machine learning

Biometric Identity Theft

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Recently I have been researching the potential of fraud and identity theft using fingerprints from photos posted on social media. Last week Amazon released its “Amazon One” Palm Scanner as a means to pay for purchases when shopping. That announcement made me wonder, what are the potential implications for fraud and identity theft using biometric data taken from images?

Man's forearm and hand, index finger extended to point to one of a series of "digital keys"
Could Photos posted on Social Media sites become the Key to Digital Identify Theft?

There are a surprising number of ways to accurately identify someone from a photo or video. Moreover, there is technology to copy fingerprints from social media photos taken up to three meters away. New technology has been proven effective at using 3D printing technology to create “fake fingerprints” that will bypass many fingerprint scanners.

Technology continues to improve at a rapid pace, which often means, “Where there is the will there’s a way.”

Since fingerprints can be copied from photos taken up to three meters away does that mean a palm print could potentially be copied from a photo taken 5-10 meters away? That question led to an interesting but unscientific experiment where I took pictures of my own hand, enlarged them, and then measured the distance between the ridges and furrows of both my fingers and my palm, and then compared the results of the two. Spoiler – probably not.

There are several areas where that distance was similar for both my fingers and palm. But, there were also areas on my palm where the average distance between “landmarks” was 3-5+ times greater. It turns out that for identification purposes a palm image is often segment into 3-4 distinct regions, likely due to this type of variation. This link was helpful to understand the process.

This research led to an idea for a chip-based embedded filter for smart devices and laptops. It would obfuscate key biometric information when extracting the data for display, without affecting the integrity of the original stored image. This functionality would automatically provide an additional layer of privacy and data protection. It would require optimized object detection capabilities (possibly R-CNN) that were highly efficient, and run on a capable but low energy processor like the Arm Cortex-M. Retraining and upgrades would be accomplished with firmware updates.

Edit 2020-10-13: This article on “Tiny ML” from Medium.com is the perfect tie-in to the idea described above.

While Amazon’s technology is much newer and presumably at least partially based on their 2019 Patent Application (which does look impressive), it makes you wonder how susceptible these devices might be to fraud given reports of the scans occurring “almost instantaneously.” Speed is one aspect of successful large-scale commercial adoption but the accuracy and integrity of the system are far more important from my perspective.

Time will tell how robust and foolproof Amazon’s new technology really is. Given their reach, this could occur sooner than later. Ultimately, multiple forms of biometric scans (such as a full handprint with shape, palm, and fingerprints, or a retina scan 2-3 minutes prior to the palm scan to maintain performance) may be required for enhanced security, especially with mobile devices.

Additional Resources:

New Perspectives on Business Ecosystems

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One of the many changes resulting from the COVID-19 pandemic has been a sea change in thoughts and goals around Supply Chain Management (SCM). Existing SCM systems were up-ended in mere months as it has become challenging to procure raw materials to components, manufacturing has shifted to meet new unanticipated needs, and logistics challenges have arisen out of health-related staffing issues, safe working distances, and limited shipping options and availability. In short, things are a mess!

Foundational business changes will require modern approaches to Change Management. Change is not easy – especially at scale, so having ongoing support from the top down and providing incentives to motivate the right behaviors, actions, and outcomes will especially critical to the success of those initiatives. And remember, “What gets measured gets managed,” so focusing on the aspects of business and change that really matter will become a greater focus.

Business Intelligence systems will be especially important for Descriptive Analysis. Machine Learning will likely begin to play a larger role as organizations seek a more comprehensive understanding of patterns and work towards accurate Predictive Analysis. And of course, Artificial Intelligence / Deep Learning / Neural Networks use should accelerate as the need for Prescriptive Analysis grows. Technology will provide many of the insights needed for business leaders to make the best decisions in the shortest amount of time that is both possible and prudent.

This is also the right time to consider upgrading to a modern business ecosystem that is collaborative, agile, and has the ability to quickly and cost-effectively expand and adapt to whatever comes next. Click on this link to see more of the benefits of this type of model.

Man's forearm and hand, index finger extended to point to one of a series of "digital keys"

Whether you like it or not, change is coming. So, why not take a proactive posture to help ensure that this change is good and meets the objectives your company or organization needs.

Changes like this are all-encompassing so it is helpful to begin with the mindset of, “Win together, Lose together.” In general, it helps to have all areas of an organization moving in lockstep towards a common goal but at a critical juncture like this that is no longer an option.

Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World

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The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore; such has managing large remote workforces – many of whom are new to working remotely, and managing risk while attempting to conduct “business as usual.” Unfortunately for most businesses, their systems, processes, and internal controls were not designed for this “new normal.”

While there have been many predictions around Blockchain for the past few years it is still not widely adopted. We are beginning to see an uptick in adoption with Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. But large-scale adoption has been elusive to date.

Image of globe with network of connected dots in the space above it.

My personal belief is that we will soon begin to see large shifts in mindset, investments, and effort towards modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, and that will lead to faster adoption by SMBs and other non-Enterprise organizations

Here are a few predictions:

  1. New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel a number of innovations in various industries including Biotech and Pharma.
    • Blockchain can provide the necessary data privacy, data ownership, and data provenance to ensure the veracity of that data.
    • New legislation will be created to protect medical providers and other users of that data from being liable for missing information or trends that could have saved lives or avoided some other negative outcome.
    • In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using the Medical IoT data, which could include Smart Contracts as a way to ensure compliance (which assumes that there is a benefit being provided to the data providers).
    • Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. This is something that I wrote about in 2013.
  2. Data Governance will be taken more seriously by every business. Today companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
    • Comprehensive Data Governance will become both a driving and gating force as organizations modernize and grow. Even before the pandemic there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
    • In a business environment where more systems are distributed there is increased risk of data breaches and cybercrime. That will need to be addressed as a foundational component of any new system.
    • One or two Data Integration Companies will emerge as undisputed industry leaders due to their capabilities around MDM, Data Provenance & Traceability, and Data Access (an area typically managed by application systems).
    • New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries.
  3. Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented as a way to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
    1. Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
    1. This will also foster the growth of next generation Business Ecosystems and collaborations that will be more dynamic in nature.

All in all this is exciting from a business and technology perspective. It will require most companies to review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.

The steps we take today will shape what we see and do in the coming decade so it is important to quickly get this right, knowing that whatever is implemented today will evolve and improve over time.

Good Article on Why AI Projects Fail

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high angle photo of robot
Photo by Alex Knight on Pexels.com

Today I ran across this article that was very good as it focused on lessons learned, which potentially helps everyone interested in these topics. It contained a good mix of problems at a non-technical level.

Below is the link to the article, as well as commentary on the Top 3 items listed from my perspective.

https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html

Item #1: 

The article starts by discussing how the “problem” being evaluated was misstated using technical terms. It led me to believe that at least some of these efforts are conducted “in a vacuum.” That was a surprise given the cost and strategic importance of getting these early-adopter AI projects right.

In Sales and Marketing you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither initially vet the solution nor quickly qualify the need for your solution when speaking with those customers or prospects. That leaves a lot of room for error when transitioning from strategy to execution.

Increased collaboration with Business would likely have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. To me, this should have been called out early as a major lesson learned.

Item #2: 

This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.

That was interesting because it appears that assumptions may have played a part in overlooking other aspects of the problem, or that the teams may have been overly confident about obtaining the correct results using the data available. In the examples cited those teams did figure those problems out and took corrective action. A follow-on article describing the process used to make their root cause determination in each case would be very interesting.

As an aside, from my perspective, this is why Explainable AI is so important. There are times that you just don’t know what you don’t know (the unknown unknowns). Being able to understand why and on what the AI is basing its decisions should help with providing better quality curated data up-front, as well as being able to identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.

Item #3: 

This didn’t surprise me but should be a cause for concern as advances are made at faster rates and potentially less validation is made as organizations race to be first to market with some AI-based competitive advantage. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.

Bonus Item:

The discussion about the value of unstructured data was very interesting, especially when you consider:

  1. The potential for NLU (natural language understanding) products in conjunction with ML and AI.
  2. The importance of semantic data analysis relative to any ML effort.
  3. The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
    • I personally believe that the biggest exception to assertion this will be from GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond those of traditional CPU based products, and have geospatial capabilities that provide an additional dimension of insight to the problem being solved.

 

Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy

This is definitely an exciting space that will experience significant growth over the next 3-5 years. The more information, experiences, and lessons learned shared the better it will be for everyone.

The Downside of Easy (or, the Upside of a Good Challenge)

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Picture of a Suzuki motorcycle

As a young boy, I was “that kid” who would take everything apart, often leaving a formerly functional alarm clock in a hundred pieces in a shoe box. I loved figuring out how things worked, and how components worked together as a system. When I was 10, I spent one winter completely disassembling and reassembling my Suzuki TM75 motorcycle in my bedroom (my parents must have had so much more patience and understanding than I do as a parent). It was rebuilt by spring and ran like a champ. Beginners luck?

By then I was hooked – I enjoyed working with my hands and fixing things. That was a great skill to have while growing up as it provided income and led to the first company I started at age 18. There was always a fair degree of trial and error involved with learning, but experience and experimentation led to simplification and standardization. That became the hallmark to the programs I wrote, and later the application systems that I designed and developed. It is a trait that has served me well over the years.

Today I still enjoy doing many things myself, especially if I can spend a little bit of time and save hundreds of dollars (which I usually invest in more tools). Finding examples and tutorials on YouTube is usually pretty easy, and after watching a few videos for reference the task is generally easy. There is also a sense of satisfaction to a job well done. And most of all, it is a great distraction to everything else going on that keeps your mind racing at 100 mph.

My wife’s 2011 Nissan Maxima needed a Cabin Air Filter, and instead of paying $80 again to have this done I decided to do it myself. I purchased the filter for $15 and was ready to go. This shouldn’t take more than 5 or 10 minutes. I went to YouTube to find a video but no luck. Then, I started searching various forums for guidance. There were a lot of posts complaining about the cost of replacement, but not much about how to do the work. I finally found a post that showed where the filter door was. I could already feel that sense of accomplishment that I was expecting to have in the next few minutes.

Picture of a folded cabin air filter for a Nissan Maxima

But fate, and apparently a few sadistic Nissan Engineers had other ideas. First, you needed to be a contortionist in order to reach the filter once the door was removed. Then, the old filter was nearly impossible to remove. And then once the old filter was removed I realized that the length of the filter entry slot was approximately 50% of the length of the filter. Man, what a horrible design!

A few fruitless Google searches later I was more intent than ever on making this work. I tried several things and ultimately found a way to fold the filter where it was small enough to get through the door and would fully open once released. A few minutes later I was finally savoring my victory over that hellish filter.

This experience made me recall “the old days.” Back in 1989 I was working for a marketing company as a Systems Analyst and was given the project to create the “Mitsubishi Bucks” salesperson incentive program. Salespeople would earn points for sales, and could later redeem those points on Mitsubishi Electronics products. It was a very popular and successful incentive program.

Creating the forms and reports was straight forward enough, but tracking the points presented a problem. I finally thought about how a banking system would work (remember, no Internet and few books on the topic, so this was reinventing the wheel) and designed my own. It was very exciting and rock solid. Statements could be reproduced at any point in time, and there was an audit trail for all activity.

Next, I needed to create validation processes and a fraud detection system for incoming data. That was rock solid as well, but instead of being a good thing it turned out to be a real headache and cause of frustration.

Salespeople would not always provide complete information, might have sloppy penmanship, or would do other things that were odd but legitimate. Despite that, they expected immediate rewards and having their submissions rejected apparently created more frustration than incentive.

So, I was instructed to turn the dial way back. I let everyone know that while this would minimize rejections it would also increase the potential for fraud, and created a few reports to identify potentially fraudulent activity. It was amazing how creative people could be when trying to cheat the system, as well as how you could identify patterns to more quickly identify that type of activity. By the third month the system was trouble free.

It was a great learning experience from beginning to end. Best of all, it ran for several years once I left – something I know because every month I was still receiving the sample mailing with the new sales promotions and “Spiffs” (sales incentives). This reflection also made me wonder how many things are not being created or improved today because it is too easy to follow an existing template.

We used to align fields and columns in byte order to minimize record size, overload operators, etc. in order to maximize space utilization and maximize performance. Code was optimized for maximum efficiency because memory was scarce and processors slow. Profiling and benchmarking programs brought you to the next level of performance. In a nutshell, you were forced to really understand and become proficient with the technology used out of necessity. Today those concepts have become somewhat of a lost art.

There are many upsides to easy.

  • My team sells more and closes deals faster because we make it easy for our customers to buy, implement, and start receiving value on the software we sell.
  • Hobbyists like myself are able to accomplish many tasks after watching a short video or two.
  • People are willing to try things that they may not have if getting started would not have been so easy.

But, there may also be downsides relative to innovation and continuous improvement simply because easy is often good enough.

What will the impact be to human behavior once Artificial Intelligence (AI) becomes a reality and is in everyday use? It would be great to look ahead 25, 50, or 100 years and see the full impact of emerging technologies, but my guess is that I will see many of the effects in my own lifetime.