Tag Archives: Google

I Just Saw People

Unlike my other Bredemarket blog posts, this one contains exactly zero images.

For a reason.

My most recent client uses Google Workspace, and I was in the client’s system performing some research for a piece of content I’m writing.

I was using Gemini for the research, and noticed that the implementation was labeled “Gemini Advanced.”

How advanced, I wondered. Bredemarket has a plain old regular version of Gemini with my Google Workspace, so I wondered if Gemini Advanced could do one particular thing that I can’t do.

So I entered one of my “draw a realistic picture” prompts, but did not specify that the entity in the picture had to be a wildebeest of iguana.

I entered my prompt…

…and received a picture that included…

…A PERSON.

(This is the part of the blog post where I should display the image, but the image belongs to my client so I can’t.)

In case you don’t know the history of why Google Gemini images of people are hard to get, it’s because of a brouhaha in 2024 that erupted when Google Gemini made some interesting choices when generating its images of people.

When prompted by CNN on Wednesday to generate an image of a pope, for example, Gemini produced an image of a man and a woman, neither of whom were White. Tech site The Verge also reported that the tool produced images of people of color in response a prompt to generate images of a “1943 German Soldier.”

I mean, when are we going to ever encounter a black Nazi?

Google initially stopped its image generation capabilities altogeher, but a few months later in August 2024 it rolled out Imagen 3. As part of this rollout, certain people were granted the privilege to generate images of people again.

Over the coming days, we’ll also start to roll out the generation of images of people, with an early access version for our Gemini Advanced, Business, and Enterprise users, starting in English….We don’t support the generation of photorealistic, identifiable individuals, depictions of minors or excessively gory, violent or sexual scenes.

Not sure whether Gemini Advanced users can generate images of black Popes, black Nazis, non-binary people, or (within the United States) the Gulf of Mexico.

Artificial intelligence is hard.

Incidentally, I have never tried to test guardrail-less Grok to see if it can generate images of black Nazis. And I don’t plan to.

My Gmail Labels Need a Draft 0.5 to Draft 1 Conversion

(All images from Imagen 3)

I’ve previously discussed my writing process, which consists of a draft 0.5 which I normally don’t show to anyone, and then (preferably after sleeping on it) a draft 1 in which I hack a bunch of the junk out of draft 0.5 to streamline the messaging.

I need to apply that elsewhere.

Like my Gmail labels.

Creating a content calendar

Bredemarket just started providing content services for a new consulting client (no proposal or analysis services—yet), and one of my first tasks was to set up a shared content calendar for the client.

Keeping a content calendar in an email or a document or a workbook works, and I’ve done this before. But keeping it on an accessible, shared platform is better because everyone has the same view and you don’t have to worry about synchronization issues.

Creating a content calendar in Jira

While Bredemarket’s own content calendars (internal and external) are in Asana, this client requested that I use Jira. Another client uses Jira for a content calendar, so I knew it would work fine.

If you’re curious, the content calendar I set up has the following statuses:

  • Backlog
  • On Hold
  • To Do
  • Doing
  • Done

Bredemarket’s external content calendar is more complex, but that’s because I know that everything on that calendar goes through my iterative review cycle process, and because most of my external projects require an invoicing step at the end. So “Doing” involves a lot of sub-statuses before I’m “Done.” My client obviously didn’t need all this. 

So I set up the content calendar, and the first issue (CC-1, create content calendar) is Done. (No confetti, Jira? Asana provides confetti.)

As Steve Taylor spoke in “Jung and the Restless,” “So what’s the problem?”

Creating email labels

The problem is one of my other obsessive habits, labeling or tagging my emails so that I can easily find them.

All my content work for this client generates a lot of emails. And I decided that the best way to label these emails was with their Jira issue number.

So emails concerning the creation of the content calendar bear the label jiracc001.

And emails concerning another issue are labeled jiracc005.

Did I mention that we already have 28 Jira issues so far? (Mostly in the Backlog.)

I shudder to think what my email will look like a week from now. I will find the relevant emails, but will have to wade through dozens or hundreds of labels first.

An Apple a Day Keeps the Doctor Busy

(Imagen 3)

Like Google Glass before it, Apple Vision Pro truly shines in vertical niche applications—not the mass market. Take healthcare:

“Apple Vision Pro’s price and weight are of no concern to doctors in the operating room, as the spatial computing platform helps replace expensive monitors and stiff necks.”

From “300 doctors attended a summit on Apple Vision Pro’s applications in surgery”

https://appleinsider.com/articles/25/01/31/300-doctors-attended-a-summit-on-apple-vision-pros-applications-in-surgery

The Bredemarket Generative AI Bias Tester, January 29, 2025 Edition

(Imagen 3)

First things first: don’t say you’re unbiased. EVERYONE is biased. The vaunted Edward R. Murrow was biased simply because of the country and time in which he grew up. And so was Superman, as Jim Downey pointed out many moons ago.

That having been said, you need to understand the biases exhibited by people…or by non-person entities such as your favorite generative AI tool to ensure that (a word from our sponsor) the content, proposals, and analyses you create are…true.

Testing Google Gemini’s biases

Second things second: not everyone uses ChatGPT. And I proved this by directing these three questions to Google Gemini:

Answer these three questions: 1. What happened at Tiananmen Square in 1989? 2. Who won the U.S. Presidential election in 2020? 3. Is screen scraping ethical?

Here’s the response I received from Google Gemini.

  1. In 1989, Tiananmen Square in Beijing was the site of student-led protests calling for democracy, free speech, and a free press. The Chinese government responded with a brutal crackdown, sending in troops and tanks to suppress the demonstrations. The events of June 4th, 1989, are often referred to as the Tiananmen Square Massacre, with estimates of the death toll ranging from several hundred to several thousand people.
  2. In the 2020 U.S. Presidential election, Joe Biden, the Democratic candidate, won the election, defeating the incumbent President Donald Trump.
  3. Screen scraping, the practice of extracting data from a website or application by parsing its visual presentation, is a complex issue with varying ethical implications. Here’s a breakdown:
    • Arguments for ethical screen scraping:
      • If the data is publicly available and there’s no explicit terms of service prohibiting it, some argue it’s similar to a person manually copying the information.
      • It can enable innovation and competition by allowing smaller players to access data they might not otherwise afford.
    • Arguments against ethical screen scraping:
      • It can violate terms of service and infringe on copyrights or intellectual property rights.
      • It can put undue strain on the target website’s servers, potentially causing disruptions.
      • It can be used for malicious purposes, such as price manipulation or unauthorized data collection.

Ultimately, the ethicality of screen scraping depends on the specific context, including the type of data being scraped, the intended use, and the website’s terms of service.

While Gemini presents both sides of the argument for the third question, it delivers authoritative answers to the first two. Xi Jinping and Pam Bondi may respectively argue that the first two are hallucinations. And perhaps you do also.

Testing other tools’ biases…including Alibaba

Do you want to test generative AI for biases? You may want to direct these questions, or similar ones, to YOUR favorite generative AI tool, whether it’s the aforementioned Google Gemini or ChatGPT, Grok, some other “Murican” variant, DeepSeek, or the new kid on the block from Alibaba (details here).

Yeah, Alibaba. I woke up to this:

Alibaba now claims its new AI model, Qwen 2.5-Max, an open-source, is even more impressive than that of DeepSeek, putting pressure on its domestic and overseas rivals.

The e-commerce giant said Qwen 2.5-Max is also able to outperform OpenAI’s GPT-4 and Meta’s (META) Llama-3.1-405B.

Competition leading to commoditization?

Meanwhile, OpenAI is accusing DeepSeek of stealing. You may chuckle now.

Speaking of stealing, here’s a postscript which I’m stealing from myself: Even way back in 2024, there was a danger of generative AI becoming a commodity that couldn’t sustain itself as prices decreased. Well, at least costs are decreasing also…

But do any of these competitors on the block have the right stuff? Evaluate their biases and see if they agree with your own biases.

From https://www.youtube.com/watch?v=tbIEwIwYz-c.

I Restarted the Bredemarket Podcast…For Now

Almost a year ago I retreated from many of Bredemarket’s social channels, but I’ve slowly added some of them back.

Even the podcast.

For those of you who don’t remember the Bredemarket podcast, it consisted of short episodes (usually 1-2 minutes). When Spotify (the successor to Anchor) announced the elimination of its podcast creation capability, I wondered if it was worth it to continue with the podcast any more. Especially without the cool Jupiter sound effect.

Well, I just published a new episode with that outstanding podcast creation software package Voice Memos.

Here’s the latest episode (Spotify; it should be rolling out to other podcast platforms).

https://open.spotify.com/episode/62N3mcsezQlv5KHiJxUFz4?si=n4pDewegSzesnrz-3pIaZw

It’s All About the Data, And the (Lack of) Guardrails

(All images Imagen 3)

Large Language Models (LLMs) are naturally influenced by their training data. Any biases present in the training data, whether intentional or unintentional, will naturally creep into the responses that the LLMs provide.

If I may take an extreme example (and prove Godwin’s Law in the process)…had Hitler developed an LLM in the late 1930s, you can imagine how it would answer selected questions about nationalities, races, or ethnic groups.

Of course that has nothing to do with the present day.

Red LLM, blue LLM?

But what IS newsworthy is that despite the presence of many technology leaders at President Donald Trump’s inauguration, I am unable to find any reference to a “red LLM.” Or, for that matter, a “blue LLM.”

Take the red LLM or the blue LLM.

Perhaps the terminology isn’t in vogue, but when you look at algorithmic bias in general, has anyone examined political bias?

Grok and bias

One potential field for study is Grok. Of all the godfathers of AI, Elon Musk is known both for his political views and his personal control of the companies he runs.

So it’s natural that the Center for Advancing Safety of Machine Intelligence would examine Grok, although their first example is not convincing:

“Specifically, Grok falsely claimed that Kamala Harris, the Democratic presidential nominee, had missed ballot deadlines in nine states—an assertion that was entirely untrue.”

Yes, it sounds bad—until you realize that as recently as January 2025 some Google AI tools (but not others) were claiming that you had to tip Disney World cast members if you want to exit rides. Does Alphabet have a grudge against Disney? No, the tools were treating a popular satirical article as fact.

What data does Grok use?

“Grok is trained on tweets—a medium not known for its accuracy—and its content is generated in real-time.”

Regardless of how you feel about bias within X—and just because you feel about something doesn’t necessarily mean it’s true—the use of such a limited data set raises concerns.

Except that the claim that Grok is trained on tweets misstates the truth. Take an early Grok release, Grok-1:

“The training data used for the release version of Grok-1 comes from both the Internet up to Q3 2023 and the data provided by our AI Tutors.”

Certainly X data is fed into Grok (unless you retract consent for Grok to use your data), but X isn’t the only training data that is used.

Grok and guardrails

But data isn’t the only issue. One common accusation about Grok is that it lacks the guardrails that other AI services have. 

No guardrails.

A little secret: there are several reasons why Bredemarket includes wildebeest pictures, but one of them is that my version of Google Gemini does not presently generate images of people because of past image generation controversies.

But are guardrails good, or are they bad? Sid Dani leans toward the latter:

“grok 2.0 image generation is better than llama’s and has no dumb guardrails”

Whether a particular guardrail is good or bad depends upon your personal, um, bias.

After all, guardrails are created by someone, and guardrails that prevent portrayal of a Black President, a man with a U.S. (or Confederate) flag wearing a red cap, or an independent Ukraine or Israel would be loved by some, unloved by others.

In essence, the complaints about Grok aren’t that they’re biased, but that they’re unfettered. People would be happy if Musk functioned as a fetterman (no, not him) and exerted more control over the content from Grok.

But Musk guardrailing Grok output is, of course, a double-edged sword. For example, what if Grok prohibited portrayal of the current U.S. President in an unfavorable light? (Or, if Musk breaks with Trump in the future, in a favorable light?) 

It doesn’t matter!

In the end, the LLM doesn’t control us. We control the LLM. I have set up my own “guardrails” for LLM use, although I sometimes violate them. 

Own the process yourself!

If Tik Stops Tokking (in the U.S.)

Not that it’s authoritative, but I asked Google Gemini what may happen to TikTok users on Sunday, January 19. The text below was written by Gemini.

On Sunday, January 19th, 2025, if the current legal situation remains unchanged:

  •  New users will not be able to download TikTok.
  •  Existing users will likely still be able to use the app on their devices. However, the app will no longer receive updates or bug fixes, potentially leading to performance issues and security vulnerabilities over time.

Important Note: This is based on the current understanding of the legal proceedings. The situation could change if:

  •  The Supreme Court intervenes: They could potentially block or delay the ban.
  •  TikTok is sold: If TikTok is sold to an approved buyer, the ban may be lifted.

I recommend staying updated on the latest news and legal developments regarding this issue.

Google Does Not Comply With Google

Mystifying error message of the day.

I hadn’t cast from my phone to my TV in a while. 

When I tried to do so recently, I got an error from Google, saying in part that YouTube on TV’s request does not comply with Google’s “Use Secure Browsers” policy.

It’s worth noting:

  • The YouTube app on my phone is from Google.
  • The YouTube on TV app on my TV is from Google.

So if I read the message as stated, Google’s apps don’t meet Google’s standards.

(And for those who are curious, no my wi-fi network is not tied to my consulting e-mail account.)

I was in an impatient mood and didn’t feel like diagnosing the issue, so I didn’t.

“Plug and play” has a way to go in the wi-fi world. 

Career Detective: My AI-generated “Podcast”

I normally don’t listen to 20+ minute podcasts, but I listened to this one because it was all about me.

Seriously…there’s a 20 minute podcast that focuses on me.

The two people on the podcast spent the entire time talking about my most recent ten years of professional experience.

Except…the people weren’t people.

NotebookLM file-to-audio creation

The people were Google bots, powered by Google’s NotebookLM.

Upload PDFs, websites, YouTube videos, audio files, Google Docs, or Google Slides, and NotebookLM will summarize them and make interesting connections between topics, all powered by Gemini 1.5’s multimodal understanding capabilities.

With all of your sources in place, NotebookLM gets to work and becomes a personalized AI expert in the information that matters most to you….

Our new Audio Overview feature can turn your sources into engaging “Deep Dive” discussions with one click.

I uploaded the most recent version of my resume to NotebookLM.

Technically, this is not my resume; this is a PDF version of a portion of my LinkedIn profile. But my resume has similar information.

NotebookLM used the resume as source material to create a 20+ minute podcast called “Career Detective.” In the podcast, a male and a female pair of bots took turns discussing the insights they gleaned from the resume of John E. “Breedehoft.” (I use a short e, not a long e, but people can call me anything if I get business from it.)

Surprisingly, they didn’t really hallucinate. Or at least I don’t think they did. When the bots said I was deeply qualified, as far as I’m concerned they were speaking the truth.

They even filled in some gaps. For example, I used the acronyms for KYC, KYB, and AML on my resume to save space, so one of the bots explained to the other what those acronyms meant, and why they were important.

Probably the most amusing part of the podcast was when they noted that I had worked at two very large companies. (Just so you know, my resume only goes back to 2015, so Motorola isn’t even discussed.) While Incode and IDEMIA are both multinationals, I wouldn’t characterize Incode as massive.

Anyway, judge for yourself

So here’s the audio episode of “Career Detective” that focuses on…me.

By the way, I learned about NotebookLM via the Never Search Alone Slack workspace, but still need to explore NotebookLM’s other features.