Career
The Future of Smart Interfaces
Recently, I was helping one of my children research a topic for a school paper. She was doing well, but the results she was getting were overly broad. So I taught her some “Google-Fu,” explaining how to structure queries to get better results. She replied that search engines should be smarter than that. I explained that sometimes the problem is that search engines look at your past searches and customize results as an attempt to appear smarter or to motivate someone to do or believe something.
Unfortunately, those results can be skewed and potentially lead someone in the wrong direction. It was a good reminder that getting the best results from search engines often requires a bit of skill and query planning, as well as occasional third-party validation.
Then the other day I saw this commercial from Motel 6 (“GasStation Trouble”) where a man has problems getting good results from his smartphone. That reminded me of seeing someone speak to their phone and get frustrated by the responses. His questions went something like this:
“Siri, I want to take my wife to dinner tonight, someplace that is not too far away, and not too late. And she likes to have a view while eating, so please look for something with a nice view. Oh, and we don’t want Italian food because we just had that last night.”
Just as amazing as the question being asked was watching him ask it over and over again in the exact same way, each time becoming even more frustrated. I asked myself, “Are smartphones making us dumber?” Instead of contemplating that question, I began to think about what future smart interfaces would or could be like.
I grew up watching Sci-Fi computer interfaces like “Computer” on Star Trek (1966), “HAL” on 2001: A Space Odyssey (1968), “KITT” from Knight Rider (1982), and “Samantha” from Her (2013). These interfaces had a few things in common:
- They responded to verbal commands.
- They were interactive – not just providing answers, but also asking qualifying questions and allowing for interrupts to drill down or enhance the search (e.g., with pictures or questions that resembled verbal Venn diagrams).
- They often suggested alternative queries based on intuition. That would have been helpful for the gentleman trying to find a restaurant.
Despite having 50 years of science fiction examples, we are still a long way off from realizing the goal of a truly intelligent interface. Like many new technologies, they were originally envisioned by science fiction writers long before they appeared in science.
A spectrum of common beliefs about modern interfaces seems to exist. On one end, some products make visualization easy, facilitating understanding, refinement, and drill-down of data sets. Tableau is an excellent example of this type of easy-to-use interface. At the other end of the spectrum, the emphasis is on back-end systems – robust computer systems that digest huge volumes of data and return the results to complex queries within seconds. Several other vendors offer powerful analytics platforms. In reality, you need a strong front end and back end to achieve the full potential of either.
But there is so much more potential…
I predict that within the next 3 – 5 years, we will see business and consumer interface examples (powered by AI and Natural Language Processing, or NLP) that are closer to the verbal interfaces from those familiar Sci-Fi shows (albeit with limited capabilities and no flashing lights).
Within the next 10 years, I believe we will have computer interfaces that understand the request (not just the string of words), intuit our needs, and quickly and easily generate correct answers. While this is unlikely to be at the level of “The world’s first intelligent Operating System” envisioned in the movie “Her,” and probably won’t even be able to read lips like “HAL,” it should be much more like HAL and KITT than like Siri (from Apple) or Cortana (from Microsoft).
Siri was groundbreaking consumer technology when it was introduced. Cortana seems to have taken a small leap ahead. While I have not mentioned Google Now, it is somewhat of a latecomer to this consumer smart interface party, and in my opinion, it is behind both Siri and Cortana.
So, what will this future smart interface do? It will need to be very powerful, harnessing a natural language interface on the front end with an extremely flexible and robust analytics interface on the back end. The language interface will need to take a standard question (in multiple languages and dialects) – just as if you were asking a person – deconstruct it using Natural Language Processing, and develop the proper query based on the available data. That is important, but it only gets you so far.
Data will come from many sources – things that we consider today with relational, object, graph, and NoSQL databases. Structured and unstructured data with inconsistent formats must be joined and filtered quickly and accurately. In addition, context will be more important than ever. Pictures and videos could be scanned for facial recognition, location (via geotagging and image searches), and, in the case of videos, analyze speech and background noises. Relationships will be identified and inferred based on a variety of sources, using both data and metadata. Sensors will collect data from almost everything we do and (someday) wear, providing both content and context.
Stylometry will identify outside content likely related to the people involved in the query and provide further context about interests, activities, and even biases. This is how future interfaces will truly understand (not just interpret), intuit (so it can determine what you really want to know), and then present results that may be far more accurate than we are used to today. Because the interface is interactive, it will allow you to organize and analyze subsets of data quickly and easily.
So, where do I think that this technology will originate? I believe that it will be adapted from video game technology. Video games have consistently pushed the envelope over the years, driving the need for higher-bandwidth I/O capabilities in devices and networks, better and faster graphics capabilities, and larger and faster storage (which ultimately led to flash memory and even Hadoop). Animation has become very lifelike and will someday be created with a few simple commands. Games are becoming more responsive to input from multiple sources, and may someday use things like eye movement for guidance. It is not a stretch to believe that the next generation of smart interfaces will come from this direction or something similar, like leading-edge defense technology (rather than from the evolution of current smart interfaces).
Someday, it may no longer be possible to “tweak” results through the use or omission of keywords, quotation marks, and flags. Additionally, it may no longer be necessary to understand special query languages (SQL, NoSQL, SPARQL, etc.) and syntax. We won’t have to worry as much about incorrect joins, spurious correlations, and biased result sets. Instead, we will be given the answers we need – even if we don’t realize that this was what we needed in the first place – which will likely be driven by AI. At that point, computer systems may appear nearly omniscient.
When this happens, parents will no longer need to teach their children “Google-Fu.” Those are going to be interesting times indeed.
Big Data – The Genie is out of the Bottle!
Back in early 2011, other members of the Executive team at Ingres and I were betting on the future of our company. We knew we needed to do something big and bold, so we decided to build what we thought the standard data platform would be in 5-7 years. A small minority of team members didn’t believe this was possible and left, while the rest focused on making it happen. We made three strategic acquisitions to fill gaps in our Big Data platform. Today (as Actian), we have nearly achieved our goal. It was a leap of faith back then, but our vision turned out to be spot-on, and our gamble is paying off today.
My mailbox is filled daily with stories, seminars, white papers, etc., about Big Data. While it feels like this is becoming more mainstream, reading and hearing the various comments on the subject is interesting. They range from “It’s not real” and “It’s irrelevant” to “It can be transformational for your business” to “Without big data, there would be no <insert company name here>.”
What I continue to find amazing is hearing comments about big data being optional. It’s not – that genie has already been let out of the bottle. Incredible opportunities await companies that understand and embrace its potential. I like to tell people that big data can be their unfair advantage in business. Is that really the case? Let’s explore that assertion and find out.
We live in the age of the “Internet of Things.” Data about nearly everything is everywhere, and we have tools to correlate it and understand so many things (activities, relationships, likes and dislikes, etc.). With smart devices that enable mobile computing, we have an extra dimension: location. And, with new technologies such as Graph Databases (based on SPARQL), graphical interfaces to analyze that data (such as Sigma), and identification technology such as Stylometry, it is getting easier to identify and correlate that data. Someday, this will feed into artificial intelligence, becoming a superpower for those who know how to leverage it effectively.
We are generating increasingly large volumes of data about everything we do and everything going on around us, and tools are evolving to make sense of that data better and faster than ever. Organizations that perform the best analysis, get answers fastest, and act on that insight quickly are more likely to win than organizations that look at a smaller slice of the world or adopt a “wait and see” posture. So, that seems like a significant advantage in my book. But is it an unfair advantage?
First, let’s remember that big data is just another tool. Like most tools, it can be misused and abused. Whether a particular application is viewed as “good” or “bad” depends on the goals and perspective of the entity using the tool (which may be the polar opposite of the groups targeted by those people or organizations). So, I won’t try to judge the various use cases; instead, I’ll present a few and let you decide.
Scenario 1 – Sales Organization: What if you could understand what you were being told a prospect company needs and had a way to validate and refine that understanding? That’s half the battle in sales (budget, integration, and support/politics are other key hurdles). Data that helped you understand not only the actions of that organization (customers and industries, sales and purchases, gains and losses, etc.) but also the stakeholders’ and decision-makers’ goals, interests, and biases. This could provide a holistic view of the environment and allow you to provide a highly targeted offering, with messaging tailored to each individual. That is possible, and I’ll explain soon.
Scenario 2 – Hiring Organization: Many questions cannot be asked by a hiring manager. While I’m not an attorney, I would bet that State and Federal laws have not kept pace with technology. And while those laws vary state by state, there are likely loopholes allowing public records to be used. Moreover, implied data that is not officially considered could color a hiring manager’s or organization’s judgment. For instance, if you wanted to “get a feeling” that a candidate might fit in with the team or the culture of the organization or have interests and views that are aligned with or contrary to your own, you could look for personal internet activity that would provide a more accurate picture of that person’s interests.
Scenario 3 – Teacher / Professor: There are already sites in use to search for plagiarism in written documents, but what if you had a way to make an accurate determination about whether an original work was created by your student? Some people will do the work and write a paper for a student for a fee. So, what if you could not only determine that the paper was not written by your student but also determine who the likely author was?
Do some of these things seem impossible or at least implausible? Personally, I don’t believe so. Let’s start with the typical data our credit card companies, banks, search engines, and social network sites already have about us. Add to that the identified information available for purchase from marketing companies and various government agencies. That alone can provide a pretty comprehensive view of us. But there is so much more that’s available.
Consider the potential of gathering information from intelligent devices accessible through the Internet, your alarm and video monitoring system, etc. These are intended to be private data sources, but history has taught us that anything accessible is subject to unauthorized access and use (just think about the numerous recent credit card hacking incidents).
Even de-identified data (medical/health/prescription/insurance claim data is one major example), which receives much less protection and can often be purchased, could be correlated with a reasonably high degree of confidence to understand other “private” aspects of your life. The key is to look for connections (websites, IP addresses, locations, businesses, people), things that are logically related (such as illnesses/treatments/prescriptions), and then accurately identify (stylometry looks at things like sentence complexity, function words, co-location of words, misspellings and misuse of words, etc. and will likely someday take into consideration things like idea density). It is nearly impossible to remain anonymous in the Age of Big Data.
There has been a paradigm shift in the practical application of data analysis, and companies that understand and embrace it will likely perform better than those that don’t. This technology also raises new ethical considerations and will likely bring new laws and regulations. But for now, the race is on!
Genetics, Genomics, Nanotechnology, and more
Science has interested me for most of my life, but it wasn’t until my first child was born that I shifted from “interested” to “involved.” My eldest daughter was diagnosed with Systemic Onset Juvenile Idiopathic Arthritis (SoJIA – originally called Juvenile Rheumatoid Arthritis, or JRA) when she was 15 months old, which also happened to be about six months into the start of my Consulting company, all while we were in the middle of a very critical Y2K ERP system upgrade and rehosting project. It was definitely a challenging time in my life.
At the time, there was very little research on JRA because it was estimated that only 30,000 children were affected by the disease, and the implication was that funding research would not have a positive ROI. The economics of medical research were eye-opening to me. This was also a few years before major breakthroughs like Enbrel for children.
One of the things that I learned was that this disease could be horribly debilitating. Children often had physical deformities as a result of this disease. Even worse, the systemic type that my daughter has could result in premature death. As a first-time parent, imagining that type of life for your child was extremely difficult.
Luckily, the company I had just started was taking off, so I decided to find ways to make a tangible difference for all children with this disease. We decided to donate 50% of our net profits to fund medical research. Our goal was to fund $1 million in research and find a cure for Juvenile Arthritis within the next 5-7 years.
As someone new to “major gifts” and philanthropy, I quickly learned that some gifting vehicles were more beneficial than others. While most organizations wanted you to start a fund (which we did), the impact tended to be more long-term, supportive, and much less immediate. Later, I met someone passionate, knowledgeable, and successful in her field who showed me a different, better approach (here’s a post that describes it in more detail).
I no longer wanted to blindly give money and hope it was used quickly and properly. Rather, I wanted to treat these donations like investments in a near-term cure. To be successful, I needed to understand research from both medical and scientific perspectives in these areas. That began a new phase in medical research and an independent learning journey in areas where I had limited understanding and expertise.
A lot was happening in Genetics and Genomics at the time (here’s a good explanation of the difference between the two). My interest and efforts in this area led to a position on the Medical and Scientific Advisory Committee with the Arthritis Foundation. Except for me, the other members were talented, successful physicians who were also involved in medical research. We met quarterly, and I asked questions and made suggestions that made a difference. But unlike everyone else on the committee, I needed to study 40+ hours for each call to ensure I understood enough to add value and not be a distraction. Every quarter, I earned my seat at that table, and soon, most of the other members respected me for that (i.e., “not a typical donor”).
A few years later, we did work for a Nanotechnology company (more info here). The Chief Scientist wasn’t interested in explaining what they did until I described some of our research projects on gene expression. He then went into great detail about what they were doing and how he believed it would change what we do in the future. I saw that and agreed. That started my thinking about the potential of leveraging advanced nanotechnology in medicine.
While driving today, I was listening to the “TED Radio Hour” and heard a segment about entrepreneur Richard Resnick. It was exciting because it got me thinking about this again – a topic I haven’t thought about for the past few years (the last time, I was contemplating how new analytics products could be useful in this space).
There are efforts today with custom, personalized medicines that target only specific genes for a specific outcome. The genetic modifications being performed on plants today will likely be performed on humans in the near future (I would guess within 10-15 years as gene editing becomes more mainstream), possibly even performed by some type of robots. Because the body is an incredibly adaptive organism, it may be very challenging to implement anything consequential that is consistently safe and effective long-term. But that day will come.
It’s not a huge leap from genetically modified “treatment cells” to true nanotechnology (not just extremely small particles). Just think, machines that can be designed to work independently within us to do what they are programmed to do and, more importantly, identify and understand adaptations (i.e., artificial intelligence) as they occur and alter their approach and treatment plan accordingly based on findings and changes. This is extremely exciting. From my perspective, being able to do things that positively impact the quality of life for children and their families is a worthy goal.
My advice is to keep learning, stay open-minded, and do what you can to make a difference. You will never know what is possible unless you try. It will also be interesting to see how technologies evolve and work together to create an even greater impact.
Are you Visionary or Insightful?
Having great ideas that go misunderstood or unvalidated is pointless, just as being great at “filling in the gaps” does little if what you are building achieves little toward your needs and goals. This post is about Dreaming Big and turning those dreams into actionable plans.
Let me preface this post by stating that both are important and complementary roles. But if you don’t recognize the difference between the two, it becomes much more challenging to execute and realize value/gain a competitive advantage.
The Visionary has great ideas but doesn’t always create plans or follow through on developing the idea. There are many reasons why this happens (distractions, new interests, frustration, lack of time), so it is good to be aware of that, as this type of person can benefit by being paired with people willing and able to understand a new idea or approach, and then take the next steps to flesh out a high-level plan to present that idea and potential benefits to key stakeholders. People may view them as aloof or unfocused.
The Insightful sees the potential in an idea, helps others understand the benefits and gain their support, and often creates and executes a plan to prototype and validate the idea – killing it off early if the anticipated goals are unachievable. They document these experiences, learn from them, and become increasingly proficient at validating the idea or approach and quantifying the potential benefits. They are usually very pragmatic.
Neither of these types of people is affected by loss aversion bias.
I find it amazing how often you hear someone referred to as a visionary, only to see that person could eliminate some of the noise and “see further down the road” than most people. While this skill is valuable, it is more akin to analytics and science than art. Insight usually comes from focus, understanding, intelligence, and being open-minded. Those qualities matter in both business and personal settings.
On the other hand, someone truly visionary looks beyond what is already illuminated and can, therefore, be detected or analyzed. It’s like a game of chess, where the visionary person thinks six or seven moves ahead. They connect the dots across various future possibilities while their competitor is still thinking about their next move.
Interestingly, this can be frustrating for everyone.
- The Visionary with an excellent idea may become frustrated because they feel an unmet need for understanding.
- The people around that visionary person become frustrated, wondering why that person can’t focus on what is important or why they fail to see/understand the big picture.
- Others view the visionary ideas and suggestions as tangential or irrelevant. It is only over time that the others understand what the visionary was trying to show them – often after a competitor has already begun implementing a similar idea.
- The Insightful, wanting to make a difference, can feel constrained in static environments, which offer little opportunity for change and improvement.
Both Insightful and Visionary people view themselves as strategic. Both believe they are doing the right thing. Both have similar goals. What’s truly ironic is that they may view each other as competitors rather than seeing the potential for collaboration.
A strong management team can boost creativity by fostering a culture of innovation and bringing these people together to work toward a common goal. Providing little time and resources to explore an idea can lead to remarkable outcomes. When I had my consulting company, I sometimes joked, “What would Google do?” to describe how amazing things were possible and waiting to be done.
The insightful person may see a payoff from their ideas sooner than the visionary person, because they focus on what is already in front of them. It may be a year or more before what the visionary person has described shifts to the mainstream and into the realm of insight – hopefully before it reaches the realm of common sense (or worse yet, is entirely passed by).
I recommend that people create a system to gather ideas, along with a description of the purpose, goals, and advantages of those ideas. Foster creativity and innovation by rewarding people for participation, regardless of what becomes of the idea. Review those ideas regularly and document your commentary. You will find good ideas with luck – some insightful and possibly even visionary.
Look for commonalities and trends to identify the people who can cut through the noise or see beyond the periphery and the areas having the greatest innovation potential. This approach will help drive your business to the next level.
You never know where the next good idea will come from. Efforts like these provide growth opportunities for people, products, and profits.
Where Ideas, Experiences, and Lessons Learned Intersect 