Thinking Deep Learning

Deep Learning is Human

ACM NEWS

Deep Learning is Human, Through and Through, By Bennie Mols

Commissioned by CACM Staff, October 18, 2022

It was 10 years ago, in 2012, that deep learning made its breakthrough, when an innovative algorithm for classifying images based on multi-layered neural networks suddenly turned out to do spectacularly better than all algorithms before it. That breakthrough has led to deep learning's adoption in domains like speech and image recognition, automatic translation and transcription, and robotics.

As deep learning was embedded into ever-more everyday applications, more and more examples of what can go wrong also surfaced: artificial intelligence (AI) systems that discriminate, confirm stereotypes, make inscrutable decisions and require a lot of data and sometimes also a huge amount of energy.

In this context, the 9th Heidelberg Laureate Forum organized a panel discussion on the applications and implications of deep learning for an audience of some 200 young researchers from more than 50 countries. The panel included Turing Award recipients Yoshua Bengio, Yann LeCun, and Raj Reddy, 2011 ACM Prize in Computing recipient Sanjeev Arora, and researchers Shannon Vallor, Been Kim, Dina Machuve, and Shakir Mohamed. Katherine Gorman moderated the discussion.

Meta chief AI scientist Yann LeCun turned out to be the most optimistic of the panelists: "There have been lots of claims that deep learning can't do this or that, and most of these claims have been proven false after a few years of more work. The last five years, deep learning has been able to do things that none of us imagined it was going to be able to do, and the progress is accelerating."

As an example, LeCun said that Facebook, owned by Meta, now automatically detects 96% of all hate speech, whereas about four years ago that was only 40%. He attributes the improvement to deep learning. "We are bombarded with enormous amounts of information every day, and this is only getting worse. We are going to need even more automated systems that allow us o sift through this information."

Shannon Vallor, a professor focused on the Ethics of Data and AI at the U.K.'s University of Edinburgh, objected to LeCun's idea that technology just moves forward as if it has its own will, and that society simply has to adapt. "That is precisely how we got into certain problems. Technology can take many forked paths and people decide which of the forked paths are optimal to follow. Deep learning systems are through and through artefacts that humans build and deploy, according to their own values, incentives, and power structures, and therefore we are still fully reponsible for them."

One of the criticisms of deep learning is that, while it is good at pattern recognition, it is currently not suitable for logical reasoning, while good-old symbolic AI is. However, both Bengio and LeCun saw no reason why deep learning systems cannot be made to reason. As Bengio observed, "Humans also use some kind of neural nets in their brains, and I believe that there are ways to get to human-like reasoning with deep learning architectures."

However, Bengio added that he doesn't think just scaling up present-day neural nets will be sufficient. "I believe that we can take much more inspiration from biology and human intelligence in order to bridge the gap between current AI and human intelligence."

It is not just that deep learning cannot reason yet, but also that we cannot reason about deep neural networks, added Sanjeev Arora, a theoretical computer scientist at Princeton University. Said Arora, "We need more understanding of what is going on inside the black box of deep learning systems, and that is what I am trying to do."

Raj Reddy was the panelist who has been involved in the AI community by far the longest, since the 1960s, when he did his Ph.D. research with AI pioneer John McCarthy. Reddy sees the glass as half-full instead of half-empty: "One important application of deep learning is helping the people at the bottom of the societal pyramid. There are about two billion people in the world who cannot read or write. All kinds of language technologies, like speech recognition and translation, are now good enough to be used. I have worked in this area for almost 60 years and I didn't think such technologies would become practical in my lifetime. Ten years from now, even an illiterate person will be able to read any book, watch any movie, and have a conversation with anyone, anywhere in the world, in their native language."

However, dealing with smaller languages is still an unsolved problem for deep learning technologies, as much less data is available for them. Dina Machuve, a data science consultant, remarked that in Africa alone, 2,000 languages are spoken for which there are no AI technologies available. It is important to go into a community and see what would work for them, so in looking for deep learning applications for Africa, Machuve concentrated on image applications. As a result, "We have developed early detection systems for poultry diseases and crop diseases based on image recognition." .... ' 

On the Cradle of the Computer

Nicely done short piece on the history of  the computer by Herbert Bruderer in the ACM. 

Where is the Cradle of the Computer?  By Herbert Bruderer, June 20, 2022  Most of the good details at the link.

 The digital computer of today arose in the first half of the 1940s independently in three different countries: Germany, the U.K. and the U.S.

In Berlin, the computer was the work of a single person, and elsewhere universities, government agencies, or industry played an important role. For political reasons, the German inventor was largely cut off from the outside world.

The English worked under top-secret conditions, because the focus was on the decoding of encrypted radio messages.

Within the Unites States, on the other hand, a certain exchange of information took place. Today's digital computer thus had several protagonists (see Table 1).  ... '

Babbages Engine Reaches 200

Am an avid historian of computation: 

Babbages Engine is 200 years old

Below just an  intro, go to the link for more and furtherlinks.

Charles Babbage’s Difference Engine Turns 200 Error-riddled astronomical tables inspired the first computer—and the first vaporware ALLISON MARSH  27 MAY 20227 MIN READ

IT WAS AN IDEA born of frustration, or at least that’s how Charles Babbage would later recall the events of the summer of 1821. That fateful summer, Babbage and his friend and fellow mathematician John Herschel were in England editing astronomical tables. Both men were founding members of the Royal Astronomical Society, but editing astronomical tables is a tedious task, and they were frustrated by all of the errors they found. Exasperated, Babbage exclaimed, “I wish to God these calculations had been executed by steam.” To which Herschel replied, “It is quite possible.“

Babbage and Herschel were living in the midst of what we now call the Industrial Revolution, and steam-powered machinery was already upending all types of business. Why not astronomy too?

Babbage set to work on the concept for a Difference Engine, a machine that would use a clockwork mechanism to solve polynomial equations. He soon had a small working model (now known as Difference Engine 0), and on 14 June 1822, he presented a one-page “Note respecting the Application of Machinery to the Calculation of Astronomical Tables” to the Royal Astronomical Society. His note doesn’t go into much detail—it’s only one page, after all—but Babbage claimed to have “repeatedly constructed tables of squares and triangles of numbers” as well as of the very specific formula x2 + x + 41. He ends his note with much optimism: “From the experiments I have already made, I feel great confidence in the complete success of the plans I have proposed.” That is, he wanted to build a full-scale Difference Engine.  ... '

About Xerox Parc

 Worked some with and met at PARC early on.    As a group that wanted to create the architecture of information, always wondered what happened to them.   They did own the computing infrastructure.     Were they really scalped by Gates and Jobs?  What do they do now?  Here a good telling of history.

XEROX PARC’S ENGINEERS ON HOW THEY INVENTED THE FUTURE—AND HOW XEROX LOST IT in IEEE Spectrum

The inside story of personal computing at the legendary research lab

TEKLA S. PERRYPAUL WALLICH04 JUN 2022N LATE 1969, C. Peter McColough, chairman of Xerox Corp., told the New York Society of Security Analysts that Xerox was determined to develop “the architecture of information” to solve the problems that had been created by the “knowledge explosion.” Legend has it that McColough then turned to Jack E. Goldman, senior vice president of research and development, and said, “All right, go start a lab that will find out what I just meant.”

This article was first published as “Inside the PARC: the ‘information architects’.” It appeared in the October 1985 issue of IEEE Spectrum. A PDF version is available on IEEE Xplore. The diagrams and photographs appeared in the original print version.

Goldman tells it differently. In 1969 Xerox had just bought Scientific Data Systems (SDS), a mainframe computer manufacturer. “When Xerox bought SDS,” he recalled, “I walked promptly into the office of Peter McColough and said, ‘Look, now that we’re in this digital computer business, we better damned well have a research laboratory!’ ”

In any case, the result was the Xerox Palo Alto Research Center (PARC) in California, one of the most unusual corporate research organizations of our time. PARC is one of three research centers within Xerox; the other two are in Webster, N.Y., and Toronto, Ont., Canada. It employs approximately 350 researchers, managers, and support staff (by comparison, Bell Laboratories before the AT&T breakup employed roughly 25,000). PARC, now in its 15th year, originated or nurtured technologies that led to these developments, among others:

The Macintosh computer, with its mouse and overlapping windows.

Colorful weather maps on TV news programs.

Laser printers.

Structured VLSI design, now taught in more than 100 universities.

Networks that link personal computers in offices.

Semiconductor lasers that read and write optical disks.

Structured programming languages like Modula-2 and Ada.

In the mid-1970s, close to half of the top 100 computer scientists in the world were working at PARC, and the laboratory boasted similar strength in other fields, including solid-state physics and optics.

Some researchers say PARC was a product of the 1960s and that decade’s philosophy of power to the people, of improving the quality of life. When the center opened in 1970, it was unlike other major industrial research laboratories; its work wasn’t tied, even loosely, to its corporate parent’s current product lines. And unlike university research laboratories, PARC had one unifying vision: it would develop “the architecture of information.”   ..... ' 

Pamela McCorDuck Dies

 We met with and used some of her  books in training exercises in the early days of AI:

Pamela McCorduck, Historian of AI, Dies at 80

The New York Times, Richard Sandomir, November 4, 2021

Pamela McCorduck, who authored a history of the first two decades of artificial intelligence (AI), has died at 80. She first co-edited an influential book of academic papers on AI at the University of California, Berkeley with computer scientists Edward Feigenbaum (an ACM A.M. Turing Award recipient) and Julian Feldman. As an English teacher at Carnegie Mellon University, McCorduck got to know AI pioneers like Turing Award recipients Herbert Simon and Raj Reddy. Feigenbaum said, "She was dumped into this saturated milieu of the great and greatest in AI at Carnegie Mellon—some of the same people whose papers she'd helped us assemble—and decided to write a history of the field." The book was "Machines Who Think: A Personal Inquiry Into the History and Prospects of Artificial Intelligence." Said Simon, "She was interacting with all the movers and shakers of AI. She was in the middle of it, an eyewitness to history."  .... 

Computing History: Looms and

Just a bit of history. Mostly pictures, at the link, but great if you were unaware of the background. 

BLOG@CACM

Charles Babbage and the Loom   By Herbert Bruderer

Charles Babbage's analytical engine (see Fig. 1), which already provided for conditional branching, is regarded as the ancestor of the modern-day computer. He wanted to control his programmable machine with punched cards similar to the automatic looms from France.

Punched tapes or punched cards joined to tapes simplified work on looms (pattern control). Among the pioneers were Basile Bouchon (see Fig. 2), Jean-Baptiste Falcon (see Fig. 3), and Joseph-Marie Jacquard (see Fig. 4). Their achievements are on view in the Musée des arts et métiers in Paris.  .... " 

About the Colossus Computer

 Am a long time follower of the history of computing since I worked with one of the people who had a part in maintaining the ENIAC computer at the U of P.  This talk does a good job of presenting the math-technical-operational challenge of breaking codes in WWII.   And also how the Colossus computer was devised earlier than other early systems like the Eniac.    Not too technical.  

Further it shows the over-eagerness of the UK to deeply bury the advances they made until the 1970s.  In part, they infer, because  it allowed the UK to sell encryption tech to allies and potential enemies after the war without revealing that they could decrypt their communications!   Some of their tech is still kept secret.  Thus ensuring that key people in the UK did not get the credit they deserved.   

The Centre for Computing History  May 4 Presentation

Chris Shore talks about Colossus, how it came to be, how it worked and how it changed the course of World War II.  Essential viewing!

Help us create more videos. Support us through Patreon : https://www.patreon.com/computinghistory​

Museum Website : www.ComputingHistory.org.uk

Twitter : https://twitter.com/computermuseum​   .... '

On the State of Quantum

 This also talks about D-Wave, which we touched with in our early examinations.  We are getting to closer to having the ability to solve complex problems very fast, and to have to worry about Quantum making some of our security less successful.

The State of Quantum Computing   By Logan Kugler, Commissioned by CACM Staff, April 8, 2021

In December 2020, a Chinese research team claimed to have successfully achieved "quantum advantage" by using quantum computing methods to perform computations that classical supercomputers can't.

Using photons, the team carried out a calculation called a boson-sampling problem. The calculation has so many variables that existing supercomputers "would take half the age of Earth" to calculate the problem, according to Nature. The Chinese team used quantum computing to achieve the calculation in a few minutes.

"[This] is certainly an impressive academic achievement, showing that quantum machinery can in some cases strain the abilities of conventional computers," said Chris Monroe, co-founder and chief scientist of IonQ, a quantum computer maker developing what it describes as a general-purpose trapped ion quantum computer and software to generate, optimize, and execute quantum circuits. "But it's important to consider that the problem they solved is a narrow application space with no known practical use, and it will be difficult to tune their experiment for any other type of problem."

Several leading companies are working to avoid that problem by developing practical quantum machines and deploying them for real commercial applications.

D-Wave Systems is one such company at the forefront of recent quantum computing developments. British Columbia, Canada-based D-Wave makes quantum machines specifically for businesses. Last year, the company announced the general availability of Advantage, its 5,000-qubit quantum system, accompanied by its quantum cloud service, Leap. The company's machines take a unique approach to quantum computing called "quantum annealing," which uses the physics of quantum phase transitions to perform computations. The company bet big on annealing early on, a bet that it says has paid off.

Murray Thom, D-Wave's vice president of software and cloud services, said, "We believed—and still believe—that annealing is the fastest path to our number-one objective: fueling customer value through practical quantum applications." The company says over 250 quantum-powered applications built with its system are now in production from Fortune 500 companies like Volkswagen, DENSO, and Accenture.  .. ...

........ "Quantum computing technology is accelerating rapidly, with new advancements and new companies getting involved nearly daily," says IonQ CEO and President Peter Chapman. "When I joined IonQ in 2019, people said that quantum would never work. We don't hear much from those people anymore."

Logan Kugler is a freelance technology writer based in Tampa, FL, USA. He has written for over 60 major publications.

Cade Metz and Nathan Benaich: Podcast on the Emergence and Direction of AI

By Nathan Benaich, Air Street Capital@nathanbenaich  ...  Looking forward to hearing this.  

Dear readers,  This week I had the opportunity to interview Cade Metz, New York Times technology correspondent and author of Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World. I’ve featured Cade’s writings (at NYT and Wired) many times in this newsletter over the years, so this was a real treat for me!

We dive into the history of AI, how academia drove industry to adopt deep learning, which companies “got it right” and which didn’t, geopolitics and China, Europe’s role on the world stage, semiconductors, AI in science, and more.

See the podcast:   'Conversation with Cade Metz in Your Guide to AI'  ... '

See you in 2 weeks for the next regular issue of Your guide to AI: March 2021! As always, thanks for hitting forward to a couple of friends  ..... 

By Nathan Benaich, 21 March 2021,   Air Street Capital | Twitter | LinkedIn | State of AI Report | RAAIS | London.AI  ....   Air Street Capital is a venture capital firm investing in AI-first technology and life science companies. We’re an experienced team of investors and founders based in Europe and the US with a shared passion for working with entrepreneurs from the very beginning of their company-building journey.  ... ' 

Decline of Computers as a General Purpose Technology

Contributed article, excerpt below in the March 2021 CACM.

My first reaction was No!  But some very good points made ... '

The Decline of Computers as a General Purpose Technology   By Neil C. Thompson, Svenja Spanuth

Communications of the ACM, March 2021, Vol. 64 No. 3, Pages 64-72  10.1145/3430936

Perhaps in no other technology has there been so many decades of large year-over-year improvements as in computing. It is estimated that a third of all productivity increases in the U.S. since 1974 have come from information technology,a,4 making it one of the largest contributors to national prosperity.

Key Insights  ...  

- Moore's Law was driven by technical achievements and a "general purpose technology" (GPT) economic cycle where market growth and investments in technical progress reinforced each other.  These created strong economic incentives for users to standardize to fast-improving CPUs, rather than designing their own specialized processors.

- Today, the GPT cycle is unwinding, resulting in less market growth and slower technical progress. 

- As CPU improvement slows, economic incentives will push users toward specialized processors, which threatens to fragment computing. In such a computing landscape, some users willbe in the 'fast lane,' benefit ing from customized hardware, and others will be left in the 'slow lane,' stuck on CPUs whose progress fades. ... 

The rise of computers is due to technical successes, but also to the economics forces that financed them. Bresnahan and Trajtenberg3 coined the term general purpose technology (GPT) for products, like computers, that have broad technical applicability and where product improvement and market growth could fuel each other for many decades. But, they also predicted that GPTs could run into challenges at the end of their life cycle: as progress slows, other technologies can displace the GPT in particular niches and undermine this economically reinforcing cycle. We are observing such a transition today as improvements in central processing units (CPUs) slow, and so applications move to specialized processors, for example, graphics processing units (GPUs), which can do fewer things than traditional universal processors, but perform those functions better. Many high profile applications are already following this trend, including deep learning (a form of machine learning) and Bitcoin mining. ... 

With this background, we can now be more precise about our thesis: "The Decline of Computers as a General Purpose Technology." We do not mean that computers, taken together, will lose technical abilities and thus 'forget' how to do some calculations. We do mean that the economic cycle that has led to the usage of a common computing platform, underpinned by rapidly improving universal processors, is giving way to a fragmentary cycle, where economics push users toward divergent computing platforms driven by special purpose processors.

This fragmentation means that parts of computing will progress at different rates. This will be fine for applications that move in the 'fast lane,' where improvements continue to be rapid, but bad for applications that no longer get to benefit from field-leaders pushing computing forward, and are thus consigned to a 'slow lane' of computing improvements. This transition may also slow the overall pace of computer improvement, jeopardizing this important source of economic prosperity.    ...."

The ENIAC Turns 75

 I used to walk past the site of the ENIAC lab on the way to class at the U of Pa, and even got a tour of the space once.  Impressive, 

ENIAC Turns 75  By Samuel Greengard,   Commissioned by CACM Staff

Programming ENIAC, the Electronic Numerical Integrator and Calculator (it was not called a computer, because "computers" at that time were people).

The history of computing is filled with mythical figures, but often lost in the shuffle is the accomplishment of John Mauchly and J. Presper Eckert, Jr. On February 14, 1946, the pair publicly unveiled the world's first true computer: ENIAC (Electronic Numerical Integrator and Computer). From their lab at the University of Pennsylvania in Philadelphia, they launched a revolution that truly changed the world.

On its 75th anniversary, ENIAC is once again in the spotlight. "It was the big bang of the information age. It set in motion a paradigm that has become the underpinning of daily life, as well as of deepest science," observes Bill Mauchly, an inventor and software architect who, as the son of John Mauchly, has also become a historian for the computer.

"ENIAC was the first digital programmable computer. It demonstrated what was possible," adds Thomas Haigh, professor of history at the University of Wisconsin, Milwaukee, and co-author of ENIAC in Action: Making and Remaking the Modern Computer (MIT Press).

A Calculated Approach

ENIAC, built at a then-astounding cost of $487,000, used 10 position ring counters to store digits. Each digit required 28 vacuum tubes that counted pulses on the ring counters to perform arithmetic. "Because it was electronic, it was thousands of times faster than anything that came before it," Haigh explains. "The machine would complete its work in a flash and spend most of its time waiting for human intervention."

The computer supported 200 decimal digits of writeable electronic memory spread across 20 "accumulators." It was a then-revolutionary development. ENIAC added by transmitting ten-digit numbers directly between accumulators, incrementing the contents of the destination counters. An "add time" was 200 microseconds. The accumulators worked in parallel, allowing up to 50,000 additions per second. A 10-digit by 10-digit multiplication required 14 add times, or a total of approximately 2,800 microseconds. A division or square root problem required 143 add times, or 28,600 microseconds.

To be sure, ENIAC was notable for more than simply being the world's first fully electronic computer. It was incredibly consequential. From the day it was introduced to the public via a front-page story in The New York Times to its retirement nearly a decade later, it tackled an array of real-world tasks including ballistics trajectory research, Monte Carlo simulations, weather predictions, and early hydrogen bomb research conducted by John von Neumann and others.

"Although the architecture and programming style were very idiosyncratic and not copied by any later machine, the ENIAC project built a foundation for more advanced computing models," says Mark Priestley, a research fellow at the U.K.'s National Museum of Computing and co-author of ENIAC in Action. This included the EDVAC (Electronic Discrete Variable Automatic Computer) design that, among other advances, introduced binary rather than decimal computing, and modern programming techniques. ... " 

History of the VPN

Intriguing history, now finally becoming universal.  Saw it first in the enterprise. 

Everything VPN is New Again in ACM Queue

The 24-year-old security model has found a second wind.

David Crawshaw  in 

The VPN (virtual private network) is 24 years old. The concept—cryptographically secure tunnels used as virtual wires for networking—was created for a radically different Internet from the one we know today. As the Internet grew and changed, so did VPN users and applications. The VPN had an awkward adolescence in the Internet of the 2000s, interacting poorly with other widely popular abstractions such as multiuser operating systems. In the past decade the Internet has changed again, and this new Internet offers new uses for VPNs. The development of a radically new protocol, WireGuard, provides a technology on which to build these new VPNs.

This article is a narrative history of the VPN. All narratives necessarily generalize and cannot capture every nuance, but it is a good-faith effort to (critically) celebrate some of the recent technical history of networking and to capture the mood and attitudes of software engineers and network administrators toward the VPN.  ... " 

Life of a Data Byte

Thoughtful look at data as beyond just the amount, but also the means of storage and use.   And  it links to the  history of our business.

Volume 18, issue 3, In ACM Queue

The Life of a Data Byte  Be kind and rewind.   By Jessie Frazelle

A byte of data has been stored in a number of different ways through the years as newer, better, and faster storage media are introduced. A byte is a unit of digital information that most commonly refers to eight bits. A bit is a unit of information that can be expressed as 0 or 1, representing a logical state. Let's take a brief walk down memory lane to learn about the origins of bits and bytes.

Going back in time to Babbage's Analytical Engine, you can see that a bit was stored as the position of a mechanical gear or lever. In the case of paper cards, a bit was stored as the presence or absence of a hole in the card at a specific place. For magnetic storage devices, such as tapes and disks, a bit is represented by the polarity of a certain area of the magnetic film. In modern DRAM (dynamic random-access memory), a bit is often represented as two levels of electrical charge stored in a capacitor, a device that stores electrical energy in an electric field. (In the early 1960s, the paper cards used to input programs for IBM mainframes were known as Hollerith cards, named after their inventor, Herman Hollerith from the Tabulating Machines Company—which through numerous mergers is what is now known as IBM.)

In June 956, Werner Buchholz (archive.computerhistory.org) coined the word byte (archive.org) to refer to a group of bits used to encode a single character of text (bobbemer.com). Let's address character encoding, starting with ASCII (American Standard Code for Information Interchange). ASCII was based on the English alphabet; therefore, every letter, digit, and symbol (a-z, A-Z, 0-9, +, -, /, ", !, etc.) was represented as a seven-bit integer between 32 and 127. This wasn't very friendly to other languages. To support other languages, Unicode extended ASCII so that each character is represented as a code-point. For example, a lowercase j is U+006A, where U stands for Unicode followed by a hexadecimal number.  ... " 

Why Computing Belongs within the Social Sciences

Thoughtful piece in the ACM.  Full text at the link. While computers started from technology, it has evolved unpredictably into something also very deeply and even alarmingly sociological.    Read the key insights below.

Why Computing Belongs Within the Social Sciences
By Randy Connolly
Communications of the ACM, August 2020, Vol. 63 No. 8, Pages 54-59  10.1145/3383444

On October 23, 2008, Alan Greenspan, the Chair of the U.S. Federal Reserve, was testifying before Congress in the immediate aftermath of the September 2008 financial crash. Undoubtedly the high point of the proceedings occurred when Representative Henry Waxman pressed the Chair to admit "that your view of the world, your ideology, was not right," to which Greenspan admitted "Absolutely, precisely."17 Fast forward 10 years to another famous mea culpa moment in front of Congress, that of Mark Zuckerberg on April 11, 2018. In light of both the Cambridge Analytica scandal and revelations of Russian interference in the 2016 U.S. election, Zuckerberg also admitted to wrong: "It's clear now that we didn't do enough to prevent these tools from being used for harm. That goes for fake news, foreign interference in elections, and hate speech, as well as developers and data privacy." ... '

Key Insights:

Mouse Co-Creator Dies

In our meeting with the co-creator of the mouse in the 80s, Doug Engelbart, was quick to mention William English as the person to create the first mouse device.  I have placed many posts about our interaction with Doug Engelbart in this blog, see the tag below.

The co-creator of the computer mouse, William English, has died aged 91.  in the BBC

The engineer and inventor was born in 1929 in Kentucky and studied electrical engineering at university before joining the US Navy.

He built the first mouse in 1963, using an idea put forward by his colleague Doug Engelbart while the pair were working on early computing.

It would only become commonplace two decades later, when personal home computers became popular.

Mr English's death was confirmed to US media outlets by his wife.  ... " 

ACM Bytecast: Donald Knuth on Computing

From my earliest days doing coding, I was reading Donald Knuth's legendary texts.  Now here is a free and open podcast interview with him.  Will be following.

" ... In the latest episode of ACM ByteCast, a new podcast series at the intersection of computing research and practice, host Rashmi Mohan interviews legendary computer scientist Donald Knuth. Knuth is the 1974 ACM A.M. Turing Laureate and author of the hugely popular textbook series, "The Art of Computer Programming." They discuss what led him to discover his love of computing as well as writing about computer programming, his outlook on how people learn technical skills, how his mentorship has helped him write “human oriented” programs, how his dissatisfaction with early digital typesetting led him to develop TeX, and the problems he is still working to solve.

The podcast is available in the ACM Learning Center https://learning.acm.org/bytecast , where you can also subscribe to an RSS feed as well as download a full transcript of each episode, and on popular podcast platforms including Apple Podcasts, Google Podcasts, Spotify, and Stitcher.  ... 

Algol: Used it

Well, ok, used it.   Mention here for historical purposes.   Was one of the first languages I ever used professionally.  The article below well describes why it is relevant.  But as a language won't have very good (or any)  libraries, so unless you are doing something very standalone, not a good choice.

ALGOL 60 at 60: The greatest computer language you've never used and grandaddy of the programming family tree ... 
By Richard Speed 15 May 2020 at 09:47    in TheRegister

Analog or Digital Mechanisms of the Past

Excellent summary of what is known to date about of the Antikythera Mechanism, a 'computer' found in an ancient Greek shipwreck, over 2000 years old.  Not technical, but a favorite topic of interest.

The Antikythera Mechanism    By Herbert Bruderer
Communications of the ACM, April 2020, Vol. 63 No. 4, Pages 108-115  10.1145/3368855

 Until the discovery of the Antikythera Mechanism, astrolabes were often considered the earliest analog mathematical devices. Such complex gearwork as in this astronomical calculator, however, only appeared (again) much later, especially in medieval clockworks. Leonardo da Vinci (1452–1519) knew gears, as his drawings show. Heron of Alexandria (1st century) used cogwheels for his pantograph. The construction of analog measuring and drawing instruments (for example, sectors, proportional dividers, compasses) and logarithmic circular and cylindrical slide rules was comparatively simple. Planimeters and (mechanical) differential analyzers were sophisticated. The first mechanical calculating machines were invented in the 17th century (Wilhelm Schickard, Blaise Pascal, Gottfried Leibniz). These digital devices required stepped drums, pinwheels, and accumulators. In the second half of the 20th century there was a competition between electronic analog computers and electronic digital computers.  ... ' 

Simple innovations are the Most Used

Anyone in this space for a while knows the drill.

Larry Tesler: Computer Scientist Behind Cut, Copy, and Paste Dies at Age 74     BBC News

Larry Tesler, inventor of the "cut," "copy," and "paste" commands, recipient of ACM SIGCHI's Lifetime Practice Award in 2011 and inducted to the CHI Academy in 2010, has died at the age of 74. Tesler's innovations helped make personal computers simple to learn and use. After New York-born Telser graduated from Stanford University, he specialized in user-interface design, the process of making computer systems more user-friendly. He worked for a number of technology firms during his long career, including the Xerox Palo Alto Research Center (Parc), Apple, Amazon, and Yahoo. Tesler "combined computer science training with a counterculture vision that computers should be for everyone," according to Silicon Valley's Computer History Museum.  ... '

End of the Blackberry

Blackberries were the first phone we issued to executives.  At their request after they saw how they could be used with their own portfolios.  And were the first that had the ability to work with mail, and later the use of 'Apps' for corporate data, which led to more concern about the security of data.  Spent lots of time considering what apps would lead to better corporate decisions  For us these were the first hand-held portable task devices.  Now it's mostly just the name that is also going away.

The end of BlackBerry phones: TCL will cease sales in August 2020
TCL's brand-name license will expire, leading to a clean, swift death.
 By Ron Amadeo in Ars Technica

BlackBerry is quitting the phone business—again. You might recall BlackBerry quit manufacturing smartphones back in 2016, but it licensed its brand name to the Chinese smartphone corporation TCL. TCL started pumping out BlackBerry-branded devices—some of which were QWERTY equipped and some of which were shameless rebadgings of existing TCL phones. TCL's Zombie BlackBerry plan apparently wasn't working too well, though, since now that's dead, too.  ... "