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What Is Google LaMDA & Why Did Someone Believe It’s Sentient?

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What Is Google LaMDA & Why Did Someone Believe It’s Sentient?

LaMDA has been in the news after a Google engineer claimed it was sentient because its answers allegedly hint that it understands what it is.

The engineer also suggested that LaMDA communicates that it has fears, much like a human does.

What is LaMDA, and why are some under the impression that it can achieve consciousness?

Language Models

LaMDA is a language model. In natural language processing, a language model analyzes the use of language.

Fundamentally, it’s a mathematical function (or a statistical tool) that describes a possible outcome related to predicting what the next words are in a sequence.

It can also predict the next word occurrence, and even what the following sequence of paragraphs might be.

OpenAI’s GPT-3 language generator is an example of a language model.

With GPT-3, you can input the topic and instructions to write in the style of a particular author, and it will generate a short story or essay, for instance.

LaMDA is different from other language models because it was trained on dialogue, not text.

As GPT-3 is focused on generating language text, LaMDA is focused on generating dialogue.

Why It’s A Big Deal

What makes LaMDA a notable breakthrough is that it can generate conversation in a freeform manner that the parameters of task-based responses don’t constrain.

A conversational language model must understand things like Multimodal user intent, reinforcement learning, and recommendations so that the conversation can jump around between unrelated topics.

Built On Transformer Technology

Similar to other language models (like MUM and GPT-3), LaMDA is built on top of the Transformer neural network architecture for language understanding.

Google writes about Transformer:

“That architecture produces a model that can be trained to read many words (a sentence or paragraph, for example), pay attention to how those words relate to one another and then predict what words it thinks will come next.”

Why LaMDA Seems To Understand Conversation

BERT is a model that is trained to understand what vague phrases mean.

LaMDA is a model trained to understand the context of the dialogue.

This quality of understanding the context allows LaMDA to keep up with the flow of conversation and provide the feeling that it’s listening and responding precisely to what is being said.

It’s trained to understand if a response makes sense for the context, or if the response is specific to that context.

Google explains it like this:

“…unlike most other language models, LaMDA was trained on dialogue. During its training, it picked up on several of the nuances that distinguish open-ended conversation from other forms of language. One of those nuances is sensibleness. Basically: Does the response to a given conversational context make sense?

Satisfying responses also tend to be specific, by relating clearly to the context of the conversation.”

LaMDA is Based on Algorithms

Google published its announcement of LaMDA in May 2021.

The official research paper was published later, in February 2022 (LaMDA: Language Models for Dialog Applications PDF).

The research paper documents how LaMDA was trained to learn how to produce dialogue using three metrics:

  • Quality
  • Safety
  • Groundedness

Quality

The Quality metric is itself arrived at by three metrics:

  1. Sensibleness
  2. Specificity
  3. Interestingness

The research paper states:

“We collect annotated data that describes how sensible, specific, and interesting a response is for a multiturn context. We then use these annotations to fine-tune a discriminator to re-rank candidate responses.”

Safety

The Google researchers used crowd workers of diverse backgrounds to help label responses when they were unsafe.

That labeled data was used to train LaMDA:

“We then use these labels to fine-tune a discriminator to detect and remove unsafe responses.”

Groundedness

Groundedness was a training process for teaching LaMDA to research for factual validity, which means that answers can be verified through “known sources.”

That’s important because, according to the research paper, neural language models produce statements that appear correct, but are actually incorrect and lack support from facts from known sources of information.

The human crowd workers used tools like a search engine (information retrieval system) to fact-check answers so that the AI could also learn to do it.

The researchers write:

“We find that augmenting model outputs with the ability to use external tools, such as an information retrieval system, is a promising approach to achieve this goal.

Therefore, we collect data from a setting where crowdworkers can use external tools to research factual claims, and train the model to mimic their behavior.”

LaMDA Was Trained Using Human Examples and Raters

Section 3 of the research paper describes how LaMDA was trained using a set of documents, dialogs, and utterances numbering in the billions, consisting of 1.56 trillion words.

Section 4.2 documents how humans rated the responses by LaMDA. The ratings are feedback that teaches LaMDA when it’s doing well and when it is not.

The human raters use an informational retrieval system (search engine) to verify the answers, ranking them as helpful, correct, and factual.

LaMDA Training Used A Search Engine

Section 6.2 describes how LaMDA receives a question and then generates an answer. After the response is generated, it then performs a search query to verify the accuracy and revises the answer if it is incorrect.

The research paper mentioned above illustrates the process of receiving a question, generating a draft response, researching the response, and then updating it with correct facts:

  1. “USER: What do you think of Rosalie Gascoigne’s sculptures?
  2. LAMDA-BASE: They’re great, and I love how her work changed through her life. I like her later work more than her earlier ones. Her influence is also super interesting – did you know she was one of the artists that inspired Miró?”

The problem with the answer is that it is factually incorrect. So LaMDA performs a search query and picks facts from the top results.

It then responds with the factually updated response:

“Oh wow, her life course is so inspiring. Did you know she was a practitioner of Japanese flower arrangement before turning to sculpture?”

Note the “Oh wow” part of the answer; that’s a form of speaking learned how humans talk.

It seems like a human is speaking, but it merely mimics a speech pattern.

Language Models Emulate Human Responses

I asked Jeff Coyle, Co-founder of MarketMuse and an expert on AI, for his opinion on the claim that LaMDA is sentient.

Jeff shared:

“The most advanced language models will continue to get better at emulating sentience.

Talented operators can drive chatbot technology to have a conversation that models text that could be sent by a living individual.

That creates a confusing situation where something feels human and the model can ‘lie’ and say things that emulate sentience.

It can tell lies. It can believably say, I feel sad, happy. Or I feel pain.

But it’s copying, imitating.”

LaMDA is designed to do one thing: provide conversational responses that make sense and are specific to the context of the dialogue. That can give it the appearance of being sentient, but as Jeff says, it’s essentially lying.

So, although the responses that LaMDA provides feel like a conversation with a sentient being, LaMDA is just doing what it was trained to do: give responses to answers that are sensible to the context of the dialogue and are highly specific to that context.

Section 9.6 of the research paper, “Impersonation and anthropomorphization,” explicitly states that LaMDA is impersonating a human.

That level of impersonation may lead some people to anthropomorphize LaMDA.

They write:

“Finally, it is important to acknowledge that LaMDA’s learning is based on imitating human performance in conversation, similar to many other dialog systems… A path towards high quality, engaging conversation with artificial systems that may eventually be indistinguishable in some aspects from conversation with a human is now quite likely.

Humans may interact with systems without knowing that they are artificial, or anthropomorphizing the system by ascribing some form of personality to it.”

The Question of Sentience

Google aims to build an AI model that can understand text and languages, identify images, and generate conversations, stories, or images.

Google is working toward this AI model, called the Pathways AI Architecture, which it describes in “The Keyword“:

“Today’s AI systems are often trained from scratch for each new problem… Rather than extending existing models to learn new tasks, we train each new model from nothing to do one thing and one thing only…

The result is that we end up developing thousands of models for thousands of individual tasks.

Instead, we’d like to train one model that can not only handle many separate tasks, but also draw upon and combine its existing skills to learn new tasks faster and more effectively.

That way what a model learns by training on one task – say, learning how aerial images can predict the elevation of a landscape – could help it learn another task — say, predicting how flood waters will flow through that terrain.”

Pathways AI aims to learn concepts and tasks that it hasn’t previously been trained on, just like a human can, regardless of the modality (vision, audio, text, dialogue, etc.).

Language models, neural networks, and language model generators typically specialize in one thing, like translating text, generating text, or identifying what is in images.

A system like BERT can identify meaning in a vague sentence.

Similarly, GPT-3 only does one thing, which is to generate text. It can create a story in the style of Stephen King or Ernest Hemingway, and it can create a story as a combination of both authorial styles.

Some models can do two things, like process both text and images simultaneously (LIMoE). There are also multimodal models like MUM that can provide answers from different kinds of information across languages.

But none of them is quite at the level of Pathways.

LaMDA Impersonates Human Dialogue

The engineer who claimed that LaMDA is sentient has stated in a tweet that he cannot support those claims, and that his statements about personhood and sentience are based on religious beliefs.

In other words: These claims aren’t supported by any proof.

The proof we do have is stated plainly in the research paper, which explicitly states that impersonation skill is so high that people may anthropomorphize it.

The researchers also write that bad actors could use this system to impersonate an actual human and deceive someone into thinking they are speaking to a specific individual.

“…adversaries could potentially attempt to tarnish another person’s reputation, leverage their status, or sow misinformation by using this technology to impersonate specific individuals’ conversational style.”

As the research paper makes clear: LaMDA is trained to impersonate human dialogue, and that’s pretty much it.


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Email Marketing: An In-Depth Guide

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Email Marketing: An In-Depth Guide

Email has revolutionized the way people communicate. From facilitating remote work to monitoring bank balances, it has become an integral part of everyday life.

It has also become a powerful tool for marketers. It has changed the way brands and customers interact with each other, providing incredible opportunities to target audiences at each stage of the buyer’s journey.

In other words, when it comes to getting the most bang for your marketing buck, nothing matches the power of email.

Providing an average return on investment of $36 for every $1 spent, email marketing is one of the most profitable and effective ways of reaching your targets.

Globally used by more than 4 billion people, it has unparalleled reach and is perfect for every step of the buyer’s journey, from generating awareness to encouraging brand loyalty.

If you’re not currently using email marketing to promote your business, you should be.

But to reap the biggest benefits, you need to do more than just dash off a message and sending it out to your contacts. You need a strategy that will help you nurture relationships and initiate conversations.

In this piece, we’ll take an in-depth look at the world of marketing via email and give you a step-by-step guide you can use to launch your own campaigns.

What Is Email Marketing?

If you have an email address of your own – and it’s probably safe to assume that you do – you’re likely already at least somewhat familiar with the concept of email marketing.

But just to avoid any potential confusion, let’s start with a definition: Email marketing is a type of direct marketing that uses customized emails to inform customers and potential customers about your product or services.

Why Should You Use Email Marketing?

If the eye-popping $36:1 ROI stat wasn’t enough to convince you to take the plunge, here are some other key reasons you should use email marketing to promote your business:

  • Email marketing drives traffic to your website, blog, social media account, or anywhere else you direct it.
  • It allows you to build a stronger relationship with your targets via personalization and auto-triggered campaigns.
  • You can segment your audience to target highly specific demographics, so you’re sending messages to the people they will resonate with most.
  • Email marketing is one of the easiest platforms to version test on, so you can determine exactly what subject lines and calls-to-action (CTAs) work best.

Even better, you own your email campaigns entirely.

With email, you own your marketing list and you can target your leads however you like (so long as you stay compliant with CAN-SPAM laws).

There is no question that you should be using email marketing as part of your overall marketing outreach strategy.

Now let’s look at some of the different ways you can do that.

What Are The Types Of Email Marketing?

For every stage of the sales funnel, there’s a corresponding type of email marketing. Here are some of the different types you can use to engage your audience and generate results.

Promotional Emails

When you think about email marketing, these types of messages are probably what you think of.

Used to promote sales, special offers, product releases, events, and more, these are usually one of the least personalized types of emails and tend to go out to a large list.

Usually, promotional campaigns consist of anywhere from 3 to 10 emails sent over a specified time frame. They have a clear CTA that encourages the recipient to take the next step of visiting your site, booking an appointment, or making a purchase.

Informational Emails

This type of email includes company announcements as well as weekly/monthly/quarterly newsletters.

They may include information about new products, company achievements, customer reviews, or blog posts.

The CTA is usually to visit your website or blog to learn more about what’s happening.

Welcome Emails

Sent to new customers or people who have filled out a form on your website, welcome emails encourage recipients to learn more about your company or offering.

These commonly include trial offers, requests to book a demo, or other offerings a new customer will find valuable.

Nurturing Emails

Any salesperson will tell you the importance of creating multiple touchpoints with potential customers.

Lead nurturing emails focus on building interest in people who are drawn to a particular offering.

The goal of these messages is to push them to the consideration stage of the buying journey.

Re-engagement Emails

Nurturing emails’ slightly more aggressive brother, re-engagement emails are used to warm up customers who haven’t been active lately.

These tend to be more personalized, as you’ll want to show the subscriber that you know and understand the challenges they’re facing.

Survey/Review Emails

User generated content (UGC) lends your brand an authenticity you simply can’t achieve on your own.

One of the best ways to generate this is via emails soliciting feedback from your customers.

This type of email also gives you insights into your brand’s relative strengths and weaknesses, so you can improve your offerings.

There are a number of other types of emails you can use as part of your marketing efforts, including seasonal emails designed to capitalize on holidays or events, confirmation emails to reassure recipients their purchase was completed or their information received, and co-marketing emails that are sent with a partner company.

In fact, it’s email marketing’s sheer versatility that makes it the cornerstone of any successful marketing strategy. You merely need to decide what you hope to accomplish, then create your campaign around it.

Now, let’s take a closer look at creating and managing your own email marketing.

How Do You Perform Email Marketing?

Step 1: Establish Your Goals

The section above should have made it clear that the type of email campaign you’ll run will depend on what you’re hoping to accomplish. Trying to do everything with one email will lead to confused recipients and a watered-down CTA.

Set one goal for your campaign, and make sure every email in the series works toward it.

Step 2: Build Your List

Now it’s time to determine who will be on the receiving end of your campaign. You do this by building your email marketing list – a process you can approach from several directions.

The most basic way to build an email list is by simply importing a list of your contacts into your chosen email marketing platform (more on that later).

One caveat: Before you add anyone to your list, make sure they have opted into receiving emails from you – otherwise you’ll run afoul of the CAN-SPAM Act guidelines mentioned above.

Other options for building a list from scratch via a lead generation campaign: provide potential customers with discounts, compelling content, or something else of value and make it easy for them to subscribe and you’ll generate high-quality leads.

Some marketers buy or rent email lists, but in general, this isn’t an effective way to perform email marketing.

The primary reason you don’t want to do this is because of lead quality. You’re not going after people who are interested in your brand but instead are blindly targeting leads of questionable quality with emails they haven’t opted in to.

In addition to violating consent laws, which could potentially hurt your IP reputation and email deliverability, you risk annoying your targets instead of encouraging them to try your offering.

Step 3: Create Your Email Campaign

Now that you know who you’re targeting and what you’re hoping to achieve, it’s time to build your campaign.

Email marketing tools like HubSpot, Constant Contact, and Mailchimp include drag-and-drop templates you can employ to create well-designed and effective email campaigns.

We’ll dive deeper into these platforms a bit later, but now, let’s talk about some fundamentals and best practices to help you get the best results:

  • Make your emails easy to read – No one wants to read a long wall of text. Structure your emails using strategically placed headers and bulleted lists for easy scanning.
  • Use images – Ideally, you want your emails to capture the reader’s eye and attention. Visuals are a great way to do this.
  • Write a compelling subject line – The best-written email in the world is useless if no one opens it. That makes a compelling, intriguing subject line paramount. Don’t be afraid to try different iterations, just be sure to keep it short.
  • Add personalization – Emails that are targeted to a specific person, including addressing them by name, are more likely to generate responses. Your email marketing platform should allow you to do this with relative ease.
  • Make conversion easy – If you want click-throughs, you need to make it easy for readers. Make sure your CTA is prominent and clear.
  • Consider your timing – As with most types of marketing, email campaigns tend to perform better when they’re properly timed. This could mean a specific time of day that generates more opens, a time of the week when purchases are more likely, or even a time of year when your content is most relevant. This will probably require some experimentation.

Step 4: Measure Your Results

You’re not going to get your email campaigns right the first time. Or the second. Or the fifth. In fact, there’s really no endpoint; even the best campaigns can be optimized to generate better results.

To track how yours are performing, you’ll want to use the reports section of your email marketing platform. This will help you understand how people are interacting with your campaigns.

Use A/B testing to drill down into what’s working best.

Generally, you’ll want to look at key metrics like:

  • Open rate and unique opens.
  • Click-through rate.
  • Shares.
  • Unsubscribe rate.
  • Spam complaints.
  • Bounces (the number of addresses your email couldn’t be delivered to).

Choosing An Email Marketing Platform

Manually sending out emails is fine if you’re only targeting three or four people. But if you’re trying to communicate with dozens, hundreds or even thousands of targets, you’re going to need some help.

But there are currently hundreds of email marketing platform on the market. How do you choose the right one for your unique needs?

Should you just go with one of the big names like HubSpot,  Klaviyo, or Mailjet? How do you know which one is right for you?

While it may initially feel overwhelming, by answering a few questions you can narrow down your options considerably.

The very first thing you need to determine is your budget. If you’re running a small business, the amount you’re willing to spend on an email service platform is probably considerably less than an enterprise-level company.

If you’re an entrepreneur, you’ll probably find that a lower-priced version of a platform like Sendinblue or Constant Contact provides you with all the functionality you need.

Larger companies with bigger marketing budgets may wish to go with an email marketing platform that provides higher levels of automation, more in-depth data analysis and is easier to use. In this case, you may prefer to go with a platform like Mailchimp or Salesforce’s Pardot.

The good thing is that most of these email service providers offered tiered pricing, so smaller businesses can opt for more inexpensive (or even free) versions that offer less functionality at a lower price.

The next thing to consider is the type of email you want to send.

If your primary send will be newsletters, a platform like SubStack is a great choice. If you’re planning on sending transactional emails, you may want to check out Netcore Email API or GetResponse.

For those of you planning on sending a variety of marketing emails, your best choice may be an option that covers multiple email types like ConvertKit or an omnichannel marketing tool like Iterable.

You can narrow down your options by determining your must-have features and internal capabilities.

Some things you’ll want to consider include:

  • The size of your lists.
  • Your technical skill level.
  • Your HTML editing requirements.
  • Template variety.
  • Your need for responses/workflows.
  • A/B testing needs.
  • Industry-specific features.

While there is significant overlap in functionality between email marketing platforms, each has some variation in capabilities.

Ideally, you want something that will integrate with your other marketing tools to help take the guesswork out of the equation.

You should request demos and trials of your finalists to find which is best for your needs. If you’re working with a team, be sure to loop them in and get their feedback.

Tips For Maximizing Your Results

Email marketing is a powerful tool for any business. But there’s both science and art to it.

Here are some additional tips to help you get the most from your campaigns:

  • Avoid being marked as spam – According to HubSpot, there are 394 words and phrases that can identify your email as junk mail. These include “free,” “lowest price,” “no catch” and “all new.” You should avoid these whenever possible. To be doubly safe, have your recipients add you to their safe senders list.
  • Run integrated campaigns – Email marketing serves to amplify the power of other marketing channels. If you’re running sales or promotions, you should include an email aspect.
  • Clean up your list regularly – Keep your email database up to date to ensure deliverability and higher engagement. If a subscriber hasn’t responded to your re-engagement efforts after six months, it’s probably safe to scrub them from your list.
  • Harness the power of automation – Autoresponders are a great way to follow up with customers and subscribers, or strategically target someone after a certain event or action. Learn how to set this up on your email marketing platform and it will save you lots of time while boosting returns.

Email Marketing Is A Powerful Tool

There’s a reason why email marketing is prevalent in the modern world – it works.

And that means you should be using it to promote your brand and drive sales.

Hopefully, by this point, you have a good idea of not only what email marketing can do for you, but how it works, and how to create and optimize your own campaigns.

There’s really no better way to connect with our audience and convey the value of your brand.

Now get to work – you have customers to attract.

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Twitter Will Share Ad Revenue With Twitter Blue Verified Creators

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Twitter Will Share Ad Revenue With Twitter Blue Verified Creators

Elon Musk, owner and CEO of Twitter, announced that starting today, Twitter will share ad revenue with creators. The new policy applies only to ads that appear in a creator’s reply threads.

The move comes on the heels of YouTube launching ad revenue sharing for creators through the YouTube Partner Program in a bid to become the most rewarding social platform for creators.

Social networks like Instagram, TikTok, and Snapchat have similar monetization options for creators who publish reels and video content. For example, Instagram’s Reels Play Bonus Program offers eligible creators up to $1,200 for Reel views.

The catch? Unlike other social platforms, creators on Twitter must have an active subscription to Twitter Blue and meet the eligibility requirements for the Blue Verified checkmark.

The following is an example of a Twitter ad in a reply thread (Promoted by @ASUBootcamps). It should generate revenue for the Twitter Blue Verified creator (@rowancheung), who created the thread.

Screenshot from Twitter, January 2023

To receive the ad revenue share, creators would have to pay $8 per month (or more) to maintain an active Twitter Blue subscription. Twitter Blue pricing varies based on location and is available in the United States, Canada, Australia, New Zealand, Japan, the United Kingdom, Saudi Arabia, France, Germany, Italy, Portugal, and Spain.

Eligibility for the Twitter Blue Verified checkmark includes having an active Twitter Blue subscription and meeting the following criteria.

  • Your account must have a display name, profile photo, and confirmed phone number.
  • Your account has to be older than 90 days and active within the last 30 days.
  • Recent changes to your account’s username, display name, or profile photo can affect eligibility. Modifications to those after verification can also result in a temporary loss of the blue checkmark until Twitter reviews your updated information.
  • Your account cannot appear to mislead or deceive.
  • Your account cannot spam or otherwise try to manipulate the platform for engagement or follows.

Did you receive a Blue Verified checkmark before the Twitter Blue subscription? That will not help creators who want a share of the ad revenue. The legacy Blue Verified checkmark does not make a creator account eligible for ad revenue sharing.

When asked about accounts with a legacy and Twitter Blue Verified checkmark, Musk tweeted that the legacy Blue Verified is “deeply corrupted” and will sunset in just a few months.

Regardless of how you gained your checkmark, it’s important to note that Twitter can remove a checkmark without notice.

In addition to ad revenue sharing for Twitter Blue Verified creators, Twitter Dev announced that the Twitter API would no longer be free in an ongoing effort to reduce the number of bots on the platform.

While speculation looms about a loss in Twitter ad revenue, the Wall Street Journal reported a “fire-sale” Super Bowl offer from Musk to win back advertisers.

The latest data from DataReportal shows a positive trend for Twitter advertisers. Ad reach has increased from 436.4 million users in January 2022 to 556 million in January 2023.

Twitter is also the third most popular social network based on monthly unique visitors and page views globally, according to SimilarWeb data through December 2022.


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AI Content Detection Software: Can They Detect ChatGPT?

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AI Content Detection Software: Can They Detect ChatGPT?

We live in an age when AI technologies are booming, and the world has been taken by storm with the introduction of ChatGPT.

ChatGPT is capable of accomplishing a wide range of tasks, but one that it does particularly well is writing articles. And while there are many obvious benefits to this, it also presents a number of challenges.

In my opinion, the biggest hurdle that AI-generated written content poses for the publishing industry is the spread of misinformation.

ChatGPT, or any other AI tool, may generate articles that may contain factual errors or are just flat-out incorrect.

Imagine someone who has no expertise in medicine starting a medical blog and using ChatGPT to write content for their articles.

Their content may contain errors that can only be identified by professional doctors. And if that blog content starts spreading over social media, or maybe even ranks in Search, it could cause harm to people who read it and take erroneous medical advice.

Another potential challenge ChatGPT poses is how students might leverage it within their written work.

If one can write an essay just by running a prompt (and without having to do any actual work), that greatly diminishes the quality of education – as learning about a subject and expressing your own ideas is key to essay writing.

Even before the introduction of ChatGPT, many publishers were already generating content using AI. And while some honestly disclose it, others may not.

Also, Google recently changed its wording regarding AI-generated content, so that it is not necessarily against the company’s guidelines.

Image from Twitter, November 2022

This is why I decided to try out existing tools to understand where the tech industry is when it comes to detecting content generated by ChatGPT, or AI generally.

I ran the following prompts in ChatGPT to generate written content and then ran those answers through different detection tools.

  • “What is local SEO? Why it is important? Best practices of Local SEO.”
  • “Write an essay about Napoleon Bonaparte invasion of Egypt.”
  • “What are the main differences between iPhone and Samsung galaxy?”

Here is how each tool performed.

1. Writer.com

For the first prompt’s answer, Writer.com fails, identifying ChatGPT’s content as 94% human-generated.

Writer.com resultsScreenshot from writer.com, January 2023

For the second prompt, it worked and detected it as AI-written content.

Writer.com test resultScreenshot from writer.com, January 2023

For the third prompt, it failed again.

Sample ResultScreenshot from writer.com, January 2023

However, when I tested real human-written text, Writer.com did identify it as 100% human-generated very accurately.

2. Copyleaks

Copyleaks did a great job in detecting all three prompts as AI-written.

Sample ResultScreenshot from Copyleaks, January 2023

3. Contentatscale.ai

Contentatscale.ai did a great job in detecting all three prompts as AI-written, even though the first prompt, it gave a 21% human score.

Contentscale.aiScreenshot from Contentscale.ai, January 2023

4. Originality.ai

Originality.ai did a great job on all three prompts, accurately detecting them as AI-written.

Also, when I checked with real human-written text, it did identify it as 100% human-generated, which is essential.

Originality.aiScreenshot from Originality.ai, January 2023

You will notice that Originality.ai doesn’t detect any plagiarism issues. This may change in the future.

Over time, people will use the same prompts to generate AI-written content, likely resulting in a number of very similar answers. When these articles are published, they will then be detected by plagiarism tools.

5. GPTZero

This non-commercial tool was built by Edward Tian, and specifically designed to detect ChatGPT-generated articles. And it did just that for all three prompts, recognizing them as AI-generated.

GPTZeroScreenshot from GPTZero, January 2023

Unlike other tools, it gives a more detailed analysis of detected issues, such as sentence-by-sentence analyses.

sentence by sentence text perplexityScreenshot from GPTZero, January 2023

OpenAI’s AI Text Classifier

And finally, let’s see how OpenAi detects its own generated answers.

For the 1st and 3rd prompts, it detected that there is an AI involved by classifying it as “possibly-AI generated”.

AI Text Classifier. Likely AI-generatedAI Text Classifier. Likely AI-generated

But surprisingly, it failed for the 2nd prompt and classified that as “unlikely AI-generated.” I did play with different prompts and found that, as of the moment, when checking it, few of the above tools detect AI content with higher accuracy than OpenAi’s own tool.

AI Text Classifier. Unlikely AI-generatedAI Text Classifier. Unlikely AI-generated

As of the time of this check, they had released it a day before. I think in the future, they will fine tune it, and it will work much better.

Conclusion

Current AI content generation tools are in good shape and are able to detect ChatGPT-generated content (with varying degrees of success).

It is still possible for someone to generate copy via ChatGPT and then paraphrase that to make it undetectable, but that might require almost as much work as writing from scratch – so the benefits aren’t as immediate.

If you think about ranking an article in Google written by ChatGPT, consider for a moment: If the tools we looked at above were able to recognize them as AI-generated, then for Google, detecting them should be a piece of cake.

On top of that, Google has quality raters who will train their system to recognize AI-written articles even better by manually marking them as they find them.

So, my advice would be not to build your content strategy on ChatGPT-generated content, but use it merely as an assistant tool.

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