Integrating ChatGPT With Google Sheets For Enhanced Data Analysis via @sejournal, @makhyan

ChatGPT remains one of the most talked about tools in the world of SEO.

Some users are finding ways to leverage the platform for content and SEO; others use it to create ads, optimize meta titles, create structured data, and be more productive overall.

And being more productive by integrating ChatGPT and Google Sheets together makes a lot of sense to me.

How To Integrate ChatGPT And Google Sheets

Integrating ChatGPT and Google Sheets can be achieved in a few ways.

While we’ll explore multiple ways to tie these two solutions together, the easiest method at the time of writing this post is to:

  • Open Google Sheets.
  • Click Extensions > Add-ons > Get add-ons.
  • Search “GPT for Sheets.”

You can also go directly to the website GPT for Work to install the add-on on Sheets – and you can use this same method to work with ChatGPT in Excel.

Add-ons make it simple to use ChatGPT with Sheets, but if the add-on becomes unsupported or stops working, you can use the methods below.

Different Ways To Integrate ChatGPT And Google Sheets

ChatGPT’s API allows developers to easily use the platform’s responses in their own code.

You can use Apps Script inside of Sheets to get this setup. First, you’ll want to:

  • Sign up for the Open API.
  • Make note of your API key (Personal > View API keys from the top menu).
  • Open Google Sheets.
  • Go to Extensions > Apps Script.

ChatGPT has the code available for easy copying, which is outlined below:

ChatGPT AppScripts Code for Google SheetsScreenshot from Google Sheets Apps Script, August 2023

Code to copy:

const OPENAI_URL = "";
const SYSTEM_MESSAGE = { role: "system", content: "You are a helpful SEO expert." };

function callChatGPT(prompt, temperature = 0.9, maxTokens = 800, model = "gpt-3.5-turbo") {
  const payload = {
    model: model,
    messages: [
      { role: "user", content: prompt }
    temperature: temperature,
    max_tokens: maxTokens

  const options = {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": "Bearer " + SECRET_KEY
    payload: JSON.stringify(payload)

  try {
    const response = UrlFetchApp.fetch(OPENAI_URL, options);
    const responseData = JSON.parse(response.getContentText());
    if(responseData.choices && responseData.choices[0] && responseData.choices[0].message) {
      return responseData.choices[0].message.content.trim();
    } else {
      console.error("Unexpected response format from OpenAI:", responseData);
      return "Sorry, I couldn't process the request.";

  } catch (error) {
    console.error("Error calling OpenAI API:", error);
    return "Sorry, there was an error processing your request.";

You’ll need to work your way through the code and change certain parameters. Primarily, you’ll replace “YOUR_OPENAI_API_KEY” with the API you jotted down previously.

Now, you can run the script and give it a try for yourself.

A quick test run of the =callChatGPT(“How can you help me?”) function will let you know if it’s working. The function should print out a list of items ChatGPT can help you with:

Google sheets chatGPT screenshot 1Screenshot from Google Sheets, August 2023

Inside your Sheets cells, you can call the Script using the following “callChatGPT(CELL-AND-ACTION-HERE),” and it will provide a response. Google might ask you to accept certain permissions, so be sure that you do if you want the script to work.

You may also utilize the function based on other cells, for example:

  • Put these keywords in cell A1: seo, chatgpt, google sheets.
  • In B1 cell input this function: =callChatGPT(“Provide a meta title for an article based on these keywords:” & A1).

You should receive a response like this:

ChatGPT response based on a single cellChatGPT response based on a single cell.

Tweak this formula based on your data, keywords, and other details.

Benefits Of Integrating ChatGPT With Google Sheets

Sheets is a product that I use quite often – it works great. But Sheets and Excel seem to lack innovations that allow you to use your data in new, exciting ways.

Integrating ChatGPT reduces the need to switch between both products and will boost your productivity in the process.

You can use the two together to:

  • Generate or translate text, which will allow you to create multiple posts on social media in a variety of target languages.
  • Create an ideas section for titles to help you come up with titles that are more clickable or user-friendly.
  • Summarize text that you can use for previews or snippets.
  • Make fast work of mundane tests, such as coming up with meta descriptions or product descriptions.

Tables help you visualize data and are great when reporting to clients, but they’re tedious to fill in. You can also use ChatGPT to generate tables for your data to better view and understand your data.

For example, you can create a table to monitor:

  • Title lengths.
  • Meta tag lengths.
  • Bounce rate changes.

When it comes to making sense of your data and analyzing it, you can have ChatGPT run the calculations for you and then create charts or tables around it.

Visualizing your data will make it easier to analyze and use it.

I’m sure you’ll find many great uses for ChatGPT and Sheets, but the following are some that I’ve found to be personally useful.

8 Ways To Use ChatGPT And Google Sheets Together

There are many ways to use ChatGPT and Sheets together, from tag generation to outlines and SEO research.

1. Generate Tags

Together, ChatGPT and Sheets make it easy to generate tags for products and build up your product tag library.

Just create a task for GPT, and it will generate tags for each product you select, saving you so much time in the process.

2. Clean Lists

Sheets and GPT can work together to help you clean up your lists.

Let’s say that you have a list of names. Because users input their names, some may be in all capital letters, and others may have emojis or inconsistencies in capitalization.

GPT can use the GPT_FILL function to clean up your name list and standardize it for easy use and organization.

3. Create Descriptions

Do you need to create product descriptions in bulk? ChatGPT can help.

Using the script, you can generate short product descriptions based on examples on your spreadsheet.

ChatGPT will analyze your example and generate descriptions that match the tone and style of your brand.

4. Generate Taglines, Ad Copy and Titles

With similar functions, you can use Sheet and ChatGPT to generate:

  • Ad copy.
  • Taglines.
  • Titles.
  • More.

ChatGPT can create ad copy that’s on-brand, captivating subject lines for emails, and other copy that will engage customers. With these tools, you can save your marketing team time and generate compelling content that converts.

5. Create Outlines

Creating outlines for blog posts can be time-consuming. Integrating ChatGPT into Sheets will save you time by generating outlines for your posts in seconds.

GPT can provide a structure for your posts and create outlines that will keep audiences engaged.

6. Keyword Research

With Google Sheets and ChatGPT, you can save time with keyword research. Just feed GPT a primary keyword and ask it to generate suggestions. The chatbot will generate a list of potential target keywords.

You can also use GPT to generate long-tail keywords.

You can use these keywords as “seeds” for your research or, at the very least, review them to ensure they’re worth targeting.

7. Generate Schema Markup Suggestions And Internal Linking Ideas

Inside of Sheets, you can use GPT to make schema markup suggestions based on the content and type.

For example, if your contact displays your address and phone number, ChatGPT can provide schema markup suggestions to help search engines better understand the information on your page.

You can also ask ChatGPT to provide you with internal linking ideas. Just provide a page topic to get more related topics for internal linking.

8. Perform Competitor And Content Gap Analysis

Want to streamline your competitor and content gap analyses? ChatGPT can help.

Just input some data about your competitors and ask GPT to provide you with some insights.

You can even ask the chatbot to provide suggestions for missing topics or areas to expand in your content.

Just provide ChatGPT with some background on your content landscape, and it can provide suggestions right in Sheets.

ChatGPT And Google Sheets: Better Together

Integrating ChatGPT into Sheets can help enhance data analysis, save time, and streamline processes.

Once you have an API key, integration is simple, and you’ll have access to a number of functions that you can use to analyze information, create charts, generate ideas, and more.

However, for simplicity, it’s easier to use add-ons that take care of the integration without scripts.

Once you connect ChatGPT and Sheets, you’ll be able to crunch numbers and ask the chatbot to begin helping you handle tedious tasks.

Even generating metadata or creating titles can be immensely helpful. You can even have ChatGPT help with creating redirects or add rules to robots.txt for you.

Example Formulas

Here are some example formulas you may want to use:

  • Input main keyword in the A1 cell and some secondary keywords in the B1 cell.
  • In C1 input: =callChatGPT(“Based on this keyword ‘”&A2&”‘ as the main keyword, and these ones as secondary keywords: “&B2&”, recommend an SEO friendly meta description. Make sure the length of your recommendation is a maximum of 150 characters, including the spaces.”).
  • In D1 input: =callChatGPT(“Based on this keyword ‘”&A2&”‘ as the main keyword, and these ones as secondary keywords: “&B2&”, recommend an SEO friendly meta title. Make the length of your recommendation to be a maximum of 55 characters, including the spaces.”).
  • In E1 input: =callChatGPT(“For a page to rank well in search engines on the topic of ‘”&A2&”‘, what would be your page content outline? Include these secondary keywords: “&B2&”, Provide the page outline with proper headings and structure. Output the outline only. Do not include a page title.”).

BONUS with script:

  • In F1 input: =callChatGPT(“Write SEO friendly FAQschema JSON, limited to 5 questions and answers, for an article with these keywords: ‘”&A2&”, “&B2&”‘ and this outline: (“&E2&”). Add the script opening and closing tags for Json”)
ChatGPT Google Sheets DemoScreenshot from Google Sheets, August 2023

Voilà – now you have an outline, meta title, meta description, and FAQ schema that goes with your keywords.

More resources: 

Featured Image: BestForBest/Shutterstock

Google Working To Remove Bard Chat Transcripts From Search via @sejournal, @MattGSouthern

Earlier this week, it was discovered that transcripts of conversations with Google’s AI chatbot, Bard, were appearing in search results.

This immediately raised privacy concerns, as one-on-one conversations were being surfaced publicly without peoples’ knowledge.

In response, Google has confirmed this indexing was unintentional and is working to block Bard chat transcripts from appearing in search results.

Google Confirms Chatbot Transcript Indexing Was Unintentional

Days later, Google’s Search Liaison shared an update on X, writing:

“While I’m not on the Bard team, I know that removing Google URLs from Google can sometimes be more complicated than for non-Google sites. You’re going to see some short-term changes in the blocking that should settle down into what makes sense for non-Google sites.”

Removing Own URLs More Complex for Google

Google’s statement acknowledges the unique challenge Google faces in removing its web pages from search, compared to non-Google sites.

While blocking regular sites can be done instantly, Google notes that removing URLs often involves “short-term changes” before achieving the desired effect.

Google is working on it, but thoroughly scrubbing Bard chat transcripts from search results will take time.

Bard Users Advised To Avoid ‘Share Chat’ Feature

As Google works to remove Bard transcripts from search results, users are advised to refrain from utilizing Bard’s “share chat” feature, as this creates a public, indexable link.

Private conversations that aren’t shared should remain unseen by search engines.

Google emphasized that search indexing was never an intentional Bard feature, and it aims to respect user privacy in the future.

However, the chatbot’s rocky launch has potentially damaged trust, making this latest privacy flub problematic.

Exercise Caution When Sharing Sensitive Info with AI

Expect Bard transcripts to dwindle from search results gradually. Exercise caution when sharing sensitive information with any AI chatbot.

While mistakes happen, Google’s handling of this incident will likely be scrutinized.

Google’s ability to swiftly solve self-created problems will be vital to maintaining trust and a positive public perception.

For a company desiring to lead in AI development, earning user trust should be Google’s top priority.

Featured Image: T. Schneider/Shutterstock

Mistral AI Launches Open-Source LLM, Mistral 7B via @sejournal, @kristileilani

Mistral AI, a burgeoning startup in the AI sector, has set out on a mission to revolutionize generative artificial intelligence (AI) with its first large language model (LLM), Mistral 7B.

The company hopes the new 7-billion-parameter model will become an open-source alternative to current AI solutions.

Mistral 7B And Mistral 7B Instruct Models

While others have set the industry standard with their “black-box” models, Mistral AI believes an open-source, community-driven approach can outpace them.

Drawing comparisons with the open-source movements in web browsers and operating systems, Mistral suggests that community-backed models are the future.

Mistral 7 B’s release comes as the company’s first significant step toward creating specialized models that compete with larger, more established AI solutions.

The raw model weights are distributed with Bittorrent and on Hugging Face. This documentation details the deployment bundle that allows to quickly spin a completion API on any major cloud provider with NVIDIA GPUs.

Mistral AI Launches Open-Source LLM, Mistral 7BScreenshot from Mistral AI, September 2023

Mistral AI’s open models aim to offer superior adaptability, enabling customization to specific tasks and user needs.

This approach is touted as advantageous for businesses aiming to keep costs low while maintaining performance.

Additionally, the company believes that open-source models will be critical tools in combating the ethical challenges associated with AI, such as censorship and bias.

As generative models continue to influence society, the ability to audit them for flaws and misuse is becoming increasingly vital.

How To Use Mistral 7B For Free

The Mistral 7B model is available for download, with documentation on GitHub or through Hugging Face.

In addition, you can chat with the Mistral 7B Instruct model on Perplexity Labs.

Mistral AI Launches Open-Source LLM, Mistral 7BScreenshot from Perplexity, September 2023

Mistral AI Made Headlines With Seed Funding

Mistral AI made headlines this summer when it raised $113 million in seed funding in June, underlining investor confidence in the open-source approach.

Funding was led by Lightspeed Venture Partners, with over a dozen investors, including Eric Schmidt, ex-Google CEO, who often discusses AI.

Mistral AI’s team is comprised of data scientists, software engineers, and machine learning engineers plucked from DeepMind, Meta, Hugging Face, and others.

Arthur Mensch, Co-founder and CEO of Mistral AI expressed excitement about what the company planned to achieve:

“Our training as AI researchers, combined with our respective professional experiences within the world’s leading technology companies, has convinced us that there is a way forward for an alternative, innovative project that will enable us to responsibly disseminate the most promising technology of our generation as widely as possible.

We are proud to initiate this global project from France, our home country, and to contribute, at our level, to the emergence of a credible new player in generative artificial intelligence from Europe. Over the coming months, we will focus all our energy and passion on honoring the trust placed in us by our investors.”

According to the pitch deck, Mistral’s plans include developing AI models superior to OpenAI’s in 2024.

In that round (Q3 2024), we expect to need to raise 200M, in order to train models exceeding GPT-4 capacities. Strong financing will allow us to train models on larger infrastructures, thereby establishing us as a research leader in AI that will be the go-to provider of the European industry.

Mistral AI hopes to progressively release new models that bridge the performance gap between its open-source solutions and proprietary offerings as part of its ongoing strategy.

France Positioned As Next Leader In AI Development?

In June, French President Emmanuel Macron, a big promoter of French tech startups, was at VivaTech, Paris’s largest European tech trade show.

He wanted to support French startups, help them expand internationally, and attract more investment in AI research and projects in France.

Technology experts have also noted that most developers (11 of 14) of Meta AI’s open-source Llama technology are French, making the latest AI developments unsurprising.

The Future Of Open-Source AI

A potentially robust and open-source competitor to existing LLMs like Mistral 7B could offer new opportunities for businesses to utilize AI, with broader customization possibilities and enhanced control over data security.

The move to open-source generative models represents a significant shift in the AI industry, challenging traditional proprietary models on ethical and performance grounds.

Featured Image: The Hornbills Studio/Shutterstock

Google Offers Publishers Control Over Bard, Vertex AI Access via @sejournal, @kristileilani

Find out how to use Google-Extended to control Google’s Bard, Vertex AI generative APIs, and future AI model’s access to content.

  • Google introduces Google-Extended, a new control for web publishers in the wake of rapid generative AI growth.
  • The control allows publishers to decide how their sites contribute to the evolution of Bard and Vertex AI generative APIs.
  • The initiative highlights the broader challenge web publishers face of managing access to various companies training data.
Google Expands Search Generative Experience (SGE) To U.S. Teens via @sejournal, @MattGSouthern

Google has announced plans to extend its new generative AI-based search feature, Search Generative Experience (SGE), to teenagers aged 13-17 in the United States.

The move follows the successful launch of Search Labs earlier this year and the feedback received from users aged 18-24.

The younger demographic finds SGE particularly valuable, Google says, as it allows them to interact with the search engine more naturally, ask follow-up questions, and mimic the flow of a human discussion.

Google is extending these capabilities to teens, believing SGE can assist them in exploring topics of interest and answering questions that traditional search engines struggle with.

Balancing Opportunities and Safety

As SGE is rolled out to a younger audience, Google emphasizes the importance of striking the right balance between opportunities and safety concerns.

To do this, Google has researched and consulted experts in teen development and implemented appropriate safeguards.

Addressing safety concerns, SGE has stricter safeguards for younger users, including stronger filters to prevent inappropriate content related to illegal substances, bullying, and other issues.

Additionally, Google published a new “AI Literacy Guide” to educate teens and parents about how the technology works.

This guide is available as part of the onboarding process for the new features in Search Labs and can be found in the Search Help Center.

Google stresses that Search Labs’ experiences are experimental and in the early stages. The company will continue gathering feedback to improve the technology.

‘About This Result’ In SGE

To ensure users can evaluate the reliability of information and sources found in SGE, Google is integrating the “About this result” feature found in traditional search results.

“About this result” is designed to give searchers a deeper understanding of how Google’s AI generated the search engine results pages (SERPs).

The feature highlights webpages and content that back up information in Google’s AI-powered summaries, allowing searchers to verify facts and sources.

Google Expands Search Generative Experience (SGE) To U.S. TeensScreenshot from:, September 2023.

Looking Ahead

The launch of SGE to a broader audience comes as technology companies face growing scrutiny around the societal impacts of large language models.

While promising to enhance search, generative AI raises concerns about the potential to spread misinformation and bias.

Google acknowledges these concerns and is committed to continuous improvement, using rigorous testing and evaluation to ensure a high-quality, helpful user experience.

This technology is in the early stages, so user feedback is essential for future updates.

Featured Image: Koshiro K/Shutterstock

What Is Quality Data And How It Connects Search, Content, And AI Success

Data is the lifeblood of search.

The remarkable evolution of AI and the introduction of generative AI has been built on data foundations.

However, the success of any innovation, product, or technological advancement boils down to the quality of that data. Utilizing the correct data is essential when connecting generative AI, search, and content marketing.

Data is exploding, with the IDC projecting that the size of global data will reach 175 zettabytes by 2025.

This is twice the amount produced last year, signaling a significant growth trend. I actually predict more!

To adapt to this data boom, professionals utilizing generative AI must evaluate their data sources and identify the most valuable functionalities for the future.

Poor Vs. Quality Data

The detrimental impact of poor data on businesses is undeniable.

Poor-quality data is the root cause of operational disruptions, inaccurate insights, and poor decision-making.

According to a report by Gartner in 2021, organizations suffer an average annual cost of $12.9 million due to bad data quality.

In the past, data quality efforts were primarily focused on structured data in relational databases.

However, marketers now face new challenges with the rise of big data systems, cloud computing, and unstructured data types like text and sensor data. Managing data quality across cloud systems has become essential.

In search and content marketing, data has never been so important. However, 57% of marketers misinterpret data, resulting in costly mistakes.

This can result from that data via disparate data sources and the related problems with processing large data sets at scale

What Is Quality Data?

Quality data combines crucial factors such as accuracy, connectivity, completeness, and reliability.

Data Quality characteristicsImage created by author, September 2023

The accuracy of the data you use defines success in search – ensuring executives, content, digital, product, marketing, and sales departments are equipped with accurate information is essential.

Reliable data is driving increasingly intelligent search decisions that impact business performance.

Additionally, quality data management plays a central role in connecting the dots between SEO and Content marketing performance.

In addition to accuracy, several other dimensions contribute to good data quality, including:

  • Completeness: Data sets should contain all the necessary data elements.
  • Consistency: Data values across different systems or data sets should not conflict.
  • Uniqueness: Duplicate data records should be avoided in databases and data warehouses.
  • Timeliness: Data should be regularly updated to remain current and readily available.
  • Validity: Data should contain the expected values and follow proper structure.
  • Conformity: Data should adhere to the standardized formats established by your organization.

By meeting these factors, data sets become reliable and trustworthy and align with data governance efforts to ensure consistent and effective data usage across organizations.

Data, Search, And Generative AI

A combination of humans and machines creates a data and content marketing battleground where quality and connectivity are crucial to success.

The adoption of AI tools, machine learning applications, real-time data streaming, and complex data pipelines has further complicated the data quality process.

Compliance with data privacy and protection laws, such as GDPR and CCPA, has increased the demand for accurate and consistent data.

While the volume of global data grows exponentially, at the same time, SEO is changing as consumer demands continue to evolve, and search engines cater to these changes by creating new experiences and experimenting with the integration of AI in search engine results pages (SERPs).

As a result, marketers need to carefully reconsider their approach to data, technical SEO, and generative AI outputs.

Data Inputs And Generative AI Outputs

The quality of generative AI outputs depends on the quality and connectivity of the data that feeds it.

Many of you will have experienced this, especially in the early days of generative AI and ChatGPT, Bing AI, and Google Bard.

This is why we see more and more prompt engineering and fine-tuning of data from large language models (LLM).

Generative AI, along with tools like ChatGPT and Google Search Generative Experiences (SGE), has been the subject of much discussion.

Generative AI based on quality data analysis is already saving time and efficiency for SEO pros.

Percent of marketers on titles and metadescriptionsImage from author, August 2023

Generative AI can help SEO and content marketers complete repetitive tasks at a faster pace and with accuracy.

Over 98% of our customers are saving valuable time crafting SEO titles and descriptions utilizing BrightEdge Copilot (Disclosure: my company).

However, the value of quality data feeding generative AI also extends beyond time savings.

By utilizing quality data, marketers can enhance their understanding of consumer and conversational intent (the key to generative AI results in the SERPS) and understand datasets by incorporating external industry classification data, ultimately reducing processing times.

Additionally, generative AI can create training and synthetic data sets to support the further development of AI and machine learning models.

However, this evolution does call for marketers to adjust their approach to data and ensure;

  • Quality and Connectivity of Data: AI outputs are only as good as the inputs. Ensure the sources you use are complete and combine historical and real-time data. Avoid multiple disparate data sources that give an incomplete picture of your consumer behavior to avoid GIGO – Garbage In, Garbage Out.
  • Integration into Enterprise Data Strategy: Generative AI should be considered an integral part of the data strategy. Ensure its inclusion from the outset and align it with your broader enterprise marketing goals.
  • Proactive Challenge Addressing: Proactively address security, bias, and accuracy challenges specific to generative AI. Assessing and mitigating these risks is essential for successful implementation and future compliance issues.
  • Focusing on Analytics Cycle Components: The initial adoption of generative AI should target specific components of your marketing campaigns and specific use cases. Testing outputs continuously to ensure applications work and guarantee success, especially when outputs are being produced at scale.
  • Prioritizing Business Impact: Prioritize programs that drive measurable business impact to your campaigns. Ensure any technologies you use are tried and tested, and innovations in generative AI are validated and backed by foundational quality, high-fidelity data sets.

Moving Forward With Data In SEO

When looking at how AI impacts SEO, it’s good to consider that every webpage has human and machine visitors: people seeking relevant content that answers their questions and needs and search engine spiders or bots analyzing technical content.

Data processing has become indispensable for assessing site content and informing digital strategies.

SEO marketers are now inundated with incremental data additions that can be overwhelming to decipher. We are fortunate that AI and automation in SEO are not new, and automated technologies can reduce manual data efforts and improve business decision-making such as;

  • Collecting and structuring big data to generate smaller, more valuable, actionable insights.
  • Improving tasks like data classification, tagging, and cleansing.
  • Online research, site auditing, and intent modeling.
  • Uncovering valuable insights into how consumers are interacting with search engines.

This also helps marketers who lack the necessary degrees or experience in data science to do this effectively.

Marketers who leverage data correctly can adapt to changing consumer expectations, keep up with granular search changes, and meet Google standards.

Utilizing a combination of unique knowledge and high-fidelity data (propriety) is crucial for staying competitive and ensuring that AI applications are successful on solid data foundations.

Marketers can harness the power of data to extract meaningful insights from the noise.

For example, retail marketers can uncover the issue of duplicate content, while marketers in banking can focus on concise content. Tailored best practices and industry-specific problem-solving give marketers a competitive edge.

This all helps create better, faster search experiences.

Data and Search Generative ExperiencesImage created by author, September 2023


Many SEO pros still do not fully utilize the value of data due to its overwhelming complexity. However, with the help of advanced AI, these hidden insights can be uncovered and understood.

By harnessing the power of AI-first technologies, marketers can optimize their content for maximum impact across multiple digital channels, adapting to changing technologies and consumer behavior.

As organizations forge ahead with their generative AI strategies, it is vital to remember the success of applications relies on the data that feeds it.

Ensure quality and connective data are central to your AI roadmap. Without it, success will be limited.

More resources: 

Featured Image: 3rdtimeluckystudio/Shutterstock

Meta’s AI Vision Revealed: Bing Integration, Celebrity Chatbots, + More via @sejournal, @MattGSouthern

At its annual Connect conference, Meta unveiled new AI innovations coming to its family of apps and devices.

Meta is rolling out an AI-powered assistant with Microsoft Bing integration, new image editing capabilities, and AI-generated stickers for messaging.

Meta’s AI assistant will provide real-time information using Microsoft Bing, with the ability to generate images from text prompts.

Among the most buzzworthy announcements is that some of the new AI chatbot characters will be voiced by celebrities like Snoop Dogg, Tom Brady, and Kendall Jenner

Here’s an overview of new features at Meta’s annual Connect conference on Wednesday.

AI-Generated Stickers Come To Messaging Apps

Meta is launching AI-generated stickers in its messaging apps. This enables you to turn text prompts into customized stickers.

The feature uses AI models like Llama 2 and Emu and aims to provide more creative ways for users to express themselves in chats and stories.

AI-generated stickers will roll out over the next month to English-speaking users.

New Instagram Tools To Edit Images With AI

Two new Instagram features will let users transform or co-create images using AI.

Using descriptive terms, Restyle, and Backdrop can overhaul existing images. These features rely on technology from Emu and Meta’s company’s Segment Anything Model.

Images created with Restyle and Backdrop will indicate they used AI.

Meta AI Assistant Coming To Apps & Devices

A new conversational AI assistant, Meta AI, is coming to WhatsApp, Messenger, and Instagram. It provides real-time info via a Microsoft Bing integration and generates photorealistic images from user text prompts.

Meta AI aims to enhance connections and productivity. In a hypothetical scenario provided by Meta, users in a group chat could use Meta AI to decide on a hiking trail or create a digital merit badge to commemorate their adventure.

Meta AI’s language model draws on Meta’s Llama 2 and other research.

New AI Characters With Unique Personalities

Beyond Meta AI, Meta unveiled 28 additional AI characters for its messaging apps. Each has a distinct backstory and personality.

Some characters are voiced by celebrities like Snoop Dogg, athletes like Tom Brady, and influencers like Kendall Jenner.

Meta wants interacting with the AIs to feel like chatting with familiar people, so the characters have social media profiles to share background info.

Currently in beta, these AI characters are set to roll out in the United States, with plans for more characters played by figures such as Bear Grylls, Chloe Kim, and Josh Richards.

More AI Creation Tools Coming

Looking ahead, Meta plans to release its AI Studio, enabling third parties to create their own AIs for Meta’s messaging services. Developers will have access to APIs for building on Messenger, with plans to expand to WhatsApp.

Businesses can design AI characters that reflect their brand values and improve customer service experiences. Creators, too, can build AIs that extend their virtual presence across Meta’s apps.

Further, Meta is developing a sandbox for users to experiment with creating their own AI.

As the company’s AI offerings grow, it plans to bring the sandbox to the metaverse, enabling users to craft AIs with greater realism and connectivity.

In Summary

Meta’s latest AI announcements show the company’s commitment to integrating advanced artificial intelligence across its platforms.

As Meta rolls out these AI capabilities, transparency about generated content and responsible development remain key considerations.

Users stand to benefit from enhancements to connectivity, productivity, and entertainment. However, the technology warrants close observation regarding ethics and safety.

Featured Image: Screenshot from, September 2023. 

GPT-4 With Vision: Examples, Limitations, And Potential Risks via @sejournal, @kristileilani

OpenAI has made waves in the tech world again with its latest innovation: GPT-4 with Vision, or GPT-4V.

GPT-4V builds on GPT-4 and incorporates visual capabilities, allowing the model to analyze images provided by ChatGPT Plus and Enterprise subscribers.

The new feature has great potential but also carries some risks for businesses.

GPT-4 With Vision Examples

As more users gain access to the new feature, they are sharing examples of how GPT-4 with Vision works.

GPT-4 with Vision can analyze handwriting.

It can create code for a website using a napkin drawing.

It can analyze memes.

In addition to these examples, I ran a few simple tests.

GPT-4 with Vision can write product descriptions for your sales pages and Amazon listings.

GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023

It can help you get started with basic coding for a particular website design based on a screenshot.

GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023
GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from W3Schools, September 2023

It can write creative Instagram captions with hashtag suggestions.

GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023

It can write an article based on data from a website or ebook, such as the State of SEO 2024.

GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023

As with all AI-generated content, it’s essential to review output from GPT-4 with Vision for accuracy. It still hallucinates and poses other risks.

OpenAI Reveals Potential Risks Of GPT-4V

OpenAI released a paper outlining potential risks associated with the use of GPT-4V, which include:

  • Privacy risks from identifying people in images or determining their location, potentially impacting companies’ data practices and compliance. The paper notes that GPT-4V has some ability to identify public figures and geolocate images.
  • Potential biases during image analysis and interpretation could negatively impact different demographic groups.
  • Safety risks from providing inaccurate or unreliable medical advice, specific directions for dangerous tasks, or hateful/violent content.
  • Cybersecurity vulnerabilities such as solving CAPTCHAs or multimodal jailbreaks.

Risks posed by the model have resulted in limitations, such as its refusal to offer analysis of images with people.

GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023
GPT-4 With Vision: Examples, Limitations, And Potential RisksScreenshot from ChatGPT, September 2023

Overall, brands interested in leveraging GPT-4V for marketing must assess and mitigate these and other generative AI usage risks to use the technology responsibly and avoid negative impacts on consumers and brand reputation.

OpenAI’s First Partner To Prepare Image Input For “Wider Availability”

OpenAI announced that the GPT-4 with Vision model will power Be My Eyes Virtual Volunteer, a digital visual assistant designed for the visually impaired.

Although the tech is still in beta, the possibilities are tantalizing. For example, this technology could assist businesses in elevating accessibility in customer service.

Be My Eyes plans to beta-test the feature with corporate clients, emphasizing its commercial potential beyond its primary audience.

The Future Of GPT-4 With Vision

The potential applications of GPT-4 With Vision for businesses, marketers, and SEO professionals could be groundbreaking.

However, all users should remain cautious due to the potential privacy, fairness, and cybersecurity issues posed by GPT-4 with Vision and other AI models.

Featured image: Tada Images/Shutterstock

AI-Powered Conversations: How To Automatically Boost Your User Experience With Phone Calls via @sejournal, @CallRail

Check out CallRail’s latest ebook to learn more about how analyzing phone calls can help:

Tip 2: Use AI Call Data To Inform Your SEO Strategy & Maximize Your Potential

You can use AI to pull important data from your clients’ phone conversations more efficiently and apply these insights to your marketing strategies with CallRail’s Conversation Intelligence®.

Then, transcribe speech to text with near-human level accuracy – you can do it instantaneously with Automated Speech Recognition (ASR).

Rather than getting bogged down with tedious manual tasks, let AI do the heavy lifting for you and spend more time on the things that really move the needle.

AI-powered tools such as CallRailʼs Conversation Intelligence can assist with sales or customer service conversations in real time.

If an agent is unsure how they should follow up on a call, for example, a generative AI model could analyze the call, compare it against the wealth of other calls it has already analyzed, and make a data-based recommendation on the next steps.

Conversational Intelligence is also a great tool for automating call summaries and aggregate trends.

It allows you to coach your clients on lead follow-up with time-saving call summaries. Plus, through call sentiment analysis, you are able to pinpoint the calls that require your clients’ immediate attention, enabling them to respond at a faster rate.

You can even utilize this technology to help you find new keywords to run campaigns against or adjust your strategy for keywords you’re already targeting.

An AI-powered tool could also recommend changes or update bids automatically, by analyzing the calls that came from these campaigns and determining whether the lead converted.

Want to learn more about making data-based recommendations with generative AI, as well as empowering the other tools in your marketing stack? Download CallRail’s new ebook.

Tip 3: Humanize Your Business & Strengthen Client Connections

By adding AI to your clients’ inbound phone calls, you can form more meaningful connections.

The data you’re able to access with this technology reveals a whole new layer to target consumers, digging deeper into their strongest needs and desires.

And by adjusting your campaign strategy to meet users where they are, you create a lasting impression that will keep them wanting more.

By tapping into AI’s ability to optimize your clients’ marketing and sales conversations, you can help them connect better with more customers.

AI exposes the quality of your clients’ connections and helps you make them more robust, satisfying, and human, rather than making them more robotic.

For marketing agencies specifically, conversational AI makes it possible to act on data you wouldn’t have otherwise had access to while automating tasks like:

  • Call transcription.
  • Summaries.
  • Keyword spotting.
  • Trend analysis.

Although the traditional phone call might appear to be teetering on the brink of obsolescence, conversations remain relevant, thanks to AI’s newfound ability to tap into their invaluable wealth of user data.

Get your free copy of CallRail’s new ebook to find out more about how these rare insights can help you explore new marketing opportunities and take your agency to the next level.

Unleashing The Power Of Conversational AI

Embracing modern tools has the power to transform the challenges associated with phone calls into opportunities.

Call tracking makes it not just possible but easy to connect incoming calls to marketing campaigns and lead records.

But more remarkably, conversational AI can unlock those rich, qualitative insights hidden inside the raw audio data of the call.

This technology helps you tap into how potential users might want to interact with your client’s product and the questions that they may have – you can then use conversational AI tools to help route them to relevant information.

With the following tips, you’ll learn how to start using this advanced form of AI to supercharge your clients’ phone conversations and boost user experience.

Leverage Your Conversation Data With CallRail & Build A Winning Marketing Strategy

Enhanced by the power of AI technology, the phone call is steadily reclaiming its relevance as a potent medium for consumer engagement.

It’s time to embrace the conversational revolution and recognize calls for what they are: a gold mine of intelligence waiting to be tapped.

So, if you’re ready to start mining gold from phone conversations, CallRailʼs Conversation Intelligence is the leading AI tool for uncovering the hidden gems of data concealed within every interaction.

Start your 14-day free trial to access the key insights that not only illuminate consumer preferences and behavior, but also pave the way for growth.

Plus, download CallRail’s new ebook to learn more about leveraging phone calls to improve your marketing strategies and unlock new opportunities.

Getty Images Launches ‘Commercially Safe’ AI Image Generator via @sejournal, @kristileilani

Getty Images, the visual content giant, recently partnered with NVIDIA, a leader in computing technology, to introduce a groundbreaking service: Generative AI by Getty Images.

This AI-driven image generation service is powered by NVIDIA Picasso, a custom AI model tailored to transform the content creation landscape in commercial safety and scalability.

Generative AI By Getty Images Offers Commercial Safety

Built on NVIDIA’s reliable computing infrastructure, the AI Generator aims to enhance creativity and content creation by synthesizing high-quality images.

The service offers an end-to-end solution that combines seamlessly with Getty Images’ extensive library of licensed content. The partnership signals a move toward automating visual design without sacrificing quality or safety.

Perhaps most intriguing is the technology’s commitment to commercial safety.

Because the NVIDIA Picasso model was trained on Getty Images’ curated library, Getty Images promises “uncapped indemnification” (financial protection) from intellectual property claims.

Such a feature must not be underestimated in a rapidly changing legal and regulatory environment.

The system’s API allows for text prompt-to-image generation.

Customers can describe their desired visuals, and the AI tool produces corresponding images in real-time.

This could prove transformative for industries such as advertising, design, and digital marketing, who can now fine-tune their visual assets easily.

Despite the technological prowess, questions about responsible AI use persist.

Getty Images ensures precautions have been taken to block prompts that could generate problematic content.

Additionally, the partnership includes a model that compensates original content creators, aligning with ongoing discussions about AI ethics and creator remuneration.

The announcement from Getty Images follows a succession of recent updates from companies working to expand generative AI image creation and editing.

Shutterstock Partners With OpenAI For Generative AI

Shutterstock launched its AI image generator in January 2023.

The company partnered with OpenAI to develop its generative AI features for customers and to compensate artists when their artwork is used as part of AI training data.

While the implications of AI in the creative field continue to be hotly debated, Shutterstock presented a well-thought-out framework that addresses ethical concerns.

It also indemnifies customers using the AI image generation tool for commercial projects.

In addition to its generative AI plans for customers, it also announced a Contributor Fund. The fund aims to equitably compensate Shutterstock contributors whose work was used in developing AI technology.

Shutterstock has also partnered with NVIDIA to create generative AI tools for 3D workflows.

Adobe Adds Firefly To Creative Cloud Membership

Adobe recently added Firefly to its lineup for Creative Cloud subscribers. Firefly’s generative AI capabilities are expansive, covering many creative tasks.

For instance, with Generative Fill and Generative Expand in Photoshop, users can utilize simple text prompts to alter content within any image. The tool even allows the canvas to be effortlessly expanded, filling the added space with matching content.

In Adobe Illustrator, the Generative Recolor feature offers the possibility of infinite color combinations through simple text prompts, speeding up the often laborious task of color matching and palette creation.

Adobe Express also gets a dose of Firefly magic. The app now includes generative AI Text to Image and Text Effects features, making creating captivating social media posts more accessible than ever.

Adobe ensures this technology’s ethical and responsible use through its commitment to the Content Authenticity Initiative (CAI) and the Coalition for Content Provenance and Authenticity (C2PA).

Like Shutterstock, Adobe plans to compensate Adobe Stock contributors whose work was used in AI training.

Canva Adds Google And OpenAI Image Apps

A week after Adobe’s announcement, Canva revealed that its users now have access to three apps for AI-powered image generation: Text to Image by Canva, Imagen by Google Cloud, and DALL·E by OpenAI.

Initially launched in 2022, Canva’s Text to Image has been a groundbreaking feature. This app allows users to input text descriptions and produce images almost instantaneously.

The rapid adoption of this feature, demonstrated by the creation of nearly 290 million images, attests to its utility across various sectors, from social media management to business communications.

In addition, partnerships with Google Cloud and OpenAI offer additional avenues of generative AI creativity for Canva users.

Unlike Text to Image, which is integrated into the Canva experience, DALL·E and Imagen are accessible through Canva’s app marketplace, bringing top-tier AI technologies into one centralized platform for creative professionals.

Getty Images Launches ‘Commercially Safe’ AI Image GeneratorScreenshot from Canva, September 2023

Though Canva’s apps operate independently, they are designed to be harmoniously integrated into the platform’s overall user experience. Users can jump from text to image without the hassle of navigating multiple tools or platforms.

While the ethical and safety aspects of AI in design are under constant scrutiny, Canva notes that it has invested heavily in responsible technology.

However, Canva does not explicitly say that images generated with AI tools are safe for use in commercial projects.

DALL·E 3 Is Coming To ChatGPT, Bing, And Microsoft Designer

OpenAI’s latest text-to-image model, DALL·E 3, will become available in ChatGPT Plus, Bing Image Creator, and Microsoft Designer this fall.

Regarding commercial use, OpenAI’s support page notes the following:

“Subject to the Content Policy and Terms, you own the images you create with DALL·E, including the right to reprint, sell, and merchandise – regardless of whether an image was generated through a free or paid credit.”

With regards to Microsoft’s tools powered by DALL·E, Bing Image Creator terms specify that images are for non-commercial use only.

General Microsoft terms also state that “Unless otherwise specified, the Services are for your personal and non-commercial use.”

Making Generative AI Tools Safer For Commercial Use

As major tech platforms and services integrate generative AI features, it democratizes the design process for marketers and SEO professionals to produce high-quality, compelling content more quickly and intuitively.

It’s important, however, for users to consider acceptable usage terms and available legal protections when using AI-generated images for commercial projects.

Featured image: Kaspars Grinvalds/Shutterstock