:
a solution looking for a problem.”
More proof needed, you gentle but skeptical reader? How about this?
According to Gartner’s Hype Cycle, blockchain is still “sliding into the trough of disillusionment,” meaning the technology is struggling to live up to the expectations created by the hype around it.
There you go. Proof from a marketing company.
DarkCyber’s view is that encryption is likely to continue to toddle forward. Also, the charm of the distributed database continues to woe some people’s attention.
There may be hope, and perhaps that is why Amazon has more than a dozen patents related to blockchain technology. We learn from the impassioned analysis:
Blockchain’s purported promise is such that everyone is willingly taking a multi-faceted approach, not giving much thought to the possibility that its potential may, in fact, be limited. Or maybe blockchain is just the first iteration of something far more powerful, a base we can build on to restore our faith in decentralized systems.
To sum up, for a dead duck, there are some feathers afloat. And there are those Amazon patents? Maybe Mr. Bezos is just off base and should stick to bulldozing outfits like mom and pop stores and outfits like FedEx?
Stephen E Arnold, December 31, 2019
Audio Data Set: Start Your AI Engines
August 16, 2019
Machine learning projects have a new source of training data. BoingBoing announces the new “Open Archive of 240,000 Hours’ Worth of Talk Radio, Including 2.8 Billion Words of Machine-Transcription.” A project of MIT Media Lab, Radiotalk holds a wealth of machine-generated transcriptions of talk radio broadcasts between October 2018 and March 2019. Naturally, the text is all tagged with machine-readable metadata. The team hopes their work will enrich research in natural language processing, conversational analysis, and social sciences. Writer Cory Doctorow comments:
“I’m mostly interested in the social science implications here: talk radio is incredibly important to the US political discourse, but because it is ephemeral and because recorded speech is hard to data-mine, we have very little quantitative analysis of this body of work. As Gretchen McCulloch points out in her new book on internet-era language, Because Internet, research on human speech has historically relied on expensive human transcription, leading to very small and corpuses covering a very small fraction of human communication. This corpus is part of a shift that allows social scientists, linguists and political scientists to study a massive core-sample of spoken language in our public discourse.”
The metadata attached to these transcripts includes information about geographical location, speaker turn boundaries, gender, and radio program information. Curious readers can access the researchers’ paper here (PDF).
Cynthia Murrell, August 16, 2019
Hadoop Fail: A Warning Signal in Big Data Fantasy Land?
August 11, 2019
DarkCyber notices when high profile companies talk about data federation, data lakes, and intelligent federation of real time data with historical data. Examples include Amazon and Anduril to name two companies offering this type of data capability.
“What Happened to Hadoop and Where Do We Go from Here?” does not directly discuss the data management systems in Amazon and Anduril, but the points the author highlights may be germane to thinking about what is possible and what remains just out of reach when it comes to processing the rarely defined world of “Big Data.”
The write up focuses on Hadoop, the elephant logo thing. Three issues are identified:
- Data provenance was tough to maintain and therefore determine. This is a variation on the GIGO theme (garbage in, garbage out)
- Creating a data lake is complicated. With talent shortages, the problem of complexity may hardwire failure.
- The big pool of data becomes the focus. That’s okay, but the application to solve the problem is often lost.
Why is a discussion of Hadoop relevant to Amazon and Anduril? The reason is that despite the weaknesses of these systems, both companies are addressing the “Hadoop problem” but in different ways.
These two firms, therefore, may be significant because of their approach and their different angles of attacks.
Amazon is providing a platform which, in the hands of a skilled Amazon technologist, can deliver a cohesive data environment. Furthermore, the digital craftsman can build a solution that works. It may be expensive and possibly flakey, but it mostly works.
Anduril, on the other hand, delivers the federation in a box. Anduril is a hardware product, smart software, and applications. License, deploy, and use.
Despite the different angles of attack, both companies are making headway in the data federation, data lake, and real time analytics sector.
The issue is not what will happen to Hadoop, the issue is how quickly will competitors respond to these different ways of dealing with Big Data.
Stephen E Arnold, August 11, 2019
MarkLogic: A NoSQL Vertical Jump for More Revenue?
July 24, 2019
Is NoSQL-database-platform-firm MarkLogic is emulating Dialog Information Services and Lexis Nexis or vertical plays for quirky controversial, niche markets like drugs and medical device specific services? MarkLogic’s push into other verticals like professional publishing and finance have not generated the type of buzz and revenue that other Silicon Valley firms have sparked. Maybe pharma is the key which will unlock massive returns for the stakeholders? MarkLogic has resisted the type of acquisition and repositioning play that kCura executed in eDiscovery? Perhaps pharma, a sector whose revenue grows as the number of global players shrinks?
The company announced the MarkLogic Pharma Research Hub, created to bring the power of federated search to the field of pharmaceutical R&D. The product description tells us:
“For pharmaceutical companies, the discovery of new molecules and the cost of developing a successful medicine can take up to 15 years and $2.6 billion — slowing potentially life-saving drugs from getting to the patients who need them and resulting in abandonment of drug trials when faced with potential failure. In this industry, even small improvements to streamline R&D processes can lead to substantially higher revenue and lower costs. To achieve those goals, pharmaceutical companies need to leverage their massive data assets that include decades of research and clinical trial data. The challenge is that researchers are often unable to access the information they need. And, even when data does get consolidated, researchers find it difficult to sift through it all and make sense of it in order to confidently draw the right conclusions and share the right results.”
The product announcement elaborated:
“The main challenge facing IT departments that serve pharma R&D is patchwork infrastructure that creates the data silos that isolate and restrict access to data. Pharmas need to leverage massive data sets, including decades of research and clinical trials information.”
In addition, we’re reminded, disparate data silos hamper collaboration, upon which researchers rely heavily. The announcement goes on to outline the platform’s advanced features: the ability to load any pharmaceutical data set, relationship visualizations and discovery, and customizable search results. Naturally, these functions are made possible by machine-learning AI.
Founded in 2001 as Cerisent, MarkLogic is based in San Carlos, California, with several offices in the U.S. and in Europe. After changing its name, it released Version 1 of its platform in 2003. The company has ingested more than $170 million in venture funding. The firm has probed the intelligence sector and marketed itself as an enterprise search solution. But revenues? MarkLogic is a privately held firm just 18 years young.
Cynthia Murrell, July 23, 2019
A Partial Look: Data Discovery Service for Anyone
July 18, 2019
F-Secure has made available a Data Discovery Portal. The idea is that a curious person (not anyone on the DarkCyber team but one of our contractors will be beavering away today) can “find out what information you have given to the tech giants over the years.” Pick a social media service — for example, Apple — and this is what you see:
A curious person plugs in the Apple ID information and F-Secure obtains and displays the “data.” If one works through the services for which F-Secure offers this data discovery service, the curious user will have provided some interesting data to F-Secure.
Sound like a good idea? You can try it yourself at this F-Secure link.
F-Secure operates from Finland and was founded in 1988.
Do you trust the Finnish anti virus wizards with your user names and passwords to your social media accounts?
Are the data displayed by F-Secure comprehensive? Filtered? Accurate?
Stephen E Arnold, July 18, 2019
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