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.
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.
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.
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.”
The discussion about the value of unstructured data was very interesting, especially when you consider:
- The potential for NLU (natural language understanding) products in conjunction with ML and AI.
- This is a great NLU-pipeline diagram from North Side Inc. in Canada, one of the pioneers in this space.
- The importance of semantic data analysis relative to any ML effort.
- 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.
I still remember my parents allowing me to stay up late to watch the first moonwalk. It was 9:30 pm, I was 5 years old, and we were huddled around an old “black and white” television that had a circular viewing area. My parents tried to convey how important and monumental that moment was – telling me that I would be telling this story to my children someday.
What I remember most was being amazed seeing the astronauts hop around with ease and not understating how that could be. We had watched the launch on TV and were getting updates nightly from Walter Cronkite on the evening news. Normally my dad would sit at a TV table to eat dinner and watch the news as my mom sat with my sister and me at our kitchen table, but this week was different.
With all of the news this past week on the 50th Anniversary of the first moonwalk it triggered a couple of memories. One of them was that I had a collectible item that I purchased in 2005 at the annual Children’s Circle of Care leadership conference in San Diego, CA. There was a luncheon held on the deck of the USS Midway Museum and afterward, I took a tour. It is an incredible place to visit if you are ever near San Diego.
Before leaving that day I went to the gift shop to get a few trinkets for my wife and children. What I found was a beautiful display, which I immediately purchased and had shipped home. This display was taken to school a couple of times for “show and tell.” It hung on my office wall for 3 years or so and then went into storage with other artwork. It then sat for the past decade and I almost forgot that I had it.
To me, this display is both beautiful to see and very inspirational as well. Human creativity is an incredible thing! As an aside, I have never seen anything like this display so I thought I would share it with you.
Today I also ran across a good article regarding this event that provided information that I had not seen before. It is very interesting and can be found here: https://go.usa.gov/xyVGh
Edit: This was another good article that discusses the advanced flight control computer used at the time – https://www.linkedin.com/pulse/apollo-11-moon-landings-fourth-crew-member-computer-far-fishman/
This anniversary is a great reminder of the power of individuals, teams, and partnerships when they are mission-focused. I find people like the men and women of NASA to be extremely motivational, and the few that I have met have all been very friendly people as well. They are the Humble Heros!
A friend posted this article on LinkedIn.com. Due to character limitations for comments, I decided to post my response here. Below is a link to the article referenced: https://hbr.org/2019/07/building-a-startup-that-will-last
The article is interesting, but the emphasis on “second and third acts” assumes that the start-up will successfully navigate the first act. Even with addressing what the author views as key points this is still a very big assumption. The reasons for Longevity and Success are far more complex and multi-dimensional, but it does place a spotlight on some of the more important areas of focus.
Long-term success requires several things: The right combination of having a unique goal that has the potential to make a big impact (think “No software” from Salesforce.com); Innovative ideas to achieve that goal; A diverse team to build the product (a mix of visionaries, insightful “translators,” technical experts, designers, planners, adept doers, etc.); Very good sales / business development / marketing to describe a better way of doing things and converting that to new business; and ultimately a management team focused on sustainable and scalable growth.
The point made about the need to, “Articulate a value framework oriented toward societal impact, not just financial achievement” seems a bit superficial and too tactical in nature.
First, there are unintended consequences to most new technologies. Social Media is a recent example, but Genetic Editing and AI are two areas that are likely to provide more examples over the next decade. Not every societal impact will be positive, and having a negative impact could very well lead to the untimely demise of that company.
Second, the two ideas (societal impact and financial achievement) are not mutually exclusive. When I owned my consulting company we had a goal of funding $1M worth of medical research that would find a cure for Arthritis. We allocated half of our net profits for this goal. Every employee was on-board with this because there was a tangible example of why it mattered (my daughter). We invested $500K, helped launch a few careers for some brilliant MD/Ph.Ds and at least one national protocol came out of their research.
Mission and Vision are so important to a company, yet so many companies fail to view this as anything more than a marketing effort. Those companies fail to realize that this is as much to motivate and inspire their employees, as it is to grab the attention of a prospective customer. These should be both inspirational and aspirational, such as the “BHAG” (Big Hairy Audacious Goals) that Collins and Porras wrote about 25 years ago.
Regarding Endurance and the assertion that “…the best businesses are intrinsically aligned with the long-term interests of society,” my take is slightly different. The best businesses are always looking for trends and opportunities in an ever-changing global competitive landscape – as opposed to looking to their competitors and trying to ride on their coattails. Companies with a culture of fostering innovation as a way to learn and grow (Amazon and Google are two great examples) are able to find that intersection of “good business” and “positive societal impact.” It is much more complex than a simple one-dimensional outlook.
But, it was a good article to help reframe ideas and assumptions around growth.
When I owned a consulting company we viewed innovation as an imperative. It was the main thing that created differentiation, credibility, and opportunity. We had an innovation budget, solicited ideas from the team, and evaluated those ideas quarterly.
Almost as important to me was that this was fun. It gave everyone on the team the chance to suggest ideas and participate in the process. That was meaningful and supported the collaborative, high-performance culture that had developed. The team was inspired and empowered to make a difference, and that led to an ever-increasing sense of ownership for each employee.
The team also had a vested interest in having the process work, as quarterly bonuses were paid based on their contributions to the company’s profitability. There was a direct cause and effect correlation with tangible benefits for every member of the team.
We developed the following 10 questions qualify & quantify the potential of new ideas:
- What will this new thing do?
- It is important to be very detailed as this was used to create a common vision of success based on the idea being presented.
- What problem(s) does this solve and how so?
- This seems obvious, but if you are not solving a problem (which could be something like “lack of organic expansion”) or addressing a pain point then selling this new product will be an uphill challenge.
- What type of organizations have those problems and why?
- This was fundamental to understanding if a fix was possible from a practical perspective, what the value of that fix might be for the target buyer, and how much market potential existed to scale this new offering.
- What other companies have created solutions or are working on solutions to this problem?
- The lack of competition today does not mean that you are the first one to attack this problem. Due diligence can help avoid repeating the failure of others, and potentially provide lessons learned by others and help you avoid similar pitfalls.
- Will this expand our existing business, or does it have the potential to open up a new market for us?
- There are upsides and downsides to each answer, but breaking into a new market can take more time and be more difficult, time-consuming, and expensive to achieve.
- Is this Strategic, Tactical, or Opportunistic?
- An idea may fall into multiple categories. When Sarbanes-Oxley (SOX) Act became law we viewed a new service offering as both a tactical means to protect our managed services business as well as an opportunistic means to acquire new customers and grow the business.
- What are the Cost, Time, and Skill estimates for developing a Minimally Viable Product (MVP) or Service?
- What are the Financial Projections for the first year?
- Cost to develop and go-to-market.
- Target selling price, factoring-in early adopter discounts.
- Estimated Contribution Margin Ratio (for comparison with other ideas being considered).
- Break-even point.
- Would we be able to get an existing customer to pre-purchase this?
- A company that is willing to provide a PO that commits to making a purchase of that MVP within a specific timeframe increased our confidence in the viability of the idea.
- What are the specific Critical Success Factors to be used for evaluation purposes?
- This was an important lesson learned over time that helped minimize emotional attachment to the idea or project, as well as providing objective milestones for critical go / no-go decision making.
This process was purposeful, agile, lean, and somewhat aggressive. We believed it gave our company a competitive advantage over larger companies that tended to respond slower to new opportunities and smaller competitors that did not want to venture outside their wheelhouse.
With each project, we learned and became more efficient and effective, and made better investment decisions that positively impacted our success. We monitored progress on an ongoing basis relative to our defined success criteria, and adjusted or sunset an offering if it stopped providing the required value.
The process was not perfect…
For example, we passed on a couple of leading-edge ideas such as a “Support Robot” in 2003 that was essentially an interactive program that used a machine-learning algorithm. It was to be trained using historical log files, could quickly and safely be tested in a production environment, refined as needed and ultimately validated.
This automation could have been used with our existing managed services and Remote DBA customers to further mitigate the risk of unplanned outages. Most importantly, it would have provided leverage to take-on new business without jeopardizing quality or adding staff – thereby increasing revenue and profit margin.
At the time we believed this would be too difficult to sell to prospective customers (“pipe dream” and “snake oil” were some of the adjectives we envisioned), so it appeared to lack a few items required by the process. Live and learn.
In summary, having a defined approach for something as important as business needs innovation to grow and prosper, as best demonstrated by market leaders like Amazon and Google (read the 10-K Annual Reports to gain a better understanding of their competitive growth strategies that are largely based on innovation).
Implementing this type of approach within a larger organization requires additional steps, such as getting the buy-in from a variety of stakeholders and aligning with existing product roadmaps, but is still the key to scalable growth for most businesses.
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.
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.
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. People 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 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. 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. By the third month the system was trouble free. It was a great learning experience. 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 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 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 technology 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. But, there may also be downsides relative to innovation and continual 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 50 to 100 years and see the full impact, but my guess is that I will see some of the effects in my lifetime.
A few months ago I purchased Fitbit watches for my children and myself. My goals were twofold. First, I was hoping that they would motivate all of us to be more active. Second, I wanted to foster a sense of competition (including fair play and winning) within my children. Much of their pre-High School experiences focused on “participation,” as many schools feel that competition is bad. Unfortunately, competition is everywhere in life, so if don’t play to win you may not get the opportunity to play at all.
It is fun seeing them push to be the high achiever for the day, and to continually push themselves to do better week-by-week and month-by-month. I believe this creates a wonderful mindset that makes you want to do more, learn more, and achieve more. People who do that are also more interesting to spend time with, so that is a bonus.
Recently my 14 year-old son and I went for a long walk at night. It was a cold, windy, and fairly dark night. We live in fairly rural area so it is not uncommon to see and hear various wild animals on a 3-4 mile walk. I’m always looking for opportunities to teach my kids things in a way that is fun and memorable, and in a way that they don’t realize they are being taught. Retention of the concepts is very high when I am able to make it relevant to something we are doing.
That night we started talking about the wind. It was steady with occasional gusts, and at times it changed direction slightly. I pointed out the movement on bushes and taller grass on the side of the road. We discussed direction, and I told him to think about the wind like an invisible arrow, and then explained how those arrows traveled in straight lines or vectors until they met some other object. We discussed which object would “win,” and how the force of one object could impact another object. My plan was to discuss Newton’s three laws of motion.
My son asked if that is why airplanes sometimes appear to be flying at an angle but are going straight. He seemed to be grasping the concept. He then asked me if drones would be smart enough to make those adjustments, which quickly led to me discussing the use potential future of “intelligent” drones by the military. When he was 9 he wanted to be a Navy SEAL, but once he saw how much work that was he decided that he would rather be Transformer (which I explained was not a real thing). My plan was to use this example to discuss robotics and how you might program a robot to do various tasks. I wanted him to logically break down the actions think about managing complexity. But, no such luck that night.
His mind jumped to “Terminator” and “I, Robot.” I pointed out that there is spectrum between the best possible outcome – utopia, and the worst possible outcome – dystopia, and asked him what he thought could happen if machines could learn and become smarter on their own. His response was that things would probably fall somewhere in the middle, but there would be people at each end trying to pull the technology in that direction. That seemed like a very enlightened estimation. He asked me what I thought and I replied that I agreed with him. I then noted how some really intelligent guys like Stephen Hawking and Elon Musk are worried about the dystopian future and recently published a letter to express their concerns about potential pitfalls of AI (artificial intelligence). This is where the discussion became really interesting…
We discussed why you would want a program or a robot to learn and improve – so that it could continue to become better and more efficient, just like a person. We discussed good and bad, and how difficult it could be to control something that doesn’t have morals or understand social mores (which he felt if this robot was that smart it would learn those things based on observations and interactions). I told him about my discussions with his older sister, who wants to become a Physician, about how I believe that robotics, nanotechnology, and pharmacology will be the future of medicine. He and I took the logical next step and thought about a generic but intelligent medicine that identified and fixed problems independently, and then sent the data and lessons learned for others to learn from. We’ll have an Internet of Things (IoT) discussion later, and I will tie back to this discussion and our Fitbit wearable technology.
After the walk I was thinking about what just happened, and was pleased because it seemed to spark some genuine interest in him. I’m always looking for that perfect recipe for innovation, but it is elusive and so far lacks repeatability. It is possible to list many of the “ingredients” (intelligence, creativity, curiosity, confidence (to try and accept and learn from failure), multi-disciplinary experiences and expertise) and “measurements” (such as a mix of complementary skills, a mix of roles, and a special environment (i.e., strives to learn and improve, rewards both learning and success but doesn’t penalize failure, and creates a competitive environment that understands the team is more important than any one individual). That type of environment is magical when you can create it, but it takes so much more than just having people and a place that seem to match the recipe. That critical mixing component is missing.
I tend to visualize things, so while I was thinking about this I pictured a tree with multiple “brains” (my mental image looked somewhat like broccoli) that had visible roots. Those roots were creative ideas that went off in various directions. Trees with more roots that were bigger and went deeper would stand out in a forest of regular trees. Each major branch (brain / person) would have a certain degree of independence, but
ultimately everything on the tree worked as a system. To me, this description makes so much more sense than the idea of a recipe, but it still doesn’t bring me closer to being able map the DNA of this imaginary tree.
At the end of our long walk it seemed that I probably learned as much as my son did, and we made a connection that will likely lead to more walks and more discussions. And in a strange way, I can thank the purchase of these Fitbit watches for being the motivation for an activity that led to this amazing discussion. From that perspective alone this was money well spent.
Earlier this week I was reading a blog post regarding the recent Gartner Hype Cycle for Advanced Analytics and Data Science, 2015. The Gartner chart reminded me of the epigram, “Plus ça change, plus c’est la même chose” (asserting that history repeats itself by stating the more things change, the more they stay the same.) To some extent that is true, as you could consider today’s big data as derivative of yesterday’s VLDBs (very large databases) and Data Warehouses. One of the biggest changes IMO is the shift away from Star Schemas and practices implemented for performance reasons such as aggregation of data sets, use of derived and encoded values, the use of surrogate and foreign keys to establish linkage, etc.
There are many dimensions to big data: Huge sample of data (volume), which becomes your universal set and supports deep analysis as well as temporal and spatial analysis; A variety of data (structured and unstructured) that often does not lend itself to SQL based analytics; and often data streaming in (velocity) from multiple sources – an area that will become even more important in the era of the Internet of Things. These are the “Three V’s” that people have been talking about for the past five years.
Like many people, my interest in Object Database technology initially waned in the late 1990’s. That is, until about four years ago when a project at work led me back in this direction. As I dug into the various products I learned that they were alive and doing very well in several niche areas. That finding led to a better understanding of the real value of object databases.
Some products try to be, “All Vs to all people,” but generally what works best is a complementary, integrated set of tools working together as services within a single platform. It makes a lot of sense. So, back to object databases.
One of the things I like most about my job is the business development aspect. One of the product families I’m responsible for is Versant. With the Versant Object Database (VOD – high performance, high throughput, high concurrency) and Fast Objects (great for embedded applications). I’ve met and worked with some brilliant people who have created amazing products based on this technology. Creative people like these are fun to work with, and helping them grow their business is mutually beneficial. Everyone wins.
An area where VOD excels is with the near real-time processing of streaming data. The reason it is so adept to this task is the way that object map out in the database. They do so in a way that essentially mirrors reality. So, optionality is not a problem – no disjoint queries or missed data, no complex query gyrations to get the correct data set, etc. Things like sparse indexing are no problem with VOD. This means that pattern matching is quick and easy, as well as more traditional rule and look-up validation. Polymorphism allows objects, functions, and even data to have more than one form.
VOD does more by allowing data to be more, which is ideal for environments where change is the norm. Cyber Security, Fraud Detection, Threat Detection, Logistics, and Heuristic Load Optimization. In each case, the key to success is performance, accuracy, and adaptability.
So, while some things stay the same, others really do change. The ubiquity of devices generating data today, combined with the desire for people and companies to leverage that data for commercial and non-commercial benefit, really is very different than what we saw 10 or more years ago. Products like VOD are working their way up that Slope of Enlightenment because there is a need to connect the dots better and faster – especially as the volume and variety of those dots increases. It is not a, “one size fits all” solution, but it is the perfect tool for this type of work.
These are exciting times.