3D printing and addressing personal needs (the opposite of mass customization)

I used to blog about the concept of mass customization, abundance and its value to the consumer. With the advent of 3D printing, mass customization is turned on its head with personalized manufacturing — which could still be thought of as customization by the masses.

Sometimes people ask me “What can you do with 3D printing?”  or “I can’t think of anything I’d make with a 3D printer.” and say I would never do/need that.

I am constantly walking around my house and thinking there has to be a better way to do XXX. An example from the last week dealt with my CNC machine an old HP 7” Android table that I use to control it (through a VNC connection to the Raspberry Pi that actually control the device).

This tablet sat on my desktop and kept getting jostled around by the competing demands for desktop space, as my efforts shift from 3D printing to CNC to laser scribing — all on the same workspace.

I have a fairly large workbench with a cabinet above it, so I thought it would be ideal to have the tablet hang down from the cabinet at something slightly below eye level, where I could still open the cabinet and yet see the tablet.

Looking at commercial options, they were in the $20-30 (or more) price range, but I am cheap and creative, so I thought I could make my own solution. It looks like:

It hooks on the cabinet and then the tablet slips inside. Note there are no ‘fasteners’ in the design. It just clips into place.

The following picture shows the design implemented with the tablet hanging under the cabinet, yet far above the desktop.

There is no way a commercial solution could be created that meets my needs so exactly.

So if you are thinking about a 3D printer and have the creative juices and problem solving skills, the problems will present themselves every day and you’ll wonder how you ever lived without it, putting up with those gnawing issues around you.

One of my mottos when I was working was:

"If it's not up to your standards, don't put up with it."

It still holds true.

Thanksgiving…

Some people view 2020 as something to be left behind. I am leaning more to being thankful. It seems you never realize what you have until it is gone. We are not going to have our whole family together this holiday season, but we can be thankful that everyone is well, some have not been as fortunate. 2020 gave us a chance to loose some things that hopefully we can all strive to get back in 2021.

One goal for 2021 is to relish whatever it was we lost in 2021, as we get them back. Sure something things we may never get back. We should be thankful we had them at all – assuming it is something missed.

I’ve not been blogging as much recently. Not because I’ve been sitting around doing nothing, but because I’ve been focused on other things, something to be thankful for as well.

Thanks for reading this…

Time and the Corona virus

I recently had a few situations that made me think about an old Alan Parson’s Project song called Time. So far in my retired life, I have been busy with meetings, activities… so time was still divided up into quanta.

With the advent of the Corona virus, time turned into a flow. Units of measure blurred to the point where even the ‘Groundhog day’ perspective broke down. When a meeting that I should attend was defined (several times) I didn’t even recognize I missed it until well after the time had flowed far past.

As social options open, it will be interesting to see if my perception of time flips back into a more calculated view. Sort of like the difference between how an integral is defined (in math) vs. the way you can calculate the area under a curve using a computer.

Integral vs. its approximation

Something tells me, things will ‘go back to normal’ much faster than I can image — but it may be a new normal. After all, time is one resource that we may be able to optimize, but it will never be abundant.

A changing perspective on the event horizon

Don’t know about you but I’ve been spending quite a bit of time lately in a socially distant mode. When the Wired magazine showed up, I thought “What a great distraction!”. I then saw that it was totally dedicated to climate change. It was a distraction, but not in the way I expected.

That topic of climate change (though important) seems less of a priority in these trying times. With the stock market and the economy in shambles and the grocery store shelves relatively empty around here – strategic issues pale in comparison.

Maslow’s hierarchy of needs is coming into play like most of us have never experienced. There was a CNBC article a few months back stating that 40% of the world’s countries will witness civil unrest in 2020. We will likely look back and view that as optimistic. Be prepared: plan for the worst and hope for the best.

Good luck out there – stay safe. Get some projects done that you’ve never been able to concentrate on before.

Well it’s 2020

Happy New Year!!

I was talking to some folks the other day who said “Gosh, it’s been 20 years since Y2K”. Some of us used to think that 2020 was impossibly far off. I used to do predictions of technology and adoption for EDS and HP. Each year (for about a decade), I’d give about 10 things to look for in the coming years and at the end of the year I’d grade my predictions.

Now that I am retired, even the predictions are receding into the rear view mirror and in some ways they appear naive. In other ways, they’ve held up well.

When I worked in HP labs (almost a decade ago), I remember writing a piece on the impact of the technology trends on services. One of the foundation elements was about the conflict within our expectations.

“We live in a world of conflict:

  • Simple, yet able to handle complexity
  • Standard, yet customizable
  • Secure, yet collaborative
  • Low cost, yet high quality
  • Sustainable, yet powerful
  • Mobile, yet functionally rich”

Some of those conflicts have been resolved to the point where they are barely background noise, while others remain as challenging as ever. A good example of that is gamification, which is now ubiquitous.

The abundance of capability (and possibility) that I tried to represent with the following illustration (that is also almost a decade old) still seems to hold true. Possibilities for new value remain around us everywhere.

Hopefully this year will allow you to expand your horizons and address the goals you’ve been making.

Back from a cruise

You know that feeling on the last day of the cruise when you are both relieved as well as sad it is all coming to an end. I thought this picture summed it up well.

If you have never been on a cruise, near the end you’ll realize the time of abundance is almost over and sanity is on the horizon fast approaching.

Someone left this ice-cream cone on the deck of the ship and it seemed like a great image that captures the thought.

Gait recognition… this made me laugh and reflect

I came across this article the other day titled: Chinese authorities use gait analysis to identify citizens on CCTVThe topic made me nostalgic, since I’d been part of some brainstorming on a very similar topic over a decade ago with some of the EDS (soon to be HP) Fellows.

We were imagining the possible uses of vibration analysis and signal processing. One of the areas discussed was gait recognition. Other ideas were monitoring patient health or even environment control, those turned into patent applications while I was at HP labs.

Seeing the article made me reflect on the great team I was part of at EDS and then HP, as well as the various truly innovative ideas we came up with and discussed with customers.

Is AI a distraction???

AutomationI was recently in an exchange with a respected industry analyst where they stated that AI is not living up to its hype – they called AI ‘incremental’ and a ‘distraction’. This caught me a bit my surprise, since my view is that there are more capabilities and approaches available for AI practitioners than ever before. It may be the business and tech decision makers approach that is at fault.

It got me thinking about the differences in ‘small’ AI efforts vs. Enterprise AI efforts. Small AI are those innovative, quick efforts that can prove a point and deliver value and understanding in the near term. Big AI (and automation efforts) are those that are associated with ERP and other enterprise systems that take years to implement. These are likely the kinds of efforts that the analyst was involved with.

Many of the newer approaches enable the use of the abundance of capabilities available to mine the value out of the existing data that lies fallow in most organizations. These technologies can be tried out and applied in well defined, short sprints whose success criteria can be well-defined. If along the way, the answers were not quite what was expected, adjustments can be made, assumptions changed, and value can still be generated. The key is going into these projects with expectations but still flexible enough to change based on what is known rather than just supposition.

These approaches can be implemented across the range of business processes (e.g., budgeting, billing, support) as well as information sources (IoT, existing ERP or CRM). They can automate the mundane and free up high-value personnel to focus on generating even greater value and better service. Many times, these focused issues can be unique to an organization or industry and provide immediate return. This is not the generally not the focus of Enterprise IT solutions.

This may be the reason some senior IT leaders are disillusioned with the progress of AI in their enterprise. The smaller, high-value project’s contributions are round off error to their scope. They are looking for the big hit and by its very nature will be a compromise, if not a value to really move the ball in any definitive way – everyone who is deploying the same enterprise solution, will have access to the same tools…

My advice to those leaders disenchanted with the return from AI is to shift their focus. Get a small team out there experimenting with ‘the possible’. Give them clear problems (and expectations) but allow them the flexibility to bring in some new tools and approaches. Make them show progress but be flexible enough to understand that if their results point in a different direction, to shift expectations based on facts and results. There is the possibility of fundamentally different levels of costs and value generation.  

The keys are:

1)      Think about the large problems but act on those that can be validated and addressed quickly – invest in the small wins

2)      Have expectations that can be quantified and focus on value – Projects are not a ‘science fair’ or a strategic campaign just a part of the business

3)      Be flexible and adjust as insight is developed – just because you want the answer to be ‘yes’ doesn’t mean it will be, but any answer is valuable when compared to a guess

Sure, this approach may be ‘incremental’ (to start) but it should make up for that with momentum and results. If the approach is based on expectations, value generation and is done right, it should never be a ‘distraction’.

Is automation forcing divergent paths of quality vs. cost?

robots-too-humanI saw an interesting post: When Robot Writers Take Over, Will Freelancers Be Obsolete? The article was focused on freelance writing, but it did make me wonder about the whole concept of freelancing, in general.

The relatively fixed and easy to automate positions in many fields are ripe for automation. Those that require creativity or unique insight should be safe for a long time to come. In fact, automation could make the freelancers life less mundane and more interesting. It reminded me of a situation earlier in my career…

Back in the early 90s, I worked in the AI space for Electronic Data Systems (EDS). We focused primarily on solving problems for GM and the US government. Somewhere around here I have a coffee cup with the moto of the group: “Make it Work, Make it Real”. Unfortunately, the folks working in the group had felt it really meant that if we could make it work, it wasn’t really AI — since someone would always say that it was just regular old programming, no matter what innovative technique or esoteric language we used.

One of the projects I led was called Knowledge-based Tool Design. We were trying to automate tooling design for clamping and welding car parts using CAD techniques, a project far ahead of its time. Programmatically determining the right type of clamp and the correct way to swing it into place was too difficult spatially, for the time. We just didn’t have the compute power and the algorithms determine orientation and approach. A good human tool designer could see the solution intuitively.

We did figure out that people are not good at pulling together the bill-of-materials to ensure that the clamp and all the hydraulic and mounting components… were defined. We shifted our attention to defining that type of detail using computers — reducing the errors and rework later in the process.

Similarly, in other industries, there are so many annoying and resource intensive, low hanging fruit to be picked that the return on investment for tackling truly intuitive problems just isn’t there. That can all change though as better algorithms and computing capabilities develop.

There are a couple of ways this could go:

  • The intuitive functions will likely become more of a freelance function, since companies will not need (or be willing to pay) for those expert roles all the time and the work will be interesting.
  • The focus shifts to less high-quality designs that can be automated.

In any case, employment as we know it will be changing.

Elastic Map Reduce on AWS

derived dataLast week, I put out a post about Redshift on AWS as an effective tool to quickly and dynamically put your toe in a large data warehouse environment.

Another tool from AWS that I experimented with was Amazon’s Elastic Map Reduce (EMR). This is an open source Hadoop installation that supports MapReduce as well as a number of other highly parallel computing approaches. EMR also supports a large number of tools to help with implementation (keeping the environment fresh) such as:  PigApache HiveHBase, Spark, Presto… It also interacts with data from a range of AWS data stores like: Amazon S3 and DynamoDB.

EMR supports a strong security model, enabling encryption at rest as well as on the move and is available in GovCloud, handling a range of big data use cases, including log analysis, web indexing, data transformations (ETL), machine learning, financial analysis, scientific simulation, and bioinformatics.

For many organizations, a Hadoop cluster has been a bridge to far for a range of reasons including support and infrastructure costs and skills. EMR seems to have effectively addressed those concerns allowing you to set up or tear down the cluster in minutes, without having to worry much about the details of node provisioning, cluster setup, Hadoop configuration, or cluster tuning.

For my proof of concept efforts, the Amazon EMR pricing appeared to be simple and predictable allowing you to pay a per-second rate for the clusters installation and use — with a one-minute minimum charge (it used to be an hour!). You can launch a 10-node Hadoop cluster for less than a dollar an hour (naturally, data transport charges are handled separately). There are ways to keep your EMR costs down though.

The EMR approach appears to be focused on flexibility, allowing complete control over your cluster. You have root access to every instance and can install additional applications and customize the cluster with bootstrap actions (which can be important since it takes a few minutes to get a cluster up and running), taking time and personnel out of repetitive tasks.

There is a wide range of tutorials and training available as well as tools to help estimate billing.

Overall, I’d say that if an organization is interested in experimenting with Hadoop, this is a great way to dive in without getting soaked.