Category Archives: Technology

Bing Chat has an attitude!

Bing chat is not above ending the chat when it paints itself into a corner.

If you don’t know the scene, it’s a classic: https://youtu.be/5oWNQSCPWy4

In an effort to get access to the new ChatGPT-4 features, I’ve started using the Bing iOS app. And, while it’s cool in some ways, I have found that wrapping GPT4 in Bing Chat often feels like they’ve wrapped Leonardo da Vinci in Chandler Bing.

My adventures started yesterday evening, in a way that has nothing to do with the GPT-4 features, but seem oddly relevant to what happened today, so I’ll describe it briefly:

I am an American ex pat living in Vienna. So my Bing Chat has my location as Vienna, my region as Austria, and my language as English. But all its notifications about breaking and local news, weather, whatever, are in German. It’s irritating because my German is not great, so I sent feedback on this through the app.

Adventures in font identification

Flash forward to this morning. Having another tech issue that has nothing to do with GPT-4, I had decided to write a post about the problems I’m experiencing with iOS 17. As part of this, I wanted to make a parody image of translation app, changing the text of a button. I decided to upload a picture of the button and ask GPT-4 if it could identify the font. OpenAI hasn’t rolled multi-modal input out to my ChatGPT Plus account yet, so Bing Chat was my only option. I gave it the image, and asked it “What font is this?”

It searches for “German button font,” which is not what I wanted, but it’s an understandable action from the AI. And, though the response does seem to include some kind of interpretation of the style of font, it is also clear that the search it performed also informed its answer, because it mentioned fonts used in central Europe, and informed me as to where I could download Old German fonts. So, as I am accustomed to having fruitful interactions with ChatGPT when I explain how its response differs from what I’m looking for, I explained: “Don’t search for German fonts, the word in the image is irrelevant, I just want to know what font is used by the way it looks.”

Denial isn’t just a river in Egypt

Bing Chat straight up denied that it searched for German fonts, and said it only used the style of the text to identify it. I respond, “Yes, you did. You searched for ‘German button font,’ and though your response might have included information based on the style of the text, it also mentioned downloading Old German fonts. I just want you to identify the font by how it looks, unbiased from the search for the word in the image.”

In response, Chandler Bing Chat pretended the connection was poor and ended the chat. I’m serious, I’m not joking: it said I had a network connection problem, and that I would have to restart the chat.

Second attempt

Now, at this point, I actually thought the network connection issue was real. But, in a fit of anthropomorphic pique, I started the new chat like this:

This time it didn’t say anything about a network connection, but it did end the chat immediately. Also, what the heck is with responding in German!? It had never done that before! I’d used it a number of times over the last several days, and Bing Chat never wrote to me in German – the German notifications I was complaining are part of the Bing iOS apps other features – not the chat. But now you see why I mentioned them in the beginning.

Hm, ok, well, fine, it’s offended now. I took the screenshots you see above (I wish I’d done so for the previous chat, but I’ll show you a recreation at the end of this article), and created a new chat.

Third time’s the…nope

This time I wanted to get back to my original question, and wanted to prevent the problem from before, so I uploaded the image, and asked it to identify the font in the image, and explicitly told it that the text in the image is irrelevant.

It said it was analyzing the image, and also searched for “font identification tool.” This time its response had nothing to do with the image – it just gave me a list of font identification cools, and then set the title of the chat to “Identificación de fuentes.” Where the heck did the Spanish come from?!

I’ve flustered it so much it is speaking in tongues!

Bing gets an attitude

Ok, at this point I decide I have to document this nuttiness, and I start this article. Because I missed out on the screenshot of the first chat, I try to recreate it as close as I can – the results are hilarious:

My analysis is that Microsoft, stung by bad press from when Bing chat professed love for a tech journalist and asked him to end his marriage, has put such strong guardrails in place that it causes the current version to be overcautious.

When AIs compare notes

It also doesn’t seem to have incorporated behind-the-scenes instructions to the ChatBot to tell it how it is working. Not sure what I’m talking about? This is something I also discovered recently. In ChatGPT Plus web interface, I have access to DALL-E 3, and it looks like this:

Now, my iOS ChatGPT app doesn’t have access to DALL-E 3 yet, but it is still possible to open the same chat via the history, and it looks like this:

Notice that’s a response from DALL-E 3 to ChatGPT. It’s not intended to be read by me, which is why it wasn’t visible to me when I first did it. But it is clearly explaining something to ChatGPT so that it doesn’t act oddly to me. And, just as clearly, Bing Chat doesn’t have something like that, and so it is left to its own devices when the app does something without the chatbot knowing.

At this point I’m worried I’m going to get my account suspended for daring to argue with the AI, but, well, this is the world now – when we snarl at our computers because they’re not behaving the way we want them to, they can argue back and then become passive-aggressive.

Why Clearing Your Phone’s Cache Might Not Be the Speed Boost You’re Looking For


It’s 2023, and if I see one more “Clear your cache for a faster browsing experience!” article, I might just throw my router out of the window. Seriously, the persistence of this piece of advice is baffling to me. And if we’re being honest, this advice isn’t just antiquated—it’s misleading.

Caches: A Brief Refresher

Browser caches store website data so that when you revisit a site, it can load faster by pulling locally stored data rather than redownloading everything from the server. This mechanism isn’t a new concept; it’s foundational to how web browsers have functioned for decades.

The Myth of the Slow Cache

Enter the narrative that a “mature” cache (one that’s been accumulating data for a while) will slow down your browsing. Picture caches as attics full of junk, needing occasional spring cleaning.

But let’s get technical for a moment:

  1. Cache Lookup Time: While this is generally faster than fetching from the network, a disorganized cache could, theoretically, introduce delays.
  2. Stale Cache Data: Old and outdated data might break website functionality if not properly validated. But, a modern browser’s validation system ensures that this is rarely a problem.
  3. Cache Pollution: Rarely visited website data taking up cache space? Maybe, but today’s browsers are designed to manage and prioritize cache effectively.
  4. Mismatched Cached Resources: While development changes can lead to outdated cache versions, browsers today are optimized to handle such mismatches with grace.

The perennially posted cache and cookie-clearing articles, if they bother to actually provide any technical basis, might mention these as potential pitfalls as justification, but they are also precisely what browser developers have spent years refining. Modern browsers are optimized to handle these scenarios efficiently, ensuring users receive the fastest and most reliable browsing experience.

Seriously, I’m picturing a browser development team lead scheduling an urgent meeting, “Everybody! I just read an article on CNET, and we have a big problem with caching we have to fix! Thank goodness the article keeps getting posted, or I might have missed it!”

Should We Automate Cache Cleaning?

If clearing the cache was this panacea, why wouldn’t operating systems just automate the process? An automated monthly clear-out, maybe? But they don’t, do they? Maybe because doing so doesn’t lead to the performance boosts that some claim?

If It Ain’t Broke…

Advising users to routinely clear their cache is like suggesting they defragment their SSDs—a relic from a bygone tech era.

We are in an age in which mobile developers are deploying sophisticated optimizations like intelligent charging to prolong battery-life, pre-loading web pages the user is likely to click on, and similar. Are we still to imagine developers would pour resources into such innovations yet neglect fundamental features? To argue that there’s some endemic issue with cache implementation is borderline absurd.

Final Thoughts

Before jumping on the “clear your cache” bandwagon, let’s discern fact from fiction. A cache is there to speed up browsing. Emptying it, especially for an average user, is counterproductive. It’s 2023, and we ought to know better than to repost dated, debunked tips as fresh tech advice.

So, the next time you see that all-too-familiar recommendation, or someone advises you to clear your cache for that “extra speed”, point them to this article. Or maybe I’m wrong, and no one is posting those articles anymore, it’s just that I have an outdated cache and I get decades-old content when I visit CNET. 😅

What would you say ya do here?

I volunteered for the career day for the 11th and 12th graders at my son’s school and they asked me to explain what I do:

More specifically, they asked me to “please provide a brief job description and list the most important aspects of your current job. This will help our students understand what you do on a daily basis.”

Wanting to be completely honest with these kids who are about to try to pick a school, pick a major, figure out a career, I sent them this:

 

I lead a plucky team of data scientists, engineers, and analysts in finding undeclared nuclear R&D around the world. We built/bought/integrated the software, begged/borrowed/took the data, fused it together into something that can swing a billion data records at our most difficult questions, and trained people in how to wield the tools we’d built.

Disciplines involved:
– large-scale data analytics
– information modeling
– programming
– machine learning
– information visualization
– persuasion
– patience
– impatience
– not knowing when to quit

What would you say?

Signal to noise

When people talk about information analysis, there’s often a lot of worry about noise in the data, and the reliability of the data sources. So when you’re building an information analysis system, there are often requirements that have to do with “filtering out bad data” or assigning “reliability scores” to data sources.

But in practice this isn’t usually necessary. With enough data, noise suffers from destructive interference, and signal interferes constructively. I first learned about this in physics class in school, and first encountered it while doing radio astronomy. I was a research assistant on a team that realized we had an opportunity to make the highest quality radio maps ever made of certain galaxies. See, when big stars go supernova, astronomers like to aim the Very Large Array of radio telescopes in New Mexico at them to study how the massive explosions grow and change. They get a lot of data, write their papers, and move on. But supernovas happen in galaxies. And scientists share data. So our PIs realized they had very long exposures of the galaxies containing the supernovae. Radio astronomy “pictures” tend to be noisier than pictures taken by optical telescopes, and noise in a picture of a distant galaxy can overwhelm the detail of star formation regions and the like. However, noise is random, whereas the bright spots (except for the supernova) don’t change noticeably. So if you take the sum of enough radio data, the random noise cancels out, and the actual bright regions become clear. Note, this is not the same thing as astronomical interferometry, although that was a technique we made use of.

The same thing is true of many other types of noisy data. If you only ever look at the data at a point in time, or watch or listen to it as it changes, it’s difficult to see the signal through the noise, but if you have a system that allows this summation to happen, and you can look at the sum, suddenly the picture becomes clear.

Suppose the “ground truth” looks like this:

A draftsman I'm not.

But we have noisy data that looks like this:

I hope that's not an EKG.

If we layer lots of noisy data, we can start to see that the signal’s there…

I drew this better on the whiteboard last week.

But if we can sum the data it looks something like this:

"The hands acquire shakes, the shakes become a warning. It is by caffeine alone I set my mind in motion." --

Now we can clearly see the signal! Is it truth? Not necessarily, but the analyst can now see that there is agreement across the data. If you want more information, you also need a system that gives you a way to dive into the details of what data contributed to the peaks. And now you also have guidance as to where to collect more data, ideally from additional sources. More on that in another post.

This is related to why Google is so good and your organization’s internal Search is so bad. Even though Google’s data source, the Internet, is way noisier than your organization’s intranet (I hope), Google is still better. This is true even if you have an in-house Google appliance. It’s because of Google’s second big data source (equally noisy): the billions of user clicks. Google doesn’t show you the sum of that data, but it does use that aggregation to decide what to show you. In essence, Google finds the peaks of agreement among billions of clicks and shows you just the peaks. Your IT department doesn’t have enough click-data to do that for your organization, no matter how good their software is.

Reliability Scores

You also probably don’t need to assign reliability scores to your data sources, even though it seems like a perfectly logical, even prudent, thing to do. The problem is that the scores will be fairly arbitrary, hard to agree on, and may present a false sense of rigor where there isn’t any. There’s lots of ways a data source can be unreliable, but we’ve found different ways to handle them that avoid these problems. For example:

Problem: There’s hardly any signal (useful information) in the data.
Solution: You’re not going to get any peaks even when you sum it all together. Don’t score it, ditch it.

Problem: There’s a lot of mistakes or inconsistencies in the data.
Solution: That’s the kind of noise that cancels out if you have enough data. If you do, then don’t sweat it.

Problem: The data has been deliberately redacted to remove what you’re looking for.
Solution: The more data there is, the harder it is to do this perfectly. If you get enough of it, you can find what was missed. Also, if you have enough of it, you’ll see mysterious quiet areas of data, because not only is the signal gone, but so is the noise. So you can detect the obfuscation, and you might even be able to catch the deceivers in a mistake.

Problem: The data is out-of-date.
Solution: This is absolutely relevant to the analysts, and it should be documented, but it’s not something you score. The analysts just need to know because data timeliness matters more for some questions than others.

Problem: There are gaps in the data coverage.
Solution: Again, it’s relevant, and should be documented, but it’s not a “reliability” issue. Maybe there weren’t enough 18-25 year-olds in the medical study you’re analyzing. Even so, if there’s a statistically significant result visible for 25-35 year-olds, you’ve still found something; you just don’t know if it works for young people.

Problem: The data’s useful, but its noise is obscuring the signal of other, cleaner data sets.
Solution: Let the users turn data sets on and off as they choose. See, this is actually what people have in mind with reliability scores – they imagine they’ll either weight more reliable data higher, or the user will use the score to decide what to look at. It’s true, you might end up doing some form of weighting. For example, a search engine might weight clicks higher for users who appear to come from a part of the world that speaks the language the results are in, so clicks from Italy have more effect on ranking Italian search results. But you don’t want to do this before you’ve had a chance to work with the data in the system. As for showing the reliability scores to the analysts…believe me, your analysts already have strong opinions on the reliability of individual data sources, and they will ignore your well-intentioned ratings. If you just give them the ability to turn them on and off, they’ll be happy and productive.

In short, signal reveals itself in noisy data if you have enough of it. And have tools that let you work with all of it in aggregate, while still letting you quickly get the details of the revealed signal.

Code.org

My story?

My dad bought a TRS-80 Color Computer when I was about 5. I didn’t learn to code, but I saw a modem, heard binary being played on our cassette drive, and learned what a kilobyte is.

Later, I learned Logo and BASIC when I was 8 and 9. Just very simple toy programs. I learned more sophisticated programming in Pascal in high school. I did have books, and Dad got me started, but my schools’ programming classes get at least half the credit.

I started getting paid to work with computers while still in high school. I have made money ever since from working with computers. Even the years I taught ballroom dancing full-time, I wrote software part-time and brought in new revenue at the studio by setting up the website and our first online sales of gift certificates.

Today I live in Vienna and manage a significant software project at the International Atomic Energy Agency. As a job, it’s amazing, and the work is important. I’m writing this from a lovely apartment in Venice where I’m vacationing with my family while the team works without me.

It’s a good life, and I’m incredibly grateful. I wouldn’t be here if I didn’t know how to code.

But it’s not just about the good work you can do and the good life you can have. It’s fun. The things we can do now with software are amazing. A programmer in the 80s would be awed by what’s possible to coders now. It’s not just faster computers, it’s the fact that so much of the world is now online. Take something simple like flight bookings: they were computerized in the 80s (probably earlier), but in closed systems. Today, there are so many ways to tie that information together that travel booking sites abound, and the best ones are so good that we can be near-omniscient about our options. We think little of booking, from our couch, vacations with airlines and hotels we’ve never heard of.

Coders regularly produce apps which do things that weren’t possible a few years ago. My phone (an anachronistic name for the hyper-connected supercomputer I carry in my pocket) can augment my reality in countless ways, but the latest is holding it up and looking through it so that all the Italian writing is replaced with English.

What’s next? Imagine writing code to do this:

  • social apps that allow you to point your finger and write in the sky…where all your friends can see it through their glasses or contact lenses.
  • designing toys and selling them online where buyers click to print them out on their 3D printers
  • building the apps to do the designing I just mentioned or building the site to broker the transactions
  • writing code to control swarms of tiny flying/crawling robots to…well, frankly the first of these will all have military or intelligence applications which may appeal to some, but, after that, there will be plenty of environmental and scientific uses.