Content creation via Artificial Intelligence (AI) for marketing isn’t quite as seamless as most businesses hoped it would be. The language is clunky, wordy, and incredibly repetitive. But what if you could train that AI to write exactly like your brand in the blink of an eye? Generative retrained transformer models like GPT-3 or GPT-4 are very close to this. In this blog, we will look at what fine-tuning is, why it’s exciting, and how it could help your business in the future. 

Understanding GPT Models

GPT models can create text, but to do that they have to consume a lot of data to learn how we write things online. Unfortunately, despite all the training these models have been put through, they are still barely coherent and only really make sense on a surface level, as a lot of what they write is repeated and regurgitated and sometimes outright plagiarism. 

The GPT-3 version of the model can write basic fiction and blogs, translate languages, write ads, and more. And the GPT-4 version is said to be more advanced. But those in the know, like writers and marketers can instantly spot a piece written by the models as despite their access to diverse data, they usually write in the same way every time. 

 Fine-Tuning Process

Enter fine-tuning, where you teach an AI model to sound like you. Fine-tuning needs your data in order to learn, and with it can write like you and possess the industry knowledge you have to boot, as long as you give it the tools. This is fantastic if you have a lot of written content in your brand voice already as you could train GPT with pre-existing data. 

To train it well, you’d need many examples; the first step would be to create a spreadsheet of inputs and outputs. To get easy inputs and outputs, you could run your text through Chat-GPT and pretend the original text is the output and the GPT text is the input. This would teach the model to turn its own way of writing into your style. 

You’d also want to feed it industry reports that are up-to-date, customer reviews so it knows what your clients/customers want, and your very best ads. Furthermore, the content you feed the model should also be pretty diverse. For example, different emotional tones, different types of platforms for which you write content, and so on. 

Once you’ve gathered all that content up you have to feed it into a specialized platform using python scripts, or other coding. The model will then scrape through all your data and test itself to make sure it can get as close to your style as possible. In some cases this process is iterative, meaning you can monitor the model’s learning progress and adjust the training data or instructions as needed.

Can I Fine-Tune My Content Yet?

Content creators and marketers alike know how invaluable a tool like this could be, turning generic text into something that is fit for a brand. But unfortunately, this isn’t widely available yet. There isn’t software that lets you directly fine-tune a model and easily spit out what you need. There are tools though that can get you close. 

For example:

  1. Cloud-based Fine-tuning Platforms: Several cloud platforms offer fine-tuning capabilities for GPT models, like OpenAI’s API access and Google’s AI Platform. These platforms require some technical expertise to figure out, like the ability to create JSON files and write python. But they give the most control over the training data and model outputs.
  1. AI Writing Assistants with Pre-Trained Models: AI writing assistants like Jarvis Jasper and ShortlyAI have created their own kind of fine-tuning so that their text sounds more natural. It’s not the brand’s voice specifically, but it’s better than ChatGPT. You can also train assistants on the platform to copy your style by feeding it lots of examples.  

Here’s a breakdown of the pros and cons to help you decide:

  • Cloud-based Fine-tuning Platforms:
    • Pros: You get a lot of control over the type of data and therefore what he model outputs. The model is then more likely to copy your voice. 
    • Cons: You need someone who has the expertise which can cost you, plus it takes some time to achieve it. 
  • AI Writing Assistants:
    • Pros: These are pretty easy to use, and you won’t need to know things like coding. Plus, it’s pretty affordable. 
    • Cons: You won’t get the same accurate results, and have less control over the training as you can’t be sure what the software is telling the model. 

Implementing Fine-Tuned GPT Models in Content and Ads

The possibilities for fine-tuning are endless. Businesses can take advantage of a model trained specifically for their style and knowledge in these areas:

  • Content Creation: better quality blogs that sound like your team are an incredible advantage, as are social media copy, and ads. Content creation would be so much faster and need less overhaul. 
  • Enhancing Advertising Strategies: Fine-tuning allows you to create targeted ad content that speaks directly to different audience segments. The model can analyze past campaign data and competitor strategies to generate ad copy that works. 

FAQs

Q: What are the common challenges in fine-tuning GPT models?

A:  It can take some time for a model to analyze your data, not to mention the hours you’d need to collect all the data to feed it. Then, you’d have to put it together using the correct codes, which only someone versed in coding languages can do well. 

Q: How much data is needed to effectively fine-tune a GPT model?

A: This depends on how complicated the task is that you want your fine-tune models to achieve. And how accurate you want it. But the more data you feed it, the better it will be. For things like writing style, writers are finding that anything over 100 examples is optimal, but preferably 200 or more, of course. 

Q: Can fine-tuned GPT models replace human creativity?

A: You still can’t train a robot to be creative, so you’d still have to babysit what it is creating. Feed the model the ideas, prompt it in the right direction, etc. It can’t make creative and unique content; just sound more like you and has access to better data. 

The Future of AI is Fine-Tuning

Fine-tuning is the direction content creation is likely going when it comes to AI. But it will never replace human creativity and will always need an expert at the wheel. Content creation for marketing is a complex process that requires an abundance of knowledge to do well.  

XAI, or Explainable Artificial Intelligence, is a group of AI where the models are understandable and transparent for consumers. Some AI can act a lot like a black box, but XAI allows you to look into its inner workings and see the why behind the decisions it makes. This is great for businesses who may worry about what AI is actually doing with their data, not to mention it gives their customers peace of mind. In this blog, we will look at why XAI is so important moving forward and the many ways it is or will be implemented. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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Understanding Explainable AI (XAI)

XAI is a type of artificial intelligence meant to be understood by you and me, not just the developers of the AI. XAI software can give clear explanations for its decision-making process, which really helps businesses understand why it’s making certain marketing choices. Understanding is incredibly important, as marketers might ultimately disagree with the choice once they see the why behind it. 

The Importance of Transparency in AI

If you want to build trust with your customers and stakeholders, you need transparency in most of your processes. Putting your content or marketing efforts behind a mystery wall is a surefire way to make everyone involved nervous. 

Customers want to know how their data is used, too–data you might be collecting from chatbots, automatic opt-ins, or even ads. Not only is this mandatory in some parts of the world, but it’s just good business practice. Businesses can really take advantage of XAI to explain AI marketing strategies they might be using, remain clear to those invested and ensure they stay ethical and honest. 

Concrete Examples of XAI in Action

Now you know what XAI actually is, here are some concrete examples of how it is used for businesses online every day:

  • Explaining Marketing Performance: Marketers will run A/B tests, XAI can explain why a certain option may have worked better for your audience. This can give invaluable insight that you can use elsewhere in your marketing/visibility efforts. 
  • Website personalization: Some AI will show specific copy to certain visitors, like different product recommendations. XAI can tell you why it chose to show a certain product to someone. 
  • Content optimization: AI can look at how people are engaging with your content online, XAI can tell you why certain pieces of content work better than others. This way, you can tailor your strategy going forward. 

XAI Software Libraries Popular Right Now


Some tools to look out for in this area are listed below:

 

Tool What it Does Description Pros Cons
SHAP (SHapley Additive exPlanations) Explains feature impact on predictions Analyzes how a model uses data to make decisions Model-agnostic, easy to understand, works with various models Can be computationally expensive for complex models
LIME (Local Interpretable Model-Agnostic Explanations) Explains individual predictions Creates simpler models to understand how a complex model arrives at a specific prediction Model-agnostic, good for complex models Less efficient for global explanations
DARPA CARES (Capturing Anthropomorphic Reasoning in Explainable Systems) Generates explanations for model predictions Designed for rule-based models, focuses on mimicking human reasoning for explanations Offers human-readable explanations, valuable for specific use cases May not be suitable for all types of machine learning models
DARPA XAI-Reasoner (Explainable AI Reasoning) Provides reasoning paths for model decisions Explains the chain of logic a model follows to reach a prediction Offers in-depth explanations, useful for complex models Can be computationally expensive, might require expertise for advanced use

 

How XAI Empowers Small Businesses in Marketing

If you want to be one step ahead of your competitors then AI in your marketing is where it’s at, but adding an extra layer of transparency to that is where you can really stand out. For example, if you’re a small online clothing boutique implementing an XAI-powered recommendation engine, you may note that the XAI system revealed that purchase decisions were heavily influenced by fabric type, color, and style.  The AI therefore recommends a certain fabric to highlight, but you know that fabric is about to be less desirable as the weather changes, so you pick its second recommendation. 

Overcoming Challenges with Explainable AI

While XAI offers some benefits, there are challenges to consider:

  • Complexity: using XAI can be more complex than traditional AI. However, the long-term benefits outweigh the initial investment.
  • Cost: XAI is newer and so there are less options and it can be more complicated plus need a higher initial investment.

Small businesses can overcome these complexities by starting small and only focussing AI on areas where time needs to be saved and by doing so more money could be saved. 

Implementing XAI in Your Marketing Strategy 

Here are some steps you can take to work with professionals today:

  1. Research XAI Tools: look into the latest software and find a good professional like a data analyst. 
  2. Start Small: pick one part of your marketing efforts to overhaul first. Perhaps this is a specific kind of ad or content creation. 
  3. Seek Expert Guidance: always consult first with experts who know what will actually work for you. They’ll be able to point you in the right direction and teach you how to streamline your marketing efforts without causing issues. 

Explainable AI Will Be Implented Everywhere Soon

As we move further into the future, AI will become more and more relevant. Businesses that refuse to catch up and use it in their marketing efforts will find themselves increasingly behind their competitors. By understanding things like XAI you position yourself to be ahead of the curve and ensure you’ll have the best chance of finding your dream clients/customers. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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Since the conception of computers, we’ve been obsessed with creating a machine that can act like a human. Being able to see is one of the pieces of that puzzle and will ultimately help computers simulate intelligent, 3D thinking. Now, AI is beginning to master this, and although it isn’t perfect, it’s good enough to be used in areas like marketing. In this blog, we will discuss what computer vision actually means and how it is changing ad targeting for good. 

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What is Computer Vision and How Does it Apply to Ad Targeting?

Computer vision is what it says on the tin. It’s the ability of the computer to see an image or video and understand what is in it. But computer vision is a little more than the vision humans know because computers can see in greater detail and analyze things with depth and context that would take us longer. 

Brands like Meta, Google, and Amazon are using it in their ads to make them more relevant. To picture this, imagine you use a platform like Instagram; sure, you will sometimes interact with text that AI can read, but it needs to be able to see the images you’re actually liking and saving so the AI can learn what you are interested in. This is why computer vision is so important–humans are visual creatures, and we don’t just listen and read; we view images for enjoyment, too. With this precious information, brands can target audiences with visual ads that will actually appeal to them, with the right aesthetics and themes. 

Analyzing Consumer Behavior with Computer Vision

AI gets more in-depth than a computer simply seeing the image you are engaged with. Computer vision also includes technology like Tobii’s eye-tracking. With this technology, brands can see how long people are looking at images and how their eyes scan across them. This is vital information, as it tells brands where to put certain messaging, what parts of the ad are actually engaging the customer, and where they can cut and add elements. Facial recognition tech is another part of computer vision that most people are aware of every day. These tools can be used as security measures, but they can also tell AI the demographic of the person viewing the ad and their facial expressions when viewing it.

Brand Safety and Consumer Privacy

Computer vision is quite exciting but like most AI it poses several problems, namely privacy. It’s somewhat disturbing for the average consumer to know that everything they look at and engage with is scrutinized to such a degree, and now computers can even see them, too. Relying solely on AI without human input also puts brands in a precarious position if their ads are shown in inappropriate contexts. Ethics surrounding AI and marketing is an important discussion that should be taken seriously by all companies. 

Future Trends and Technologies

Companies like Apple and IBM are taking the future of computer vision very seriously. IBM is even working with NASA on some tech-related to computer vision that will help with climate change. Another way computer vision might be used in the future is to diagnose people based on their MRIs and other visual scans. Computer vision will also help with cars and how they view the road as they drive. 

When it comes to marketing, you need to look no further than edge computing, which is the ability to process information immediately on devices; this will make marketing lightning-fast and relevant. Plus, marketing will become more saturated with deep fakes as time goes on, and things like computer vision will help combat that by viewing ads and determining if they are real or fake. 

Augmented reality is another tech that will require computer vision; this is the ability to overlay information in the real world in real-time. In the future, this will unlock new ways to market to people. For example, imagine wearing augmented reality glasses as your toaster breaks and immediately getting recommendations for new ones. 

Computer Vision in Action

A company called Memorable is taking up the torch of computer vision to help brands stay in the minds of their customers. It does this by using computer vision and learning what people look at first and over time in ads. Based on this data, they also did studies on the retention of information. 

Unilever has a hair care brand called Sedal or Sunsilk, depending on location. They wanted to know how to stand out with their new products containing trend-setting ingredients. So, the Memorable platform looked through at least 130 assets from campaigns that had already run to see which ads would work best. And it worked. Sedal saw a 5.5% increase in ad recall, 70% in logo saliency and 67% more attention in their new product line. 

Hellman’s also used this tech to see how to shift certain aspects of their ad to make it more memorable. They discovered that changing the size of certain elements within the ad itself made a big impact on retention. 

Another way that computer vision can work in marketing products is with an example like L’oreal. They have a process where you can upload an image of yourself, which the app can analyze, and then recommend products specifically for your features. 

It’s tech like this that is going to change the way we create ads online, and make our ads more effective. 

A Vision for the Future of Advertising

Computer vision in AI will open up many new ways for us to market to people and get it right the first time, so less money is wasted on discovering what works best to find the clients and customers who need our services. But of course, all AI needs humans to look over what it is doing to ensure it reaches the goals we set. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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AI audio ads are just another way that marketers can take advantage of artificial intelligence (AI) to build interesting and engaging ads that are targeted for better conversions. The idea of AI in audio ads is to reach people who might not be on social media but rather listen to the radio or podcasts. Some big names to look out for in this space are Adthos and Instreamatic, who use platforms like Spotify to help businesses find customers. In this blog, we’ll look at how AI audio ads actually work and how businesses are using them.

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What are AI Audio Ads?

AI audio ads use AI and voice AI technologies to help create interesting sound-based ads that are made for your customers and, therefore, are more likely to convert. The interesting thing about incorporating AI in ads is that changes can be made in real time rather than running the campaign and making changes once data is collected. These sorts of innovations simply didn’t exist before and are changing how we target and find new customers based on things as nuanced as even mood. 

How AI Audio Ads Work

In the past, if companies wanted to create an audio ad, they would do so for a set target audience, hire a writer, buy studio time, hire editors, etc., and then release it on platforms where both that audience and others may hear it. Now, AI can create multiple versions of ads at lightning speed, each made for a different segment of your audience. 

For instance, you may have a broad audience of a person looking for a personal injury lawyer. Plenty of demographics fit this, but different ads will appeal to females in their 30s versus males in their 20s who need a personal injury lawyer. Before, you would try to create an ad that appealed to both, but because AI can track demographics and write/create ads quickly, you can now create more personalized audio ads with ease and actually speak directly to that customer on the apps they use every day. 

There are even synthetic voices now that sound incredibly real and can create ads much faster and cheaper than if you had to find time in a studio, write the ad, record, polish, and test. Of course, there are downsides to this, like being inauthentic and the fact that a lot of audiences simply don’t trust AI. 

How do AI Audio Ads Improve Engagement and ROI?

Because AI audio ads can be personalized to each person at similar cost margins to traditional audio ads, they can create a lot more engagement. People are more likely to act on things that speak directly to their needs. AI can do this by analyzing data about that person and what they actually need, taking out the guesswork. 

AI audio ads can also: 

  • Reach audiences at the right time, as they can be created quickly, allowing you to jump on trends 
  • Saves money for companies by cutting out a lot of middlemen 
  • Allows for brand retention as the ad makes sense to the person hearing it 
  • Let the ad change the location it talks about based on where the listener is located
  • Or if you sell multiple products, it can highlight different ones based on what the user is looking for 

How Audio Advertising Has Grown

The start of audio advertising began really with radio spots. The company would create a catchy jingle of an ad and hope it stuck in someone’s mind when they needed to call them. This was long before we reached for phones in our pockets to Google companies. 

Now, a lot of audio ads are found on podcasts or between music apps like Spotify. Instead of catchy jingles, advertisers try to find the pain points of that specific listener and attempt to talk directly to them so their brand is unforgettable (as they promise to fix a problem the customer has right there and then). This is why current ads are less jingles and phone numbers and more emotional-based. 

As we move into the future, we’ll be able to do even more unimaginable things, like creating audio ads simply based on images. That’s right, audio ads in the AI sphere are becoming so advanced that there is now software that allows you to upload an image of a product you sell and let the AI do all the work of creating the ad–from conception all the way to the finished product. This new tech is from Adthos. According to the brand, it can even identify your target market just by looking at your brand images, and it tells you exactly what you need to know to capture the attention of the people best suited for your product/service. 

Case Studies: AI Audio Ads in Action

It’s often helpful to see how other bigger companies are using or may use a technology so you can imagine how it might apply to your business. 

Corona recently decided to use ads to target people by location. They wanted customers who were heading to the beach to pick their beer over other brands. To achieve this, they played the sound of waves and music in the background of ads to people near beaches. For their audience located further in-land, they used a more generic summer sound. 

Another way this sort of ad could be used is by looking at a company that sells items such as hiking gear. That company could use a customer’s upcoming hiking vacation (based on spending or searches) to recommend the perfect gear for that hike with sounds that make sense–like birds in forest landscapes or jungle sounds if Amazonian. 

Challenges and Ethical Considerations

Like all AI ads, this territory comes with a lot of problems. Using synthetic voices raises ethical questions about authenticity or whether it puts voice actors out of work. The content can also be misleading if someone isn’t constantly monitoring it. Using humans in your work as touchstones when utilizing AI is always the way to go. 

Is the Future of Advertising Listening?

AI audio ads are certainly right for some audiences. Not every audience likes to read blogs, check their emails, or browse social media some listen to podcasts instead, or the radio on the way to work, or during their free time. If you want to target all audiences, you need to take this into account. As technology gets better, brands will use audio ads with personalization more and more in our everyday lives. It’s certainly an exciting side of marketing for businesses to keep their eye on. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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There are a lot of interesting things coming out of Artificial Intelligence (AI) development that can help us with marketing for businesses. Reinforcement learning (RL) is just one of many. In this blog, we will look at how Artificial Intelligence (AI) uses reinforcement learning in the marketing space and how it is different from traditional machine learning paradigms.

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Why Reinforcement Learning Matters for AI Marketing

Reinforcement learning reacts to its inputs and makes decisions based on the consequences of its actions. The great thing about RL is that it can be a proactive way of marketing rather than responding to how the market is at that moment. It can change how our customers are influenced to make decisions in new and innovative ways. Below are some real applications of RL to get a better understanding. 

1. Optimizing Bid Strategies for Higher ROI

The most interesting side of RL is bid optimization for ad auctions. RL can learn from the outcomes of auctions and adjust bids as it goes to get the best return on investments. 

2. Content Selection for Targeted Advertising

A massive part of creating ads that convert is choosing the right content for the ad. RL algorithms analyze a lot of data to help predict which content will work best for your ad.

3. Maximizing Customer Lifetime Value

RLs can personalize ad recommendations. This obviously attracts new customers and encourages loyalty by showcasing ads to them. RL can understand individual preferences and craft irresistible offers. 

4. Predicting Responses to Pricing Changes

Pricing offers set the right way are important for every company. RLs can predict how customers will react when changes are made to the cost of services or items. This helps companies make better decisions and find the perfect prices for them. 

Real-World Success Stories

Understanding RL is a lot easier when you look at real-life use cases from brands everyone knows well. It can also showcase how well the AI works. You can look to companies like Google Ads, for instance, who use RL algorithms to analyze a lot of incoming user data so that they can keep tweaking bids to make sure campaign performance is optimized. The great thing about this is an increase in click-through rates compared to other methods, which improves conversions and, therefore, ROI.    

Product recommendations are another thing that RL is useful for. Static algorithms can get stale and miss some of the intricate information that things like RL can gather. Amazon for instance uses RL to make sure that products you see on their site are personalized to you in their ads. The AI looks at past purchases, browsing behavior and even external factors to send the user the product that is most likely to end in conversion. 

Types of Reinforcement Learning Algorithms

There are many types of RL algorithms that are put to use to do all sorts of different things. Below is a quick overview:

  • Monte Carlo, Q-Learning, SARSA: These are foundational algorithms used for simpler tasks. These algorithms train through rewards so the agent can learn the best action to take based on trials.
  • Deep Q Network (DQN): This is powerful when used in complicated environments like video games. It uses deep learning techniques to handle a lot of information so the agent can learn how to do more intricate tasks.
  • Asynchronous Actor-Critic Agent (A3C): This is an advanced algorithm where someone can trail lots of agents at once. They learn faster by swapping experiences between themselves. 
  • Deep Deterministic Policy Gradient (DDPG) and NAF: These algorithms are good for agents that need to keep going, like the movement of a self-driving car. They can help the agent learn things like smooth movement and good control techniques.

Challenges and Considerations of RL

RL isn’t all positive; for instance, using batch RL (past data) isn’t keeping the AI up to date, but when you use online data to learn from, that is always changing, so what the AI is learning at that moment will completely change soon enough. 

There’s also the issue of “reward training”. RL learns by getting positive feedback, but when to give that feedback can be tricky. Should it be after each task or every move? 

RL can also learn through trial and error, which can make it incredibly slow for more complex tasks. The AI might make a lot of mistakes before figuring out the right thing to do. An AI can also work great in a training environment but not so well in the real world. Overcoming these challenges is possible, but they must all be considered when using RL. 

Some Important Questions About RL

What distinguishes reinforcement learning from traditional machine learning?

Traditional machine learning grabs a lot of data, like images of butterflies, and analyzes them so it can recognize butterflies. Where as RL learns by trying things out and getting rewarded for correct outputs.  

How does RL optimize ad bidding strategies?

RL can do things like testing different ad prices to see which ones are getting the most clicks. It learns from its findings to give the best price for each ad so you get the optimal ROI. 

What role does content selection play in RL AI marketing?

The AI can test lots of headlines, images and other content in ads to see which gets the most engagement. Over time it can then tailor the content to what works best for your audience. 

How can RL enhance customer lifetime value and respond to pricing strategies?

This AI is pretty good at experimenting with different offers or discount types to see what your customers enjoy most and what gets them to spend more money with you. This way you can optimize your pricing for long-term promotions and give your customers the best value to make them feel good about your brand. 

RL Learning in Marketing Needs a Human Touch

Reinforcement Learning is really important for optimizing campaigns and keeping your customers engaged and happy with offers and ads. The idea is to meld both the company’s needs with what is best for the customer. When you personalize content with rewarded learning you have the best opportunity to actually connect with your audience. But it is never wise to rely on AI alone. To use AI the right way you really need someone who understands how the AI is working in your favor and how to monitor the results. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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Many companies are beginning to add generative AI content into their rotation, especially as part of their blogs, web pages, or social media. The appeal of being able to create content incredibly quickly is hard to ignore, but when generative content isn’t done the right way, a lot can go wrong. In this blog post, we will take a look at those top 5 mistakes that you should avoid with generative AI content and how to use this tool the right way. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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Generative AI in Content Creation?

It isn’t that big of a mystery as to why so many companies are flocking to use generative AI content at scale. It takes the portion of your budget that would otherwise go to research, writers, and editing and gives it all to an AI that claims it can do it all for a fraction of that price. But unfortunately, this isn’t the case if you want to stay ahead of competitors with good content that does well to convert, engage, and perform against social and search engine algorithms. For that you will need to take a few more steps. 

Mistake 1: Over-reliance on Automation

When companies use AI, the worst thing they can do is over-rely on it and assume it is accurate. AI often makes mistakes–it can misunderstand the assignment or just simply make stuff up. You need a human working alongside the AI to help it do a good job. So you don’t rely too much on AI:

  • Hire someone to look over everything the AI creates  
  • Have someone add something new and interesting to the AI content 
  • Make sure your brand’s voice is clear

Mistake 2: Ignoring SEO and Engagement Metrics

AI generated content does not have access to the myriad of tools that an SEO expert does. These tools allow us to look at what keywords are trending, what people are searching for, and what the intent for keywords are. AI will often take a guess at these keywords and then plug them into the content. To make sure your SEO remains relevant:

  • Use AI alongside correct SEO research practices 
  • Make sure the AI isn’t keyword-stuffing and repeating itself to do so 

Mistake 3: Failing to Adapt Content for Different Platforms

Every platform that you create content for has its own needs, its own kind of audience with different demographics, likes, and a different algorithm to play with. You cannot simply take content from one platform and paste it into another. So you don’t fall victim to this issue:

  • Ensure your AI understands the platform its writing for, what tone it should use, the style and formatting, etc. 
  • Create strategies for each platform and use human oversight to check on the AI content and whether this strategy is being implemented

Mistake 4: Neglecting Brand Voice and Personality

When AI writes, it sounds “samey”. In fact, writers who work with different platforms, like ChatGPT or Claude2, can often tell which AI wrote the content just by reading it. AI often repeats words and phrases again and again. Because of this, you should implement some ways to make sure the content sounds like your brand and not AI’s: 

  • Have a human who understands AI writing–and knows what sounds like AI–go in and make tweaks so the content sounds like your brand
  • Make sure you are using the best prompts possible when creating the content to begin with so it doesn’t come out completely generic 
  • Delete where needed, as a lot of the time AI will just repeat itself in different ways 

Mistake 5: Not Regularly Updating AI Training Data

Some models are not updated that regularly, meaning it doesn’t have access to all the latest information. This can be devastating for many brands; for example, if you’re writing content for a dental office and the AI starts using outdated information to inform your patients of something now considered bad practice, this is a big problem. You can stay ahead in a couple of ways:

  • If you can, update the training data, but often, we don’t have access to do this 
  • So the next step is to make sure you do your own research and fact-check everything the AI has written 

Generative AI Needs Human Oversight 

Generative AI can make a lot of processes faster for many businesses, but it isn’t a plug-and-play solution yet. It’s one of those things where you really need someone who has experience using it and who actually understands content well to make sure what the AI is spitting out actually makes sense. AI should always be viewed as a tool to help your content strategy along and not something to replace people with, as in the long run, it can’t create, engage, or research in the way that a human can.  

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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As more content is created via AI, a question has emerged: Is AI copyrighted material? This is an important discussion to have, as AI has been trained on content written by humans without their permission. The outcome of this question will affect businesses who use AI all across the globe. 

In this blog, we will examine whether AI can actually commit copyright infringement, whether businesses should stop using it altogether until this is resolved, and the laws around copyright and how that applies to AI. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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The Rise of AI in Content Creation

First, it’s important to understand what AI content creation actually is. AI can create lots of different content, from blog posts to images or even music. An SEO marketing agency might use AI content creation to fill out pages and blog posts for a site with optimized keywords. It’s a quick and easy way to create content, but it raises a lot of questions about copyright. This is because AI is trained by gathering information from other websites, which is all original content created by others. The AI is essentially stealing that information to create its own version. The question of whether this is copyright infringement, though, is very nuanced. After all, isn’t this how humans learn? By seeking information from other sources?

The Legal Labyrinth of AI Authorship

Copyright laws were intended to protect creators from other human creators. The Copyright Act prevents “unauthorized copying of work of authorship.” Currently only humans are protected by this law. But it creates a strange grey area: If content is created by AI and then edited by a human, can it fall under copyright law? Is the AI the author or is the human using the AI the author? Answers to these questions will dramatically change the legal recourse that companies can take. 

Answering this conundrum of whether AI is an author who can copy work and, therefore, infringe on copyright will help us decide how we can use AI moving forward. Most jurisdictions currently state that AI is not an author and cannot legally own copyright because it’s human creativity that creates the criteria for eligibility for copyright. Therefore, the human who used the AI tool or programmed it is the author. But the many court cases currently happening will ultimately be the deciding factor. 

AI Copyright: Some Case Studies

Some businesses are fighting back against AI-generated content as they feel it was trained on their human content and took advantage of it. Here are some examples:

  • Getty Images vs. Stability AI (2023): Getty Images is accusing Stability AI of scraping its website images to gather all sorts of information to help Stability AI’s image generator, called Stable Diffusion, make artistic images. They allege this is infringing on Getty’s copyright by using these images without their permission. This raises the question: does training AI on copyrighted material fall under fair use? We’re yet to see the outcome. 
  • Thomson Reuters vs. ROSS Intelligence (2020): ROSS is an AI legal research tool. Thomson Reuters sued them for training their AI on its copyrighted legal material. The issue will boil down to fair use since the AI model is not made to simply copy but learn from the content. The answer to this should come sometime in May 2024. 
  • New York Times v. Artificial Creativity LLC (2023): The New York Times has sued an AI company called Artificial Creativity because it used its articles to train its own system to create similar articles. The Times is arguing that this is copyright infringement, but Artificial Creativity is claiming fair use. This case is similar to Thomson Reuters vs ROSS intelligence and will pave the way for how AI can be trained using information that already exists online. 
  • Artists vs. AI Image Generators (2023): A group of artists got together to sue Midjourney, Stable Diffusion, and DreamUp because they believe their artwork was used to train these AI models. The artists use their own style, which they have honed for years, to sell unique art, which begs the question, is it legal for AI to simply copy that and sell its own version?

Unfortunately, these are just a couple of examples, but the questions that these cases will really ask are:

  • Is it fair use to train an AI on copyrighted data?
  • Who owns the copyright on AI generated material?
  • How can we protect creators from AI while encouraging innovation?

Practical Tips for Businesses Using AI to Avoid Copyright Infringement 

Businesses that need content creation to stay afloat, perhaps for blogs, product descriptions, selling art, or creating books are in a difficult position. Using AI can make content creation much easier and help them find an audience who is looking for what they sell. But trying to make sure content doesn’t infringe on copyright is currently a stab in the dark. It’s important to use caution when using AI generated content and ensure there is a human behind it making tweaks, researching, staying on top of laws and regulations, and making sure the content is accurate and good quality. Here are some practical tips:

  • Transparency is Key: Make sure that your audience is aware of your use of AI to build trust. 
  • Understand Copyright Laws: Stay on top of copyright laws when it comes to the AI you are using and how you are using it. 
  • Human Oversight: Never use AI without a human who checks over the content it generates. This will help with quality control and copyright issues in case the AI copies work exactly. 
  • Intellectual Property Rights: Make sure your company actually owns the content that AI is generating. The AI tool may have in their terms and conditions that they own anything their AI creates. 
  • Ethical Guidelines: Be ethical. Create guidelines that protect everyone involved to avoid copyrighting, plagiarism, your brand’s values, and minorities.  

AI and Authorship

AI will probably be the future for all sorts of content generation. It’s quick, streamlined, and serves as a great assistant for specialists in content creation. The challenge is whether AI is ethical. Copyright is just one way that AI can infringe on ethics. In the future, we will probably come across other questions, such as copyright issues, that will play out in court. Until these cases are resolved we won’t really know whether AI is actually infringing on copyright or not. But the answer to these questions will completely change how AI is trained and how we create content for websites forever. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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When you understand neural networks, you give yourself the option to remain competitive with other businesses that are using them to market their products and services. In this blog, we have put together some important and interesting information on how neural networks are implemented in marketing, why people are using them for this purpose, and what that will look like for the future of ads and content marketing. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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Neural Networks vs. Traditional ML Models

Most AI ran on traditional machine learning (ML) models, which are pretty good at helping marketers with certain types of data and, therefore, to find leads or customers. It could also create new content using linear predictive models. The downside to this, though, is that MLs are not very creative. The upside? They are the less expensive option. So, if as a marketer you’re looking for information–like how to detect spam or what price something will be in the future–this isn’t a bad model to use. 

However, neural networks are the harder-hitting model. Neural networks are like the human brain, which is likely how it got its namesake. It uses a neuron-type system with lots of layers to process the information it is given from lots of different angles. It will take all this data and keep learning, too. By continuously learning, it can pick out subtler patterns and some hidden gems in the data without being specifically programmed to do so. The great thing about neural networks is their ability to handle data that isn’t as structured and is a little more messy, like video, for instance. Take a look at the differences between neural networks and MLs below. 

Main Differences:

  • Structure: A traditional model has pre-made rules inputted, and it follows them step-by-step. A neural network learns as it goes. 
  • Data: MLs are great with structured data, and sometimes that is all you need. A neural network can handle that and more complex data. 
  • Complexity: MLs do well when given a specific task, and neural networks are great at complicated problem-solving and finding unique patterns.
  • Interpretability: It is easy to understand why MLs come to the conclusions they do when one looks at the data they are trying to analyze, but neural networks are much like a black box; understanding their decision-making process is much harder.
  • Prediction: Neural networks can see all elements of an ad including color and images, so it can make better predictions than ML alone. 

Practical Ways Neural Networks Can Be Implemented

One way that marketers understand the customers coming to their websites and buying their products or services is by using segmentation. They take certain groups of people who all have certain things in common in order to target them with ads that might appeal to them as a group. Neural networks can help with this process by making those groups even more niche; therefore, you’re not losing some of the customers on the edges who aren’t quite interested in what you have to offer. The AI does this by watching behavior on the website. 

You can connect with people so much better when you talk to them as an individual, so as you can imagine static ads and mass messaging will become an outdated tactic. There is more than just this though, neural networks can also:

  • Predict future behavior: You can use past data to guess what trends you might see next in your industry and make better sales. Or predict what people might be looking for in your services over the next few months. 
  • Optimize campaigns: When your content is live, it can be changed on the go, depending on how people are reacting to the ad or visuals. 

Neural Networks are Branching Out

Creating content is a big task for businesses online; it’s one of those time-consuming things that everyone has to do to stay relevant or gain leads. Neural networks can actually help with this in a couple of different ways, like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Below, we explain what these two are and what that means. 

Variational Autoencoders vs. Generative Adversarial Networks

Variational autoencoders will condense data, so when you feed it the data you have on your customers, it can clean it up and put it into something called latent space. This space will take only the important details and get rid of the bits it doesn’t need. Once it’s managed that, it will explore that latent space to take some samples of the data, which allows it to create new data points. These new data points will feel familiar because they share the same features as the original points, but it will also be completely new ideas. That’s how you can get ad copy or product descriptions that feel like copy you’ve seen before but are new. 

GANs, on the other hand, are like two talking heads. One side of the AI will create an image, video, or whatever you need it to. The other side probes and tries to see if it can tell if the content is AI. They go back and forth like this and then spit out something that should feel incredibly human. Of course, AI isn’t human, so it isn’t perfect, but it’s a start.  

Real-Life Case Uses for Neural Networks

Netflix Personalization Engine

We discussed personalization with neural networks a bit above, and Netflix definitely takes advantage of it. You can tell by the spookily accurate predictions about which shows you will actually like. They have it down to such a science that they’ll even put the likelihood as a percentage. In the past, if you were a 20-year-old female, they might just recommend rom-coms because it would be more likely you would want to watch them than a 40-year-old male. But we all know that’s not really how life works. Everyone is different. With neural networks, they can grab the attention of a viewer with actual movies and shows they want to watch. This obviously maximizes their ROI. 

Spotify’s Discover Weekly Playlist

There is this cool feature on Spotify that’s called the Discover Weekly Playlist. This playlist will feed you songs once a week that it thinks you will really enjoy. It does this by looking at the past data of what you have listened to by analyzing things like the tempo of the music or the genre. This keeps you using the Spotify app as you find new music much easier and, therefore, want to listen for longer. 

AI Neural Networks Are the Future of Marketing

Marketers will start to use Neural Networks more and more in the AI they use for marketing. It has so many benefits that if you don’t jump on board, you might be left behind. However, you need an expert on your team because the ethical concerns of AI, as well as other nuances, can land you in hot water if you don’t know what you’re doing. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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AI-generated content is really tempting for SEOs and companies because it makes a process that takes a lot of work easy by spitting out a result in seconds that can even help you rank on Google. But there has been a massive debate amongst SEO experts about whether AI-created content is actually going to help bolster website SEO or if it will ultimately just be considered spam and cause Google to blacklist websites in the long run. And it seems as if Google is finally bringing down that hammer with its March update. 

In this blog, we’ll discuss why AI-generated content will penalize your website when not created correctly and why you need an expert with experience in content creation to handle your website copy. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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Why AI Content is Sometimes Bad

There are a lot of AI writing tools available now, mostly because of the appeal of ease. Writing is a skill–one that takes time to hone. SEO is a massive barrier for businesses whose expertise is in their products or services. And professional sales copywriting? That’s even more advanced. So when companies sell you a solution of written blogs in seconds that have SEO, keywords, and sales copy advertised as a perk, and more–of course, businesses want to jump on it. But unfortunately, AI just isn’t there yet. It can’t actually research keywords in the way an expert does or understand the psychology of copywriting. 

In the end, AI-generated content ends up thin and robotic sounding, repeating concepts over and over to hit keyword goals and leaving readers with little value. This is exactly why Google’s update wants to target this writing. 

Why Google Cares (and Why You Should Too)

Google’s core mission is to be a search engine that answers queries accurately. It doesn’t always get this right since experts are constantly looking for ways to optimize content to take advantage of their algorithm; for example, recipe blogs are filled to the brim with random information in order to keyword stuff instead of just giving the recipe the searcher is looking for. Between this and the ever-changing way that people search for information, Google has to keep updating how they handle queries. 

Google considers keyword stuffing and trying to take advantage of the algorithm SEO spam. The practice completely undermines Google, hurts users looking for information, and stops legitimate businesses from finding their audience. Google wants to help its users, and businesses should want to find their paying audience, so SEO spam hurts everyone involved who has good intentions. 

Decoding Google’s March 2024 Update

Google gave a statement on what their March update is going to involve, and it’s all about taking a hammer to spam. Since 2022, Google has had its eye on trying to boost helpful content. One clear way you can see this is in our current search results. In 2022, we would see a lot of the same blog posts re-written over and over again because that’s how you could get a blog to rank and find your audience. Now, there are unique blogs when you search for things, each with different tidbits of information. Google’s update is intended to be helpful; it’s intended for users to be able to find blog posts that actually answer their questions in different ways. But it doesn’t always get this right, hence the many updates. 

What Will Google Consider Spam?

Google did say some of the things that they are going to consider spam. Here are some important examples and what they mean:

Sites Created for Specific Search Queries – Seeing this statement from Google alone can send any business into panic because, of course, every blog or page on a website is created for a specific search query. In fact, if you’re doing SEO well, you want to see what is being searched and then create content based on that because that is what will actually help your audience. Thankfully, Google isn’t talking about this when making this statement. What Google means is if you create a boring page stuffed to the brim with keywords without actually giving any help to the user (i.e., answering a question or selling a product), it will be considered spam. This is nothing new, but now Google is saying it will now take targeted action to blacklist these websites or stop their traffic. 

Scaled Content Abuse – Google has had so much trouble in the past with companies that have created hundreds of pages and blogs a month, all with awful-quality text. Typos, boring speech, unhelpful keyword stuffing, etc. One way sites are doing this now is by using AI. By simply telling ChatGPT to write a blog post about a subject, posting it up, and moving on to the next topic has made the practice of scaled content abuse ten times worse. Google wants to target anyone doing this. But it made sure to clarify it’s not just targeting AI content. Google doesn’t care how the content was written; it cares about the abuse of scaled content. 

Site Reputation Abuse – Sometimes, incredible sites with high-quality posts will host content from other sites in order to link back to that site. The site that gained the link will get authority, and the site that hosted the content will be paid for doing so. This practice in and of itself is not the problem. What is the problem? A lot of the time, the sites hosting the content from another site will accept bad quality content because they want the pay cheque. Google is going to bring down the hammer on any site accepting bad-quality blog posts. Meaning sites that host guest blogs for backlinking will need to ensure that guest posts are of the same quality as their other content. 

Expired domain abuse – Sometimes, a company will buy an expired domain that has some authority already behind it. Essentially, the domain used to exist and did well in search rankings but now no longer exists. The company will buy it and use its reputation to bolster its own content. This practice, in and of itself, will not be penalized if the company creates fantastic content. But if companies use it to create spam content, just to help it rank, Google will stop traffic going to that site. 

Cloaking – SEO experts can show different content to Google and the user. One easy way of doing this is by making text on the page the same color as the background. This way, they can add a bunch of keywords that Google will see whilst hiding it from the user. Cloaking is trying to cheat the system and will be targeted in this update. 

What Makes Content “Unhelpful”? 

According to Google, unhelpful content includes:

  • Content made just to get clicks, with exciting titles and no substance.
  • Content that lacks any nuance, depth, originality, or analysis of any kind. 
  • Content that is just full of keywords, repeating itself over and over again to achieve this, and doesn’t care about the user. 

What Google’s March Update Means for You

Anyone with good intentions who is really passionate about their topic and knows how to write well to answer queries shouldn’t have to worry about Google’s new update, in theory. Besides that, AI content, in general, isn’t even the problem. It’s not about who wrote it but how well it’s written. Here are some goals to ensure you will not be affected:

  • Focus on User Intent: When doing keyword research, you have to understand why a user puts that LSI or keyword into the search engine. Then, you can create content that is actually relevant to that search. Just throwing that keyword into your content at random is spammy and bad practice.
  • Become an Authority: Build your reputation the right way. Become an authority in your field and always add something unique to your content that isn’t available elsewhere.
  • Prioritize Readability and Engagement: Make sure your writing is clear and easy to read. Make it conversational and engaging. You want people to actually read what you have to say.
  • Maintain Transparency and Credibility: Whether you use AI or not to create content, make sure what is on the page is actually correct. Use credible sources to find information and build trust with your audience.
  • Quality of Quantity: Invest your time in building out well written blog posts that will be shared by others. Rather than writing hundreds of them at a time with little unique information.

So, Does This Mean AI Content is Dead?

AI writing tools are ultimately not the problem. The problem is scaled content on mass that isn’t being vetted. This has always been a problem, even before AI. Before AI, companies would just pay writers, usually from countries outside of the US so it was cheaper, to create hundreds of blogs for pennies on the dollar. Whether this tactic or AI, the blogs aren’t helpful, are full of mistakes, and make users distrust Google. Google wants users to find exactly what they are looking for and enjoy using Google. As long as your content achieves this, you’re in the clear. But if you are using AI generated content, here are some ways to ensure it isn’t against Google’s policies:

  • Humanize Your Content: The content you make through AI might sound great to you, professional sounding and maybe even intelligent. But it doesn’t sound human to most users. The way AI talks isn’t full of nuance. You need to edit it, refine it, and make sure it sounds like your brand. 
  • Prioritize Value Over Volume: Stop creating tens of pages a month full of AI content because you think it will help people find you. It won’t. It will just make Google hide your site from users. Instead, create a steady stream of really good posts that actually help people. Sometimes, all you need is a couple of incredible posts to get great traffic that will help users find you. 
  • Maintain Editorial Control: Don’t just stick up AI content on your site without checking whether it is correct. AI hallucinations are a thing and the AI will make stuff up as it goes along. 

SEO is a Marathon, Not a Sprint

SEO can be a slow game compared to paid ads, which are instant gratification. SEO takes time to build and, when done right, will last too. Google’s new update is here to remind businesses that actually helping their users, even when it takes time, is what will get them loyal customers. When you focus on giving value, putting the user first, and vetting content no matter who writes it, you will get ahead. This new AI way of creating content is reminding everyone that robots can’t engage in human spaces in the way we can. It can’t reach people and teach people like we can. But it can serve as an assistant to our goals. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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When it comes to online content, artificial intelligence (AI) is making things a lot easier for businesses. Many companies and marketers who use AI can create content faster and find more accurate predictions for whether content will work for their audience. But there is a massive problem with AI generated content that many businesses are unaware of, and that is AI hallucinations. This is when AI spits out nonsensical, inaccurate, or downright dangerous content. Find out what AI hallucinations are, how to counter them, and why they are a problem below. 

ClickGiant is a leading digital marketing agency serving clients nationwide. Get in touch today to request a free site audit.

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Understanding AI Hallucinations

AI content creation, when done right, should be a collaborative effort between the machine and humans. The AI is trained on a wide range of data (what data that is will depend on the model), and the human is trained in their field of expertise and, of course, has the innate sense for creativity and nuance. It should be a good match, in theory. However, sometimes the AI tries to understand the inputs, data, and patterns, and it goes horribly wrong, leading to “hallucinations,” which is when the content it generates goes completely off script or is made up. This happens more than you would think. 

Causes of AI hallucinations:

  • Incomplete or biased training data: the AI is only as good as the data it is trained on; if it has biased or incorrect information, it will spit out the same.  
  • Over-reliance on data patterns: when the AI focuses heavily on statistics and patterns, it may miss the nuance needed to understand the data fully. 
  • Lack of human oversight: if AI is allowed to run and do whatever it wants without interruption, it will steer further from helpful content, and hallucinations will slip through the cracks. If this is an AI that learns from each interaction, and those interactions are full of hallucinations, the problem will get worse. 

The Price of AI Hallucinations for Your Business

Even if a business unintentionally creates inaccurate content, it can still have severe consequences. Spreading misinformation damages your brand and the integrity you have worked hard to build. Once a customer sees this, their trust will be lost; trust you need to make a conversion. 

For example, imagine sharing biased financial advice or publishing factually incorrect health information—the repercussions could range from customer anger to legal issues. 

This can then bleed into your website trust factor, too, which can then affect your ads. The domino effect of this one small issue is vast. Always verify content created by AI and never trust it to do the job of a content creation specialist, especially one well-versed in SEO. 

Real-World Examples of AI Hallucinations

Below are some examples of AI hallucinations and what happened:

  • Law Gone Wrong: A lawyer used ChatGPT to prepare for court proceedings and cited fake cases suggested by the AI. He was fined $10,000 as a result. 
  • Health Hoaxes: A healthcare chatbot tasked to help cancer patients gave the wrong advice 12% of the time, according to Jama Oncology. 
  • Unintended Insults: An AI language model by Microsoft–used to create Tweets for X–generated racist and offensive tweets by learning from other users. 
  • Copyright Issues: The New York Times made accusations against AI and Microsoft for a copyright lawsuit. Because OpenAI had access to Times’ content and learned from it, New York Times would be able to have a case if they could prove that the generated content was somewhat similar to their style.
  • Algorithmic Bias: AI can be trained in data to approve resumes or loan approvals. If, however, the data the AI is trained from happens to be biased towards a certain group of people, the AI, too, will be biased, unfortunately. This happened for Amazon in the tech sector, where men were chosen over women due to inputted bias.

Mitigating AI Hallucinations

There are steps you can take that will help to stop AI hallucinations from popping up in your content as much. You can’t eliminate it altogether, but here are some steps you can take:

  • Ample training data: your AI tool needs to be equipped with data that is high quality and factual.
  • The right roles for AI: you have to define explicitly what job you want to delegate to AI. You also have to define what the person in charge of the content would need to do to make it as accurate as possible, i.e., research statistics and facts.
  • Quality and accuracy metrics: a clear guideline needs to be set within the company on measuring the quality and accuracy of the AI content.
  • Human eyes: no content should be published that has not been looked over. 
  • Learn Prompts: use super prompts, prompts from experts, or create clear, detailed, and precise prompts for directing your AI.

Tips for crafting effective prompts:

  • You must describe the desired format for your content, including the length and layout. 
  • Tell the AI which tone it should write in, if unsure feed it content with the intended tone and ask for the AI to describe it and write like it. 
  • Provide the AI correct stats and details to use. 
  • Include references and examples if possible.
  • Set clear limitations. Remember to tell the AI what not to do. 

Striking the Balance Between Innovation and Responsibility

AI content creation is very promising for businesses and marketers despite it being in its early stages. Knowing the risk of AI hallucinations and making sure you do all you can to avoid it puts you in the best place to create accurate content that brings about trust and authority in your brand. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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