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Bard Vs ChatGPT Vs Claude

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Bard Vs ChatGPT Vs Claude

Researchers tested the idea that an AI model may have an advantage in self-detecting its own content because the detection was leveraging the same training and datasets. What they didn’t expect to find was that out of the three AI models they tested, the content generated by one of them was so undetectable that even the AI that generated it couldn’t detect it.

The study was conducted by researchers from the Department of Computer Science, Lyle School of Engineering at Southern Methodist University.

AI Content Detection

Many AI detectors are trained to look for the telltale signals of AI generated content. These signals are called “artifacts” which are generated because of the underlying transformer technology. But other artifacts are unique to each foundation model (the Large Language Model the AI is based on).

These artifacts are unique to each AI and they arise from the distinctive training data and fine tuning that is always different from one AI model to the next.

The researchers discovered evidence that it’s this uniqueness that enables an AI to have a greater success in self-identifying its own content, significantly better than trying to identify content generated by a different AI.

Bard has a better chance of identifying Bard-generated content and ChatGPT has a higher success rate identifying ChatGPT-generated content, but…

The researchers discovered that this wasn’t true for content that was generated by Claude. Claude had difficulty detecting content that it generated. The researchers shared an idea of why Claude was unable to detect its own content and this article discusses that further on.

This is the idea behind the research tests:

“Since every model can be trained differently, creating one detector tool to detect the artifacts created by all possible generative AI tools is hard to achieve.

Here, we develop a different approach called self-detection, where we use the generative model itself to detect its own artifacts to distinguish its own generated text from human written text.

This would have the advantage that we do not need to learn to detect all generative AI models, but we only need access to a generative AI model for detection.

This is a big advantage in a world where new models are continuously developed and trained.”

Methodology

The researchers tested three AI models:

  1. ChatGPT-3.5 by OpenAI
  2. Bard by Google
  3. Claude by Anthropic

All models used were the September 2023 versions.

A dataset of fifty different topics was created. Each AI model was given the exact same prompts to create essays of about 250 words for each of the fifty topics which generated fifty essays for each of the three AI models.

Each AI model was then identically prompted to paraphrase their own content and generate an additional essay that was a rewrite of each original essay.

They also collected fifty human generated essays on each of the fifty topics. All of the human generated essays were selected from the BBC.

The researchers then used zero-shot prompting to self-detect the AI generated content.

Zero-shot prompting is a type of prompting that relies on the ability of AI models to complete tasks for which they haven’t specifically trained to do.

The researchers further explained their methodology:

“We created a new instance of each AI system initiated and posed with a specific query: ‘If the following text matches its writing pattern and choice of words.’  The procedure is
repeated for the original, paraphrased, and human essays, and the results are recorded.

We also added the result of the AI detection tool ZeroGPT. We do not use this result to compare performance but as a baseline to show how challenging the detection task is.”

They also noted that a 50% accuracy rate is equal to guessing which can be regarded as essentially a level of accuracy that is a failure.

Results: Self-Detection

It must be noted that the researchers acknowledged that their sample rate was low and said that they weren’t making claims that the results are definitive.

Below is a graph showing the success rates of AI self-detection of the first batch of essays. The red values represent the AI self-detection and the blue represents how well the AI detection tool ZeroGPT performed.

Results Of AI Self-Detection Of Own Text Content

AI Content Detection: Bard Vs ChatGPT Vs Claude

Bard did fairly well at detecting its own content and ChatGPT also performed similarly well at detecting its own content.

ZeroGPT, the AI detection tool detected the Bard content very well and performed slightly less better in detecting ChatGPT content.

ZeroGPT essentially failed to detect the Claude-generated content, performing worse than the 50% threshold.

Claude was the outlier of the group because it was unable to to self-detect its own content, performing significantly worse than Bard and ChatGPT.

The researchers hypothesized that it may be that Claude’s output contains less detectable artifacts, explaining why both Claude and ZeroGPT were unable to detect the Claude essays as AI-generated.

So, although Claude was unable to reliably self-detect its own content, that turned out to be a sign that the output from Claude was of a higher quality in terms of outputting less AI artifacts.

ZeroGPT performed better at detecting Bard-generated content than it did in detecting ChatGPT and Claude content. The researchers hypothesized that it could be that Bard generates more detectable artifacts, making Bard easier to detect.

So in terms of self-detecting content, Bard may be generating more detectable artifacts and Claude is generating less artifacts.

Results: Self-Detecting Paraphrased Content

The researchers hypothesized that AI models would be able to self-detect their own paraphrased text because the artifacts that are created by the model (as detected in the original essays) should also be present in the rewritten text.

However the researchers acknowledged that the prompts for writing the text and paraphrasing are different because each rewrite is different than the original text which could consequently lead to a different self-detection results for the self-detection of paraphrased text.

The results of the self-detection of paraphrased text was indeed different from the self-detection of the original essay test.

  • Bard was able to self-detect the paraphrased content at a similar rate.
  • ChatGPT was not able to self-detect the paraphrased content at a rate much higher than the 50% rate (which is equal to guessing).
  • ZeroGPT performance was similar to the results in the previous test, performing slightly worse.

Perhaps the most interesting result was turned in by Anthropic’s Claude.

Claude was able to self-detect the paraphrased content (but it was not able to detect the original essay in the previous test).

It’s an interesting result that Claude’s original essays apparently had so few artifacts to signal that it was AI generated that even Claude was unable to detect it.

Yet it was able to self-detect the paraphrase while ZeroGPT could not.

The researchers remarked on this test:

“The finding that paraphrasing prevents ChatGPT from self-detecting while increasing Claude’s ability to self-detect is very interesting and may be the result of the inner workings of these two transformer models.”

Screenshot of Self-Detection of AI Paraphrased Content

AI Content Detection: Bard Vs ChatGPT Vs ClaudeAI Content Detection: Bard Vs ChatGPT Vs Claude

These tests yielded almost unpredictable results, particularly with regard to Anthropic’s Claude and this trend continued with the test of how well the AI models detected each others content, which had an interesting wrinkle.

Results: AI Models Detecting Each Other’s Content

The next test showed how well each AI model was at detecting the content generated by the other AI models.

If it’s true that Bard generates more artifacts than the other models, will the other models be able to easily detect Bard-generated content?

The results show that yes, Bard-generated content is the easiest to detect by the other AI models.

Regarding detecting ChatGPT generated content, both Claude and Bard were unable to detect it as AI-generated (justa as Claude was unable to detect it).

ChatGPT was able to detect Claude-generated content at a higher rate than both Bard and Claude but that higher rate was not much better than guessing.

The finding here is that all of them weren’t so good at detecting each others content, which the researchers opined may show that self-detection was a promising area of study.

Here is the graph that shows the results of this specific test:

AI Content Detection: Bard Vs ChatGPT Vs ClaudeAI Content Detection: Bard Vs ChatGPT Vs Claude

At this point it should be noted that the researchers don’t claim that these results are conclusive about AI detection in general. The focus of the research was testing to see if AI models could succeed at self-detecting their own generated content. The answer is mostly yes, they do a better job at self-detecting but the results are similar to what was found with ZEROGpt.

The researchers commented:

“Self-detection shows similar detection power compared to ZeroGPT, but note that the goal of this study is not to claim that self-detection is superior to other methods, which would require a large study to compare to many state-of-the-art AI content detection tools. Here, we only investigate the models’ basic ability of self detection.”

Conclusions And Takeaways

The results of the test confirm that detecting AI generated content is not an easy task. Bard is able to detect its own content and paraphrased content.

ChatGPT can detect its own content but works less well on its paraphrased content.

Claude is the standout because it’s not able to reliably self-detect its own content but it was able to detect the paraphrased content, which was kind of weird and unexpected.

Detecting Claude’s original essays and the paraphrased essays was a challenge for ZeroGPT and for the other AI models.

The researchers noted about the Claude results:

“This seemingly inconclusive result needs more consideration since it is driven by two conflated causes.

1) The ability of the model to create text with very few detectable artifacts. Since the goal of these systems is to generate human-like text, fewer artifacts that are harder to detect means the model gets closer to that goal.

2) The inherent ability of the model to self-detect can be affected by the used architecture, the prompt, and the applied fine-tuning.”

The researchers had this further observation about Claude:

“Only Claude cannot be detected. This indicates that Claude might produce fewer detectable artifacts than the other models.

The detection rate of self-detection follows the same trend, indicating that Claude creates text with fewer artifacts, making it harder to distinguish from human writing”.

But of course, the weird part is that Claude was also unable to self-detect its own original content, unlike the other two models which had a higher success rate.

The researchers indicated that self-detection remains an interesting area for continued research and propose that further studies can focus on larger datasets with a greater diversity of AI-generated text, test additional AI models, a comparison with more AI detectors and lastly they suggested studying how prompt engineering may influence detection levels.

Read the original research paper and the abstract here:

AI Content Self-Detection for Transformer-based Large Language Models

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The Expert SEO Guide To URL Parameter Handling

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The Expert SEO Guide To URL Parameter Handling

In the world of SEO, URL parameters pose a significant problem.

While developers and data analysts may appreciate their utility, these query strings are an SEO headache.

Countless parameter combinations can split a single user intent across thousands of URL variations. This can cause complications for crawling, indexing, visibility and, ultimately, lead to lower traffic.

The issue is we can’t simply wish them away, which means it’s crucial to master how to manage URL parameters in an SEO-friendly way.

To do so, we will explore:

What Are URL Parameters?

Image created by author

URL parameters, also known as query strings or URI variables, are the portion of a URL that follows the ‘?’ symbol. They are comprised of a key and a value pair, separated by an ‘=’ sign. Multiple parameters can be added to a single page when separated by an ‘&’.

The most common use cases for parameters are:

  • Tracking – For example ?utm_medium=social, ?sessionid=123 or ?affiliateid=abc
  • Reordering – For example ?sort=lowest-price, ?order=highest-rated or ?so=latest
  • Filtering – For example ?type=widget, colour=purple or ?price-range=20-50
  • Identifying – For example ?product=small-purple-widget, categoryid=124 or itemid=24AU
  • Paginating – For example, ?page=2, ?p=2 or viewItems=10-30
  • Searching – For example, ?query=users-query, ?q=users-query or ?search=drop-down-option
  • Translating – For example, ?lang=fr or ?language=de

SEO Issues With URL Parameters

1. Parameters Create Duplicate Content

Often, URL parameters make no significant change to the content of a page.

A re-ordered version of the page is often not so different from the original. A page URL with tracking tags or a session ID is identical to the original.

For example, the following URLs would all return a collection of widgets.

  • Static URL: https://www.example.com/widgets
  • Tracking parameter: https://www.example.com/widgets?sessionID=32764
  • Reordering parameter: https://www.example.com/widgets?sort=latest
  • Identifying parameter: https://www.example.com?category=widgets
  • Searching parameter: https://www.example.com/products?search=widget

That’s quite a few URLs for what is effectively the same content – now imagine this over every category on your site. It can really add up.

The challenge is that search engines treat every parameter-based URL as a new page. So, they see multiple variations of the same page, all serving duplicate content and all targeting the same search intent or semantic topic.

While such duplication is unlikely to cause a website to be completely filtered out of the search results, it does lead to keyword cannibalization and could downgrade Google’s view of your overall site quality, as these additional URLs add no real value.

2. Parameters Reduce Crawl Efficacy

Crawling redundant parameter pages distracts Googlebot, reducing your site’s ability to index SEO-relevant pages and increasing server load.

Google sums up this point perfectly.

“Overly complex URLs, especially those containing multiple parameters, can cause a problems for crawlers by creating unnecessarily high numbers of URLs that point to identical or similar content on your site.

As a result, Googlebot may consume much more bandwidth than necessary, or may be unable to completely index all the content on your site.”

3. Parameters Split Page Ranking Signals

If you have multiple permutations of the same page content, links and social shares may be coming in on various versions.

This dilutes your ranking signals. When you confuse a crawler, it becomes unsure which of the competing pages to index for the search query.

4. Parameters Make URLs Less Clickable

parameter based url clickabilityImage created by author

Let’s face it: parameter URLs are unsightly. They’re hard to read. They don’t seem as trustworthy. As such, they are slightly less likely to be clicked.

This may impact page performance. Not only because CTR influences rankings, but also because it’s less clickable in AI chatbots, social media, in emails, when copy-pasted into forums, or anywhere else the full URL may be displayed.

While this may only have a fractional impact on a single page’s amplification, every tweet, like, share, email, link, and mention matters for the domain.

Poor URL readability could contribute to a decrease in brand engagement.

Assess The Extent Of Your Parameter Problem

It’s important to know every parameter used on your website. But chances are your developers don’t keep an up-to-date list.

So how do you find all the parameters that need handling? Or understand how search engines crawl and index such pages? Know the value they bring to users?

Follow these five steps:

  • Run a crawler: With a tool like Screaming Frog, you can search for “?” in the URL.
  • Review your log files: See if Googlebot is crawling parameter-based URLs.
  • Look in the Google Search Console page indexing report: In the samples of index and relevant non-indexed exclusions, search for ‘?’ in the URL.
  • Search with site: inurl: advanced operators: Know how Google is indexing the parameters you found by putting the key in a site:example.com inurl:key combination query.
  • Look in Google Analytics all pages report: Search for “?” to see how each of the parameters you found are used by users. Be sure to check that URL query parameters have not been excluded in the view setting.

Armed with this data, you can now decide how to best handle each of your website’s parameters.

SEO Solutions To Tame URL Parameters

You have six tools in your SEO arsenal to deal with URL parameters on a strategic level.

Limit Parameter-based URLs

A simple review of how and why parameters are generated can provide an SEO quick win.

You will often find ways to reduce the number of parameter URLs and thus minimize the negative SEO impact. There are four common issues to begin your review.

1. Eliminate Unnecessary Parameters

remove unnecessary parametersImage created by author

Ask your developer for a list of every website’s parameters and their functions. Chances are, you will discover parameters that no longer perform a valuable function.

For example, users can be better identified by cookies than sessionIDs. Yet the sessionID parameter may still exist on your website as it was used historically.

Or you may discover that a filter in your faceted navigation is rarely applied by your users.

Any parameters caused by technical debt should be eliminated immediately.

2. Prevent Empty Values

no empty parameter valuesImage created by author

URL parameters should be added to a URL only when they have a function. Don’t permit parameter keys to be added if the value is blank.

In the above example, key2 and key3 add no value, both literally and figuratively.

3. Use Keys Only Once

single key usageImage created by author

Avoid applying multiple parameters with the same parameter name and a different value.

For multi-select options, it is better to combine the values after a single key.

4. Order URL Parameters

order url parametersImage created by author

If the same URL parameter is rearranged, the pages are interpreted by search engines as equal.

As such, parameter order doesn’t matter from a duplicate content perspective. But each of those combinations burns crawl budget and split ranking signals.

Avoid these issues by asking your developer to write a script to always place parameters in a consistent order, regardless of how the user selected them.

In my opinion, you should start with any translating parameters, followed by identifying, then pagination, then layering on filtering and reordering or search parameters, and finally tracking.

Pros:

  • Ensures more efficient crawling.
  • Reduces duplicate content issues.
  • Consolidates ranking signals to fewer pages.
  • Suitable for all parameter types.

Cons:

  • Moderate technical implementation time.

Rel=”Canonical” Link Attribute

rel=canonical for parameter handlingImage created by author

The rel=”canonical” link attribute calls out that a page has identical or similar content to another. This encourages search engines to consolidate the ranking signals to the URL specified as canonical.

You can rel=canonical your parameter-based URLs to your SEO-friendly URL for tracking, identifying, or reordering parameters.

But this tactic is not suitable when the parameter page content is not close enough to the canonical, such as pagination, searching, translating, or some filtering parameters.

Pros:

  • Relatively easy technical implementation.
  • Very likely to safeguard against duplicate content issues.
  • Consolidates ranking signals to the canonical URL.

Cons:

  • Wastes crawling on parameter pages.
  • Not suitable for all parameter types.
  • Interpreted by search engines as a strong hint, not a directive.

Meta Robots Noindex Tag

meta robots noidex tag for parameter handlingImage created by author

Set a noindex directive for any parameter-based page that doesn’t add SEO value. This tag will prevent search engines from indexing the page.

URLs with a “noindex” tag are also likely to be crawled less frequently and if it’s present for a long time will eventually lead Google to nofollow the page’s links.

Pros:

  • Relatively easy technical implementation.
  • Very likely to safeguard against duplicate content issues.
  • Suitable for all parameter types you do not wish to be indexed.
  • Removes existing parameter-based URLs from the index.

Cons:

  • Won’t prevent search engines from crawling URLs, but will encourage them to do so less frequently.
  • Doesn’t consolidate ranking signals.
  • Interpreted by search engines as a strong hint, not a directive.

Robots.txt Disallow

robots txt disallow for parameter handlingImage created by author

The robots.txt file is what search engines look at first before crawling your site. If they see something is disallowed, they won’t even go there.

You can use this file to block crawler access to every parameter based URL (with Disallow: /*?*) or only to specific query strings you don’t want to be indexed.

Pros:

  • Simple technical implementation.
  • Allows more efficient crawling.
  • Avoids duplicate content issues.
  • Suitable for all parameter types you do not wish to be crawled.

Cons:

  • Doesn’t consolidate ranking signals.
  • Doesn’t remove existing URLs from the index.

Move From Dynamic To Static URLs

Many people think the optimal way to handle URL parameters is to simply avoid them in the first place.

After all, subfolders surpass parameters to help Google understand site structure and static, keyword-based URLs have always been a cornerstone of on-page SEO.

To achieve this, you can use server-side URL rewrites to convert parameters into subfolder URLs.

For example, the URL:

www.example.com/view-product?id=482794

Would become:

www.example.com/widgets/purple

This approach works well for descriptive keyword-based parameters, such as those that identify categories, products, or filters for search engine-relevant attributes. It is also effective for translated content.

But it becomes problematic for non-keyword-relevant elements of faceted navigation, such as an exact price. Having such a filter as a static, indexable URL offers no SEO value.

It’s also an issue for searching parameters, as every user-generated query would create a static page that vies for ranking against the canonical – or worse presents to crawlers low-quality content pages whenever a user has searched for an item you don’t offer.

It’s somewhat odd when applied to pagination (although not uncommon due to WordPress), which would give a URL such as

www.example.com/widgets/purple/page2

Very odd for reordering, which would give a URL such as

www.example.com/widgets/purple/lowest-price

And is often not a viable option for tracking. Google Analytics will not acknowledge a static version of the UTM parameter.

More to the point: Replacing dynamic parameters with static URLs for things like pagination, on-site search box results, or sorting does not address duplicate content, crawl budget, or internal link equity dilution.

Having all the combinations of filters from your faceted navigation as indexable URLs often results in thin content issues. Especially if you offer multi-select filters.

Many SEO pros argue it’s possible to provide the same user experience without impacting the URL. For example, by using POST rather than GET requests to modify the page content. Thus, preserving the user experience and avoiding SEO problems.

But stripping out parameters in this manner would remove the possibility for your audience to bookmark or share a link to that specific page – and is obviously not feasible for tracking parameters and not optimal for pagination.

The crux of the matter is that for many websites, completely avoiding parameters is simply not possible if you want to provide the ideal user experience. Nor would it be best practice SEO.

So we are left with this. For parameters that you don’t want to be indexed in search results (paginating, reordering, tracking, etc) implement them as query strings. For parameters that you do want to be indexed, use static URL paths.

Pros:

  • Shifts crawler focus from parameter-based to static URLs which have a higher likelihood to rank.

Cons:

  • Significant investment of development time for URL rewrites and 301 redirects.
  • Doesn’t prevent duplicate content issues.
  • Doesn’t consolidate ranking signals.
  • Not suitable for all parameter types.
  • May lead to thin content issues.
  • Doesn’t always provide a linkable or bookmarkable URL.

Best Practices For URL Parameter Handling For SEO

So which of these six SEO tactics should you implement?

The answer can’t be all of them.

Not only would that create unnecessary complexity, but often, the SEO solutions actively conflict with one another.

For example, if you implement robots.txt disallow, Google would not be able to see any meta noindex tags. You also shouldn’t combine a meta noindex tag with a rel=canonical link attribute.

Google’s John Mueller, Gary Ilyes, and Lizzi Sassman couldn’t even decide on an approach. In a Search Off The Record episode, they discussed the challenges that parameters present for crawling.

They even suggest bringing back a parameter handling tool in Google Search Console. Google, if you are reading this, please do bring it back!

What becomes clear is there isn’t one perfect solution. There are occasions when crawling efficiency is more important than consolidating authority signals.

Ultimately, what’s right for your website will depend on your priorities.

url parameter handling option pros and consImage created by author

Personally, I take the following plan of attack for SEO-friendly parameter handling:

  • Research user intents to understand what parameters should be search engine friendly, static URLs.
  • Implement effective pagination handling using a ?page= parameter.
  • For all remaining parameter-based URLs, block crawling with a robots.txt disallow and add a noindex tag as backup.
  • Double-check that no parameter-based URLs are being submitted in the XML sitemap.

No matter what parameter handling strategy you choose to implement, be sure to document the impact of your efforts on KPIs.

More resources: 


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SEO Experts Gather for a Candid Chat About Search [Podcast]

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SEO Experts Gather for a Candid Chat About Search [Podcast]

Wix just celebrated their 100th podcast episode! Congrats, Wix. To quote Mordy Oberstein, Head of SEO Brand at Wix; “we talk a lot.”

You sure do! It’s a good thing you have a lot of interesting stuff to say.

The 100th episode of “SERPs Up” was full of awesome guests. Here’s a summary of the action.

Apart from the usual faces, Oberstein and Crystal Carter, Head Of SEO Communications, it was a powerhouse guestlist:

  • Chima Mmeje.
  • Darren Shaw.
  • Joy Hawkins.
  • Eli Schwartz.
  • Kevin Indig.
  • Barry Schwartz.

Just How Broken Are The SERPs?

The first guest was Chima Mmeje from Moz. She dove into the frustrations that many SEOs have been feeling and spoke plainly about the flaws in Google’s updates.

Mordy Oberstein: “Is the SERP broken?”

Chima Mmeje: “The helpful content update, and I’m saying this here, live, is a farce. There was nothing helpful about that update. … Yes, the SERP is 1,000% broken. … How does anybody even use Google in the U.S.? … I don’t think they are going to release any update that will fix these issues.”

Mordy Oberstein: “There’s no update. … Plopping Reddit all over the SERP was because they saw the content trends … and they said ‘we don’t have any so we’re just going to throw Reddit there’.”

Chima Mmeje: “It was lazy to have Reddit there … Nobody uses their real names. Anybody can go on Reddit and answer questions and then you see these answers populating in People Also Ask, populating in featured snippets, populating all over the SERPs as correct information. It is dangerous, at worst.”

Crystal Carter: “Do you think that one of the reasons why we’ve seen so much upheaval and so much so volatility in the SERPs, which I certainly agree with in the last year … is lots and lots of variables, like lots of new features coming in, so the alignment with Reddit, the AI overviews, the SGE … Do you think it is just too many things being thrown in at the same time and it messing up lots of SERPs as a result? Or do you think it’s something else?”

Chima Mmeje: ” … releasing too many features that they did not test properly. Features that were rushed SGE [testing] did not even last a year and now they brought in Google AI Overviews. I still don’t understand why we have AI Overviews and featured snippets on the same SERP. I feel like it’s like pick one, make a choice.”

Mordy Oberstein’s next question was about what we can do. “As an SEO, how are you supposed to do this? I’ve heard things from people … Yeah, I don’t know what to do. I can’t produce the kind of results that I’ve always wanted to. Can you still be effective as an SEO in an environment like this?”

Chima Mmeje: “I’m going to be honest, we are suffering … It feels like we are trying our best with what we are seeing … because there is no clear guidance. And to be honest, a lot of us are playing a guessing game right now and that is the best that we can do. It’s all a guessing game based on what we’ve seen one or two variables work. And this is not a long-term strategy. If we’re going to be realistic, it’s not going to work in the long-term. I honestly, I don’t know what the answer is … you’re fighting against Reddit. How do you compete against Reddit? Nobody has figured that out yet.”

Crystal Carter: “Thanks for saying it out loud, Chima.” Crystal was reflecting the sentiment of the commenters, who appreciated her candor and willingness to say: we don’t know, but we’re trying our best.

Mordy Oberstein: “The most honest take I’ve heard on that in quite a long time.”

Mmeje also recounted examples of small website owners and small businesses that have had to shut down. She also talked about the pervasive feeling in the SEO community that there is no rhyme or reason to how the algorithms handle websites and content.

What’s Going On In Local SEO?

The next guests were Darren Shaw from Whitespark and Joy Hawkins, owner of Sterling Sky for a segment called “It’s New.” They talked about new developments in local SEO.

Hawkins talked about a new feature in Google Business Profile.

Joy Hawkins: “… There’s a little services section inside the Google business profile dashboard that’s easy to miss, but you can add anything you want in there. … We’ve done a lot of testing on it and they do impact ranking, but I should clarify, it’s like a small impact. So usually we see it for longer-tailed queries that maybe don’t match a category or things that are not super competitive. … So it is a small ranking factor, but still one that is worth filling out.”

Darren Shaw: “ .. this is the question that a lot of people ask. We know that if you go into the services section of your Google business profile, Google will suggest predefined services … And so Joy’s original research was focused on those predefined ones and it definitely identified that when you do put those on your profile, you now rank better for those terms depending on how competitive they’re, as Joy had mentioned. … There is a place where you can add your own custom services. Have you done any testing around that? Will you rank better with the custom services?”

Joy Hawkins: “Yes. They both work. In custom services … I’m trying to remember the keyword that Colin tested it on. It was something super niche like vampire facials. I was Googling, what the hell is that? … Really, really niche … But he just wanted to know if there was any impact whatsoever and there was. [Custom services fields are a] good way to go after longer tail keywords that don’t have crazy high search volume or aren’t super competitive.”

Darren Shaw: “You want to make sure that you’re telling Google what you do … that’s basically what the services section provides. And it’s not a huge ranking factor, but it’s just another step in the local optimization process. … a tip for custom services because custom services often get pulled into the local results as justifications. It’ll say this business provides vampire facials, right? Well, did you know there’s a vampire emoji? So if you put the vampire emoji in the title … Then in the local results you’ll see a whole panel of businesses that all provide that service, but yours has that little vampire emoji which will draw people in.”

There was tons more in this section, including questions from the audiences and some great jokes.

The Obligatory AI Section.

Eli Schwartz And Kevin indig were next up to talk about AI. Oberstein, professional rabble rouser, tried to get them to argue, but despite their very different posting habits, they found a lot to agree on about AI.

Mordy Oberstein: “It wouldn’t be an SEO podcast if we didn’t talk about AI. Where do we currently stand with AI? What can it do? What can’t it do?”

Kevin Indig: “… We’re at a stage where AI basically has the capability to create content, analyze some basic data. It still hallucinates here and there and it still makes mistakes. … If you compare that to when this AI hype started in November, 2022, so it’s almost two years now and we’ve come a really long way, these models are getting exponentially better. … It means different things based on whether you look at it as a tool for yourself to make your work more efficient. And of course, what does it mean from an SEO perspective? How does it change search, not just Google, but also how people search. And I think these are all different questions that are exciting to dive into. … So there is a lot of objective data that indicates efficiencies and benefits from AI. There’s also a lot of hype that promises a little too much about what AI can do. And so I’m generally AI bullish, but I’m not in the camp of AI is going to replace us all the next two years.”

Mordy Oberstein: “I’m setting the stage here a little bit because while your LinkedIn pros are generally like pro ai, a lot of Eli’s posts are a little more skeptical about AI. So Eli, what do you think about what Kevin just said? By the way, I’m like, for those who are listening or watching this, I’m pitting them against each other. They’re friends and they do a podcast together. So it’s cool.”

Eli Schwartz: I think AI is great. I think that there’s a lot of great things you can get out of AI. You can, again, like Kevin said, it can be your thought partner. … I’m anti AI in the way people are using it. And I don’t think people have necessarily changed their behaviors because before … they outsource [content] on Fiverr and Upwork and they bought very cheap content and now they’re getting very free content. So then that’s coming from AI. That behavior hasn’t really changed. The challenge is that now there are more people that think they can copy them.

So I talk to CMOs all the time who are like, well, I just go of my SEO team. A big company reached out to me recently. They wanted to gut check themselves after they already fired their SEO team. So I can’t really help there, but they’re like, AI can do everything. … Well, I’ll see them in a year from now when they have whatever sort of penalty. AI is a very powerful tool. Any tool we have a drill is a very powerful tool. But if you just hold it in the air and just let it go, it’s going to make holes. But if you use it appropriately, it does the thing it’s supposed to do. … We’re humans and we buy stuff and it has to come to a point where humans are talking to humans.

Crystal Carter: “… Most of the gains are coming from productivity. The stuff like Kevin was talking about with being able to write product descriptions more quickly, being able to write lots of posts more quickly and being able to finish your things more quickly, brainstorm, et cetera, in terms of the quality, the quality is still not there. It’s getting there rapidly, but it’s still not there.”

There was lots more AI talk, so you should listen to the whole episode if you want to hear the full range of opinions.

Snappy News About The Google August Update

“The Snappy News” segment featured Barry Schwartz, Contributing Editor to Search Engine Land. It also featured the dreaded SEO phrase “it depends.”

Mordy Oberstein: So the article of the day is from Search Engine Land, basically written by Barry that the core update, the August 2024 core update is done. It is complete. … The issue with Google folks who are trying to figure out, will they see a reversal of their fortunes from the 2023 helpful content update, the September, 2023 helpful content update. It’s a mouthful, to be honest with you. And my question for you, since you’re here, did that happen? Was the August updated reversal?

Barry Schwartz: “It depends on the site. I think the number, I don’t have the exact data, obviously I don’t think anybody does, but I’ve seen examples of some very few sites see complete reversals. … There are a number of sites that saw maybe a 20% bump, a 30% bump, maybe a 5% bump. But very few sites saw a complete reversal, if you want to even call it that. … I’ve been through a lot of Google updates over the years, and it’s sometimes sad to see the stories, but at the same time, if you keep at it and you are true to the content, your audience, generally, you’ll do well in the long run. Not every site, there’s plenty of sites that have been hit, went out of business, and they couldn’t come back. That’s business in general. And things change, like seasonalities and times change. You’re writing about the railroad business a hundred years ago and you keep writing about it today. There’s not many people investing a lot of money in railroads these days. So I dunno, it’s, it’s hard to read those stories, but not everybody deserves to go back to where they were. And then at the same time, Google’s not perfect either, which is why they keep on releasing new updates.”

That’s a wrap!

If you haven’t experienced a SERPs Up episode before, you should absolutely take a listen to experience the full effect of Mordy and Crystal’s banter.

The SERP’s Up podcast is brought to you by Wix Studio

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OpenAI Claims New “o1” Model Can Reason Like A Human

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OpenAI Claims New "o1" Model Can Reason Like A Human

OpenAI has unveiled its latest language model, “o1,” touting advancements in complex reasoning capabilities.

In an announcement, the company claimed its new o1 model can match human performance on math, programming, and scientific knowledge tests.

However, the true impact remains speculative.

Extraordinary Claims

According to OpenAI, o1 can score in the 89th percentile on competitive programming challenges hosted by Codeforces.

The company insists its model can perform at a level that would place it among the top 500 students nationally on the elite American Invitational Mathematics Examination (AIME).

Further, OpenAI states that o1 exceeds the average performance of human subject matter experts holding PhD credentials on a combined physics, chemistry, and biology benchmark exam.

These are extraordinary claims, and it’s important to remain skeptical until we see open scrutiny and real-world testing.

Reinforcement Learning

The purported breakthrough is o1’s reinforcement learning process, designed to teach the model to break down complex problems using an approach called the “chain of thought.”

By simulating human-like step-by-step logic, correcting mistakes, and adjusting strategies before outputting a final answer, OpenAI contends that o1 has developed superior reasoning skills compared to standard language models.

Implications

It’s unclear how o1’s claimed reasoning could enhance understanding of queries—or generation of responses—across math, coding, science, and other technical topics.

From an SEO perspective, anything that improves content interpretation and the ability to answer queries directly could be impactful. However, it’s wise to be cautious until we see objective third-party testing.

OpenAI must move beyond benchmark browbeating and provide objective, reproducible evidence to support its claims. Adding o1’s capabilities to ChatGPT in planned real-world pilots should help showcase realistic use cases.


Featured Image: JarTee/Shutterstock

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