Month: January 2024

So. Many. Layoffs.

~ Taran ~ 1 Comment

I’ve been looking at getting back into the ring of software engineering, but it doesn’t seem like a great time to do it.

When Google was laying off workers, I shook my head a bit. It ends up that Google spent 800 million dollars in layoffs just this month. Just this month!

By comparison, Google spent $2.1 billion dollars on layoff expenses for more than 12,000 employees over the course of 2023. Other Google employees only knew about people being dismissed when people’s emails got bounced back last year in February.

With so many layoffs, hopefully they’re getting better at it. Well, maybe not. Google employees have been told more layoffs are coming this year.

I imagine that there are some pretty high quality resumes floating around. As far as the tech field goes, Google is probably considered top tier, and landing a position against someone with Google on their resume is going to be tough.

There’s a problem with that, though. More than 25,000 tech workers from 100 companies got the axe in first few weeks of 2024. Meta, Amazon, Microsoft, Google, TikTok and Salesforce are included in that… and Microsoft numbers may account for the Blizzard/Activision layoffs that happened this past week, sadly.

Blizzard was one of those dream jobs I had as a significantly younger developer way back when. They were often late on delivery for a new game, but it was pretty much worth it. I still play Starcraft II.

It’s become an employer’s job market – maybe it was before, but definitely more so now, and in an era when artificial intelligence may be becoming more attractive for companies and software development, as well as other things. For all we know, they may have consulted artificial intelligence for some of the layoffs, though. It wouldn’t be the first time that happened, though that was in Russia.

I can’t imagine that Google, Microsoft, Meta and Amazon aren’t using big data and AI for this, at least behind the scenes, but it’s probably not being explained because of the blowback that might cause. ‘Fired by AI’ is not something that people would like to see.

When tech companies axe companies, Wall Street rewards them, so stock prices go up – and there are more unemployed technology folk in a period when AI tools are making so many types of productivity easier. Maybe too much easier.

This reminds me so much of the 1990s. The good news is that tech survived the 1990s despite the post-merger layoffs.

Of course, the correction on the NPR article (at the bottom) is something I wish I had caught earlier. “Nearly 25,000 tech workers were laid in the first weeks of 2024. Why is that?” would definitely be an article worth reading.

Possibly Building a Raspberry Pi 5 Home Theater in the Future.

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There was a time when I would have gotten the news about the Raspberry Pi 5 launch much sooner. I’ve always liked the idea of these single board computers, and Raspberry Pi has been and continues to be a standard.

The trouble for me is finding a use for them. I have always got a bunch of decaying technology sitting around waiting for a purpose.

I decided to look into it for a home theater system, since what I have now is an old ASUS gaming laptop connected to a wide & flat screen television with a new Bluetooth speaker system – the cables in the old (10 years+) speaker system have had the insulation decay enough where I can hear things from other stuff that I’m not supposed to hear.

Normally I’d install Linux on systems and keep them going, but when it comes to home theater, Linux gets screwed over by manufacturers with audio device drivers.

As a little side procrastination project, I looked into the Raspberry Pi 5 for such an endeavor, replacing the laptop that is closing in on 10 years old itself. On “paper”, it looks like it can do everything I would want. It’s been out for about 3 months now, so I poked around for reviews.

This post on the Raspberry Pi said all the positive things I wanted to read. However, it didn’t seem to have enough in it to say, “Verily, we have used it.” That was a product announcement.

ArsTechnica, on the other hand, didn’t disappoint. “What I learned from using a Raspberry Pi 5 as my main computer for two weeks” was a lot more thoughtful, and someone had used it.

The Pi modal window looks like a severe annoyance, so I would opt to use a Linux distro. The monitor issues encountered were of interest to me because… home theater. Firefox locking up the Pi also is concerning because I’m not a fan of corporate browsers, particularly in an age of AI where they learn more about you by what you do than you might think. I don’t feel like training a corporate AI, thank you.

Of course, he lost a little credibility when he wrote this line: “Linux is a teetering stack of independently developed software projects that, on a good day, just barely manage to work together.” If that were the case, Linux likely wouldn’t be used as much as it is. However, he’s talking about the desktop, and while desktop Linux has come a long way, these tiny distros used for the Pi are still “Old School” because no one seems to have really brought them up to speed for the desktop. It’s a catch-22.

That review served as a cautionary note, as well as opened up some realistic possibilities. Maybe the Raspberry Pi 5 isn’t ready for what being a Home Theater system quite yet. The caveat on that is apparently he was trying to run 2 monitors at a time. I’ve only done that in business environments, because someone always wants to email you about a meeting, a status report, and “Do you think we can…” emails and having that second monitor comes in handy for that stuff.

The Pine rabbit hole.

The comments, though, were worth exploring. It mentioned the Pinebook Pro, as an example, which I didn’t know about despite having built a cluster with Pine64s. I went down the rabbit hole on those reviews, and while the Pinebook Pro looks like a great Linux based laptop it doesn’t fit what I’m looking for presently.

I checked out the Pine store, and while they have some really fun stuff in there like the QUARTZ64 Model-A 8GB Single Board Computer I glanced over, I wasn’t sold on it because the reviews on it don’t include stuff I am looking at doing – it could be fun to play with, though.

…And I’m still likely going to get one.

Having looked it all over, I think the Raspberry Pi 5 is my go to on doing this because Linux is not something that worries me much, the hardware is pretty well done though the Broadcom hardware is as problematic as the Broadcom brand for anything to do with open source, something that they have really worked hard at.

They are supporting things til January 2035, they say in their obsolescence statement, so it might outlast me.

The lead times on orders seems to be a bit long now, which is fine because I’ve found time is often an ally when it comes to buying stuff for the household.

At present, I think I would go with the CanaKit Raspberry Pi 5 Starter Kit – Turbine Black since it gets a lot of stuff out of the way, though I’d really prefer the aluminium case. Still, I think the Raspberry Pi store itself might be the place I buy things. I’ll wait and see.

A Basic Explanation of how AIs Identify Objects.

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I’ve been experimenting with uploading images to ChatGPT 4 and seeing what it has to say about them. To me, it’s interesting because I gain some insight into how far things have progressed, as well as how descriptive ChatGPT can be about things.

While having coffee yesterday with a friend, I was showing him the capabilities. He chose this scene.

He, like others I showed here in Trinidad and Tobago, couldn’t believe it. It’s a sort of magic for people. What I like when I use it for this is that it doesn’t look at the picture as a human would, where the subject is pretty obvious. It looks at all of the picture, which is worth exploring in a future post

He asked me how it could do that, give the details that it did in the next image in this post. I tried explaining it, and I caught that he was thinking of the classic “IF…THEN… ELSE” sequence that came from ‘classical’ computer science that we had been exposed to in the 1980s.

I tried and failed explaining it. I could tell I failed because he was frustrated with my explanation, and when I can’t explain something it bothers me.

We went our separate ways, and I went to a birthday party for an old friend. I didn’t get home til much later. With people driving as they do here in Trinidad, my mind was focused on avoiding them so I didn’t get to think on it as I would have liked.

I slept on it.

This morning I remembered something I had drawn up in my teens, and now I think I can explain it better to my friend, and perhaps even people curious about it. Hopefully when I send this to him he’ll understand, and since I’m spending the time doing just that, why not everyone else?

Identifying Objects.

As a teenager, my drawing on a sketch pad page was about getting a computer to identify objects. It included a camera connected to the computer, which wasn’t done commercially yet, and what one would do was rotate the object through all the axes and the computer would be told what the object was at every conceivable angle. It was just an idea of a young man passionate about the future with the beginnings of a grounding in personal computing.

What we’ve all been doing with social media for some time is tagging things. This is how we organized finding things, and the incentive was for people to find our content.

A bat in the bathtub where I was staying in Guyana, circa 2005, while I was doing some volunteer IT stuff. It was noteworthy to me, so I did what I did then – took a picture and posted it to Flickr.

Someone would post something on social media, as I did with Flickr, and tag it appropriately (we would hope). I did have fun with it, tagging things like a bat in a photograph as being naked, which oddly was my most popular photo. Of course it was naked, you perverts.

However, I also tagged it as a bat. And if you search Flickr for a bat, you’ll come up with a lot of images of bats. They are of all different sorts of bats, of all angles. There are even more specific tags for kinds of bats, but overall we humans pretty much know a bat when we see one, so all those images of bats could then be added to a training model to allow a computer to come up with it’s own algorithmic way of identifying bats.

And it gets better.

The most popular versions of bats on Flickr, as an example, will be the ones that the most people liked. So now, the images of bats are given weight based on their popularity, and therefore could be seen as the best images of bats. Clearly, my picture of the bat in the bathtub shouldn’t be as popular a version.

It gets even better.

The more popular an image is, the more likely it is to be used on the Internet regardless of copyright, which means that it will show up in search engine rankings if you search for images of bats. Search Engine ranking then becomes another weight.

The more popular images that we collectively have chosen become the training models for bats. The system learns the pattern of the objects, much as we do but differently because they have different ways of looking at the same things.

If you take thousands – perhaps millions – of pictures of bats and train a system to identify it, it can go around looking for bats in images, going through all of the images available looking for bats. It will screw up sometimes, and you tell it, “Not a bat”. It also finds the bats that people haven’t tagged.

Given the amount of tagged images and even text on the Internet, doing it with specific things is a fairly straightforward process because we don’t do anything. We simply correct mistakes.

Now do that with all the tags of different objects. Eventually, you’ll get to where multiple images in a picture can be identified.

That’s basically how it works. I don’t know that they used Flickr.com, or search engines, but if I were doing it, that’s probably how I would – and it’s not a mistake that people have been encouraged to do this a lot more over the last years preceding artificial intelligence hitting the mainstream. Now look at who is developing artificial intelligences. Social networks and search engines.

The same thing applies to text.

Then, when you hand it an image with various objects in it, it identifies what it knows and describes them based on the words commonly associated with the objects, and if objects are grouped together, they become a higher level object. Milk and cookies is a great example.

And so it goes, stacking higher and higher a capacity to recognized patterns of patterns of patterns…

And this also explains how the revolution of Web x.0 may have carried the seeds of it’s own destruction.

When Is An Algorithm ‘Expressive’?

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Yesterday, I was listening to the webinar on Privacy Law and the United States First Amendment when I heard that lawyers for social networks are claiming both that they have free speech as a network as a speaker, as well as claiming not to be the speaker and claiming they are simply are presenting content users have expressed under the Freedom of Speech. How the arguments were presented I don’t know, and despite showing up for the webinar I am not a lawyer1. The case before the Supreme Court was being discussed, but that’s not my focus here.

I’m exploring how it would be possible to claim that a company’s algorithms that impact how a user perceives information could be considered ‘free speech’. I began writing this post about that and it became long and unwieldy2, so instead I’ll write a bit about the broader impact of social networks and their algorithms and tie it back.

Algorithms Don’t Make You Obese or Diabetic.

If you say the word ‘algorithm’ around some people, their eyes immediately glaze over. It’s really not that complicated; a repeatable thing is basically an algorithm. A recipe when in use is an algorithm. Instructions from Ikea are algorithms. Both hopefully give you what you want, and if they don’t, they are ‘buggy’.

Let’s go with the legal definition of what an algorithm is1. Laws don’t work without definitions, and code doesn’t either.

Per Cornell’s Legal Information Institute, an algorithm is:

“An algorithm is a set of rules or a computational procedure that is typically used to solve a specific problem. In the case of Vidillion, Inc. v. Pixalate Inc. an algorithm is defined as “one or more process(es), set of rules, or methodology (including without limitation data points collected and used in connection with any such process, set of rules, or methodology) to be followed in calculations, data processing, data mining, pattern recognition, automated reasoning or other problem-solving operations, including those that transform an input into an output, especially by computer.” With the increasing automation of services, more and more decisions are being made by algorithms. Some examples are; criminal risk assessments, predictive policing, and facial recognition technology.”

By this definition and perhaps in it’s simplest form, adding two numbers is an algorithm, which also fits just about any technical definition out there. That’s not at issue.

What is at issue in the context of social networks is how algorithms impact what we view on a social networking website. We should all understand in the broad strokes that Facebook, Twitter, TikTok and their ilk are in the business of showing people what they want to see, and to do this they analyze what people view so that they can give people what they want.

Ice cream and brownies for breakfast, everyone!

Let’s agree every individual bit of content you see that you can act on, such as liking or re-transmitting, is a single item. Facebook sees you like ice cream, Facebook shows you posts of ice cream incessantly. Maybe you go out and eat ice cream all the time because of this and end up with obesity and diabetes. Would Facebook be guilty of making you obese and diabetic?

Fast food restaurants aren’t considered responsible for making people obese and diabetic. We have choices about where we eat, just as we have choices about what we do with our lives outside of a social network context. Further, almost all of these social networks give you options to not view content, from blocking to reporting to randomly deleting your posts and waving a finger at you for being naughty – without telling you how.

Timelines: It’s All A Story.

As I wrote elsewhere, we all choose our own social media adventures. Most of my social networks are pretty well tuned to feed me new things to learn every day, while doing a terrible job of providing me information on what all my connections are up to. It’s a real estate problem on social network sites, and not everyone can be in that timeline. Algorithms pick and choose, and if there are paid advertisements to give you free access, they need space too.

Think of it all as a personal newspaper. Everything you see is picked for you based on what the algorithms decide, and yet all of that information is competing to get into your eyeballs, maybe even your brain. Every story is shouting ‘pick me! pick me!’ with catchy titles, wonderful images, and maybe even some content – because everyone wants you to click to their website so you can hammer them with advertising.4

Yet when we step back from those individual stories, the social networking site is curating things in a chronological order. Let’s assume that what it thinks you like to see the most is at the top and it goes down in priority based on what the algorithms have learned about you.

Now think of each post as a page in a newspaper. What’s on the front page affects how you perceive everything in the newspaper. Unfortunately, because it’s all shoved into a prioritized list for you, you get things that are sometimes in a strange order, giving a weird context.

Sometimes you get stray things you’re not interested in because the algorithms have grouped you with others. Sometimes the priority of what you last wrote about will suddenly have posts related to it covering every page in that newspaper.

You might think you’re picking your own adventure through social media, but you’re not directly controlling it. You’re randomly hitting a black box to see what comes out in the hope that you might like it, and you might like the order that it comes in.

We’re all beta testers of social networks in that regard. They are constantly tweaking algorithms to try to do better, but doing better isn’t necessarily for you. It’s for them, and it’s also for training their artificial intelligences more than likely. It’s about as random as human interests are.

Developing Algorithms.

Having written software in various companies over the decades, I can tell you that if there’s a conscious choice to express something with them, to get people to think one way or the other (the point of ‘free speech’), it would have to be very coordinated.

Certain content would have to be weighted as is done with advertising. Random content churning through feeds would not fire things off with the social networking algorithms unless they manually chose to do so across users. That requires a lot of coordination, lots of meetings, and lots of testing.

It can be done. With advertising as an example, it has been done overtly. Another example is the last press against fake news, which has attempted to proactively check content with independent fact checkers.

Is that free speech? Is that freedom of expression of a company? If you look at this case again, you will likely draw your own conclusions. Legally, I have no opinion because I’m not a lawyer.

But as a software engineer, I look at it and wonder if this is a waste of the Court’s time.

  1. It should be in the interest of software engineers and others about the legal aspects of what we have worked on and will work on. Ethics are a thing. ↩︎
  2. It still is, and I apologize if it’s messy. This is a post I’ll likely have to revisit and edit. ↩︎
  3. Legal definitions of what an algorithm is might vary around the world. It might be worth searching for a legal definition where you are. ↩︎
  4. This site has advertising. It doesn’t really pay and I’m not going to shanghai viewers by misrepresenting what I write. It’s a choice. Yet to get paid for content, that’s what many websites do. If you are here, you’re appreciated. Thanks! ↩︎

The Greek AI Connection.

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This is not Sophia. There are plenty of images of Sophia across the Internet. You can see her here.

For those that have been busy doing real things, the fact that Sophia the Robot is heading to Greece to do a meet and greet. She apparently identifies as a woman, which I suppose is just one more thing that women will put up with. She didn’t make woman of the year, though, so there is that.

The Greek Reporter covered it, Al Jazeera has a video of Sophia, and last year Sophia was on a panel of ‘humanoids’ that told the UN that they could do a better job of running things than humans.

Taking a look around, I’d be hard pressed to disagree. Humans are good at creating new problems like AI without dealing with the old problems running rampant.

What is particularly weird about this is her status. She has a passport. She’s being treated as a human, and is also being treated to at least a degree as a woman.

It’s a bit concerning when people on the planet don’t even have those rights.

Why I don’t link to Amazon.com.

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Lately, writers have been paying attention to a few things related to artificial intelligence because the large language models are competent at rolling dice to put words together really quickly.

One of the annoyances I wrote about was that AI was being used to create summaries of books and published on Amazon.com not long after the books are actually published. The only plausible solution to it would seem to be just writing the authorized version of a summary and undercutting the people wielding AI to create summaries of your books.

There have been no articles on it other than on Wired.com, which is a little weird. Granted, the whole copyright and AI issue has been punted to the courts. At around the same time, Stephen Wolfson wrote a nicely done post on why Fair Use is good for training generative AI. The book summaries do get protected by Fair Use, which leads to another post later this week.

Yet when I was poking around, I ended up reading “Goodreads was the future of book reviews. Then Amazon bought it.” It was published on the Washington Post roughly 6 months ago, but it goes on to talk about how book reviews are abused on GoodReads. That lead me to write this post.

KnowProSE used to do Book Reviews.

I used this site in one of it’s iterations to do book reviews, and it was going ok. I hadn’t hit critical mass by any stretch, but there was room for it given other things I had done with the site. Publishers would send me books, and I would write reviews.

I’d get a free book, generally worth reading, and I could link to Amazon.com with affiliate links so I could pick up residual income (generally used to buy books for myself!). This became problematic when I changed geography because those at Amazon.com could not figure out how an account could change geography. In fact, it became such an issue I just created another account, as they suggested, but the problem then becomes dealing with all the old links.

You’d think they could handle someone moving between countries with their affiliate stuff, but it’s too hard it would seem. Given my background, that’s a pretty disappointing response from a tech company, but from a business it just falls into the, “You’re not statistically significant”, which is something I’m used to.

While all of that was happening, Amazon.com was pushing hard on their own reviews on the site, and you could not link off of Amazon.com to your own review on their site. Amazon, though, was built on affiliates, so this was a shift for them. The Amazon we all knew before it became popular was an Amazon that depended on people’s affiliate links. They got popular enough, and affiliates really didn’t matter to them anymore.

The whole Goodreads issue could have been handled by not centralizing book reviews. A bunch of independent people on their websites doing reviews was better for everyone. Sure, social networks work too, but we’ve seen how terrible social network algorithms are for getting eyeballs.

It’s a bit disappointing. I watched Amazon.com’s rise, and was happy with them when they cared beyond their profit margins. I had a beginning symbiosis with them of sorts, and it was beginning to work out. Between “it’s too hard” and “all book reviews are belong to us“, Amazon just became another business.

That it’s getting flooded with bad reviews from one of their companies and are putting out spammy book summaries isn’t the Amazon.com we started with. It’s the Amazon.com we ended up with.

That’s why when I mention books I link elsewhere. It’s not because of the money. It’s that I don’t do business with jerks unless I have to, and overall, Amazon.com has become a jerk. You’ll find some old links, I’m sure, but… too many headaches to be worried about.

Artificial Intelligence is not making Amazon.com better. It’s making it even worse.

KnowProSE.com doesn’t make any money right now. It hasn’t for some time. It isn’t a business as much as a place to write about some of the stuff I write about, and hopefully creating some value.

Beyond the AMIE-Better-Than-Doctors posts.

~ Taran ~ 1 Comment

As a former Navy Corpsman, it’s hard not to be at least a bit excited about Google’s AMIE, which the Google Research Blog announced on Friday, January 12th. Posts on social media flared like a diagnosed case of hemorrhoids to my senior software engineer self.

I dug in and researched.

Reality is that it’s not as much of an advance as some posts and titles may have people believing. Doctors aren’t going to be replaced anytime soon, particularly since the paper’s conclusion was very realistic.

The utility of medical AI systems could be greatly improved if they are better able to interact conversationally, anchoring on large-scale medical knowledge while communicating with appropriate levels of empathy and trust. This research demonstrates the significant potential capabilities of LLM based AI systems for settings involving clinical history-taking and diagnostic dialogue. The performance of AMIE in simulated consultations represents a milestone for the field, as it was assessed along an evaluation framework that considered multiple clinically-relevant axes for conversational diagnostic medical AI. However, the results should be interpreted with appropriate caution. Translating from this limited scope of experimental simulated history-taking and diagnostic dialogue, towards real-world tools for people and those who provide care for them, requires significant additional research and development to ensure the safety, reliability, fairness, efficacy, and privacy of the technology. If successful, we believe AI systems such as AMIE can be at the core of next generation learning health systems that help scale world class healthcare to everyone.

“Towards Conversational Diagnostic AI“(PDF), Conclusion, Many authors (see paper), Google Research and Google Deep Mind, 11 Jan 2024.

In essence, this is a start, and pretty promising given it’s only through a text chat application. Clinicians – real doctors – that took part in the study were in a disadvantage, because they normally have a conversation with the patient.

As I quipped on social media with a friend who is a doctor, if the patient is unresponsive, the best AMIE can do is repeat itself in all caps:

“HEY! ARE YOU UNCONSCIOUS? DID YOU JUST LEAVE? COME BACK! YOU CAN’T DIE UNLESS I DIAGNOSE YOU!”

In that way, the accuracy comparison of 91.3%, compared to 82.5% for physicians should be taken with Dead Sea levels of salt. Yes, the AI beat human doctors by 11.2% when we tied a doctor’s human experience behind their back.

Interestingly, sometimes doctors aren’t the ones who do the patient histories, too. Sometimes it’s nurses, in the Navy it was often Corpsmen. Often when a doctor walked in the room to see a patient they already had SOAP notes to work from, verify, and add on to.

The take from Psychology Today, though, is interesting, pointing out that AI and LLMs are charting a new course in goal-oriented patient dialogues. However, even that article seemed to gloss over the fact that this was all done in text chat when they pointed out in terms of conversation quality, AMIE scored 4.7 out of 5, while physicians averaged 3.9.

There is a very human element to medicine which involves evaluating a patient by looking and listening to them. In my experience as a Navy Corpsman taking medical histories for the doctors, patients can be tricky and unfocused, particularly when in pain. Evaluation often leans more on what one observes more than what the patient says, particularly in an emergency setting. I’ve seen good doctors work magic with patient histories, ordering tests based not on what the patient told them but what they observed, ruling things out diagnostically.

Factor in that in what I consider a commodification of medicine in my lifetime, doctors can be time constrained to see more patients in unit time and that certainly doesn’t help things – and that’s a human induced human error when it crops up. Given the way the study was done, I don’t think it was as much a factor here but it’s worth considering.

When we go to the doctor as patients, when sitting with the doctor in the uncomfortable uniform of the patient on an examination table that is designed to draw all the heat from your body through your buttocks, we tend to think we’re the only person the doctor is dealing with. That’s rarely the case.

I do think we’re missing the boat on this one, though, because one of the best ways to pull artificial intelligence into checking patient charts, which would be a great exercise of what a large language model (LLM) artificial intelligence is good at: evaluating text and information and coming up with a diagnosis. Imagine an artificial intelligence evaluating charts and lab tests when they come back, then alerting doctors when necessary while the patient is being treated. Of course, the doctor gets the final say, but the AI’s ‘thoughts’ are entered into the chart as well.

I’m not sure engaging a patient for patient history was a good first step for a large language model in medicine, but of course that’s not all that Google’s research and Deep Mind teams are working on, so it may be part of an overall strategy. Or it might just be the thing that got funding because it was sexy.

Regardless, this is probably one of the more exciting uses of artificial intelligence because it’s not focused on making money. It’s focused on treating humans better. What’s not to like?

AI, Confirmation Bias and Our Own Insanity.

~ Taran ~ 1 Comment

In unsurprising news, if you feed artificial intelligences the output of artificial intelligences they become a bit insane. I’d covered that before in Synthetic Recursion, which seemed pretty intuitive even before I wrote that, but scientists at Rice and Stanford University wrote a paper: “Self Consuming Generative Models Go MAD“.

So, we can say that’s been verified.

What’s even worse is apparently, Taylor Swift, Selena Gomez and Kim Kardashian have been saying things that they did not say – organized disinformation that has appeared all over, and if in vacuuming copyrighted content OpenAI’s ChatGPT might get infected. It’s not just artificial intelligences, output from people willfully misleading others can easily make it in.

Fortunately, I verified with ChatGPT4 and they got it right by… using Bing. I don’t use Bing. Why does ChatGPT4? Because of the same reason you can’t have a Coke with your Kentucky Fried Chicken.

While this time it has been caught – it started in November 2023 – it demonstrates how inaccuracies can crop up, how biases can be pushed, and how many problems we still have with misinformation without involving artificial intelligence. Every time we get anything on social media these days we have to fact check, and then we immediately get blowback about fact checking being flawed.

Why? It fits their confirmation biases. Given the way large language models are trained, we can say that we’re getting a lot right and yet we’re collectively also under a delusion that humanity has collected is right. What is true is that what we believe we know just hasn’t been proven wrong yet, with different thresholds for that varying from person to person.

With science, there’s a verification process, but science has been under fire increasingly because of who pays for the papers to be written and published. Academia has to be funded, and we don’t fund that as much as we should so others do sometimes, to their own ends. That’s why it’s important to read the papers, but not everyone has the time to do that. There is good science happening, and I’d like to think more of it is good than bad.

With AI tools, I imagine more papers will be written more quickly, which creates a larger problem. Maybe even an exponentially larger problem.

We accept a lot, and since we don’t know what’s in learning models, we don’t know what has been verified until we find things that aren’t. This means we need to be skeptical, just like when we use Wikipedia. There are some people who don’t like doing that footwork because what they see fits their confirmation biases.

Should we be surprised that our tools would have them too based on what we feed them?

It’s almost as if we need to make sure we’re feeding these learning models with things of value. That should come at a cost, because when we write, when we express ourselves in any way, it’s based largely on experience, sometimes hard won.

Meanwhile, artificial intelligence tools are being created to write summaries of books authors took years to write. Amazon is being flooded with them, apparently, and if I see another advertisement about microlearning on Facebook that seems to use these sort of precis notes, I might throw up through my monitor on someone else’s keyboards.

22 Years Old: KnowProSE.com

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This morning I woke up and thought, “Man, how old is KnowProSE.com?”. I’ve been blogging since the late 90s, but when I started someone had hosted me for free just for me to write. No kidding.

It went so well that I decided to get my own domain name and KnowProSE.com began. The Internet Wayback Machine’s first look at KnowProSE.com is November 24th, 2002. From what I can tell, I was still writing at CramSession.com about programming. That was mainly C/C++, but I wrote a lot of content for new programmers thehre in the 1990s.

Also interesting is that I was displaying the International Webmaster’s Association graphic, and was running the Greymatter blogging software.

I know I’d had the domain for a while, but… looking back through the Internet archive, I can say it’s been a long while.

The things I have seen. 🙂

Copyright, Innovation, and the Jailbreak of the Mouse.

~ Taran ~ 1 Comment
Not produced by Disney, generated by deepai.

On one hand, we have the jailbreak of Steamboat Willie into the public domain despite the best efforts of Disney. I’m not worried about it either way; I generated the image using Deepai. If Disney is upset about it, I have no problem taking it down.

There’s a great write-up on the 1928 version of Mickey over at the Duke Center for the Study of the Public Domain, and you can see what you can do with the character and not through some of the links there.

So we have that aspect, where the Mickey Mouse Protection Act in 1998 allowed for the copyright protection further. As Lessig pointed out in Free Culture, much of the Disney franchise was built on the public domain where they copyrighted their own versions of works already in the public domain.

Personally, it doesn’t matter too much to me. I’ve never been a fan of Mickey Mouse, I’m not a big fan of Disney, and I have read much of the original works that Disney built off of and I like them better. You can find most of them at Gutenberg.org.

In other news, OpenAI has admitted that it can’t train it’s AI’s without copyrighted works.

Arguably, if there was more content in the public domain, OpenAI could train it’s AIs on stuff that is in the public domain. Then there’s the creative commons licensed content that could also be used but… well, that’s inconvenient.

So on one hand, we have a corporation making sure people don’t overstep with using Mickey of the Public Domain, which has happened, and on the other hand we have a corporation complaining that copyright is too restrictive.

On one hand, we have a corporation defending what it has under copyright (which people think went into the public domain but didn’t, just that version of Mickey), and on the other hand we have a corporation defending it’s wanton misuse of copyrighted materials.

Clearly something is not right with how we view copyright or innovation. Navigating that with lawyers seems like a disservice to everyone, but here we are.