SearchResearch (11/13/25): How good is AI at recognizing images? What should you know?

 Recognizing images is an impressive AI feat.  But... 

Remarkable desserts. What are they? 

.... it's true that the state of the art of image recognition has changed over the past several years.  It gets better, it gets worse, the functionality changes, some things are removed, others are added.  

But it's still an amazing thing... IF you know what works and what doesn't now.  I'm afraid that means you have to stay up on what's going on in the world of image search.  So let's dive into it... 

Here are a few images that I'd like for you to identify--the key question for each is what's going on in this image?  What is it?  (And if you can, where is it?)  

For each image I've given you a link to the FULL image (no sneaky reduction in resolution or removal of metadata, as our blogging tools tend to do).  I recommend you use that image for your search.  

1. The image above (the dessert display) is from a cafe.  Can you figure out what KIND of desserts these are?  Yes, I know you can read the labels, but these are from a particular region of the world.  What kind of cafe is it?  (Image link to full image.)

2. Here's a photo I took while on a walk in San Francisco the other day.  What a strange, strange place!  It's clearly supposed to have a statue on top of the pedestal.  What happened here?  Why is it bereft?  (Image link)  

3. Here's a great picture of a cloud that Regular Reader Ramon sent in for identification.  What's going on here?  (Image link)  

P/C SRS Regular Reader Ramon

4. This little bridge is in a lovely town somewhere in the world.  Can you figure out where it is, and when it was built?  (Image link)

The point of this week's Challenge is to give you a bit of familiarity with the different image reco tools.  They're sometimes called "Reverse image search" tools, but as you'll find out, they have very, very different capabilities.  

When you write in to let us know what you found, be sure to (a) tell us what tools you tried, (b) if they worked well, and (c) whether or not you find the answer believable. 

Next week I'll write up my findings and summarize what everyone else found... along with a description of the tradeoffs involved in the different tools.  

Keep searching! 

SearchResearch (11/5/25): Pro tips on using AI for deep research

A few friends... 

Gemini's conception of [hyperrealistic image of scholar doing deep research].
Not sure it's hyperrealistic, but definitely interesting. 

... have recently written posts of their own about using AI for deep research. Since they've got some great nuggets, I'm going to leverage their writings and give a quick summary of the top methods for doing high quality deep research with LLMs. 

In this post, I'm drawing extensively on a post written by Maryam Maleki (UX Researcher at Microsoft) for people doing product research:  How to Do High-Quality AI Deep Research for Product Development  Here, I've generalized it a bit and given it my own flavor.

Here are the top few tips about getting Deep Research mode to work well for you:  

Be clear about what you want.  

Keep in mind: You want credible content. Prompt it that way. 

In order for the AI to work, you need to tell it what kind of sources you think are reliable and credible.  If you can, give it a list of several resources as guidance. 

In these patterns below, items in { } and italics are variables.  You need to pop in the values you need to get the effect you want.  

Pattern:   

[ Do deep research on {TOPIC}. Generate {n} credible sources with links that can be used for this research.

Prioritize: {BOOKS / ACADEMIC PAPERS / CASE STUDIES}

For each source, provide: the Title, the URL, a short snippet about why it's relevant, tell me the Source type. ] 

Example: 

[ Do deep research on Rocky Mountain locusts. Generate 10 credible sources with links that can be used for this research.

Prioritize: academic papers

For each source, provide: the Title, the URL, a short snippet about why it's relevant ] 

Doing this in Gemini will create a 4,000 word essay about Rocky Mountain Locusts.  It will ALSO give you section VII, which has Ten Credible Sources for Rocky Mountain Locust Research.  It also creates a reference list for the entire document, with section VII containing the best of the entire list.  

By contrast, doing this in ChatGPT 5/Thinking or Claude Sonnet 4.5 gives you exactly what you asked for--they give you the list-of-ten.   

Review the AI-generated results for quality 

I note in passing that the Gemini-created document is pretty good, but the list of 10 papers was a little mixed in quality.  (One paper was very tangential, one paper was just a link to Wikipedia, and one paper wasn't accessible at all.)  I clicked through all of the links to verify that they were real and on-target.  

If the results aren't what you want, feel free to iterate until you get the result quality you need.  

Press enter or click to view image in full

Ask for contrary points of view
(don't just confirm!)  

Research isn’t just about collecting references — it’s also about understanding the space, both in terms of what you know and what counterarguments you might want to consider. 

In reading through the Rocky Mountain Locust collection, you'll notice that one of the main hypotheses about the disappearance of the locust is that the rangeland where it lived and bred was increasingly plowed up for farmland.  

You should ask about other opinions:  

Pattern:   

[ GIve me different explanations for {TOPIC}.  Are there other points of view that have been considered in the literature?  

For each source, provide: the Title, the URL, a short snippet about why it's relevant. ] 

Example:

[ Give me different explanations for why the Rocky Mountain Locusts disappeared.  Are there other points of view that have been considered in the literature?  For each source, provide: the Title, the URL, a short snippet about why it's relevant. ] 

Interestingly, Gemini merely did an okay job of this step--ChatGPT was reasonably good, but Claude did a spectacular job of highlighting 11 different hypotheses about what happened.  (To see Claude's output, here's the document.)  This also suggests that you should get multiple AI opinions to improve the quality of your research!   

Double Check Everything

We still live in a hallucinatory world. As great as AI generated content is, I still double check everything.  In her post, Maryam has a great set of questions (below).  This is what is on my mind as I read through EVERY claim and EVERY linked document.  You should too.  

• Source Quality — Is it recent, reputable, and methodologically sound?
• Fact Containment — Only use approved notes/sources. 
• Triangulation — Every claim needs at least two independent sources.
• Original-Source Tracing — Don’t rely on LinkedIn slides, Twitter posts, or a quote in a blog. Find the earliest credible publication.
• Hallucination Sweep — Audit the final draft. Remove or qualify any claim not directly supported.

Search Research Summary

When using AI for deep research, keep in mind 3 heuristics: 

1. Be clear about what you want.  Not just in content, but in form and quality.  Be explicit--give examples--ask for everything you want. 

2. Review the results for quality. Do this step immediately, and change the prompt if need be to get what you really seek.  Iterate!  

3. Ask for contrary points of view.  Don't give in to confirmation bias--proactively ask about other perspectives on the questions you're researching. 

4. Double check everything.  No surprise here, but be sure to leave enough time to do this.  Don't just copy/paste what you've found. 

 

Thanks again to Maryam for her excellent post.  

Keep searching!

SearchResearch (10/29/25): The 1 trick you need to know to use AI for deeper, better reading

 I absolutely adore... 

P.G. Wodehouse.  P/C Wikimedia

... the writings of P. G. Wodehouse.  Whenever I need a lift in the old spirits, I pluck a volume from the bookshelf of Wooster and Jeeves, I read a bit, and in the blink of an eye, all is right with the world.  As Wodehouse might say, God is in His heaven and the celestial choirs sing again.  

If you don't know Wodehouse, drop what you're doing and read a short story or two. Better yet, pick up a Wodehouse novel and dive in.  

I'd recommend Right Ho, Jeeves, which is an excellent place to start.  

The writing is droll and the language--especially the language--just tickle my humorous bones.  

BUT, Wodehouse is satirizing the language and behaviors of the early 1900s upper class.  They are a rich vein to mine, but roughly once each page, there is a phrase or word that escapes my understanding or offers up a nuance that completely misses my brain.  

For instance: 

Butter-and-egg man (An investor with a lot of money)

Absquatulate (To depart suddenly or abscond)

Cattywampus (Used to mean something that was directly across from something else, as opposed to its modern meaning of being askew or in disarray)

Those are fairly easy to look up.  But the more tricky phrases are things like: 

"Only that she’s a blister.”

Or... 

"Deprived of Anatole’s services, all he was likely to give the wife of his b. was a dirty look."  

I know what a blister is, but the obvious definition makes no sense here.  And what is "...the wife of his b."?  That's clearly not the end of a sentence, but feels like an abbreviation for something--but what? 

Here's where your friendly, local LLM comes in handy.  Here's what I did to figure out each expression:  I asked an LLM (Gemini in this case) to explain it to me in the context of the book... 

And when you need to be even more specific, give the name of the story in the context you provide to the LLM.  

In both of these cases, it's not clear that any amount of contextual reading would have taught me these meanings.  

This is a brilliant use of an AI to augment your ability to deeply read a text.  

On the other hand, use caution:  AI still makes mistakes, and they can be subtle. 

Here I asked a question about the mention of a device in a book written about the same time as Wodehouse: 

This completely checks out.  (Of course I double check everything.  Don't you?) The Veeder box is indeed a type of odometer made at the time.  

However... see this next part of the explanation: 

That mention of "By the time Evelyn Gibb and her husband were bicycling the West Coast in 1909..." is completely made up.  The book is NOT about Evelyn Gibb and her husband, but is about Vic McDaniel and Ray Francisco, friends who cycled 1,000 miles from Santa Rosa, California, to Seattle, Washington, for the Alaska-Yukon-Pacific Exposition. The author (Evelyn Gibb) is Vic's daughter, not his wife.  

SearchResearch Lessons 

1. Using an AI to give insights into obscure texts can be incredibly handy.  By virtue of having ingested so much text, an AI can often give you a perspective about a fragment of text that you don't understand. 

2. CAUTION:  Check everything--there are still hallucinations about!  Double check everything!

Hope you find this useful SRS method!  

Keep searching.  

SearchResearch (10/24/25): The shifting of SearchResearch

 I've noticed a subtle shift.  

The longer I write this blog, SearchResearch, the more changes I see.  Content on the web changes, the tools we use change--the whole ecology of writer, reader, producer, and consumer has dramatically shifted since I began writing back in January, 2010.  That was 15 years ago.  In Internet years, that's about 1500 years.  (I figure Internet years are about 100-to-1 with Human years.)  I've written 1478 posts and we've had 6.23 million reads. You have written around 10 comments / post, for which I thank you.  

In my first blog post I wrote: 

I have to warn you before you start reading: In the back of my head, I want something tangible to emerge from this. Ideally, a book, or a series of books, about how people search... how they research... and how they get good at doing this.

Congrats. We've done that.  The Joy of Search came out in 2019 to reasonable success.  I'm happy about how well it worked as a book.  

And I see that this blog is shifting a bit too.  

As you've seen, our typical pattern is that one week I'll pose a Challenge--usually a question about some interesting aspect of the world that requires using a particular research skill that you, dear reader, need to figure out.  Good news here, you figured out some deep skills.  

Some of my favorites have been skills like knowing Control-F (the skill of finding text),  using site: restriction (to search just within a particular website), or using deep resources like Google Books or the Newspaper archive.  

But with the rise of AI tools to help out with doing deep online research, it seems that our skills need to shift as well.  You still need Control-F, but I find myself using tools like site: less-and-less these days.  

So... I think we need to shift the way the SRS blog will operate.  As I wrote in 2010:  

When you think about it, search is not something you're born with--there's no inherent, latent skills for research (the way there is, say, for walking or spitting). Some people are really good at it, others just never quite get the basics. 

That's still really true--but more people know Control-F these days, and AI is doing a lot of the search-specific skill. 

HOWEVER... I still find myself using somewhat more subtle online research skills. The technical problem for this blog is that it's hard to frame the skills in terms of motivating Challenges.  So the blog is shifting a bit as well to try and communicate those sensemaking and deep research skills.  

I WILL pose interesting Challenges from time-to-time when I just can't resist their siren call, just not every other week as we've been doing.  

Instead, I want to point out some of the deep research skills we need to cultivate.  And that will require me telling stories, rather than posing a research Challenge.  

Bear with me as we try to figure out the new format.  I'm confident that we'll find something that's deeply interesting and fun.  Stay tuned as SRS starts a few experiments.  

In my next post I'm going to point you to some people who are writing about this new, AI-linked research methods.  That will be entitled, Key skills you need to have to be an effective online researcher and will be a collection of some posts by other folks who have good things to teach us as well.  

Stay tuned.  Keep reading, keep leaving comments.... 

And keep searching.  

 

Answer: How can the same locust look so different?

 

It's difficult to understand... 

Rocky Mountain locust. P/C Wikimedia

... how variable the appearance of an animal might be.  In this case, how can this particular insect--the Rocky Mountain locust--be so variable in appearence that biologists thought that the two different forms of the insect were actually different species?  

So how could biologists mistake the two different looks of a locust for two different species?  

1. How often has it happened that biologists have seen two (or more) species when it was really just one in different clothing?  Can you find another case of two (or more) species being reconciled into one? 

This seems like a great question for an LLM.  When I copy/pasted this into Gemini, I learned that this happens more often than I thought.  The Gemini answer talked about "lumping" and "splitting" a species definition--by "lumping together" two organisms that were thought to be different, and "splitting apart" organisms that look the same, but are actually genetically different.  

That sounds right, but the words "lumping" and "splitting" are probably NOT what biologists call this process.  

A quick query of: [what do you call it when biologists find that two different appearing animals are found to be the same species and they reclassify them in a new species name] taught me that biologists refer to this process (which happens a LOT), as synonymization. ("Synonymization" is the process of identifying and combining different scientific names that refer to the same organism. This happens when a species is described multiple times by different scientists, or when a species is reclassified into a different genus, or when different organisms are discovered to be variants of a common organism.)

I revised the question to learn about "splitting" and learned that this is just regular old speciation, which then leads biologists to a taxonomic revision in the textbooks.  

This is often the result of cryptic species, which are species that appear identical but are reproductively isolated.  

To find examples of "splitting" I asked the obvious query: [can you give me examples of organisms that seem very similar and were once thought to be one species, but are now understood to be multiple species?]  and found several examples.  The elephant in the room is obviously the African elephants...  

African elephant (Loxodonta africana).  P/C K. Russell

Historically classified as a single species, the African elephant, has now been distinguished as two separate species: the African bush elephant (Loxodonta africana) and the African forest elephant (Loxodonta cyclotis). They have nearly identical appearances, but DNA analysis revealed them to be genetically distinct and reproductively isolated, with the forest elephant being slightly smaller and having straighter tusks.

And the converse:  [can you give me examples of organisms that seem very different and were once thought to be different species, but are now understood to be just one species?] 

Leptocephalus, the larval form of Anguilla anguilla. Yes, they are transparent.

A lovely answer:  For centuries, the transparent, ribbon-like leptocephalus larva was believed to be a separate species from the adult eel (Anguilla anguilla). It was only in the early 20th century that scientists realized the leptocephalus is the eel’s larval stage and not a different organism.  

And when I asked about locusts in particular, I learned that for centuries, naturalists thought that the grasshopper and the swarming locusts were entirely different insects. (For the record, they also wrote that caterpillars and butterflies were completely different insects as well...)  

The solitary form of the locust lives alone as a grasshopper, while the gregarious or swarming form appears during outbreaks.  It's larger, brightly colored (often yellow and black), with longer wings, stronger flight muscles, and completely different behavior, preferring to fly in massive swarms.  

They were so different in appearance and habits that early entomologists gave them different scientific names. 

Then, around one hundred years ago (1921), Sir Boris Uvarov recognized that two locust species are one species but appearing in two different phases, a solitarious and a gregarious phase.  This phenomenon of phase polymorphism, is now called polyphenism.   (See a nice review paper on Uvarov's discovery, "One hundred years of phase polymorphism research in locusts.") 

It turns out that under crowded conditions, young locusts experience tactile stimulation on their hind legs and undergo phase transformation, triggering massive physiological and behavioral changes. This transformation affects color, size, brain structure, metabolism, and social behavior, switching them from a solitary to a gregarious form — leading to the famous locust swarming behavior.  

2. It's clear that organisms can have multiple shapes / patterns / colors (we've discussed this before in the context of plant mimicry).  Can you find an organism that has a huge number of different appearances?  Any idea WHY they have such variability?  

 

I put this question to Claude as [Can you find an organism that has a large number of variable appearances?  That is, what is the most polymorphic organism?]

All of the AIs--Claude (and Gemini and ChatGPT)--gave variations on a good answer, pointing out that both the Great Mormon Butterfly (Papilio memnon) and certain snails (e.g., the Grove snail, Cepaea nemoralis) are famous for their polymorphism, leading biologists to classify the different forms as different species.  

Polymorphisms in Papilio memnon.  P/C Wikimedia

The Grove snails have widely varying shells, which can be different colors (brown, pink, yellow) and have various banding patterns. These different morphs can look so distinct they puzzled early researchers, and the variety is controlled by a complex of closely linked genes.

Polymorphism in Grove snails (Cepaea nemoralis). P/C Wikimedia

And there's the answer: there are many organisms with widely varying morphs--ants, bees, fish, snails, and locusts.  

WHY this is so can be seen in the many shapes of dogs around the world.  How can one species be SO variable in size, shape, and color, yet all be one species?  

Another LLM query:  [what are the genetic factors the cause extreme polymorphism in some species?]   You can do that query yourself and read the details, but it boils down to this: there are a small number of genes that control a LOT of the variation in coat kind, coat color, size, muzzle shape, etc.  With a great deal of selective breeding over the eons (by people), the variation has been amplified into the great number of dog varieties that we see today.  (For lots of insights, see:  Boyko, Adam R., et al. "A simple genetic architecture underlies morphological variation in dogs." PLoS biology 8.8 (2010).  

Search Research Summary

1. The AIs worked well.  One of the nice surprises of this Challenge is how well the LLMs answered each question.  This is largely due to this being a not-especially controversial area--nobody bothers to push out pathological content about the genetics of insects or dogs.  

2. Double check everything.  HOWEVER... for each result I write about here, I double-checked each claim.  In some cases I triple checked. It's just what you have to do these days. 

On the upside, most of the explanations were quite good.  (The business about "lumping" and "splitting" aside--those are common terms that work well, but are not terms of art.)  

A few times I had to dive a little deeper into the topic area to fully understand what was going on.  But that's a big part of The Joy of Search.  

Hope you enjoyed this week's Challenge.  

Keep searching. 

SearchResearch Challenge (10/8/25): How can the same locust look so different?

It's difficult to understand... 

Rocky Mountain locust. P/C Wikimedia

... how variable the appearance of an animal might be.  

Sure, people look very different around the globe, and both dogs and cats have wildly variable appearances.  But in every case, you'd say that they're all of one species. 

So how could biologists mistake the two different looks of a locust for two different species?  

A bit of background here.

I've been reading Jeffrey A. Lockwood's brilliant book Locust: the devastating rise and mysterious disappearance of the insect that shaped the American frontier. (Basic Books, 2009.) 

Part of the book tells the story of the Locust Plague of 1874.  Locusts swarmed over an estimated 2,000,000 square miles (5,200,000 square kilometers) of the plains states in North America, causing millions of dollars' worth of damage. 

Residents described swarms so thick that they covered the sun for up to six hours. The swarms of Rocky Mountain locusts (Melanoplus spretus) were larger than the state of California and comprised some 12.5 TRILLION insects.

They would eat grass, trees, even the clothes off people's backs.  

But less than 30 years later, the entire species was extinct. Gone.  Vanished.  

That's the subject of Lockwood's book--how is it possible for such a vast number of insects to simply disappear?  

A cartoon of the locusts arriving in Nebraska

Laura Ingalls Wilder’s book, On the Banks of Plum Creek  has a description of what it was like to live through the literal plague of locusts arriving on the farm:  

Plunk! something hit Laura's head and fell to the ground. She looked down and saw the largest grasshopper she had ever seen. Then huge brown grasshoppers were hitting the ground all around her, hitting her head and her face and her arms. They came thudding down like hail. 

The cloud was hailing grasshoppers. The cloud was grasshoppers. Their bodies hid the sun and made darkness. Their thin, large wings gleamed and glittered. The rasping whirring of their wings filled the whole air and they hit the ground and the house with the noise of a hailstorm.

You might think of this extinction as the most spectacular “success” in the history of economic entomology — the only complete elimination of an agricultural pest species.  But it seems as if it was a total accident.  

(For all the details, I encourage you to read Lockwood's book--a fascinating detective story of a past extinction. Also check out the Wiki articles Locust Plague of 1874 and Rocky Mountain locust. For more details, Lockwood has a short article about his sleuthing, The Death of the Super Hopper.)  

But that's not our Challenge for this week.  Instead, I want to focus on that first question I raised earlier--So how could biologists mistake the two different looks of a locust for two different species?  

1. How often has it happened that biologists have seen two (or more) species when it was really just one in different clothing?  Can you find another case of two (or more) species being reconciled into one? 

2. It's clear that organisms can have multiple shapes / patterns / colors (we've discussed this before in the context of plant mimicry).  Can you find an organism that has a huge number of different appearances?  Any idea WHY they have such variability?  

It's fascinating stuff--hope you enjoy reading about it as much as I did.  

Be sure to tell us HOW you found the answers to this week's Challenge.  Regular search?  AI?  If so, what prompts did you use... and how well did it work for you?  

We want to hear about successes as well as disasters! 

Keep searching.