Tags: computing

Creativity cannot be computed

The slides from Hidde’s presentation at Beyond Tellerrand.

She Built a Microcomputer Empire From Her Suburban Home

The story of Lore Harp McGovern is like something from Halt And Catch Fire.

Capt. Grace Hopper on Future Possibilities: Data, Hardware, Software, and People (Part One, 1982) - YouTube

Wow! Grace Hopper has always been a hero to me, but I had no idea she was such a fantastic presenter. She’s completely engaging, with the timing and deadpan delivery of a stand-up comedian at times.

The Gods of Logic, by Benjamín Labatut

Benjamín Labatut draws a line from the Vedas to George Boole and Claude Shannon onward to Geoffrey Hinton and Frank Herbert’s Butlerian Jihad.

In the coming years, as people armed with AI continue making the world faster, stranger, and more chaotic, we should do all we can to prevent these systems from giving more and more power to the few who can build them.

No Web Without Women

From Ada Lovelace to Nicola Pellow.

How do we build the future with AI? – Chelsea Troy

This is the transcript of a fantastic talk called “The Tools We Still Need to Build with AI.”

Absorb every word!

Beyond the Frame | Untangling Non-Linearity

A fascinating look at the connections between hypertext and film editing. I’m a sucker for any article that cites both Ted Nelson and Walter Murch.

When Mathew Modine’s character first shows up in Christopher Nolan’s Oppenheimer, I figured the rest of the cinema audience wouldn’t have appreciated me shouting out “VANNEVAR BUSH IN THE HOUSE!” so I screamed it on the inside.

The Manhattan Project was not his only claim to fame or infamy. When it comes to the world we now live in, Bush’s idea of the memex has been almost equally influential. His article As We May Think became a touchstone for Douglas Engelbart and later Tim Berners-Lee.

But as Matt Thompson points out:

…the device he describes does not resemble the internet or anything I’ve ever found on it.

Then he says:

What Bush was describing sounds to me like what you might get if you turned a browser history — the most neglected piece of the software — into a robust and fully featured machine of its own. It would help you map the path you charted through a web of knowledge, refine those maps, order them, and share them

Yes! This!! I 100% agree with the description of browser history as “the most neglected piece of the software.” While I wouldn’t go as far as Chris when he says web browsers kind of suck, I’m kind of amazed that there hasn’t been more innovation and competition in this space.

If anything we’ve outsourced the management of our browsing history to services like Delicious and Pinboard, or to tools like Obsidian and Roam Research. Heck, the links section of my website is my attempt to manage and annotate my own associative trails.

Imagine if that were baked right into a web browser. Then imagine how beautiful such a rich source of data might look.

Like Matt says:

I don’t think anything like this exists. So Bush’s essay still transfixes me.

Quite a few people have been linking to this list on The Verge of what they consider the greatest tech books of all time.

To be clear, this is a fairly narrow definition of technology. It’s really a list of books about the history of computing. But there’s some great stuff in there.

I’ve been thinking the books about computing and technology that I’ve managed to get around to reading, and which ones made an impact on me. Some of these made it on The Verge’s list too, which is nice to see.

Broad Band by Clare L. Evans

I was blown away by the writing and the stories uncovered in “the untold story of the women who made the internet.” Here’s what I wrote when I read the book:

This book is pretty much the perfect mix. The topic is completely compelling—a history of women in computing. The stories are rivetting—even when I thought I knew the history, this showed me how little I knew. And the voice of the book is pure poetry.

It’s not often that I read a book that I recommend wholeheartedly to everyone. I prefer to tailor my recommendations to individual situations. But in the case of Broad Band, I honesty think that anyone would enjoy it.

Uncanny Valley by Anna Wiener

I read this one in 2020, not too long after it came out. In my end of year round-up, I described it like this:

A terrific memoir. It’s open and honest, and just snarky enough when it needs to be.

Close to the Machine by Ellen Ullman

I read this in 2018, many years after it first came out. Here’s how it came across to me:

Lots of ’90s feels in this memoir. A lot of this still resonates today. It’s kind of fascinating to read it now with the knowledge of how this whole internet thing would end up going.

Abolish Silicon Valley by Wendy Liu

This book is mostly excellent. But as I wrote when I got my hands on an advance copy, the juxtaposition of memoir and manifesto didn’t work for me:

Abolish Silicon Valley is 80% memoir and 20% manifesto. I worry that the marketing isn’t making that clear. It would be a shame if this great book didn’t find its audience.

The Victorian Internet by Tom Standage

Okay, this isn’t technically about computing, it’s about the telegraph. But it’s got the word “internet” in the title, and it’s a terrific read. Here’s what I wrote when I put it in Matt’s book-vending machine:

A book about the history of telegraphy might not sound like the most riveting read, but The Victorian Internet is both fascinating and entertaining. Techno-utopianism, moral panic, entirely new ways of working, and a world that has been utterly transformed: the parallels between the telegraph and the internet are laid bare. In fact, this book made me realise that while the internet has been a great accelerator, the telegraph was one of the few instances where a technology could truly be described as “disruptive.”

When Jason linked to the list of books on The Verge he said:

I’m baffled that Tracy Kidder’s amazing The Soul of a New Machine didn’t make the top 5 or even 10.

I’m more surprised that this book is held in such high esteem. It has not aged well. I read it in 2019 and had this to say:

This is a well-regarded book amongst people whose opinion I value. It’s also a Pulitzer prize winner. Strange, then, that I found it so unengaging. The prose is certainly written with gusto, but it all seems so very superficial to me. No matter how you dress it up, it’s a chronicle of a bunch of guys—and oh, boy, are they guys—making a commercial computer. Testosterone and solder—not my cup of tea.

In new AI hype frenzy, tech is applying the label to everything now

Today’s AI promoters are trying to have it both ways: They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks. But they also define AI so broadly as to include almost any large-scale, statistically-driven computer program.

Under this definition, everything from the Google search engine to the iPhone’s face-recognition unlocking tool to the Facebook newsfeed algorithm is already “AI-driven” — and has been for years.

AI Hype-Driven Development - Parallels in History -

Simulmatics as a company was established in 1959 and declared bankruptcy in 1970. The founders picked this name as a mash of ‘simulation’ and ‘automatic’, hoping to coin a new term that would live for decades, which apparently didn’t happen! They worked on building what they called the People Machine to simulate and predict human behavior. It was marketed as a revolutionary technology that would completely change business, politics, warfare and more. Doesn’t this sound familiar?!

The man who tried to redeem the world with logic - Big Think

The fascinating—and tragic—story of Walter Pitts and Walter McCulloch whose lives and work intersected with Norbert Wiener and John von Neumann:

Thanks to their work, there was a moment in history when neuroscience, psychiatry, computer science, mathematical logic, and artificial intelligence were all one thing, following an idea first glimpsed by Leibniz—that man, machine, number, and mind all use information as a universal currency. What appeared on the surface to be very different ingredients of the world—hunks of metal, lumps of gray matter, scratches of ink on a page—were profoundly interchangeable.

AI isn’t the app, it’s the UI - Stack Overflow Blog

In some ways, the fervor around AI is reminiscent of blockchain hype, which has steadily cooled since its 2021 peak. In almost all cases, blockchain technology serves no purpose but to make software slower, more difficult to fix, and a bigger target for scammers. AI isn’t nearly as frivolous—it has several novel use cases—but many are rightly wary of the resemblance. And there are concerns to be had; AI bears the deceptive appearance of a free lunch and, predictably, has non-obvious downsides that some founders and VCs will insist on learning the hard way.

This is a good level-headed overview of how generative language model tools work.

If something can be reduced to patterns, however elaborate they may be, AI can probably mimic it. That’s what AI does. That’s the whole story.

There’s very practical advice on deciding where and when these tools make sense:

The sweet spot for AI is a context where its choices are limited, transparent, and safe. We should be giving it an API, not an output box.

Make Something Wonderful | Steve Jobs

This anthology of Steve Jobs interviews, announcements and emails is available to read for free as a nicely typeset web book.

Picture someone tediously going through a spreadsheet that someone else has filled in by hand and finding yet another error.

“I wish to God these calculations had been executed by steam!” they cry.

The year was 1821 and technically the spreadsheet was a book of logarithmic tables. The frustrated cry came from Charles Babbage, who channeled his frustration into a scheme to create the world’s first computer.

His difference engine didn’t work out. Neither did his analytical engine. He’d spend his later years taking his frustrations out on street musicians, which—as a former busker myself—earns him a hairy eyeball from me.

But we’ve all been there, right? Some tedious task that feels soul-destroying in its monotony. Surely this is exactly what machines should be doing?

I have a hunch that this is where machine learning and large language models might turn out to be most useful. Not in creating breathtaking works of creativity, but in menial tasks that nobody enjoys.

Someone was telling me earlier today about how they took a bunch of haphazard notes in a client meeting. When the meeting was done, they needed to organise those notes into a coherent summary. Boring! But ChatGPT handled it just fine.

I don’t think that use-case is going to appear on the cover of Wired magazine anytime soon but it might be a truer glimpse of the future than any of the breathless claims being eagerly bandied about in Silicon Valley.

You know the way we no longer remember phone numbers, because, well, why would we now that we have machines to remember them for us? I’d be quite happy if machines did that for the annoying little repetitive tasks that nobody enjoys.

I’ll give you an example based on my own experience.

Regular expressions are my kryptonite. I’m rubbish at them. Any time I have to figure one out, the knowledge seeps out of my brain before long. I think that’s because I kind of resent having to internalise that knowledge. It doesn’t feel like something a human should have to know. “I wish to God these regular expressions had been calculated by steam!”

Now I can get a chatbot with a large language model to write the regular expression for me. I still need to describe what I want, so I need to write the instructions clearly. But all the gobbledygook that I’m writing for a machine now gets written by a machine. That seems fair.

Mind you, I wouldn’t blindly trust the output. I’d take that regular expression and run it through a chatbot, maybe a different chatbot running on a different large language model. “Explain what this regular expression does,” would be my prompt. If my input into the first chatbot matches the output of the second, I’d have some confidence in using the regular expression.

A friend of mine told me about using a large language model to help write SQL statements. He described his database structure to the chatbot, and then described what he wanted to select.

Again, I wouldn’t use that output without checking it first. But again, I might use another chatbot to do that checking. “Explain what this SQL statement does.”

Playing chatbots off against each other like this is kinda how machine learning works under the hood: generative adverserial networks.

Of course, the task of having to validate the output of a chatbot by checking it with another chatbot could get quite tedious. “I wish to God these large language model outputs had been validated by steam!”

Sounds like a job for machines.

50 Years Later, We’re Still Living in the Xerox Alto’s World - IEEE Spectrum

A profile of the Xerox Alto and the people behind it.

Light Years Ahead | The 1969 Apollo Guidance Computer - YouTube

This video was in my “Watch Later” queue for ages but I finally got ‘round to watching it this weekend. It’s ace! Great content, great narrative, great delivery—would’ve made a good dConstruct talk.

Shift Happens

Marcin’s book is coming along nicely—you just know it’ll be a labour of love.

You’ve never seen a book on technology like this. Shift Happens is full of stories – some never before told – interleaved with 1,000+ beautiful full-color photos across two volumes.

The Kickstarter project launches in February. In the meantime, there are some keyboard-based games here for you to enjoy.

Jack Rusher ☞ Classic HCI demos

At Clarity last week, I had the great pleasure of introducing and interviewing Linda Dong who spoke about Apple’s Human Interface Guidelines. I loved the way she looked at the history of the HIG from 1977 onwards. This collection of videos is just what I need to keep spelunking into the interfaces of the past:

A curated collection of HCI demo videos produced during the golden age from 1983-2002.

The Thorny Problem of Keeping the Internet’s Time | The New Yorker

This story of the Network Time Protocol hammers home the importance of infrastructure and its maintenance:

Technology companies worth billions rely on open-source code, including N.T.P., and the maintenance of that code is often handled by a small group of individuals toiling away without pay.