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Source Code: Bill Gates’ Harvard Days

May 29, 2025 By Jack Vaughan

Gates Source Code BookWith the likes of Sam Altman and Elon Musk dashing about, we crouch for shelter now in an era where well-funded high-tech bros can live a life that was once reserved only for Doctor Strange.

That tends to make Bill Gates’ “Source Code: My Beginnings” (Knopf, 2025) a much more warmfy and life-affirming book than it might otherwise have been. In this recounting of his early days, and founding of Microsoft, he paints a colorful picture of a bright and excitable boy making good. Much of Source Code is set in “the green pastures of Harvard University.”

The boy wonder to be was born in Seattle in 1955, when computers were room sized, and totally unlike the consumer devices  which humans now ponder like prayer books as they walk city streets.

His family was comfortable and gave him a lot of room to engage a very curious imagination. His mother called it precociousness, and it’s  a trait he dampered down when he could. He had a fascination with basic analytical principles, which held him in stead when the age of personal computers dawned. [Read more…] about Source Code: Bill Gates’ Harvard Days

Why tinyML?

MARCH 26, 2021 — In about 2004 this reporter asked a top IBM software development leader what cloud computing looked like to him. “It looks like a mainframe,” he said with only half a smile. True enough, cloud is a centralized way of computing, which is beginning to raise questions.

One of which is: Will machine learning be forever in the “glass room?” That is the old-time descriptor for the home sweet home of the immortal mainframe era, where numbers got crunched and good ideas went to die.

Today, technologists are working to bring machine learning out of the closet and into the real world in the form of Edge computing.

For that to happen machine-made observations and decisions will have to succeed on individual chips in devices and on boards, far from the cloud data center where a lot of electrical power allows infinite compute.

For that to happen, machine learning at the edge, which is often more project than reality today, will have to become productized. It will have to work within much tighter constrains. That is the motivation behind TinyML, which — thank goodness — is more a way of doing things, than it is a standard or product.

Issues facing TinyML as it struggles to leave the cocoon are worth consideration. As with client server and other computing paradigm shifts, the outcome will rely on how teams on the cloud and on the edge deal with the details of implementation.

That was seen in a panel at this week’s tinyML Summit 2021. It afforded opportunity for such consideration. Here I am going to share some comments and impressions from a panel that featured expert implementers working to make it happen.

The lively panel discussion entitled “tinyML inference SW – Where do we go from here?” was moderated by Ian Bratt, Distinguished Engineer & Fellow, Arm. Joining Brat were Chris Lattner, President, Engineering and Product, SiFive; Tianqi Chen, CTO, OctoML; Raziel Alvarez, Technical Lead for PyTorch at Facebook AI; and Pete Warden, Technical Lead, Google. (A link to the  panel recording on YouTube is found at the bottom of this page.)

A familiar view emerged, one that showed the creators of the trained machine learning model handing off their work, hoping a dedicated engineer can make the code run in the end. That conjures the old saw about ‘throwing it over the wall,’ and hoping system programmers can do the finished carpentry.

The tableau suggested the objectives of the researchers in a sort of ivory tower of cloud machine learning were somewhat at odds with the objectives of the front-line inference engineers at the edge where cycle economy is paramount and power consumption is crucial.

That echoes yet another developer saw that goes ‘it worked on my machine’ – one of the classic crunch time excuses over the history of computing.

Other issues:

-It may take top gun skills to make a trained model work in practice. “Somebody has to do magic to get it into production,” said Raziel Alvarez.

-People are able to deploy these models but the effort is very considerable. The many different cogs in machine learning (for example, the link between a CPU and a GPU) have to be managed deftly. In practice that means  “people have to go outside their [practice] boundaries,” said T.Q. Chen.

-They hope to deploy inference on a variety of hardware, but each hardware version and type requires special tuning. And, low-level hardware changes can effect a cascading chain of changes all the way up the machine learning development stack. “As soon as you get heterogenous hardware, models tend to break,” said Peter Warden.

Hmmm, maybe that is enough on the ‘challenges.’ Obviously, people go at this to succeed, not to loll in obstacles. But obstacles go with the move to production for machine learning inference. As one tinyML Summit 2021 panelist said of recent history, “we have found a lot of what doesn’t work – we know what we don’t know.”

It will be interesting to see if and how the machine learning technology moves to the edge from the cloud. In architecture, the devil isn’t in the details, but in building, it is. What is likely is that the leap from science project to useful product will depend on the future work of the participants at tinyML Summit 2021 and other conferences to come. – Jack Vaughan

 

 

Edmund Morris’s Edison

Thomas Edison was born in the years before the American Civil War, and as a lad came under the thrall of popular writings on electricity. The phenomenon had only recently evolved from spooky mystery thing to moneymaking entity. In the form of the commercial telegraph was forming the basis for new industries and ways of life in the United States. As a boy in Port Huron, Michigan, he muddled through some schooling, became a news boy, and then a telegraph operator as the Civil War commenced.

He used his time. Riding the rail running from Port Huron to Detroit, and he began to produce his own paper in a baggage car on the train. In Detroit between the morning and evening trains he’s go to the Young Men’s Society Reading Room (public library) and commence to read every book, starting at the beginning of book shelf (that’s something I heard, not something detailed as such in the book we’re going to look at here). Read Les Misérables, Robert Burden’s The Anatomy of Melancholy, the Penny Encyclopedia. And perhaps Newton’s Principia Mathematica. Although he probably didn’t comprehend its contents. He would rummage around the city, bartering for or commandeering refuse copper, which he fashioned into electric batteries and components in the evenings back in Port Huron.

From the very first, Edison had a penchant to ponder the ramifications of telegraph transmission and all the complementary components involved. His wondering upon the problems of long distance induction and interference that disrupted the transmission of signals led to enhancements, improvements and breakthroughs that changed the fabric of modern life.

Edison’s position as the image of the inventor may fade, as the next generation fixes on new icons of invention such as Steve Jobs or Elon Musk. That makes Edmund Morris’s recent (2019) biography particularly welcome.

It is welcome even though the book has a hurried feel, with only some of the prose majesty exhibited in earlier Morris works. Maybe it is wrong to suggest he rushed to completion due to failing health – Morris completed the book just before his own death. Edison has some of a spectral, episodic quality and is less solid by far than Morris’s remarkable and award-winning biography of Edison contemporary Theodore Roosevelt.

Yet, the book is indeed a valuable addition to the copious collections already devoted to Edison, the archetypal inventor of the first commercial age of electricity.

He was a principal wizard behind the lightbulb, phonograph, cinema, significant improvements to battery technology as well as the telegraph and telephone, and a dizzy string of more forgotten contraptions. A self-taught polymath, he is noted here as a man of amazing concentration. He could look intently at what the world lay before him – and was able to uncover deep first principles as he tinkered with pieces of metal, carbon, vulcanite, lamp black and assorted materials.

More than that, he had a gift for imaginative re-application of principles to conjure new products, and improve on existing successes. This was all to result from long nights and extended work stints.

His schoolteachers considered him slow, and he set out to work along the railroad, itself a new invention. Edison was a fast copyist, a task he learned in his days as a railroad telegraph man. This was during the Civil War, when telegraphy was becoming increasingly important as a media for the country. He’d practiced, becoming a fast transcriber. In this he acquired the aspects of an automaton – an entranced taker of code, rather than words. By 1863, as a 16-year-old, he was eligible for the Civil War draft, but with the job of sparker, or lighting slinger for the telegraph company, he avoided the Civil War draft. Instead, he conveyed the messages of soy prices, battles and death.

At one point, as night operator on the Grand Trunk railroad, he nearly lost his job, having nearly signaled two trains into collision. The work involved monitoring the movement of trains by means of telegraphic messages exchanged with other stations along the line, a kind of thing that continues to be an intrinsic fascination for high tech. He persisted

Morris suggests Edison saw Newton, Franklin and Faraday as exemplars, but held that all inventions were in fact rediscoveries that could be found in the reorganization of primal matter. His Edison stands as the classic paradigm for the lone inventor, at the same time he comes to stand as well for the more modern model of an inventor as part of an organization.

He was also involved in technology as a business, and was not shy about touting his vision in the media of the day. There are echoes of Edison in the efforts of Jobs and Musk, both of who’s learnings and interests were wide in terms of technology, business and marketing.

One cannot review this book without pointing to an odd conceit in Morris’s narration. He tells Edison’s life story backwards. As with the odd techniques he applied in his biography of Ronald Reagan (“Dutch”), the effects do not shed any special light on the subject, and tend on the main to disconcert. We’re sure that others will, as we did, read the book backwards to achieve a familiar chronological sequence.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

While Edison was impressively interested in discovery, as a person, he erected shields to block discovery of what made him tick. Successes and failures made him wealthy and catch-as-can. In his love life, in his family life, with his lab buddies or his worldly business friends, he left little to evidence his innards. We would have to leave a judgment on Morris’s forensic success here to others — but suggest he should get an ‘E’ for effort.

A story from his childhood seems to provide a ghostly electric clue. At about the age of 10, Edison goes to the creek with young friend George Lockwood, who disappears in the eddies to drown. Edison observes the creek water for a long time, maybe rapt by the dying Lockwood’s breath bubbles. At last, after the long wait for Lockwood to surface, Edison finally goes home to dinner and to bed without telling anyone about the event. Meanwhile, a party searches for Lockwood – eventually they come to hear Edison’s story of his drowning.

Of course, given the reverse order telling, this early life story occurs at the end of Morris’s chronicle. To me it seemed to explain a lot, while maintaining the veiling of the real Edison.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We read the brunt of this book during the contentious 2020 presidential campaign, which was nearly derailed by controversy around voting processes and voting machines. Funny to find out that Edison’s first patent (granted during his days in Boston) was for an electro-chemical vote recorder.

The device was meant to speed up the laborious process of vote counting in legislative bodies, and used electricity to do this. He called it his record-o-graph. As it turns out, speedy voting was not a ‘must-have’ for politicians in the passing of legislative bills, and the work was much for naught.

Edison resolved, going forward, that he would focus efforts only on things that people wanted to use. He was seldom off that mark.

What we take away from the book is a mark we will look to apply to dissection of technologies here at the Progressive Gauge atelier — there is no technology without useful case. – Jack Vaughan

Related
Edison – by Edmund Morris – Amazon.com

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