Cybersecurity, PDUs and LinkedIn Training

I hope you had a wonderful Christmas and a happy New Year 2025.  During the holiday season, I had to get fourteen professional development units (PDUs) to maintain my Project Management Professional (PMP) certification by mid-January 2025.  I had my PMP since mid-January 2013.  

The next big question I had was where to get my PDUs and what course I should invest in to earn 14 PDUs.  I decided to go with the LinkedIn Training service because I have been impressed by their improvements in IT learning.  

After watching this video called Satya Nadella On Evolution Of SaaS via Devan Patel's LinkedIn Post

I realized that the world of business applications will be significantly impacted.  Business applications as we know them may not exist in the next five to ten years.  However, I envision cybersecurity staying around and evolving.  Therefore, I took the following cybersecurity classes, which helped me earn my 14 PDUs and learn more about cybersecurity.  I was impressed with the content.  I took the following Cybersecurity classes on LinkedIn Training:

• The Future of Work and Cybersecurity on the Metaverse: 0.75 PDU
• Building and Auditing a Cybersecurity Program: 1.75 PDUs
• Cybersecurity Foundations: Governance, Risk, and Compliance (GRC): 1.00 PDU
• Artificial Intelligence for Cybersecurity: 1.50 PDUs
• Cybersecurity Awareness: Cybersecurity Terminology: 1:00 PDU
• Cybersecurity Awareness: Protect Your Privacy: 1.25 PDUs
• Building an Application Security Program: 1.50 PDUs
• Building a Cybersecurity Awareness Program: 1.25 PDUs
• Emotional Intelligence for Cybersecurity Professionals: 0.50 PDU
• Insider Threat Risk Management: 0.50 PDU
• Cybersecurity Awareness: Cloud Security: 0.50 PDU
• Cybersecurity Awareness: Phishing Attacks: 1.00 PDU
• Leveraging AI for Governance, Risk, and Compliance: 0.25 PDU
• The OWASP Top 10 for Large Language Model (LLM) Applications: An Overview: 0.75 PDU

That totaled to 13.50 PDUs.  I took the following acquisition classes on LinkedIn Training:

• Outsourcing Management: 0.50 PDU

Overall, I enjoyed all of the courses, and here are my favorites from the list:

• Cybersecurity Awareness: Protect Your Privacy - It provides practical information on how we can protect our privacy online.
• The Future of Work and Cybersecurity on the Metaverse provides insight into how people are addressing cybersecurity in new technologies.
• Artificial Intelligence for Cybersecurity - It does a good job of defining some of the threats in the AI space.
• The OWASP Top 10 for Large Language Model (LLM) Applications: An Overview - It's an overview course that provides insights into the security vulnerabilities of LLM technologies.

I am fortunate to have access to the LinkedIn Training service via my employer and my county library system.  I recommend you check with your county library system to get access to LinkedIn Training since it is an excellent resource if you want to get credits for your professional development to maintain your respective certificates or develop a learning culture with your team.   

Unicorns have an exciting future

I recently began taking courses to obtain a Digital Transformation certificate from Stanford University's School of Engineering.  I highly recommend it since it flexes your critical thinking and strategic acumen; a few weeks ago, I completed the Building an AI-Enabled Organization course, which showcases how data science and artificial intelligence (AI) can be applied within the context of the business and corporate world.   I thought I completed my training when I obtained a master's in data analytics and business administration.    

A couple of items that stuck out to me from the course:

1. Data Drifts & Model Drifts -  Data drifts are when the statistical distribution of the data changes over time. For instance, electric cars didn't exist before the 2000s.  The percentage of gasoline-powered cars reduced after the 2000s.  Model drifts, aka concept drifts, are learning models that are outdated since they were developed using the pre-data drift model.
2. Unicorns—Steven Geringer's Data Science Venn Diagram v2.0 showcases the rare breed of knowledge worker called unicorns.

Steven Geringer's Data Science Venn Diagram v2.0 (Mokrian, n.d.)

These unicorns are rare since these data scientists have insights into the subject matter expertise, computer science and a background in math and science.  These unicorns reminded me of professionals who work with bioinformatics, nursing informatics, aviation informatics, and other informatics.  According to Wood (2023), the subject of informatics is to design, develop and maintain systems that capture, store, and manage domain-specific data.  Wood (2023) also states that data science is to get insights into the data and develop predictive analytics to enable key staff to make decisions.  Even though informatics and data sciences are distinct disciplines, they depend on each other to help any key organization or program.  For instance,  data scientists can only gain insights if the data quality is good.  Data quality is an outcome of how systems capture and store the data.  If organizations want their data scientists to leverage multiple data sources to gain insights, then data management is key.  In other words, data scientists are dependent on informatics specialists since their work is important.

Suppose you or your loved ones want to be the next Unicorn or be part of the Unicorn team. In that case, I recommend that you pursue an informatics degree in a specific domain and take a handful of data analytics and data science courses.  The informatics degree will train you in a specific subject matter and expose you to computer programming.  Here is a list of the colleges and universities and the types of informatics degrees they offer:

• Applied Informatics - University of Technology, Russia;
• Business Informatics - Modul University, Austria;
• Cybersecurity Informatics - San Jose State University;
• Health Informatics - George Mason University;
• Informatics for Logistics - College of Logistics, Czech Republic;
• Integrative Informatics - Allegheny College;
• Maritime Logistics Informatics - University of Tasmania, Australia;
• Social Informatics - HSE University, Russia;
• Urban Informatics - Northeastern University; and
• Others.

For domains, like sports, that already use data science,  colleges offer specialized degrees like:

• Crime Analytics - Toronto Metropolitan University, Canada;
• Nutrition and Health Analytics - Munster Technological University, Ireland;
• Sports Analytics - American University; and 
• Others

I predict colleges and universities will offer more domain-specific analytics degrees in a matter of years.  With more domain-specific data being generated by various industries, data scientists will tighten up the algorithms to be specific to each domain for better insights and more focused decision-makers.  I don't think there will be a slowdown in folks getting informatics degrees since industries still need human smarts and brains to design how data should be captured, stored, and processed.    With the demand for platform technologies like Salesforce, Appian, and others, as well as platform businesses like  Fiverr, Etsy, LinkedIn, and others,  folks with data science and computer science degrees will have jobs.  Suppose you have these skills and expertise in niche domains like maritime, aviation, urban, nutrition, and others. In that case, you have a very bright future that can be translated into more money for the next several years.  I also envision platforms that support specific domains will be created and may thrive.  I say "may thrive" because the folks who run these platform businesses need savvy executives who have a feel for the markets and adopt agile business strategies.  The future is exciting in this space for unicorns, entrepreneurs,  and disrupters.  Generative AI technologies will complement this space, but I don't see them ever replacing the unicorns since Generative AI is nothing more than a chatbot that overlays the data science engines and the unicorns who work with them.

In summary, if you or your loved ones want to make a living as unicorns, there are avenues to pursue, and I see an exciting future for the next five to ten years or even longer.

As always, please comment if you agree or disagree with my entry and subscribe to this blog.

References

Morkian, P. (n.d.).  Building an AI-Enabled Organization.  Stanford Online University.

Wood, T. (2023, October 10).  Healthcare Data Science vs. Healthcare Informatics (and why the difference matters).  Fast Data Science.  https://bit.ly/4hbH0u3

digitalEnoch: voice -generated-AI

"Radio station that produces 'Radiolab'"—That was the clue on the Wednesday, July 24th, 2024, New York Times (NYT) crossword puzzle.  The answer was WNYC, which in my mind stood for W—New York City (NYC).  Intrigued, I went to my podcast app and searched for RadioLab and WNYC, and discovered the Radiolab podcast.  

If you are a science/technology geek and listen to podcasts like me, please check out the Radiolab weekly podcast.  WNYC drops new episodes on Fridays, which have expanded my sphere of knowledge and learning.  For instance, did you know dolphins sleep with half their brain while they use the other half to swim and breathe, or the killer instincts of Argentine ants?  Sometimes, I wish the Argentine men's soccer team had killer instincts against their opponents. 

As someone who works with software, cybersecurity, and data, including artificial intelligence (AI), I find yesterday's RadioLab podcast episode called Shell Game simply fascinating.  It is a good example of how AI voice leveraging generative AI like ChatGPT, Google Gemini, and others are getting sophisticated.  The RadioLab host Latif Nasser interviews Evan Ratliff, a tech journalist who writes about the seedier side of technology.  Evan and his team created a voice AI that sounds just like Evan, and during the episode, Latif plays recorded sessions of Evan's interaction with regular people on the phone.  For instance, in the podcast, I heard the digital Evan interfaces with an online therapist, a $1200/hr attorney, entrepreneurs, etc.  

It made me wonder how a digital version of myself—let’s call it digitalEnoch—would fare.  How would an AI model learn from me and mimic my behavior?  Would digitalEnoch be more analytical or more empathetic? For instance, would digitalEnoch use the word "perfection?"  You will need to listen to the RadioLab podcast to find out about the word "perfection."

In summary, check out this podcast if you want to see how generative voice AI is evolving.  Put on your cybersecurity hat; it's a fascinating future.  I wouldn't say scary because policies, processes and technology are also evolving to put "bad guys" in check.   Here are a couple of links:

• RadioLab podcast episode called Shell Game - http://www.wnycstudios.org/story/shell-game/
• Evan Ratliff podcast with his digital version interacting with several individuals -https://www.shellgame.co/podcast

As always, please feel free to comment below and follow this blog.

Give me the list of the top 50 quarterbacks

It's that time of the year again.  I am doing my online research and scouring the internet for tidbits to get ready for this fall and winter's football season.  As a University of Kansas (KU) alum,  I follow my Jayhawks and may go to a game this year.  No, I won't be traveling to Lawrence, Kansas, but I may catch the game in Morgantown, WV.  Thanks to the crazy geographical dispersion of universities in the various significant colleges' sports conferences.  Can we use machine learning and language learning models (LLM) to determine the ideal sports conferences?  We can leverage factors like fan base, endorsements, level of competition, revenue, travel costs, and others to figure out which colleges should reside in which college conferences like the Big Twelve (BIG XII), Big Ten (BIG 10), and others.  

Since this is a technology-centric blog entry, let's get back to the topic at hand.  I get excited about fantasy football every fall, and the pinnacle of any fantasy sports league is the player draft.  I have also been fascinated by how good these LLMs are at meeting my fantasy sports needs.  As someone who has played fantasy sports since 1996,  I have trusted sources.  For this blog entry,  I am going to use the rankings from Yahoo!  Fantasy Sports.  This is what I did.

Purpose:  Leveraging ChatGPT, Google Gemini Advanced, Meta AI and MSN Copilot chatbots, I asked them to give the top 50 quarterbacks for this season.   

Scope:  I focused on NFL quarterbacks because only 32 quarterbacks are playing any given week.  Unlike kickers, quarterbacks have a significant impact on a football game.  

Language Learning Models Used:

AI Vendor	LLM
OpenAI Chat GPT	GPT-4o
Google Gemini	Gemini 1.5 Pro
Meta AI	Llama 3.
MSN CoPilot	Unknown

The prompt I used for the four sites was:

"Give me the list of the top 50 quarterbacks for my fantasy American football league.  Here is the scoring key: 

Offense League                              Value (points)      

Passing Yards:                                  25 yards per point; 5 points at 350 yards 

Passing Touchdowns:                       6 

Interceptions:                                  -1 

Rushing Yards:                                 10 yards per point; 5 points at 200 yards 

Rushing Touchdowns:                      6 

Receptions:                                       1

Receiving Yards:                              10 yards per point; 5 points at 200 yards 

Receiving Touchdowns:                   6 

Return Touchdowns:                        6 

2-Point Conversions:                       2 

Fumbles Lost:                                 -2 

Offensive Fumble Return TD:         6"

I compared the LLM results with Yahoo!  Fantasy Sports' top 50 quarterbacks (QB).  As of August 16th, 2024, here is the list:

1. Josh Allen - Buffalo Bills
2. Jalen Hurts - Philadelphia Eagles
3. Lamar Jackson - Baltimore Ravens
4. Patrick Mahomes - Kansas City Chiefs
5. Anthony Richardson - Indianapolis Colts
6. C.J. Stroud - Houston Texans
7. Kyle Murray - Arizona Cardinals
8. Joe Burrow - Cincinnati Bengals
9. Dak Prescott - Dallas Cowboys
10. Jordan Love - Greenbay Packers
11. Jayden Daniels - Washington Commanders (rookie)

NOTE: The rest of the list is here: Top 50 QBs - 8/16/2024 - Google Sheets

Here is how the various LLM models ranked these quarterbacks.

Yahoo!  Rank	Name	ChatGPT	Meta AI	MSN Copilot	Google Gemini
1	Josh Allen	1	2	2	1
2	Jalen Hurts	2	4	4	2
3	Lamar Jackson	4	3	3	4
4	Patrick Mahomes	3	1	1	3
5	Trent Richardson	5	5	5	19
6	C.J. Stroud	6	6	6	28
7	Kyle Murray	8	8	8	10
8	Joe Burrow	7	7	7	6
9	Dak Prescott	9	10	10	14
10	Jordan Love	11	9	9	21
11	Jayden Daniels	17	11	11	NOT LISTED

It appears Google Gemini's results (https://gemini.google.com/app/1ab3b15202d9e4f1) did not incorporate the 2024 NFL Draft or the changes in free agency.

Unlike Google Gemini,  ChatGPT, Meta AI, and MSN, Copilot aggregated ranking from other sites.  These sources are listed in the various result sets.  Here are the links to the prompts and the appropriate result sets:

• ChatGPT - https://chatgpt.com/c/7d1ef462-f5b1-44fe-9621-a53e775c0a83
• Meta AI - https://www.meta.ai/c/8a581989-559e-497c-8063-280cd5cf2254
• MSN Copilot - https://copilot.microsoft.com/sl/in1H9YyMRZQ

The three LLMs didn't do number crunching to generate the results but instead pulled data from other websites and aggregated the data.

Since I was doing a rudimentary analysis of the various LLMs to meet my needs with my upcoming Fantasy Football draft, I didn't write any code but simply leveraged existing algorithms in Microsoft Excel.   

Results:

Here is the summary of the results:

No.	Player	Teams	Depth	Yahoo!  Sports	AI Rank
1	Josh Allen	Buf	1	1	2
2	Jalen Hurts	Phi	1	2	4
3	Lamar Jackson	Bal	1	3	3
4	Patrick Mahomes	KC	1	4	1
5	Anthony Richardson	Ind	1	5	5
6	C.J. Stroud	Hou	1	6	6
7	Kyle Murray	Ari	1	7	8
8	Joe Burrow	Cin	1	8	7
9	Dak Prescott	Dal	1	9	10
10	Jordan Love	GB	1	10	9
11	Jayden Daniels	Was	1	11	11
12	Brock Purdy	SF	1	12	12
13	Jared Goff	Det	1	13	15
14	Tua Tagovailoa	Mia	1	14	13
15	Caleb Williams	Chi	1	15	16
16	Trevor Lawrence	Jax	1	16	14
17	Kirk Cousins	Atl	1	17	17
18	Matthew Stafford	LAR	1	18	19
19	Justin Herbert	LAC	1	19	18
20	Geno Smith	Sea	1	20	22
21	Aaron Rodgers	NYJ	1	21	23
22	Deshaun Watson	Cle	1	22	20
23	Baker Mayfield	TB	1	23	21
24	Will Levis	Ten	1	24	24
25	Derek Carr	NO	1	25	26
26	Taysom Hill	NO		26
27	Daniel Jones	NYG	1	27	25
28	Bryce Young	Car	1	28	28
29	Bo Nix	Den	1	29	30
30	Justin Fields	Pit	2	30	27
31	Russell Wilson	Pit	1	31	29
32	JJ McCarthy	Min	1	32	35.5
33	Jacoby Brissett	NE		33	33
34	Sam Darnold	Min		34	32
35	Gardner Minshew II	LV	1	35	34
36	Drake Maye	NE	1	36	31

Regarding the AI Rank, I removed the Google Gemini results using the Standard Deviation algorithm STDEV and the Variance algorithm VAR  since they were outliers compared to the other results.  Here is the link to the spreadsheet (ai-fantasyqb.xlsx - Google Sheets)

Conclusion:

I didn't expect the diversity of the results from the four AI engines and how these AI engines leverage third-party content from other respectable websites.  The other thing I didn't expect is that the results generated from their APIs are quite different since they don't pull data from third-party websites.  In summary, I envision using something other than AI and the various respective LLMs to address my fantasy sports needs.  I expect sports vendors like ESPN, Yahoo!, and sporting organizations to probably have sophisticated AI, LLMs, and machine learning technologies, which will be more reliable for at least the next couple of years.

On a personal note,  if you are an aspiring data scientist, don't be discouraged because you don't have a programming background or understand various algorithms like backward propagation neural networks.  You should be curious to learn from the data.

Large Language Models Asserting Themselves

Wow!  My last post on this blog was on July 26, 2020.  It was four years and 14 days ago, prior to President Joe Biden being elected, hoards of disgruntled individuals storming the US Capitol, and ChatGPT asserting itself on the technology world.  

What is ChatGPT?  It's OpenAI's chatbot.  It is based on artificial intelligence, specifically Large Language Models (LLM).  If you ask ChatGPT, "Can you write me a blog entry about ChatGPT and "LM?" it gives me the following answer: "Certainly! Here’s a blog entry about ChatGPT and Large Language Models (LLMs).” Assuming you clicked on the link,  you can see it did my work for you.  Initially, it was great, but the more I played around with it, I realized that, like any other technology,  it has its benefits and drawbacks.

It's great for prototyping and brainstorming.  I, however, would not put merit into developing new products with the appropriate quality measures.  For instance, I created outlines for strategy documents, but the selection and sequencing of the document sections were based on what I was trying to communicate to the audience.  Recently, I began developing a board game for kids, and I wanted to create a quick prototype on the web using ReactJS.  The prototype is to refine the gameplay, and I didn't spend a lot of time architecting the ReactJS application.  Still, I realized that it wasn't easy to customize ChatGPT-generated code. I didn't have a good grasp of the ReactJS fundamentals.  I, therefore, decided to take a brief pause and focus on the fundamentals, including the syntax.  A few days ago, I got selected to be an AI tester for Outlier.ai.  It's fascinating how the backend works, and products like ChatGPT, Google Gemini, and others need refinement.  Since I was introduced to cybersecurity at work around the time of my previous blog post, I am interested in seeing how the cybersecurity landscape will evolve LLMs.   Black hat hackers and other "baddies" are the creative ones since they are constantly exploiting technologies to make a profit.  I am specifically interested in how data, cyber, and IT systems intersect without impacting user experience and optimizing business.  

Will computers be allowed to do more than "aping"

Yesterday I completed reading, "What Can Bonobos Teach Us About The Nature of Language," in the July-August 2020 edition of the Smithsonian Magazine. Lindsay Stern (Twitter, LinkedIn) wrote the article, which is about the Bonobo ape and their ability to grasp complex language concepts using lexigrams. The research was pioneered by Dr. Sue Savage-Rumbaugh (Linkedin). Sue was able to communicate with lexigrams to the Bonobos apes. She would show a banana and point to a specific symbol. Over time, the bonobos mapped the symbol for bananas, apples, and other objects and communicated with Sue and other researchers. Dr. Savage-Rumbaugh was eventually able to communicate complex thoughts using the lexigrams. Dr. Savage-Rumbaugh was eventually replaced due to the close bond she developed with the bonobos. The current researches state that Dr. Savage-Rumbaugh deviated from the scientific rigors in her research and, in many ways, neglected the apes.

In her Smithsonian article, Stern questions if humans don't want to develop a relationship with the bonobos because it makes us less human or the apes more human. The thought is intriguing, and it led me to ponder if humanity will a similar struggle with machines in the near or distant future. After all, we teach computers using deep-learning algorithms to map images, which can be equated to lexigrams, to objects. Unlike animals, machines are predictable and can be harnessed to superhuman tasks like crunching through petabytes of data in a matter of minutes. If a PC or a Mac says, "I don't want to do the task, " then we replace the machinery. If an employee says, " I don't want to do the task," then the employee is disciplined or even fired. Is it a control thing; or is it our biases (acquired or built-in)

As a trained data scientist, I was taught that it is tough to remove the audience's biases. The best a data scientist can do is to provide the data in a consumable format that will enable the audience to get insights and make decisions.   

Organizations and governments fund research to learn about things that can be leveraged to benefit their respective groups and, eventually, humanity. However, these entities will not fund research that questions the underlying belief systems and makes us less human or our research subject or technology more human. Hopefully, you enjoyed the blog entry because the Smithsonian article made me reflect on the work we do and how we contribute and transform the "human race."

Surprise, Surprise,...blah tech isn't working

Surprise. Surprise. A few days ago, IBM replaced its CEO Ginni Rometti, with Arvind Krishna. According to Barrons, IBM sales have fallen by 25% in the last eight years, while other tech giants sales are doing well. In the previous eight years, I haven't seen IBM make the rounds to sell their products like DB2, Watson, WebSphere, and others.  They are not marketing their services well.

These days when I think about IBM, I see it as a "blah" technology company that lacks excitement. I used to think of Microsoft the same way. Things changed after Satya Nadella took reigns at Microsoft. They made a few exciting acquisitions that tell me Microsoft (specifically the current CEO) have a vision. These acquisitions diversify their offering of services. Here is the timeline:

1. Microsoft made its flagship product Microsoft Office accessible on non-Windows devices.
2. Microsoft purchased Minecraft for $2.5 billion. I thought this was a head-scratcher, but my kids love Minecraft. Right off the bat, Microsoft made inroads with the next generation, which will be earning income in the five to ten years.
3. Microsoft introduces Windows 10, and it is a solid Operation System with a smooth user experience. When l look at Windows 7 and others, I am reminded of Steve Balmer and his poor image of screaming at developers. Steve Balmer and his version of Microsoft.  Steve is not missed. He is better off running the Los Angeles Clippers.
4. Microsoft then bought LinkedIn.
5. Microsoft purchased Github.  As a developer,  I thought it was huge because it showed Microsoft's commitment to the developer community and the open-source community.

When I see IBM,  I don't see any innovation, and it appears to be the "Big Blue Stale Machine."  Is IBM's Watson ready to collect its Social Security checks because it seems to be aging pretty fast?  The company I am really concerned about is Google. Google recently stopped offering its core product Google Search Appliance, and it bundled it with its email.  Gmail is good, but it doesn't provide the email experience of Outlook.  Google's founders stepped away from leadership and gave it an up-and-coming CEO  Sundar Pichai.  The sudden disappearing act by founding computer scientists Larry Page and Sergey Bring doesn't build my confidence in Google.  Memes like this one below or The Wallstreet Journal article will not help Google's image.

For Google to build their trust with me, I want to see Google's founders take a more active role in the technical community.  I would also like to see user-centric innovation.  Having the most robust and secure cloud in the market is not appealing to me because they don't market it, and their tools are not user-friendly.  It's time for Larry and Sergi to stop playing in their lab and step up with the big boys like Satya Nadella, MarkZuckerburg, Reed Hastings, Tim Cook, and others.

Speaking about Tim Cook, who is the CEO of Apple Inc.  He has not done anything earth-shattering, but he has kept Apple with the times.  The launch of Apple TV is great, but I would like to see more innovation come out of Apple. Shrewd CEOs are strategic, operations focused, and can be ruthless.  Very few leaders are also innovative.  I see Steve Jobs in this category.  As you may know, the US government is trying to wrestle with Apple to get into its devices via a backdoor.  It will be interesting to see if Apple will every give-in.

Unlike Apple, Mark Zuckerburg, and his company Facebook seem to give in to external pressures.  According to Facebook, they are going to let politicians publish political ads.  Facebook is trying to diversify its portfolio of products, but I don't know how they can much progress since I don't trust.  It's going to be hard for Facebook to get back in the game until they gain back the public trust. It can be done.

I am interested to see how Netflix will evolve. Scott Hastings and his team revolutionized streaming digital content to mass audiences. Netflix has created a great market for actors, actresses, producers, directors, and other auxiliary roles for making movies, documentaries, and tv shows. With Disney+, Peacock TV, CBS Access, HBO, and others are trying to catch up to Netflix. According to my graphic, Netflix needs to move into gaming and devices to differentiate itself from other sorts of offerings.

I believe Amazon has reached its potential with e-commerce, and I like the way they are going with Amazon cloud and digital content. Amazon will evolve and begin to the left on my image via Amazon Web Services (AWS).

In summary,  big tech companies need to offer a diverse set of services which gives them the flexibility to grow and help them to minimize the risk.

ABCDEF - Astros, Baseball, Cheating, Data, Ethics, and Finances

“…Luhnow is widely considered to be one of the most successful baseball executives of his generation, credited with ushering in the second “analytics” revolution in baseball and rebuilding the Houston Astros into a perennial Postseason contender. But while no one can dispute that Luhnow’s baseball operations department is an industry leader in its analytics, it is very clear to me that the culture of the baseball operations department, manifesting itself in the way its employees are treated, its relations with other Clubs, and its relations with the media and external stakeholders, has been very problematic. At least in my view, the baseball operations department’s insular culture – one that valued and rewarded results over other considerations, combined with a staff of individuals who often lacked direction or sufficient oversight…”

-          Excerpt from the Statement of the Commissioner

If you are a baseball fan, then your world got rocked by Major LeagueBaseball’s (MLB) Commissioner Rob Manfred’s penalties on the Houston Astros General Manager (GM) Jeff Luhnow and manager A J Hinch.  They are banned from MLB baseball for a year without pay.  Subsequently the owner of Houston Astros Jim Crane fired both of them.  Jeff and A J were fined because they oversaw the 2017 Houston Astros baseball team, which cheated in baseball games using technology.  The following few days after the firings, Alex Cora,  the Boston Red Sox manager, and Carlos Beltran, the New York Mets manager, stepped down.  Both of them were mentioned in Rob Manfred's Statement of the Commissioner report. Alex was bench coach of the 2017 Houston Astros, and Carlos Beltran was a player on the 2017 Houston Astros team.

The 2017 Houston Astros used technology to steal the opposing team’s catcher’s signs.  The catcher’s signs tell the pitcher what pitch the pitcher will throw.  If the batter knows the pitch that he will see, then the batter has a better chance of hitting the ball.  It alters the odds provides an edge to the batter.

If you don't know much about the game baseball, here is a video that provides the basics of the game.

Other than baseball pundits and historians debate if the penalities on Jeff and AJ were harsh enough and the adverse impact to the game,  the following statements in the commissioner's report caught my attention,

"Luhnow’s baseball operations department is an industry leader in its analytics," and

"the baseball operations department’s insular culture – one that valued and rewarded results over other considerations."  

Ideally, in any data and analytics organization, decisions are based on data and not on emotions and "gut feeling."  The Houston Astros focused on the data and results and forgot their mission, which is to play honest baseball and compete.   MLB players, managers, and teams are tempted to cheat because they can win, which in turn leads to more lucrative financial contracts.

Speaking about money. Several years ago, I distinctly remembered listening to a Fresh Air episode on the National Public Radio (NPR).  In the show, the Fresh Air host interviews Michael Lewis about his book Flash Boys.  The show caught my attention.   The Fresh Air website states the following,
"Flash Boys is about the form of computerized transactions known as high-frequency trading, in which the fastest computers with the highest connection speeds get the information first, and make the trade before anyone else can. A millisecond — even a nanosecond — can make all the difference between how much money is made or lost on any transaction."

With machines making trade decisions in a few seconds,  investment banks, hedge funds are exploiting these types of legal cheating using technology.

I believe US law enforcement agencies like the Federal Bureau of Investigation (FBI), US Securities and Exchange Commission (SEC), and others are looking into these types of legal cheating using technology.

Major League Baseball (MLB) punished the cheating team, but I believe they are of the tip of the iceberg.  With financial institutions using machine learning, artificial intelligence, and other technologies to legally cheat, we, as a society, need to develop policies based on data ethics.

I, however, haven't read much about data ethics in any major publications like Harvard Business Review, Medium, Gartner, Forrester, and others.  These publications still promote the objectivity of data-driven decisions since it enables organizations to make ideal decisions.  With high-frequency trading,  stealing baseball signs, and others,  I would like to read about how businesses and organizations are promoting data ethics with their workforce.  The domain of Data Ethics should include data stealing,  skewing the data to achieve desirable results, data sharing, speedy data access, and more.

If we don't have penalties for cheating with technology and data, then we may be cheated out of our money.  Companies sharing our personal data for monetary gains is just the being.  Folks need to know what is right and wrong when these individuals work with data and analytics.  We cannot afford to have our trusted institutions to be like the 2017 Houston Astros.

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NOTE: The ABCDEF image is from https://www.123rf.com/photo_10483641_flip-clock-letters-a-b-c-d-e-f.html.

Thank you for the 2010s and looking forward to the 2020s.

Happy New Year!!!  As we look forward to the new decade,  we should reflect on the past decade.  

Content-driven services
A lot happened in the 2010s,  Netflix, Amazon Prime, ESPN 3 made streaming content "cool."  I went this route because my DVDs were getting scratched, and I was not happy with my cable service.  Overall I saved money, and I was pleased with its flexibility. I could watch content on my handheld devices.  

Prediction for the 2020s:  

• I envision these types of streams will continue to grow, and the cable TV will eventually disappear.  Folks will pay for recorded and live content rather than specific tv channels.  I would rather directly pay the NBA, NFL, MLB, and NHL rather than go through ESPN and FOX sports.  
• I envision broker companies that will focus on aligning users, content providers, and marketing companies.  The broker companies will develop user profiles based on patterns.  They will use  AI and machine learning to build and improve user profiles.  These profiles will then leveraged to provided target adds.  Amazon is already taking steps to go this route via Amazon Channels[1].  

Technical Debt

Currently, Chief Information Officers (CIO) are struggling with aging infrastructure and the lack of funds to upgrade the infrastructure, which includes network infrastructure, databases, applications, and other IT-related components.  Hackernoon.com does a great job of defining Technical Debt[2], and Technical Debt is one of the significant causes of cybersecurity vulnerabilities.

Prediction for the 2020s:  

• Companies will be forced to address this problem in the 2020s because of security vulnerabilities that be in exposed in the old infrastructure.
• I envision start-up companies that will specialize in upgrading infrastructure, which includes moving digital assets (e.g., applications, data, and others) to the cloud. These companies will bring processes like DevSecOps[3] and the associated tools to the project.
• These companies will need to provide a holistic approach in assessing the issues, developing a strategy, and executing the plan. 

Leverage platform services

In the 2010s,  companies recognized that they need more enterprise IT services. Email isn't the only enterprise service. Services like Identity and Access Management (IAM) services, enterprise search,  document management, and others need to be considered as well. Companies bought the infrastructure and hired the staff to develop the service and maintain it.  Unfortunately, a document management SME doesn't have the bandwidth to understand the rapidly evolving cybersecurity attacks.  This leaves the businesses behind the eight-ball when it comes to sustaining an enterprise service and be vigilant of cyber threats.

Prediction for the 2020s:  

• Large vendors like Google, Facebook, Microsoft, and others will offer cloud-based enterprise services.  These services will have simple and clean interfaces.  By having a clean and straightforward interface,  the potential targets for a cyber attack are reduced. The current set of examples of cloud-based enterprise services are the Google Identity Platform [4], Tableau Online [5], and others. 
• I envision IT groups focus on training their staff on how to integrate their business applications with their enterprise services.  

CXO roles

Currently, the Chief Information Officer (CIO) is the chief IT officer in any business organization.  Typically the Chief Information Security Officer (CISO) and the Chief Data Officer (CDO) report to the CIO.  I envision these roles will change in the future.

Prediction for the 2020s:

• Due to the continuous threat of cyber attacks, the role of the CISO will be elevated in the business organization.  The CISO will be equivalent to the CIO.  
• The job of the CDO will continue to be a tough one[6].  The CDO role will not be funded well and may not have a lot of authority.  The CDO will need to please two masters, the CDO and CISO.
• The CDO role will follow a similar pattern to the Gartner Hype Cycle [7], where the hype will go down because the CDO role is not an operations role.  The CDO will be on the same level as the Chief Technology Officer (CTO) or lower.
• The Cloud Architect will be elevated to the C-Suite due to the complexity of the cloud and the cybersecurity threats. On December 30th, 2019, The Wallstreet Journal published an article, Ghosts in the Clouds: Inside China’s Major Corporate Hack[8], which discusses how the Chinese hackers went in through the cloud providers.

NOTE: Image for this blog post is located at https://pixabay.com/illustrations/new-year-s-day-2020-new-year-s-eve-4722431/.

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[1] Casey, H., and Reisinger, C. (2019, May 13). What Is Amazon Channels and Is it Worth It? Tom's Guide. Retrieved from https://www.tomsguide.com/us/amazon-channels-faq,review-4125.html.
[2] Hackernoon.com. (2018, Jan. 25). There are 3 main types of technical debt. Here’s how to manage them. Retrieved from https://hackernoon.com/there-are-3-main-types-of-technical-debt-heres-how-to-manage-them-4a3328a4c50c.
[3] RedHat, Inc., (n.d.). What is DevSecOps?  Retrieved from https://www.redhat.com/en/topics/devops/what-is-devsecops.
[4] Google, LLC (n.d.). Google Identity Platform. Retrieved from https://developers.google.com/identity
[5] Tableau, LLC (n.d.). Tableau Online. Retrieved from https://www.tableau.com/products/cloud-bi.
[6] Bennett, Jo. (2016, Apr. 11). How chief data officers can tackle formidable roadblocks, including people, culture, and internal resistance. Smarter With Gartner. Gartner, Inc.  Retrieved from https://www.gartner.com/smarterwithgartner/half-of-cdos-succeed/.
[7] Gartner, Inc. (n.d.). Gartner Hype Cycle.  Retrieved from https://www.gartner.com/en/research/methodologies/gartner-hype-cycle.
[8] The Wallstreet Journal (2019, Dec. 30). Ghosts in the Clouds: Inside China’s Major Corporate Hack. Retrieved from https://www.wsj.com/articles/ghosts-in-the-clouds-inside-chinas-major-corporate-hack-11577729061.

Data generated by its citizens as a national asset...

The following statement in a Harvard Business Review (HBR) article, which was published on December 18, 2019, caught my eye,
"India is a nation state, it would treat the data generated by its citizens as a national asset, store and guard it within national boundaries, and reserve the right to use that data to safeguard its defense and strategic interests."[1]  The HBR article is about India's proposed legislation to protect its consumer data. The bill is called the Personal Data Protection Bill (DBP).

On December 24, 2019, The New York Times published an article called Pentagon Warns Military Personnel Against At-Home DNA Tests.[2] The article discusses a Department of Defense internal memo which discourages military personnel from taking mail-in DNA tests.  According to DoD leadership, they:

• Are unreliable;
• Negatively impact service members' careers; and 
• Create security risks.

I am not sure if there is any relationship between the two articles, but it highlights the dangers of international privacy laws, which are still evolving.  I believe our DNA information is our most private and personal information.  If countries like India push laws like the DBP,  which can use its citizens' personal information, including DNA for national and strategic interests, then folks to be careful about their personal data.  Nations cannot use their citizens' data if the data isn't generated.

The next question to ask if these laws will get less complicated.   The answer lies in the history of privacy laws.

According to the HBR article's authors, the proposed DBP is based on the European Union's (EU) General Data Protection Regulation (GDPR).  According to an EU website, the GDPR was released on May 24, 2016, and the EU enforced the GDPR on May 25, 2018.[3] The EU used the US Department of Commerce's National Institution of Standards and Technology (NIST) recommendation called Guide to Protecting the Confidentiality of Personally Identifiable Information (PII) (Special Publication 800-122)[4], which was released in April 2010.  The state of California issued the California Consumer Privacy Act (CCPA) in June 2018.  The CCPA is based on GDPR with modifications.[5]

With each data regulation, bill, and recommendation released over time, nations and states are trying to protect its citizens and their data, but countries (like India, China, and others) may use the data to further political and national security reasons.

As members of the digital age, we cannot cut off ourselves from the internet, but we need to be aware of the risks, policies, and rights since they are constantly evolving like the technologies around us.  Military and other governments may ask their citizens to share their DNA for the good of their countries. Still, I am not a fan of businesses monetizing, or criminal syndicates stealing my data. I believe privacy laws, in general, are good, but we in the US need a cohesive single data protection legislation and a watchdog organization that focuses on digital privacy.  I am not a policy wonk, but I believe national and international privacy laws need to mature as data grows exponentially, and technologies like artificial intelligence proliferate our lives in the next ten years.

Until then, please be careful what you share on the web, including your DNA results.  Currently, our DNA information in the US is protected via the Genetic Information Nondiscrimination Act of 2008 (GINA) [6]. That being said, presidents change, legislators change, supreme government justices change, governments may change, but your DNA information doesn't change.

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[1] Govindarajan, V., Srivastava, A., & Enache, L. (2019, Dec., 18). How India Plans to Protect Consumer Data. Retrieved from https://hbr.org/2019/12/how-india-plans-to-protect-consumer-data.
[2] Murphy, H., & Zavehri, M. (2019, Dec. 24). Pentagon Warns Military Personnel Against At-Home DNA Tests. The New York Times. Retrieved from https://www.nytimes.com/2019/12/24/us/military-dna-tests.html.
[3] Data protection in the EU | European Commission.  Retrieved from https://ec.europa.eu/info/law/law-topic/data-protection/data-protection-eu_en.
[4] McCallister, E., Grance, T., & Scarfone, K. (2010, Apr.). Guide to Protecting the Confidentiality of Personally Identifiable Information (PII). NIST Special Publication 800-122. Retrieved from https://csrc.nist.gov/publications/detail/sp/800-122/final.
[5] Korolov, M. (2019, Oct. 4). California Consumer Privacy Act (CCPA): What you need to know to be compliant. Retrieved from https://www.csoonline.com/article/3292578/california-consumer-privacy-act-what-you-need-to-know-to-be-compliant.html.
[6] The Genetic Information Nondiscrimination Act of 2008 (2008, May 21). U.S. Equal Employment Opportunity Commission (EEOC). Retrieved from https://www.eeoc.gov/laws/statutes/gina.cfm.
[7] Picture for this blog post retrieved from https://pixabay.com/illustrations/dna-matrix-genetics-control-3888228/.

"Dear Santa" letter from a vigilant kid

Dear Santa,

I cannot send you my Christmas letter because the letter will have a lot of my personal data aka personally identifiable information (PII) data. Due to privacy laws and potential stalkers, my parents forbade me to send you a letter or an email. With the various cybersecurity issues, I am not sure if my email will end up in the wrong hands and my daddy, mommy, brother, sister and I may get socially engineered and phished.

Since I still believe in you, here is a compromise. I will share my Amazon wish list and you can use it to send me toys. It needs to be magically because my parents forbade me to share my address with you. I noticed that now Amazon recommends items that I can add to my Amazon wish list. I wonder if Amazon is learning my behavior on Amazon.com. Doesn't that compromise my privacy? Mommy and Daddy tell me privacy laws are here to protect me but I am not sure if websites like Amazon.com, Google.com, Yahoo.com should use my data to make money.

Nevertheless, please send me your email address and I will share my Amazon wish list.

Merry Christmas Santa,

Your Friend

P.S.  My parents forbade me to share my name with you.

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NOTE: I used the picture from https://spaceshipsandlaserbeams.com/20-free-printable-letters-to-santa/.

Hack the LIME - the story about open-source AI

Leave it to a lawyer to spoil the fun!  Andrew Burt, a lawyer, and writer, recently wrote an article for the Harvard Business Review, which discusses the risks of open-source Artifical Intelligence (AI) models   The HBR post, "The AI Transparency Paradox," explains the dangers of making artificial intelligence models transparent.  On the one hand, businesses want to know how companies engineered their AI algorithms.  Is the AI algorithm too sexist, racist, and just not good?  For instance, in James Vincent's article, Google ‘fixed’ its racist algorithm by removing gorillas from its image-labeling tech, Google "fixed" its 2015 deep learning algorithm, which labeled humans as monkeys. I am sure the appropriate stakeholders at Google wanted to know how the algorithm was written and how can the developer fixed the inappropriate bias in the algorithm.

On the one hand, if Google opened its AI code to the open-source community, then the AI algorithms could be significantly improved.   On the other hand, as (our no-fun lawyer friend), Andrew Burt cautions businesses that they would be vulnerable if they opened their AI algorithms to the world, which includes bad actors like hostile governments.  Andrew uses the research paper titled, “Why Should I Trust You?” Explaining the Predictions of Any Classifier.  The paper is about the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.  Andrew then talks about a research paper. How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods,  which was published in November 2019.  The document discusses a "novel scaffolding technique," which can be used to hack the LIME algorithm.  Is there a return on investment if companies made their algorithms more transparent?

Folks may argue that Google successfully made its TensorFlow machine learning (ML) platform open source without much impact.  Before the TensorFlow platform question can be answered,  the following terms need to be defined:

• Machine Learning;
• Deep Learning; and 
• Artificial Intelligence.

According to Geeks for Geeks portal, "Machine Learning is a technique of parsing data, learn from that data, and then apply what they have learned to make an informed decision." Machine Learning includes supervised training where the machine builds the model on most of the data (70% to 90% of the complete dataset) and then test the data for accuracy with the remaining portion of the data.  Unsupervised training involves machine learning from the full dataset.  There is no testing of the data.

Deep Learning algorithms are a specific subset of Machine Learning algorithms, which are primarily composed of neural networks. A common use of deep learning algorithms is for image recognization.

Gartner defines Artificial intelligence (AI) is the application of "...advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions."

Gartner recommends several open-source Data Science and ML platforms, including TensorFlow, to develop ML-based solutions.    Does this mean Mr. Burt is incorrect?

Andrew Burt is correct because comprised algorithms, which are the core of all AI solutions, will cause businesses to make poor decisions and ultimately discredit the AI solution. The underlying technology, which enables to invoke the algorithm, pull the data, process the data, and reports the outcomes, can be open source.  The other critical piece of any AI solution is the data itself.   Sensitive data is never unless asked by law enforcement or regulatory agency.

In conclusion,  Andrew Burt is correct by bringing up valid points, but at the same time, the AI field is still pretty young.  I am a strong believer in open-source software, and I believe we will have open-source AI models available.  We will also have AI models that will validate other models using technologies like blockchain technologies.

NOTE: The video below is about the LIME algorithm, and please keep in mind researchers recently hacked this algorithm.

Check out Manna and "Empathy driven AI"

My summer reading included a book called Manna: Two Visions of Humanity's Future by Marshall Brain,  which described a world with robots, and artificial intelligence (AI). In the book, there were two specific implementations of this technology.  One focused on automating businesses, and the other focused on improving the quality of life. 

I am not sure if we will reach either state, but it was good to get a perspective on how the world could be.  Personally, as a trained data scientist,  I don't envision robots taking over my life.  With any technology,  we need to use it with pragmatism.  AI will enable decision-makers to be aware of their biases and tendencies.  "Based on gut feelings" will be reduced.  At the same time,  all decision-makers shouldn't be making identical decisions since they may not have similar emotions about a decision.  

Speaking about emotions, currently, AI engineers are currently developing  AI algorithms to mimic and sense human emotions  Chatbots, and virtual assistants like Alexa, Google Assistant, and others want to detect emotion via user interaction and provide a better answer.  AI-driven emotions should strive for better sales and improved customer experience. A Senior Analyst (now former) Allison Snow at Forrester posted a blog entry on November 12, 2018, titled AI Tech Shines A Path To Empathetic Triggers where connects three independent entities empathy, insights, and AI within the context of B2B marketing. On October 14, 2019,  Gartner published the article, Digital Empathy: A New Lever for Earnings Growth by Kristin Moyer, Dave Aron, and Don Scheibenreif.  Gartner typically takes a concept and brands it with its own buzz words.  In this case, Gartner calls AI-driven emotions "Digital Empathy."  

With all innovative ideas, you will find folks who will talk about the dangers of these ideas.  In the article, Empathy in Artificial Intelligence on Medium.com, Jun Wu writes about AI, human emotions, and the ethics around.  Last month Harvard Business Review (HBR) published an article called The Risks of Using AI to Interpret Human Emotions by three Accenture employees (Mark Purdy, John Zealley, and Omaro Museli).  Accenture talks about biases seeping in the AI since AI is nothing more than decisions based on past data.  

Overall the AI-driven empathy concept is entirely new. I wonder how the Nigerian prince scam would work if we were effectively duped into giving money. I feel bad for the security guys, but at the same time, there is a business opportunity. All of these ideas are reflected in the book Manna: Two Visions of Humanity's Future.  Check out the book!

NOTE: I used the image from Amazon.com.