Makers

What’s new?

The most recent issue of Make magazine (Summer 2020) has articles about making PPE for COVID-19 protection, a backyard wind turbine, and a robot monkey. It also has articles with information about capacitors, wood glazes, and MIDI (Musical Instrument Digital Interface). 

What does it mean?

Make magazine (available for free online or by subscription in print) publishes articles about how to make things. The long history of tinkerers, artisans, and crafters, in the past nurtured by Popular Mechanics, Erector sets, Edmund Scientific kits, and craft stores has morphed into the maker movement. The maker culture is inclusive and eclectic, emphasizing the participation of everyone. The Maker Movement Manifesto by Mark Hatch highlights nine verbs: make, share, give, learn, tool up, play, participate, support, and change. Makerspaces in schools, libraries, universities, and even street front locations provide space, tools, equipment, and community to support makers.

As a retired professor of engineering, I embrace the maker movement because it encourages involvement and fun. I think the movement can recruit young people into engineering. But I also cringe at the lack of understanding that can result. I have had elementary school students tell me that they have made a robotic arm (“we already did that!”) just like the one that took our senior engineering students a whole semester to design and make. Of course, the elementary school robotic arm is nothing like the college robotic arm, but try to explain that to excited sixth graders. Should I even try? Worse perhaps are the inventors who have invented perpetual motion machines (or the equivalent) and refuse to be taught about the laws of thermodynamics. Should I even try?

Good maker culture encourages experimentation and understanding. My strategy in teaching engineers is to start with an example and then extract the theory; that method of teaching requires using a good example, one that can be grasped with some common sense and intuition but one that is also deep enough to enable me to generalize from it and to draw out the underlying physics and math. The Make article on capacitors in the Summer 2020 issue starts with simple experiment involving discharging a capacitor to light an LED and then recharging the capacitor. Then the author (Charles Platt, author of Easy Electronics) explains how a capacitor works, including circuit schematics and an explanation of the difference between a battery and a capacitor. The explanations will be beyond some readers, will elevate or solidify understanding for others, and may prompt some to learn more.

Having stuff available to play with is a strong part of the maker culture. When I was a child in the 1950s, my father worked for Bell Labs, which had the policy that employees could take stuff home, so I had access to and played with batteries and electrical components, supplemented by kits from Edmund Scientific, and Erector sets. I also learned to sew on my mother’s sewing machine. I majored in math in college, but then went on to graduate school in engineering. A maker culture can nurture engineers.

What does it mean for you?

Education, especially in engineering, should be fun. And education in engineering has to be grounded in theory. Hands on with mind involvement. If you interact with children, make sure you give them those messages, in words and in actions. For example, the many excellent programs of the Boys & Girls Clubs of America (I am on the board of directors of the Boys & Girls Clubs of Pueblo County) include DIY STEM, with components on energy and electricity, engineering design, food chemistry, and the science of sports.

As the maker culture and maker movement have spread, schools have incorporated hands on activities in STEM (or even STEAM, adding the A for Arts) into all levels of education. When the students from those programs reach the workforce and college, how do they change the established cultures in those organizations? Universities are learning that telling a student to take 20 credits of calculus and physics before you think about having any ideas is increasingly unacceptable. Engineering programs use introduction to engineering courses, just-in-time teaching, and makerspaces to try to keep the fun and enthusiasm alive. I tell students that calculus is the foreign language they need to come into engineering land and just like learning any foreign language, it is easier to learn when you are using it to accomplish some task.

Maker culture is also spreading into the organizations that employ the products of this culture. Can useful employees emerge from this maker culture? Yes, of course. Hands on, self taught technicians can be valuable, but a backyard welder may need some education and training to become certified. The certification should reflect the tasks the welder needs to do, but for all welders, safety practices must be taught and adhered to, reflecting the importance of safety in all organizations. And you need to think about how to test the ability of any employee to apply their knowledge outside of routine tasks if you expect them to do that on the job. Maker motivated engineers still have to learn the physics and math to understand how devices work.

More broadly, the culture and values of makers may have positive influence on your organization. Makers want to try ideas out in physical things, are willing to fail and try again, want and give critical evaluation of prototypes, and believe in educating and involving everyone. How would those traits work in your organization?

Where can you learn more?

A great place to start is Mark Hatch’s book The Maker Manifesto, which expands on each of the nine verbs I listed earlier.

Luminary Labs urges organizations to embrace the maker culture by getting exposure to maker culture, bringing making inside, and investing in a future of makers. Wired argues for the adoption of maker values: be open, embrace imperfection, love the process, and build community. Simmi Singh in the MIT Sloan Management Review says we need to embrace makers, not just entrepreneurial innovators, by embracing the creator identity, fostering interaction among creators, insisting on fluidity, and understanding the effectiveness of novel play. The company Stanley Black & Decker has developed an array of programs to empower makers.

Explaining AI

What’s new?

The Information Commissioner’s Office (in the United Kingdom) issued its first draft regulatory guidance into the use of AI (artificial intelligence). One part of the guidance advises organizations to “make your use of AI for decision-making obvious and appropriately explain the decisions you make to individuals in a meaningful way.” This guidance applies to decisions that use personal information to make a decision with legal or similarly significant effects.

What does it mean?

Many methods in artificial intelligence use large databases to generate a mathematical model that fits the data well. The model is not built up from knowledge from human experts, but rather by finding the mathematical patterns in the database. The mathematics is very complicated in order to capture patterns not obvious even to the humans who were involved in generating the data. The resulting model can then be used to make a prediction about a case not included in the original database.

For example, artificial intelligence could use a large database on a bank’s decisions whether to grant loans to create a mathematical model that would, with great accuracy, duplicate those previous decisions. Then the model could be used by a loan officer who inputs data on the current applicant in order to generate a recommendation on whether to grant or deny the loan. In most cases, the human being (the loan officer in this case) could still make a different decision than that recommended by the model, but in the future, the decision could be totally automated with no human intervention.

The mathematical models used in such an approach to artificial intelligence are often quite sophisticated and complicated. The result is that the model is so dense that it is difficult to generate an explanation of the prediction in a traditional sense. While a bank might previously have said, “we denied your application for a loan because of your bad credit rating, the lack of collateral, and the poor forecast for growth in your line of business,” with an AI model, the bank might only be able to say that the model generated a low score. Some fear that the mathematics may be capturing biases in past decisions, for example, denying loans to racial minorities that would be granted to other applicants.

Fairness and transparency argue that someone denied a loan should be able to receive an explanation of the decision. Thus, regulators are pushing for (1) transparency so that the person knows that a model was used to deny the loan and (2) explanation of the decision.

Issues raised by these requirements include defining what is an adequate explanation (not simply “the computer said so”) and deciding who is accountable for a decision (the loan officer can’t say “the computer made me do it”). Without an understandable explanation the person denied a loan cannot appeal, cannot correct incorrect data that drove the decision, and cannot improve the important factors so a future loan will not be denied.

The proposed guidance describes several types of explanations: rationale explanation (the reasons for the decision), responsibility explanation (who was involved), data explanation (what data was used to train the AI), fairness explanation (what steps were taken to eliminate bias and ensure equity), safety and performance explanation (steps taken to ensure accuracy, reliability, and security of decisions), and impact explanation (the impact of the use of the AI system more widely on society).

What does it mean for you?

The guidance described above is only proposed and only affects the United Kingdom. However, it indicates a possible trend in other countries. You may find that any AI application used by your organization may need to meet such requirements in the future. Using an AI application that cannot give meaningful explanations may open your organization to legal challenge of bias.

But, more importantly, you should consider the need for your customers and clients to trust your organization not to treat them capriciously. You may not be able to be completely open about the basis for decisions even without an AI element, for example, if you need to protect some competitive secrets, but starting with the premise of explaining decisions to your customers is part of a customer focus for your organization.

Where can you learn more?

The three parts of the ICO report are available here: https://ico.org.uk/about-the-ico/ico-and-stakeholder-consultations/ico-and-the-turing-consultation-on-explaining-ai-decisions-guidance/

The 34-page “Part 1: The basics of explaining AI” is very readable and could be the focus of a discussion in your organization of principles you want to use concerning AI. “Part 2: Explaining AI in practice” is 108 pages and gives more concrete guidance to an organization about the decisions to be made in deciding what type of explanation to provide. Finally, “Part 3: What explaining AI means for your organization” covers organizational roles, policies and procedures, and documentation in 23 pages. While the three parts are oriented toward organizations (rather, organisations) in the United Kingdom, much of the advice applies in any country.

Source info: New Scientist, issue 3259, December 7-13, 2019, page 10. By Adam Vaughan

Businesses and other organisations could face multimillion-pound fines if they are unable to explain decisions made by artificial intelligence, under plans put forward by the UK’s data watchdog today.

The Information Commissioner’s Office (ICO) said its new guidance was vital because the UK is at a tipping point where many firms are using AI to inform decisions for the first time. This could include human resources departments using machine learning to shortlist job applicants based on analysis of their CVs. The regulator says it is the first in the world to put forward rules on explaining choices taken by AI.

About two-thirds of UK financial service companies are using AI to make decisions, including insurance firms to manage claims, and a survey shows that about half of the UK public are concerned about algorithms making decisions humans would usually explain. AI researchers are already being called on to do more to unpack the “black box” nature of how machine learning arrives at results.

Simon McDougall of the ICO says: “This is purely about explainability. It does touch on the whole issue of black box explainability, but it’s really driving at what rights do people have to an explanation. How do you make an explanation about an AI decision transparent, fair, understandable and accountable to the individual?”

The guidance, which is out for consultation today, tells organisations how to communicate explanations to people in a form they will understand. Failure to do so could, in extreme cases, result in a fine of up to 4 per cent of a company’s global turnover, under the EU’s data protection law.

Not having enough money or time to explain AI decisions won’t be an acceptable excuse, says McDougall. “They have to be accountable for their actions. If they don’t have the resources to properly think through how they are going to use AI to make decisions, then they should be reflecting on whether they should be using it all.” He also hopes the step will result in firms that buy-in AI systems rather than building their own asking more questions of how they work.

Produced in conjunction with the Alan Turing Institute, the guidance is expected to take effect in 2020.

Openness

What’s new?

According to ZDNet, sometime in March, someone accessed a Microsoft employee’s account at GitHub and downloaded about 1200 private repositories. The person threatened to publish some of the stolen material online, but Microsoft employees said that the material accessed is not sensitive.

What does it mean?

Because most software projects are large and complicated, programmers work in teams, reuse code from previous projects, and update existing code when problems are detected by users of the product or when new features are added. Created in 2005 by Linus Thorvalds (who also created the widely used Linux operating system), Git is a tool that supports version control, that is, it tracks all the changes that have been made to a piece of code (or any file), allowing restoration to earlier versions if problems arise. Git can track the branching and merging of versions by different users, thus supporting team work on a project. GitHub, one of several hosts for Git, was founded in 2008 and acquired by Microsoft in 2018 for $7.5 billion. GitHub is used by many companies for the development of proprietary products, but is also used by teams developing open source software.

What does it mean for you?

Computer security is a constant problem. While the ZDNet article does not say so, the case may be one where someone obtained the login information of a Microsoft employee. Often the human is the weakest point in computer security, even if that issue was not the case here. I recently heard from a company that about half of their incoming email is rejected by various filters; that percent has increased since the COVID19 crisis has kept people at home. Not all of those emails are attempts to obtain information, but a significant proportion are. A hospital in my community is still recovering from a ransom situation regarding their software. You already know that you need professional help to maintain the security of your computer systems.

Software projects are huge and complicated. While counting the number of lines of code in a piece of software is only a poor measure of the size or complexity of a project, it does enable some comparisons. The infographic “How Many Millions of Lines of Code Does It Take?” shows that the Space Shuttle software has about 400,000 lines of code, the Hubble Space Telescope several million lines, the Android operating system about 12 million lines, and Facebook over 60 million lines of code.

Coding is teamwork. Because of the size of the projects, a team of programmers writes and maintains the code. Software development methods aim to ensure that the resulting software meets the needs of the clients, just as with any other product. These methods have built upon and contributed to ideas about teamwork and customer satisfaction. For example, some software development methods are called agile and focus on being able to handle changing requirements by close collaboration with the customer. The tensions among speed to produce working software, responsiveness to the customer, creating clear documentation, and ensuring that different parts of the software are compatible are issues that every team will recognize. Different methods of software development involve different levels of up-front planning, meetings to review progress at various levels of frequency, sizing of tasks assigned to each programmer, frequency of contact with client, and methods for finding and fixing bugs in the software.

Coding is iterative. James Michener is often cited as the person who said “I’m not a very good writer, but I’m an excellent rewriter” and many writers would express a similar sentiment. Software companies often emphasize speed to market in order to capture market share; features can be added and problems fixed in response to feedback from customers. DevOps is a set of practices designed to provide software updates at a blistering pace and software updates are almost constant with many products.

As a writer and an engineer, I struggle with the programming approach of putting out a product that is good enough, letting users give feedback, and then improving the product. This piece I am writing now will reach a final stage and I will post it.  Many engineering products (bridges, for example) have safety requirements built in from the start and are not meant to be strengthened or rebuilt in response to failure. Starting with a “minimal viable product” for, say, an autonomous vehicle shocks me and makes me conscious of the ring I wear on my right pinky.

The most interesting aspect of this story for me is that many of the users of GitHub are working in teams to produce open source products. The first and still the most famous example of open source software is Linux, software for the Unix operating system. Open source software may be used, changed, and distributed to others under a license specifying the terms. The open movement has many flavors and philosophies combining in various ways community, sharing, transparency, inclusivity, peer review, the value of public goods, giving software away for free, and protection of intellectual property rights. The words “free,” “open,” and “libre” are used in specific, although not always consistent, ways to make distinctions.

Open source software has led to other concepts of open, such as Open Educational Resources. I have written two textbooks (one an introduction to industrial engineering and one on probability, statistics, and Six Sigma) that I give away for free. I have taught students well and saved them tens of thousands of dollars. I took this approach to my books because it freed me to write the books I want (not the books that publishers think I should write). I distribute the book under a Creative Commons license, which specifies what users may and may not do with the text. One of the books has been translated into Turkish and has also been recombined with material written by another professor for use in her classroom. Many examples of Open Educational Resources are collaborative efforts.

The open movement is increasingly being applied in other domains. Wikipedia, citizen journalism, the open wireless movement, and an open source group for watch making are just some examples.

The open movement challenges our thoughts about how work should be done. The highly paid professionals behind some of the most highly valued companies are part of community that argues about how to work together to create their products; many of them have concluded that open and collaborative work is better than closed and individual work. Furthermore, the open movement challenges our thoughts about the nature of labor, pay, and the common good. Some open source software is produced by people who work for pay, but in other projects all the work is done by unpaid people. Why do people do this work? Because people want to be part of something larger than themselves and people want to work on something that feeds their passion. In an economy in which almost everyone survives by selling their labor, what does this movement mean?

As we talk about returning back to normal during the COVID19 crisis, I argue we should be talking instead about moving forward to better. The economic system of the US is a mechanism for organizing work and for delivering products and services to people. Other mechanisms are possible and the open movement may give us some ideas.

Where can you learn more?

The 1999 book The Cathedral and the Bazaar by Eric S. Raymond contrasts software development under tight control with software development in the public eye, arguing for the latter based on, for example, the observation that involving more people leads to quicker detection of bugs in the software.

The 2008 book Two Bits by anthropologist Christopher M. Kelty argues that the Open Software movement created a new type of entity, a recursive public. “A recursive public is a public that is vitally concerned with the material and practical maintenance and modification of the technical, legal, practical, and conceptual means of its own existence as a public; it is a collective independent of other forms of constituted power and is capable of speaking to existing forms of power through the production of actually existing alternatives.” The geeks he study both act within the open source movement but also consciously work to preserve the environment that allows the open source movement to exist.

The Electronic Frontier Foundation defends “digital privacy, free speech, and innovation.”

“The Free Software Foundation is working to secure freedom for computer users by promoting the development and use of free (as in freedom) software and documentation.”

The Open Source Initiative provides various licenses for open source software.

Open Education Global is a consortium of organizations supporting open education.

Making masks

What’s new?

In an effort to help people during the COVID-19 pandemic, many makers have applied their knowledge and technology to make Personal Protective Equipment (PPE), especially face masks, and to make medical equipment that is in short supply in some places, especially ventilators. The Center for Disease Control (CDC) provides advice on how to make a mask, including a no sew alternative from an bandanna. ActivArmor, a company making 3D printed casts and braces in my home town of Pueblo pivoted quickly to making 3D printed FDA compliant fitted face masks.  GM partnered with Ventec Life Systems to produce ventilators, delivering the first products in mid April.

What does it mean?

Making a fabric face mask requires some thought. Tightly woven fabric or multiple layers do best at filtering out small particles. Loosely woven or stretch fabric have larger holes that allow viruses through. Adding a filter from items available in many households (for examples, coffee filters) may or may not help and may expose the human to other unsafe fibers. The mask needs to fit snugly to prevent air from getting around the edges. Masks should be washed regularly. The wearer should be cautious about touching the mask to adjust it, possibly transferring virus between the hands to the face. But even with those cautions, any face mask may be better than none, at least at preventing the wearer from transmitting the virus to others.

Masks intended for use by medical personnel require even more thought. The FDA describes the differences among types of surgical masks (surgical, isolation, dental, or medical procedure masks). These are meant to be used once and disposed of. Surgical N95 respirators are designed to block “at least 95 percent of very small (0.3 micron) test particles.” The FDA (working with CDC NIOSH) regulates these items. These regulations rely on ASTM standards concerning bacterial filtration efficiency, particulate filtration efficiency, fluid resistance, pressure differential, and flame spread. An ISO standard applies for skin sensitivity and cytotoxic tests “to ensure that no materials are harmful to the wearer.”

Ventilators are sophisticated devices that deliver air to a patient’s lungs, and include controls, monitors, and safety devices to make sure the device is helping, not harming, the patient. Initial excitement by makers cooled down when people realized the difficulty of making a safe and useful ventilator.

What does it mean for you?

The lessons to be learned are about materials, processes, technology, and safety. The biggest lesson is about expertise. Meaning well is sometimes hard to translate into doing well.

Kaoru Ishikawa, one of the founders of Japanese quality, invented the Ishikawa (or fishbone) diagram, in which the causes of a problem in quality are brainstormed and displayed, often in six categories: Man (people), Machine, Material, Method, Measurement, and Mother Nature (environment). The design and manufacture of any product or service has to consider these factors in depth in order to reliably deliver.

People who will do the work must be trained completely. Machines and equipment (including computers) used in production have to be capable of producing products and services meeting the specification of the customer. Materials must be chosen that can stand up to the uses to which they will be put. Methods of production have to be refined and standardized so the desired quality is achieved every time. The measurement devices used in every step must be capable of measuring the desired critical-to-quality measurements. And consideration must be given to how to control the natural variation in the environment so quality is not harmed.

In the face of the global pandemic, it has been heartening to see so many people step up to help – helping their neighbors with food, helping by slowing the spread of the disease, and helping to produce products to support medical care. But designing a product or service and designing the production process to reliably produce that product or service to meet the desired specifications are hard work – and require expertise. The final story about the world’s response to COVID-19 is a long way from being written, but certainly the ongoing battle between respect for and rejection of expertise and the need to identify who actually has expertise will be parts of that story.

In your organization, you provide leadership – in the many forms that can take – but you know that you must select and listen to experts that you can rely on. My field, industrial engineering, provides the expertise you need for producing services and products to meet customer specifications – reliably and consistently. Industrial engineering sometimes seems like organized common sense, but common sense is, regrettably, not always that common, and a task that may seem easy can, in fact, be hard. As I watched the initial efforts to make PPE and medical equipment, I was warmed by the enthusiasm and the desire to help, but dismayed by my knowledge that those efforts would, inevitably, need to be refined and perhaps even abandoned. Meaning well is sometimes hard to translate into doing well. Experts do know more than nonexperts.

Where can you learn more?

Industrial engineering is about efficiency, quality, and safety. It has roots in methods invented by Taylor and the Gilbreths that became time-and-motion studies, mathematical methods of optimization used to improve efforts in World War II that became operations research, quality tools developed by Shewhart, Deming, and others that led to control charts and quality principles, the invention of electronics that led to computers, automation, and controls, and much more. Key ideas are processes, systems, flow, control, optimization, continuous improvement, and safety. Industrial engineers are the engineers who think most about the people. I tell my students that being an industrial engineer means you are always dissatisfied: if it ain’t broke, it can still be improved.

Many professional organizations support the creation and dissemination of knowledge in industrial engineering and the networking of professionals. The lead society for industrial engineers is the Institute of Industrial and Systems Engineers (IISE), which has subgroups for all the specialties withing the field. Other relevant organizations include the American Society for Quality (now called ASQ), the Institute for Operations Research and the Management Sciences (INFORMS), the Society of Manufacturing Engineers (now called SME), and the Human Factors and Ergonomics Society (HFES). If you want to deliver a product or service that meets customer requirements with efficiency, quality, and service, hire the experts: industrial engineers.