An image of a cave drawing of horned cow. There is another one coming up behind it as well. There are four dots as described by the researchers on the main cow's back.

Writing – So Easy A Caveperson Could Do It

February 13, 2024 by Navarre Bartz 10 Comments

We modern humans tend to take writing for granted, and often forget that like any other technology, somebody had to invent it. Researchers from Cambridge believe they’ve determined the purpose of one of the earliest writing beta-tests.

Examining a database of images taken in caves throughout Europe and dated to the Upper Paleolithic, the researchers found “three of the most frequently occurring signs—the line <|>, the dot <•>, and the <Y>—functioned as units of communication.”

It appears the <|> and <.> symbols when “in close association with images of animals” denote time relating to lunar months of the year, starting with spring as the new year. The <Y> symbol appears to carry the meaning <To Give Birth> allowing early people a way to tell others information about the prey of a region, which would be pretty handy when hunting and gathering are your only options for food.

We’ve covered other ancient technologies like storytelling and abrasives. If you’re curious what the climate was like for our ancestors, perhaps paleoclimatology will tickle your fancy.

AI’s Existence Is All It Takes To Be Accused Of Being One

February 11, 2024 by Donald Papp 31 Comments

New technologies bring with them the threat of change. AI tools are one of the latest such developments. But as is often the case, when technological threats show up, they end up looking awfully human.

Recently, [E. M. Wolkovich] submitted a scientific paper for review that — to her surprise — was declared “obviously” the work of ChatGPT. No part of that was true. Like most people, [E. M. Wolkovich] finds writing a somewhat difficult process. Her paper represents a lot of time and effort. But despite zero evidence, this casual accusation of fraud in a scientific context was just sort of… accepted.

There are several reasons this is concerning. One is that, in principle, the scientific community wouldn’t dream of leveling an accusation of fraud like data manipulation without evidence. But a reviewer had no qualms about casually claiming [Wolkovich]’s writing wasn’t hers, effectively calling her a liar. Worse, at the editorial level, this baseless accusation was accepted and passed along with vague agreement instead of any sort of pushback.

Showing Your Work Isn’t Enough

Interestingly, [Wolkovich] writes everything in plain text using the LaTeX typesetting system, hosted on GitHub, complete with change commits. That means she could easily show her entire change history, from outline to finished manuscript, which should be enough to convince just about anyone that she isn’t a chatbot.

But pondering this raises a very good question: is [Wolkovich] having to prove she isn’t a chatbot a desirable outcome of this situation? We don’t think it is, nor is this an idle question. We’ve seen how even when an artist can present their full workflow to prove an AI didn’t make their art, enough doubt is sown by the accusation to poison the proceedings (not to mention greatly demoralizing the creator in the process.)

Better Standards Would Help

[Wolkovich] uses this opportunity to reflect on and share what this situation indicates about useful change. Now that AI tools exist, guidelines that acknowledge them should be created. Explicit standards about when and how AI tools can be used in the writing process, how those tools should be acknowledged if used, and a process to handle accusations of misuse would all be positive changes.

Because as it stands, it’s hard to see [Wolkovich]’s experience as anything other than an illustration of how a scientific community’s submission and review process was corrupted not by undeclared or thoughtless use of AI but by the simple fact that such tools exist. This seems like both a problem that will only get worse with time (right now, it is fairly easy to detect chatbots) and one that will not solve itself.

Raspi-Powered Typewriter Is A Real MUSE

September 5, 2023 by Kristina Panos 4 Comments

Thanks to parenting and life in general, [Brendan] had fallen out of the habit of writing and wasn’t happy about it. If you write anything ever, you already know there are endless distractions when it comes to doing so on a computer. Sure, there always typewriters, but it’s difficult to do anything with the fruits of a typewriter other than scan it in or make copies, and it’s basically un-editable except by hand.

Instead of just sitting down and writing, [Brendan] did what any of us would do — took the time to create an elegant solution. The Most Unusual Sentence Extractor, or MUSE, is a Raspberry Pi-based typewriter with the best of both worlds. It’s essentially a word processor, but it can save to the cloud.

[Brendan] found beautiful inspiration in the Olympia Traveller de Luxe typewriter, a delightfully boxy affair made in the 1960s and 70s with lovely keys. Starting with a 68Keys.io board, [Brendan] set about re-creating the lines of the Traveller de Luxe in Tinkercad.

Since it doesn’t really need a platen, this was the perfect place to mount a screen using black PVC. At first, [Brendan] was going to use an e-ink screen, but a mishap led to a better solution — an LCD touchscreen that makes document navigation a breeze.

We absolutely love the look of this machine, which was obviously a labor of love. And yeah, it does the trick:[Brendan] is writing again. Though it maybe be inconvenient, we agree that it really is nice to have a dedicated workstation for certain things.

Looking for the complete opposite of this project? How about a Chat GPT-assisted daisywheel typewriter?

Better Coding Through Sketching

December 3, 2022 by Al Williams 25 Comments

Back in the late 1970s and early 1980s, engineering students would take a few semesters of drafting and there would usually be a week or two of “computer-aided drafting.” In those days, that meant punching cards that said RECTANGLE 20,30 or something like that and getting the results on a plotter. Then we moved on to graphical CAD packages, but lately, some have gone back to describing rather than drawing complex designs. Cornell University researchers are trying to provide the same options for coding. They’ve built a Juypter notebook extension called Notate that allows you to sketch and handwrite parts of programs that interact with traditional computer code. You can see a video about the work below.

The example shows quantum computing, but the idea could be applied to anything. The example has sketches that generate quantum circuits. Naturally, there is machine learning involved.

Continue reading “Better Coding Through Sketching” →

Blog Title Optimizer Uses AI, But How Well Does It Work?

August 21, 2022 by Donald Papp 11 Comments

[Max Woolf] sometimes struggles to create ideal headlines for his blog posts, and decided to apply his experience with machine learning to the problem. He asked: could an AI be trained to optimize his blog titles? It is a fascinating application of natural language processing, and [Max] explains all about what it does and how it works.

The machine learning framework [Max] uses is GPT-3, a language model that works with natural-seeming human language that is capable of being tweaked in different ways. [Max] uses OpenAI’s GPT-3 API (which, by the way, is much easier to experiment with than one might think) and here is the basic workflow for his title optimizer:

The optimizer takes as input a blog post title to optimize.
OpenAI’s pre-trained GPT-3 engine is used to generate six alternate titles.
For each of those alternate titles, a fine-tuned version of GPT-3 is consulted to judge how “good” they are based on custom training data. (“Good” in this context means “similar to titles of successful submissions on Hacker News“, but more on that in a moment.)
Print the results.

Continue reading “Blog Title Optimizer Uses AI, But How Well Does It Work?” →

A 3D-Printed Nixie Clock Powered By An Arduino Runs This Robot

June 22, 2022 by Jenny List 16 Comments

While it is hard to tell with a photo, this robot looks more like a model of an old- fashioned clock than anything resembling a Nixie tube. It’s the kind of project that could have been created by anyone with a little bit of Arduino tinkering experience. In this case, the 3D printer used by the Nixie clock project is a Prusa i3 (which is the same printer used to make the original Nixie tubes).

The Nixie clock project was started by a couple of students from the University of Washington who were bored one day and decided to have a go at creating their own timepiece. After a few prototypes and tinkering around with the code , they came up with a design for the clock that was more functional than ornate.

The result is a great example of how one can create a functional and aesthetically pleasing project with a little bit of free time.

Confused yet? You should be.

If you’ve read this far then you’re probably scratching your head and wondering what has come over Hackaday. Should you not have already guessed, the paragraphs above were generated by an AI — in this case Transformer — while the header image came by the popular DALL-E Mini, now rebranded as Craiyon. Both of them were given the most Hackaday title we could think of, “A 3D-Printed Nixie Clock Powered By An Arduino Runs This Robot“, and told to get on with it. This exercise was sparked by curiosity following the viral success of AI generators, which posed the question of whether an AI could make a passable stab at a Hackaday piece. Transformer runs on a prompt model in which the operator is given a choice of several sentence fragments so the text reflects those choices, but the act of choosing could equally have followed any of the options.

The text is both reassuring as a Hackaday writer because it doesn’t manage to convey anything useful, and also slightly shocking because from just that single prompt it’s created meaningful and clear sentences which on another day might have flowed from a Hackaday keyboard as part of a real article. It’s likely that we’ve found our way into whatever corpus trained its model and it’s also likely that subject matter so Hackaday-targeted would cause it to zero in on that part of its source material, but despite that it’s unnerving to realise that a computer somewhere might just have your number. For now though, Hackaday remains safe at the keyboards of a group of meatbags.

We’ve considered the potential for AI garbage before, when we looked at GitHub Copilot.