I had the pleasure of seeing this performed in 1997 with dear friends from my days at Hampshire College. It was performed by Ossie Davis, Susan Sarandon, Mandy Patinkin, Pete Seeger and others. Quite a thought provoking night.
Richard Hamming was a 20th century mathematician who contributed greatly to our understanding of information and information encodings. In this talk he shares his thoughts on how to be good at what you do. In short: work on the right problem, at the right time, and using the right methods. Were it so easy!
Here’s an interesting throwback to my early days of computing. I learned about computing and programming on a Times-Sinclair 1000 back in 1982. For a “reasonable” cost, it was a keyboard and computer all-in-one that you plugged into your TV set (for display) and audio cassette player (for data storage).
This is a nice intro to the new Raspberry Pi 400 all-in-one computer… This is 1,000 generations follow-on from the Times-Sinclair 1000. And yet, it’s so very similar: all-in-one, connect to your monitor, etc. Also, it’s the same current dollars cost: The Sinclair cost $99 in 1982 dollars and the PI 400 costs $100 in 2020 dollars for a kit that includes a hefty beginner’s guide, power supply, mouse, and video cable.
These kinds of devices allow for discovery and tinkering in a way that tightly controlled ecosystems such as the iPhone, iPad, and even Android platforms generally do not. Here’s hoping such systems inspire more generations to explore the possibilities of computing.
“The only freedom which deserves the name, is that of pursuing our own good in our own way, so long as we do not attempt to deprive others of theirs, or impede their efforts to obtain it.”
“…doing as we like, subject to such consequences as may follow: without impediment from our fellow-creatures, so long as what we do does not harm them, even though they should think our conduct foolish, perverse, or wrong.”
There are plentiful examples of spreadsheet applications leading analysts astray. Believe all the scary stories. Spreadsheets can silently damage your data, converting numbers to dates or dropping leading zeros from what should be fixed-length identifier (where did the U.S. Zip Code 01002 go?).
One night, when I was 15 or 16, my father called me to his bedside to pronounce. “Your mother tells me,” said he, “that you don’t feel you can talk to me.” Truth. “I wan’t you to know you can talk to me about anything. It’s just that I’m never going to say another word to you.” He was true to his word; one of the very few times that I’m aware of. He died 10 years later with us having never exchanged another word.
You will be asked to write. Think about your writing process carefully and be open to new ideas about how to approach it.
Writing is, at a minimum, a two-step process: you write, you edit. Writing and editing are distinct steps. Don’t try to edit your writing as you write your first draft; you’ll trip over your own creativity. Get your ideas down, then edit. Then get more ideas down and edit them.
Ideally, you would repeat the writing-editing process several dozen times to further refine your prose. After you’ve written and edited your work, you’ll need to share your draft with others, who will edit it and rewrite portions of it. Set your ego aside. Focus on improving the writing and, through that experience, your default writing style.
Are there problems for which the best possible approach is to perform a brute-force search of every possible solution?
During the Soviet-era, the perebor problem (перебор) addressed this question. There are connections here to the question that arose in Western computer science: P versus NP. That is, is the set of problems that are easy to verify the correctness of necessarily also easy to solve?
The Golden Rule relates the perebor problem to Communist ideology: a desire to believe that some problems rightly require effort and that the search for shortcuts—also known as more efficient solutions, as were pursued in the west—was anti-Marxist.
Whether or not one decides to anthropomorphize complexity, it has long fascinated me that the “East” and “West” divisions of the 20th century carried over into the conceptualization of fundamental properties of computational complexity.
When a learner asks a question, I often hear their peers and teachers respond in a way that suggests “don’t ask that question; learn something different”. I see the same thing occurring on Q&A support boards all the time:
Questioner: “I was wondering how to cook an egg in the microwave.”
Supposed answerer: “Don’t use a microwave, use a pressure cooker.”
Supposed answerer: “Why would you want eggs? Go vegan.”
As an educator myself, I find it’s better not to say NO to someone interested in learning and instead say, “yes… and…….” to find out what interests them and connect the topic to their interests.
It’s a lesson I draw from the improv and acting communities: “no, but…” (or even “yes, but…”) stops conversations. “yes, and…” encourages them.
I’m often taken in by restoration and conservation stories. Recently, the thoughtful machine learning algorithms at YouTube suggested to me a set of videos related to Alec Steele and company’s efforts to install an industrial power hammer in their steelwork shop.
This is industrial equipment at a scale with which I have no experience. Yet, the sheer joy and curiosity exhibited by this crew as they work to address practical, physical, and design issues with making this equipment functional is glorious.
The Machine Stops, a story ahead of its time being published in 1909, foretells of a society in which individuals are almost completely physically isolated from one another in an underground enclave where communication is achieved only with technology and all life’s necessities are attended to by a vast, unseen network of tubes.
What happens when, as always must happen, the machine stops?
Good, dear friends recently inspired a journey into the world of radios, speakers, and design. I love nothing more than being challenged to consider deeply a topic that’s otherwise new to me and through that to broaden my understanding of what I value and enjoy.
The first computing device I remember using was The Little Professor (1976). Designed like a calculator, the Little professor worked backward: it presented unsolved equations the user then needed to solve. Many years later, future-me would see a Little Professor in a computing exhibit at London’s Science Museum and wish that past-me could have known.
My first programmable computer was the Timex-Sinclair 1000 (1982) with 2K of RAM. I don’t remember what—other than the low price of $99—spurred my mother to buy this for me. It was connected to a Radio Shack audio cassette recorder for data storage and the family’s TV in the living room via an RF converter switch for video output.
“Now, Therefore THE GENERAL ASSEMBLY proclaims THIS UNIVERSAL DECLARATION OF HUMAN RIGHTS as a common standard of achievement for all peoples and all nations, to the end that every individual and every organ of society, keeping this Declaration constantly in mind, shall strive by teaching and education to promote respect for these rights and freedoms and by progressive measures, national and international, to secure their universal and effective recognition and observance, both among the peoples of Member States themselves and among the peoples of territories under their jurisdiction.”
“Both students and instructors perceive standard-error statistics as a confusing collection of specialized tools. To improve student learning, instructors long for a reduction in the number of topics needed to support statistical thinking. This book is a roadmap for instructors who wish to simplify inference while continuing to teach using traditional tools.”
“I hope that this little book can help instructors see that statistical inference can be handled as one topic among the many needed for modern statistics. Inference, important though it be, does not need to be such a sprawling set of methods and details taking up so much of the introductory course that other essential topics get neglected.”
I like this recent GOTO conference talk about the role of linguistics in understanding the language of coding. It touches upon many issues I’ve noted over the years as well as newer-to-me issues in non-English programming.