Consumer Technology enables SCALE and RAPID INNOVATION

Consumers enable SCALE and RAPID INNOVATION in Technology. As I walked around the Consumer Electronics Show (CES), I could see how the technology will “bubble up” into business and into enterprises quickly. Initially, technology came from business to consumers – think PCs. The sheer size of consumer market, hunger for new functionality, and its willingness to put up with beta releases makes the consumer world the ideal proving ground for the less fault tolerant enterprise world. Companies that span both world can leverage the consumer world for its SCALE and RAPID INNOVATION and bubble those innovations into the enterprise world for higher profits.

Drones are an example of bubbling up. While they started in the military, they are now a big segment of the consumer market. Drones or autonomous flying vehicles have been improving including automated stabilization, 4K cameras, enhanced flying times, etc. Many of them have dozens of computers on board and some rather impressive programming to make them simple to use. First it was movies, then multi-million dollar homes and now you see mid-market homes with drone footage. It has become a toy for teenagers, too.

Due to the wide-spread usage of drones in the consumer market, they are vastly improved and far less expensive. One of the leader’s in the industry (Drone Market Map)( https://www.droneii.com/top20-drone-company-ranking-q3-2016)), DJI’s basic drone is just under $500, flys for ~25 mins, includes GPS tracking, tracks subjects based photo recognition using a 1080 camera for photos and stills. Lots of knock offs are even cheaper.  “Toy Drones” are just $50! Five years ago, the DJI basic drone would have been a top of the line $5K drone, if even available.

Part of the attractiveness of the consumer world is scale. The other factor is that the consumer world is filled with willing beta testers and relatively low liability costs. The consumer world is an agile one where cycles occur very quickly. A typical enterprise development cycle is 18 months. In the same time in the consumer world, you’d see a major hardware, firmware, and at least 30+ releases of software.

The demand for new and the tolerance for risk is high in the consumer market. Recent releases of drones from reputable consumer companies come with lots of complaints on the internal boards of them not flying well, not following waypoints, and simply flying away. In the business world, failure to perform would be a breach of contract and might result in loss of property or life. In the consumer world, the drone manufacturer can just send a firmware update, a letter, a coupon, award you status on their web site as hero or pioneer, or at worst – replace the drone. It’s a trivial price enabling those dipping into the consumer world to advance faster than those in the business world.

While scale makes the money, it is the willing beta tester that enables advancement. Haven’t you signed up to be a beta or an alpha tester. I know I am for many of IBM’s early release programs. We have marvelous internal site called “Technology Adoption Program” where individuals submit their software inventions. Many have become key enablers of IBM’s business. They grew up fast by being adopted and depended on by IBM’s business.

What else might bubble up? Virtual Reality has real possibilities for training. Augmented Reality with heads up displays and glasses will be welcomed the field. Giving schematics, UV and Infrared vision, and more to workers. What will make it become easily affordable and useful – another Pokemon Go that plays with glasses pushing it onto millions of users’ foreheads.

3D printing is coming of age, but I can see point where 25% of households have plastics printer and your hardware store has a metal one. No inventory of 500K parts – just print it. Lots more like LED lights, Home Automation, Sports Fitness, etc. will bubble up.

Finally, Artificial Intelligence (AI) may be the biggest winner from the bubble up effect of the consumer. The key to AI is having huge knowledge base or corpus and lots of training. Where better than the consumer market with a potential of 7 billion users – the population of the world – to train your AI. Whether it is Siri, Alexa, Cortana, Watson, or Google, these companies’ AI programs will benefit from the consumer training it. You get a voice interface and they get you to train their AI.

What do you think will be the next big bubble up technology from the consumer world to the enterprise world?

 

 

Our 2 fears of Artificial Intelligence (AI)

We have two (2) overarching fears of AI. AI domination is the most irrational fear where AI becomes smarter than organic intelligence and wipes out or subjugate the organic life forms. This plays out in number of number of science fiction works like “Transformers”, “Terminator” and “I, Robot”. In “I, Robot”, the AI unit is claiming to do it in service of humanity. I’d argue AI domination is the least likely scenario of doom and maybe in dealing with our second fear, we can solve our AI domination fear, too.

The second fear is that of misuse of AI. I’d argue that is the same argument has been used against every technological advancement. The train, automobile, nuclear fission, vaccines, DNA, and more have all been cited for ending the world. I suspect someone said the same thing against the lever, wheel, fire, and bow. Each has changed the world. Each has required a new level of responsibility. We’ve banded together as humans to moderate the evil and enhance the positive in the past. Ignoring it or banning it has never worked.

Amazon, DeepMind/Google, Facebook, IBM, and Microsoft are working together in the “Partnership on AI” to deal with this second fear as described by the Harvard Business Review article “What will it take for us to trust AI” by Guru Banavar. It is a positive direction to see these forces coming together to create a baseline set of rules, values, and ethics upon which to base AI. I’m confident others will weigh in from all walks of life, but the discussion and actions needs to begin now. I don’t expect this to be the final or only voice, but a start in the right direction.

I hope the rules are as simple and immovable as Issac Asimov’s envisioning of the  3 laws of robotics on which the imagined, futuristic positronic brains power AI robots. Unfortunately, I doubt the rules will be that simple. Instead they will probably rival international tax law for complexity, but we can hope for simplicity.

The only other option is to stop AI. I don’t think it is going to work. The data is there and collecting at almost unfathomable rate. EMC reports stored data growing from 4.4ZB in 2013 to 44ZB in 2020. That is 10^21 (21 zeros) bytes of data. AI is simply necessary to process it. So unless we are going to back-out the computerized world we live in, we need to control AI rather than let it control us. We have the option to decide our fate. If we don’t then others will move forward in the shadows. Openness, transparency, and belief in all of human kind have always produced the best results.

In the process of building the foundation of AI, maybe we can leave out worst of human kind – lust for power, greed, avarice, superiority. Maybe the pitfalls in humans can simply NOT be inserted in AI. It will reflect our best and not become the worst of human kind – a xenophobic dictator.

Putting the AI genie back in the bottle will not work. So I think the Partnership on AI is a good first step.

 

Saving $1.8T but at what cost? and do we have a choice?

We continue to automate and improve business systems. I’ve spent my whole career improving business efficiency. Each time we do so, we mostly disrupt lower level service jobs and now some medium level professional jobs. We do this because making a business more efficient, effective, and cost competitive keeps that business ahead of its competition.

The recent article by CIO Insight “How Repetitive Tasks Waste $1.8 Trillion” made me consider the consequences, both bad and good. That $1.8 Trillion amounts to a lot of people’s jobs. The downside is elimination will be the elimination of jobs. I once recall discussing how we were going to put in telephonic automation for the service desk when someone said “you know, we just fired 300+ people.” We observed about 30 seconds of silence, swallowed hard, and then finished our task of designing the solution. It was going to happen regardless as most of their competitors had already eliminated large human level 1 service desks. Now we are observing the impact of readily available cloud wiping out many small and medium data center and application support people’s jobs. I’m certainly not against cloud solutions. IoT, Mobile, and SaaS solutions all stem from basic cloud capability and are creating NEW job markets and careers.

Jobs are both a way wage along with an identity for most of us, so I take it personally and seriously. I’ve done both the laying off of people and been laid off. Neither is fun. After I had to lay off my staff, I was physically ill and just thinking about it gives me the chills. I was able to get the best of them lined up with new job opportunities. No one wants to be told they are no longer needed and can be discarded.

To the positive, people can be moved to new jobs. The best companies work with their people to find them jobs that can help the company grow. As individuals, we all need to be on the look out for the possibility we’ll be disrupted by new technologies. There is no job that is immune entirely. Hands on trades people are probably the least susceptible, but even they must learn new skills constantly to stay employed. If you are in job that can be digitized, you need to start planning how to adapt. Your job will be under threat inevitably.

Companies are not social employment agencies and I don’t advocate socialism. I think it is in their best interest to be part of the community, since ultimately it is the community who consumes from them and makes them successful. Companies in capitalistic market that must out compete each other and to do so must make money for the owners / stockholders. In addition, if a company does not continue to move forward ahead of its competition, it will fail and NO ONE will be working for that company.

In the end, the march of improvement and technology is inevitable part of human history. Stopping progress is neither possible or wise. We can and should think about how to do it humanely by recognizing the impact and helping those impacted find ways to be productive members of society. We can use it wisely to improve our conditions as a planet and as human beings.

 

Healthcare technology dependent on new Healthcare Regulation

Recently, Andy Slavitt, CMS Acting administrator, wrote “Pitching Medicaid IT in Silicon Valley“. His requests are sensible and well founded, but will fail. I’m not saying he won’t have modest gains, but his gains will be held back by overly burdensome Code of Federal Regulations that regulates all regulated industries including health care providers, pharmaceuticals, utilities, etc.

I am currently working at a global Pharma company and it is embarrassing the volume of labor and the amount of paper we consume in the name of quality. We are switching to a digital system, but the effort will remain the same. Every item has to be written up for expectations, dry run testing to get 100% correct, written, printed in screen print, and then signed. Any exception such as an extra temporary file in the directory results in a hand written explanation. I’m not convinced that any of this QA work will ever add anything to quality of the pharamceuticals the company produces.

I fully get the need to test, but this test so we can’t be questioned model of micro-testing and lack of awareness of digital images will continue to hold back the industry. Cloud, at any level (IaaS, PaaS, SaaS) requires the ability to certify the template and assume all instantiations are also certified if they have no errors in the instantiation process. Again, I’m not against testing or quality, but the redundancy is a waste.

The other 2 issues that will need to be conquered for healthcare is: 1) privacy and 2) standardized data formats at least for header records. Privacy is pretty obvious in that I don’t want anyone reading my information unless required and released by me. At the same time, it is important that my “data” goes into the pool for analytics to give researchers the ability to learn from the population. There is no perfect answer for anonymizing data, but good security and good tools are well-known, understood, and can be implemented.

Data standards are not a new issue in our industry. While 80% of data is non-structured, the 20% that is structured never seems to line up so we spend anywhere from 60-80% of our analytics money messaging data. To tie together all this data, we’ll need to have at least some standards for header records.

Again, I salute the efforts by Andy Slavitt (@aslavitt), CMS Acting Administrator, for his efforts. I hope he gets his interns and can make a big impact. He’s got a big job to do.

 

Wish I was going to Sapphire 2016

Since 2005, SAP Sapphire meant panicking for 6+ weeks of April and half of May. Since I’m no longer in the IBM SAP Practice Global CTO, I won’t be there. I’m still deeply involved and interested in IBM‘s efforts in the SAP world. It impacts most of my clients and I spend a lot of time on the interfacing of SAP software to many of IBM’s latest capabilities like Bluemix and Watson and most recently in developing an FDA compliant cloud for SAP. SAP is still on my mind, still important, and I wish I could go to Sapphire to see my friends who have become like family over the decade.

The focus is on Digital Transformation for all IBM’s SAP Practice. It aligns perfectly with IBM’s focus on Cloud, Cognitive, and Industries. Take some of your valuable time to speak with the IBM experts in booth #104 to understand how the unique partnership between IBM and SAP on Digital Transformation can benefit you and your company.

You can go beyond just discussing Digital Transformation, you can touch it. You can touch it in the IBM Booth #104. Gagan Reen, who leads the LSS, and his team will be launching Digital Transformation Cognitive Solutions as part of the IBM and SAP Digital Transformation initiative.

Please let me know how Sapphire goes this year. What is new? What is pure hype and what is real? Have a great show and I will remain calm all of May, but I will miss of you, my extended work family, at Sapphire.

 

Why I believe IBM will succeed

I believe IBM will succeed even in this next era of rapid innovation. There is no doubt IBM is founded on innovation. Whether you measure it by 23 years of leading in number of patents or by sheer number of innovations found in its history (DRAM, Hard drives, Tabulation Machines, System 360, major innovation around relational datbases, etc.), IBM is innovative.

I think the question is not “can IBM innovate”, but can IBM innovate with enough speed and follow through. It is tough for any large company to move fast with heirarchies, communities, and sheer mass. It can be done.

One key is having a clear vision. IBM’s vision is Cloud, Cognitive, and Industries. Cloud in all it’s forms including IaaS, PaaS, and SaaS. Recent announcements like putting IBM Box, IBM’s cloud for file sharing, on Amazon shows a willingness to follow requirements of the market. Clients are saying no one cloud solution, even IBM’s cloud, is enough. Speed and diversity are as important as cost, or more.

Cognitive is the peak of IBM’s data strategy. Beneath is everything from ETL to IoT to cloud based integration. Getting to Watson is rarely a first step for most clients. Rather we find we need to do a lot of data hygene just to be ready for standard analytics. Eventually, they do get to Watson and Cognitive services. It is a journey.

I really find Watson on Bluemix especially interesting. IBM is offering access in nibble size chunks access to Watson via standard APIs. It is an amazing shift to see IBM offering the power of its flagship product for pennies. It is a new model for IBM. IBM has always ruled in the realm of big projects with high margins. To take on the tiny, an API at a time and a penney at a time, is huge change in business model for IBM. You can check out the services, via RESTfull API’s, on the developer cloud and for modest use it is even FREEhttp://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

Under the banner of Cognitive is IoT. The ability to interact and understand our world via the digital world seems like a SciFi dream. The possibilities are endless. We see capabilities like controling our environment just by thinking about it. I love the story about the IBMer who is using his mind to control a Sphero toy. I confess, I want one. https://developer.ibm.com/bluemix/2015/12/18/the-force-bb8-emotiv-insight-bluemix/  or Youtube (~3 mins).

Industries runs through everthing at IBM. IBM’s entire organization is organized by Sector (Industrial, Distribution, Financial, etc.) and below that into Industries. Every go to market effort is passed through an industry focus and a lot of the investment in new ideas is based on the question of “what does this industry require.” You can even filter our Institute for Business Value by Industries to find unique value for your business. Watson even has its own Watson Healthcare division – another focus on an industry.

In the fast moving world of IT innovation, being innovative last year is not going to save you; however, IBM has a long history of remaining an innovation leader. We working to see how we can leverage all IBMers’ great minds.  I’m optomistic as we are now working on innovations for rapid innovation at cloud speed and beyond. Cloud, Cognitive, and Industries is great springboard into our future.

 

The Data Scientist Oath (Part 2)

Today, we are bombarded by data and information as FACTS. We accept what is in chart from any printed source as if it is a FACT and therefore true. Let me illustrate with sublime example.

half & half pie chart
Chart 1: A simple pie chart

If the title of this chart is “Households with Gourmet Cooks”, you might be influenced to run out an buy stock in a company that makes gourmet cooking equipment like Middleby Corporation the makers of Viking kitchen equipment. If the title of this chart is “Households with Gourmet Cooks (sample size = 2)” or “Households with Gourmet Cooks (Std. Dev. = N/A), you’d probably not. In fact, you’d probably wonder why I built the chart, and yet we frequently fall into this trap and don’t even ask for the transparency. The data scientist presenting his data should always tell you how they arrived at their conclusion.

Unfortunately it happens almost everyday in trusted sources ranging from news magazines, newspapers, documents at work, and in every aspect of the Internet (social, web sites, e-mail, etc.). The Internet is huge force multiplier, which enables one zealot to look like an entire movement and their arguments look like widely accepted FACTS.

 

 

There are even more subtle ways of influencing you especially with cultural and emotional cues. If you look at the charts below, what happens if the title for both charts which are identical is “Evil is winning the war over Good.”

In the first chart, you might initially draw that conclusion that evil is wining big time. We assume red represents evil since red is the color of the devil in the Western world. We react to the color and not the fact fact that evil is a 2% green slice. Plus the text in the legend is small and hard to read. In the second chart, you’d look at it and think the author is an idiot since the evil slice is clearly a tiny 2%. How information is represented is almost as important as the source data and methods used to turn it into information. Even good information can be displayed poorly.

PT Barnum said “you can’t fool all the people all the time, but you can fool all of the people some of the time” and I agree we all do get fooled occasionally. It is each individual’s responsibility to consume data carefully and consider the source and how the information is being displayed to minimize the “some of the time” to almost always never.

It is the responsibility of the data scientist to try to present the information in good faith, transparently, and with as little bias as possible. Stan Lee via Spiderman Comics said via wise Uncle Ben Parker “that with great power comes great responsibility.” If you work with data and publish it then understand the potential influence and power over people’s opinions, thoughts, feelings, and potential actions and use it wisely.

Start with a simple Data Scientist oath or code. “As a Data Scientist, I will understand the veracity and validity of my data and its sources, and I will clearly, transparently and with minimal cultural bias display the results so the end consumer can make valid conclusions.”

It is that simple. How cool is that you get to side with Stan Lee and all his comic book heroes and become a real hero by making every effort to represent the truth as plainly and obviously with unflinching transparency as humanly possible.