Healthcare organizations rely on web analytics tools to gather insights into website performance and user behavior. Google Analytics is one of the most popular tools for this purpose. However, when it comes to handling Protected Health Information ("PHI"), the Health Insurance Portability and Accountability Act ("HIPAA") sets stringent privacy and security standards that Google Analytics does not meet. Despite this, it is possible to use Google Analytics in a HIPAA-compliant manner by anonymizing the data and not sending PHI to Google Analytics.

## Using Google Analytics 4 with Google Tag Manager in a HIPAA-Compliant Way
To ensure HIPAA compliance when using [Google Analytics 4](/services/ga4) (GA4) with Google Tag Manager (GTM), follow these steps:

1. Data Layer Configuration: Set up a data layer in GTM to control and manage the information sent to GA4. This prevents PHI from being accidentally transmitted.
2. Anonymize IP Addresses: Enable IP anonymization in GA4 settings to mask the last part of visitors' IP addresses.
3. Disable Data Sharing: Turn off all data sharing settings in GA4 to prevent Google from accessing your data for its purposes.
4. Avoid Collecting PHI: Do not track or collect any information that could be classified as PHI, such as names, email addresses, or medical record numbers.
5. Enable 'Restricted Data Processing': Use this setting in GA4 to limit how data is processed and ensure compliance with privacy regulations.
6. User Consent: Implement user consent mechanisms on your website to comply with various privacy laws and ensure that users agree to your data collection practices.
7. Regular Audits: Conduct regular audits of your tracking tags and triggers in GTM to ensure that no PHI is being captured or sent to GA4.

## HIPAA-Compliant Analytics Alternatives: Grafana, Prometheus, and Loki
For healthcare organizations seeking alternatives to Google Analytics that are HIPAA-compliant, tools like Grafana, Prometheus, and Loki offer a powerful combination for monitoring and analyzing data while adhering to HIPAA regulations. Using the Grafana stack, [Fruition's Cloud hosting](/services/hosting) is setup with HIPAA compliant analytics and logging! 

At Fruition, we utilized the following setup on hardening infrastructure to provide HIPAA compliant website analytics for our healthcare clients: 
1. Grafana: An open-source analytics and interactive visualization web application that allows you to query, visualize, alert on, and understand your metrics. Grafana can be configured to use data that has been properly secured and anonymized.
2. Prometheus: An open-source monitoring system with a dimensional data model, flexible query language, efficient time series database, and modern alerting approach. Prometheus is set up within a HIPAA-compliant infrastructure to monitor system performance without handling PHI.
3. Loki: A horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost-effective and easy to operate, as it does not index the contents of logs, but rather a set of labels for each log stream.

### When integrating these tools:

1. Ensure Data Encryption: Data in transit and at rest should be encrypted to protect any sensitive information.
2. Access Control: Implement strong access controls to ensure that only authorized personnel can access the monitoring and analytics tools.
3. Logging and Auditing: Maintain comprehensive logs of access and changes to monitoring systems to ensure traceability and accountability.
4. Data Anonymization: Ensure that any data processed by these tools is anonymized and does not contain PHI.
5. Compliance Documentation: Keep thorough documentation of your compliance measures, data flows, and data handling procedures.

By taking these steps and utilizing HIPAA-compliant tools, healthcare organizations can gather valuable insights from their data analytics practices while maintaining the privacy and security of patient information as mandated by HIPAA.

## Fruition's HIPAA Compliant Analytics 
Fruition hosts many healthcare organizations websites and other applications. We have Grafana, Prometheus, and Loki setup so the sites we host are compliant from day one (including the staging environments). 

## HIPAA Covered Entities Can Use Google Analytics, Just Don't Send PHI to Google
While Google Analytics is not HIPAA-compliant out of the box, with careful configuration and data handling practices, it can be used in a manner that respects patient privacy. Alternatively, using tools like Grafana, Prometheus, and Loki can offer a more straightforward path to HIPAA-compliant analytics. The key to success lies in a thorough understanding of HIPAA requirements and diligent management of data flows and analytics processes.
### 

Additional Research: 
[HIPAA and Online Tracking Guidance](https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/hipaa-online-tracking/index.html)

[Google Analytics Data Privacy and Security](https://support.google.com/analytics/answer/13297105?hl=en)

No, GA4 is not HIPAA compliant. However, you can still use Google Analytics if you are covered by HIPAA. Just don't send PHI to GA4. This article explains how to avoid a HIPAA privacy rule violation when using Google Analytics

HIPAA Compliance with Google Analytics and HIPAA-Compliant Alternatives

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# What is the Accessibility Law for Colorado State and Local Government?

The State of Colorado has enacted the Information Technology Law ([HB21-1110](https://oit.colorado.gov/accessibility-law)) in accordance with its commitment to equal access to information and services for all its residents. This law is aimed at ensuring that state and local government websites meet the requisite accessibility standards. This summary encapsulates the salient points of the law and the necessary steps that government organizations should take to comply with its provisions.

The law mandates that all state agencies and political subdivisions should make their websites accessible to individuals with disabilities. It is geared towards making sure that every Coloradan, regardless of their physical abilities, can access information and services provided by the government. The law takes its cue from the Web Content Accessibility Guidelines (WCAG) 2.0 Level AA, a set of international standards that are widely recognized in the digital industry. It requires all state and local government sites to meet these standards, effectively ensuring that their digital content is accessible to all. All State agencies and local governments must be compliant with state standards by July 1, 2024. 

## Steps To Meeting 2024 ADA Standards

To ensure compliance with the law, state and local government organizations need to take several critical steps.

First, state and local government organizations should conduct an audit of their current websites and digital platforms to evaluate the level of accessibility. This audit should be thorough and cover all aspects of the site, including images, text, forms, and multimedia content.

Based on the audit, organizations should identify areas that need improvement, and develop a plan to address the identified shortcomings. The plan should be comprehensive, detailing the changes to be made, the timeline for these changes, and the person or team responsible for implementation.

Next, state and local government organizations need to implement a plan. This may necessitate hiring or consulting with experts in digital accessibility and training for staff on how to create and maintain accessible digital content.

Once the plan has been implemented, accessibility compliance work has only just begun.  It is crucial to monitor and maintain the accessibility of the site on an ongoing basis. Changes in content, technology, and user needs will necessitate ongoing adjustments. Regular audits should be conducted to ensure continued compliance with WCAG 2.0 Level AA standards.

In conclusion, the Accessibility Law for Colorado State and Local Government is a vital step towards digital inclusion for all Colorado citizens. Government organizations should take active steps not just to comply with the law, but also to ensure that their services are accessible to all Coloradans, thereby affirming their commitment to equality and inclusivity.

## How can Fruition Assist its Partners with Meeting Accessibility (ADA) Compliance Requirements?

Fruition is a leading expert in ensuring websites meet Accessibility (ADA) Compliance requirements and is positioned to assist Colorado State and Local Government partners in meeting and exceeding these standards.

Our services are designed to bring your website's code and theme layer into compliance with ADA WCAG 2.2 AA requirements. We can work with you to determine the necessary effort to strive towards accessibility compliance, regardless of whether your website is in the building stage, newly launched, or already existing. While we do not provide legal advice, our services are tailored to align your digital presence with the highest standards of accessibility and inclusivity.

Fruition offers two levels of accessibility services for new and existing websites:

### Accessibility Services For New Websites

Our basic service includes Automated Scans and Remediation. As part of a new website build project, we will conduct automated scans and tests for compliance with WCAG 2.2 AA accessibility using third-party software. All issues revealed by the scans that violate WCAG 2.2 A & AA best practices will be remediated by our team before site launch. This service focuses on code, templates, and assets that are controllable within the building of the website, but does not include UI/Design accessibility testing, or testing of third-party imbeds, migrated content, or PDFs. It's important to note that the basic service approach does not guarantee that a website will be ADA-compliant at launch.

For a more comprehensive approach on a new website build project, our premium service includes Manual Review/Live User Testing and Remediations. Automated ADA compliance scans generally flag only 25% to 30% of the WCAG 2.2 A & AA guidelines. The remaining guidelines can only be tested via manual user testing using various screen readers and other accessibility tools. Premium service includes a manual review of a specified number of templates/components, review, feedback, verification, and consulting during Design, Development, and QA, and user testing upon completion of the site. Additionally, we provide conformance documentation and an Accessibility Statement.

### Accessibility Services For Existing Websites

In addition to the New Website Build ADA compliance services, Fruition also offers Accessibility (ADA) Compliance services for existing websites, and/or post-launch to ensure your website remains compliant as your website evolves along with the ever-changing ADA standards. As your site is frequently updated with new content, images, alt-tags, menu and link structures, it's important to maintain compliance. 

Our basic service - Automated ADA Scans, recognizes that ADA compliance is not a one-off task but an ongoing effort. We offer weekly, monthly, or quarterly Automated ADA scans as part of an ongoing engagement. The process includes an automated scan of the website to assess HTML pages, analysis of scan results, prioritizing remediations based on impact, and re-scanning to ensure the remediations are working. Again, automated scans will not ensure ADA compliance, but will allow you to address readily identifiable gaps and work toward compliance.

For a more comprehensive approach, our premium service - Manual Review ADA Audit - is recommended on a quarterly or bi-annual basis. As automated ADA compliance scans generally flag only 25% to 30% of the WCAG 2.2 A & AA guidelines, this service manually tests the remaining guidelines using various screen readers and other accessibility tools. The Manual Review Audit includes initial conformance documentation, proactive accessibility statement, automated and manual testing to WCAG 2.2AA, identification of issues, estimation of efforts to complete prioritized remediations, final round of automated & manual testing, QA review of remediated code implementation, meetings to review results, and final conformance documentation.

Alongside any effort you elect to undertake with Fruition with respect to ADA Compliance, we also recommend training your internal team members on ADA best practices for website management, designating an ADA officer responsible for managing ongoing efforts, and maintaining an ADA policy on your website.

Fruition understands the importance of ADA compliance and is committed to helping your organization meet and exceed the standards set by HB21-1110, ensuring a truly inclusive digital environment for all users.

If you need support in getting your site ADA compliant, Fruition is here to help. [Contact us today to get started! ](https://fruition.net/contact)

Source: https://oit.colorado.gov/accessibility-law

The State of Colorado has enacted the Information Technology Law (HB 18-1189) in accordance with its commitment to equal access to information and services for all its residents. This law is aimed at ensuring that state and local government websites meet the requisite accessibility standards. This summary encapsulates the salient points of the law and the necessary steps that government organizations should take to comply with its provisions.

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What is the Accessibility Law for Colorado State and Local Government? 

# 7 Google Analytics 4 (GA4) Best Practices for Optimizing Your Analytics

Google Analytics 4 (GA4) is a potent tool for recording and reporting key data gathered from visitors to your firm's website or app. To harness the full potential of the platform, you must optimize your Google Analytics 4 (GA4) property. Here we'll delve into seven foundational best practices that daily users of GA4 should implement immediately.

## 1. Review how (and why) you're tracking data before switching to GA4
To optimize analytics performance, focus on key questions about how users are interacting with your site:

1. Is our website attracting visitors that match our target audiences and markets?
2. Where do our visitors originate?
3. How are users engaging with our website?
4. Is our website successful at converting visitors into leads?

## 2. Ensure your data is flowing smoothly
Understanding the basics of GA4 functions is crucial, starting with setting up your Google Analytics Accounts, initiating a data stream, confirming the data flow, and regularly monitoring your analytics to identify patterns and trends.

## 3. Set up internal filtering
Filter out internal traffic, including data from your marketing team, main office employees, contractors, etc., to get an accurate representation of your actual user base.

## 4. Understand the difference between event types 
In GA4's tracking system, there are four broad categories of events you should be aware of:

1. Automatically Collected Events
2. Enhanced Measurement Events
3. Recommended Events
4. Custom Events

## 5. Learn the basics of Google Tag Manager & Google Looker Studio
Google Tag Manager (GTM) is for collecting data, GA4 is for storing data and basic analysis, and Looker Studio is for presenting that data to key stakeholders. Utilizing these tools to their full potential improves data collection accuracy and analytics reporting.

## 6. Integrate with Google Search Console & Google Ads
Integration with other data sources enhances the depth and accuracy of the data you collect on GA4. Equally, it helps in more precise ad targeting and comprehensive data collection.

## 7. Leverage GA4 data to improve your user experience
The primary reason for tracking data is to improve the user experience for those visiting your site. GA4 provides the ability to make informed decisions about refining digital strategies effectively.

In the realm of digital analytics, there's a significant power in knowledge. Using GA4 is not just about accumulating traffic data but understanding how your users interact with your website and improving the overall experience.

By following a few GA4 best practices, you can more effectively optimize your website for your audience and drive stronger conversions.

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7 Google Analytics 4 (GA4) Best Practices for Optimizing ...

# How to Verify an Image's Copyright: A Comprehensive Guide

The proliferation of images on the internet has made it increasingly important to understand image copyrights thoroughly. Unauthorized use of an image could lead to serious legal and financial consequences, emphasizing the need to verify its original source, seek appropriate permissions, and understand the terms of use.

## Identifying if an Image is Copyrighted 

Progressively, every new creative work, including an image, is copyrighted the moment it undergoes creation, except for a few exceptions. The challenge, however, lies in determining the actual owner of these copyrights, essential to seeking necessary permissions for usage.

## Steps to Check Image Copyright

To verify the owner of an image, one might employ the following methods:

**1. Look for an image credit or contact details**

If an image is sourced online, it may include a caption naming the creator or the owner of copyright. It may also provide contact details or a link to the creator's website, facilitating direct communication to negotiate terms of usage.

**2. Look for a watermark**

The presence of a watermark signifies a copyrighted image. It often contains the owner's name or company, easing the search for contact details. However, using software to remove this watermark is against copyright laws and could be held against you legally.

**3. Check the image's metadata**

Creators often embed valuable information into an image file's metadata. This information can be accessed by inspecting the image's properties, and may reveal the copyright owner's name or a copyright notice. 

**4. Use Google's reverse image search**

If owner details remain elusive, Google's reverse image search can be used to trace the original source. This function, accessible via the camera icon on Google Image Search, enables one to search using the image itself. This may reveal other instances of the image's usage online, and possibly the owner's information.

**5. Consult the U.S. Copyright Office Database**

Lastly, if a reasonable amount of owner information is at hand, further verification can be done via the U.S. Copyright Office Database. Images officially registered for copyright can be found here. However, registration is not a requirement for an image to be copyrighted—it is an optional step providing the creator additional legal leverage.

**Reminder: When unsure, refrain from usage**

In the event that the image source and copyright owner cannot be conclusively determined, avoid using the image. Numerous image banks provide high-quality images which are legally compliant for varying specifications and budgets. 

## Circumstances for Usage Without Permission

An image can be used without explicit permission only under specific circumstances: 

- The image is explicitly stated as free for use, like on Unsplash. 
- The image is within the public domain, signifying an abandonment of rights by the owner.
- The image falls under a Creative Commons (CC) license. However, the specific terms and conditions of permissible use should be thoroughly understood.

In every instance, make sure you possess all necessary information to legally and safely use the image. When in doubt, always reach out to the image creator.

## Concluding Remarks

The nuances of image copyright can be complex and it is critical to understand them completely before using someone else's imagery. The responsibility lies in being 100% sure about copyrights before image use. This pre-emptive step could spare users from expensive and unpleasant consequences. 

_Fruition has been upholding best practices in digital rights management, content creation, and SEO services for over two decades, supporting artists and creators globally in safeguarding their digital copyrights._

"Understand the crucialities of image copyrights with our step-by-step guide. Unveil the techniques to identify copyright ownership and use images legally."

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# Verifying Image Copyright: 5 Proven Steps to Establish Ownership

The surge of images on the internet heightens the necessity to grasp image copyrights thoroughly. Misuse of an image could end in severe legal and fiscal outcomes, hence the urgency to trace its original source, acquire suitable permissions, and apprehend the usage terms.

## How to Prove If an Image is Copyrighted 

Promptly, each innovative work, like an image, is copyrighted upon its creation, barring a few exceptions. The predicament, nevertheless, lies in pinpointing the precise copyright owner, crucial for securing required permissions for usage.

## Follow These Steps to Confirm Image Copyright

To trace the proprietor of an image, consider these methods:

**1. Scan for image credit or contact details**

An online-sourced image might include a caption earmarking the creator or copyright owner. It could also offer contact info or a link to the creator's portal, streamlining direct negotiations for usage terms.

**2. Seek a watermark**

Watermark presence indicates a copyrighted image. It often carries the owner's identity or firm, simplifying the contact details' search. Conversely, using tools to eliminate this watermark infringes copyright laws and could result in legal ramifications.

**3. Inspect the image's metadata**

Creators usually incorporate valuable data into an image file's metadata. Inspecting this, via the properties of the image, might disclose the copyright owner's identity or a copyright notice. 

**4. Employ Google's reverse image search**

When owner specifics are elusive, Google's reverse image search can locate the original source. Accessible through the Google Image Search's camera icon, this feature facilitates a search via the image, unveiling other online usages of the image, and potentially the owner's data.

**5. Probe the U.S. Copyright Office Database**

If a reasonable level of owner data is available, the U.S. Copyright Office Database can further confirm it. Officially registered copyrighted images can be tracked here. Nevertheless, registration isn't a pre-requisite for an image to be copyrighted—it's an extra step offering the creator more judicial foothold.

**Remember: When in doubt, refrain from usage**

Should the image source and copyright owner remain indecipherable, steer clear from using it. Multiple image banks provide superior quality images complying legally for various specs and budgets. 

## Specific Usage Scenarios Without Permission

An image can be in use without direct permission only in certain conditions: 

- The image is declared free for usage, like on Unsplash.
- The image is within the public realm, indicating owner rights' abandonment.
- The image falls under a Creative Commons (CC) license. However, ensure you understand the specific terms and conditions of permitted use.

Never fail to possess all necessary data for the lawful and safe use of the image. Always consult the image creator if uncertain.

## Conclusion

Image copyright intricacies can be convoluted. Fully comprehend them before making use of someone's visuals. Being certain about copyrights before image use could avert costly and unfavorable experiences.

_At Fruition, we respect best practices in digital rights management, content creation, and SEO services for over 20 years, supporting global artists and creators in protecting their digital copyrights._

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Uncover Image Copyright: 5 Proven Steps to Verify Image Ownership

# Understanding Generative AI: A Comprehensive Guide

Artificial intelligence (AI) has always been captivating; however, when the term "generative" is added to it, it sparkles with an irresistible combination of automation and creativity. Generative AI has been making headlines recently in a variety of sectors, including healthcare, finance, art, and entertainment. But what exactly is generative AI? And why has it become so popular today? This guide delves into the intricate world of generative AI, exploring its technology, history, various subfields, practical applications, and ethical concerns.

## What is Generative AI?

Generative AI uses algorithms to understand the nuances of the input or training data provided to it, leading to a versatile approach to data generation. It can discern and classify elements within a given dataset, like determining whether an email is spam (similar to discriminative models). However, generative algorithms also understand existing frameworks and use this understanding to create new information or data that convincingly fits in with the rest of the dataset. 

The real magic of generative AI comes from its ability to comprehend complex structures and patterns in its training data. It uses various statistical techniques and deep learning architectures—such as Neural Networks, Convolutional Neural Networks (CNNs) for image tasks, and Recurrent Neural Networks (RNNs) for sequential data—to gain a deep understanding of their training data. Leveraging this knowledge, generative AI can produce synthetic data that follows similar patterns or characteristics as the training data, producing results that are novel yet contextually and structurally consistent with the original dataset.

## Statistical Foundations and Underlying Technologies in generative AI

Generative AI draws from foundational statistics, frequently employing methods like Bayesian inference, Markov Chains, and maximum likelihood estimation. It is largely powered by deep learning and neural networks. Specific variants of neural networks, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are used for image-related and sequential data tasks respectively. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) form the essential building blocks for generating high-quality data.

## Evolution of Generative AI

Generative AI has evolved significantly over time:

- **The early 2010s**: The advent of deep learning algorithms and a significant increase in computational power made generative AI viable. 
- **2014**: Introduction of GANs, thereby setting a milestone for modern generative algorithms.
- **2015–2016**: Increasing popularity of Variational Autoencoders, giving data producers more control.
- **2017–2018**: Terms like "creative AI" became popular, encompassing AIs that can produce music, art, and even stories. 
- **2018–2019**: Significant advancements in natural language processing (NLP) models like GPT-3 and BERT.
- **2022 and beyond**: Generative AI continues to expand, finding applications in multiple disciplines and sectors.

## Types of Generative AI

Generative AI finds application in multiple fields:

- **Text Generation**: Models like GPT-3 can write code, create news articles, respond to user queries, and even generate poetry.
- **Image Production**: GANs have been used to produce realistic and detailed images, with applications ranging from creating fictional vistas to producing medical imagery.
- **Audio Generation**: Audio generation algorithms can create simple tones or more complex compositions like music or speech.
- **Data Augmentation and Simulation**: When data is scarce or expensive, generative algorithms can be used to simulate scenarios or generate extra training data.
- **3D modelling**: The capability of generative models to create intricate 3D models is gaining interest in areas such as gaming, architecture, and healthcare.

## Examples in Real-world Applications

Generative AI is already seeing applications in various industries:

- **DeepFakes**: Using GANs to replace faces in videos to make it appear as if someone is saying or doing something they haven't done.
- **Customer service and chatbots**: Use of advanced chatbots like GPT-3 to automate customer service.
- **Arts and Crafts**: Platforms like DALL-E produce artworks and AI like Jukebox composes entire musical works.
- **Drug Research**: Simulating the effects of various compounds for faster drug discovery.
- **Finance**: Generation of various economic scenarios for risk analysis and financial planning.
- **Automotive Sector**: Improving existing car models for aerodynamics and fuel efficiency.

Despite these applications, generative AI also comes with a set of moral dilemmas related to data reliability, ownership and copyright, potential for manipulation and falsification, and more. 

## Drawbacks of Generative AI

Some of the potential challenges while working with generative AI include:

- **Reliance on High-Quality Data**: High-quality data is a necessary input for generative AI models to produce accurate results.
- **Computation-Related Costs**: Training these models is computationally intensive, which leads to high energy consumption and carbon footprint.
- **Loss of Employment**: Automation could potentially displace workers in fields where generative AI can generate outputs.
- **Legal and Ethical Grey Areas**: Issues related to the use and misuse of AI-generated content and the ownership of AI-generated works still need to be worked out.
- **Accessibility**: Creating high-quality generative AI models requires substantial resources and expertise, potentially exacerbating existing disparities in access.

## Case Study: Using Generative AI to Simplify Digital Marketing Content Production

Generative AI can play a crucial role in automating the creation of content for digital marketing campaigns, acting as an efficient strategy for reducing costs and enabling hyper-personalization at scale. This, however, requires careful planning, addressing ethical considerations, and continuous performance monitoring.

## Conclusion

Cleary, generative AI—a promising, tech marvel—has the potential to revolutionize many sectors. As we unlock the vast capabilities of generative AI, the ride is filled with exciting developments, ethical obligations, and potential challenges. Our journey through this territory goes beyond being just users or developers—we are pioneers charting the course of digital evolution. As we continue to script this generative AI saga, it's our responsibility to ensure a future that syncs technological power with ethical responsibility. It's not just about predicting the future but also creating it. Let's make it a masterpiece.

Delve into the world of generative AI, exploring its evolution, statistical foundations, and diverse applications across sectors. Uncover its challenges, real-world usage, and the looming ethical dilemmas.

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Grasping Generative AI: From Foundations to Future Developments

# Master Customer Interaction with Multi-Turn Conversations in Digital Marketing

## What exactly is a multi-turn conversation?

During interactions with digital assistants, we usually experience commands like setting timers or initiating calls. These activities often involve a single response with no follow-up questions or exchanges, concluding in one conversational turn. A conversational 'turn' is characterized by one alternating interaction: a user expresses and the digital assistant replies, or vice versa.

Should an interaction include more than a solitary turn, it progresses into a multi-turn conversation. Such dialogues necessitate that digital assistants recall context throughout the conversation, comprising information already gathered. This aspect introduces a degree of complexity to programming.

## The significance of context in multi-turn conversations

Tasks of a simple nature can usually be executed in one response, such as setting a timer or making a call. However, tasks of greater complexity may require a digital assistant to recall context and prior shared information. Absent the capacity to recall context, a digital assistant might not comprehend ongoing requests, reducing its utility.

## The impact of multi-turn conversations on digital marketing

Marketing initiatives are crafted to draw in customers and resolve their queries effectively. Multi-turn conversations play a pivotal role in customer engagement by allowing for natural conversation sans a rigid script.

Rather than adhering to a strict question pattern such as "What is your name? What products interest you? How can we further assist you?" a multi-turn conversational setup permits businesses to gather information in a more organic manner. 

## Multi-turn conversations enhancing digital marketing campaigns 

A well-established multi-turn conversation engine offers a richer, smoother customer engagement experience. Routine voice assistants may exhibit decreased successes after numerous conversational turns due to error accumulation. Precise interactions are pivotal for resolutions to succeed. 

Yet, armed with a well-designed multi-turn system, businesses can anticipate high accuracy even amidst intricate interactions, significantly elevating the customer experience. The system's ability to manage interruptions, query for clarity, and understand human nuances are critical aspects of user engagement in successful digital marketing campaigns.

## Multi-turn Agents by Fruition for Today's Customer Engagement

Fruition creates multi-turn agents for our digital marketing platform. This approach allows for more complex tasks to be performed autonomously.

## Essential facts about multi-turn conversations in digital marketing:

1. Multi-turn conversations include more than a single back-and-forth interaction.
2. The retention of context during multi-turn conversations is vital.
3. Multi-turn conversations give rise to an intuitive and engaging customer experience.
4. The effectiveness of a multi-turn conversation engine is significantly influenced by its accuracy.
5. Multi-turn conversation systems contribute significantly to improving digital marketing strategies.

Discover how multi-turn conversations boost customer engagement in digital marketing. Enhance your marketing campaigns using context retention and intuitive customer interactions.

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Maximize Customer Engagement with Multi-Turn Conversations in Digital Marketing

# Google's Defense of Generative AI Users: An Indemnification Not Grounded in Goodwill 

Google recently announced its decision to indemnify users of generative artificial intelligence systems against intellectual property violation claims. It is imperative, however, to understand that this decision emerges not from goodwill but from the recognition that AI-generated works do not infringe copyright laws.

Google's pledge proffers a shield to the users of its Google Cloud and Workspace platforms, aligning itself with other tech leaders such as Microsoft and Adobe who have affirmed similar commitments. An apt analogy is if I said I'd protect you from Mike Tyson but I know that Mike Tyson is so stoned that he doesn't have any teeth left. I of course kid, Mike Tyson would still mess you up... Kidding aside, copyright law doesn't have any teeth when it comes to generative AI. 

## Generative AI & Intellectual Property Battles

Leading technology corporations, Google included, have been increasingly relying on generative AI, incorporating these advanced systems into their product offerings. However, this has invited several lawsuits from copyright owners who argue that the use of their works to train AI systems and the content produced by these systems infringes their rights. 

Google's two-pronged approach to indemnity covers both these areas of concern; it is a first in the industry, according to the company spokesperson.

## Applicability of Indemnity 

This new policy from Google applies to its suite of software, notably its Vertex AI development platform and Duet AI system. Both these programs generate text and images in Google Workspace and Cloud programs. Their AI chatbot program Bard, however, was not mentioned in this context.

It is noteworthy that the indemnity will not apply if users intentionally make or utilize the AI-generated output to infringe others' rights.

## Alternative to Google's Shallow Offer
Use a private AI service like [FruGPT](https://frugpt.com). Create your own AI models to protect your data and prevent IP leakage. 

## Legal Claims & Google's Stance

The burgeoning legal tussles over generative AI focus mainly on the companies owning these systems, including Google, rather than individual end users. Employing internet-scraped data for system training is defended as fair use under U.S. copyright law, according to the AI defendants.

Google's indemnity offer is an insightful step in this direction. Despite the appearance, it is rooted in the clarity of AI's non-violation of copyright laws rather than being a manifestation of goodwill. Technology companies are constantly grappling to integrate AI into their products, and such indemnities offer a steadying hand in this competitive race.  It is clear that Google's stand serves both a strategic purpose and a reassurance to AI system users that their usage of generative AI programs stands on the right side of copyright laws.

Google said on Thursday that it will defend users of generative artificial-intelligence systems in its Google Cloud and Workspace platforms ...

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Google to defend generative AI users from copyright claims

# Google October 2023 Broad Core Update: An Overview

Google's most recent broad core update, released in October 2023, has sent waves through the SEO and digital marketing industry. This update, like its predecessors, serves as a stark reminder of the constant and crucial need to stay updated with Google's algorithm changes to maintain or improve your website's search engine ranking.

## Release and Completion

This October 2023 update is the third one from Google this year, following the March and August updates. The rollout for the update has commenced and is expected to be fully implemented within two weeks.

## Impact and Response

With any update Google introduces, there's always a potential fluctuation in search engine rankings. While there may be no explicit actions to effectively recover from a negative impact, Google has previously suggested a review of the pages affected, indicating a decline in ranking may not necessarily mean that something is wrong with your pages. Notably, recovery between core updates is marginal, with the most considerable changes usually occurring after a subsequent core update.

## Previous Updates 

Google's core updates prior to this October 2023 edition include:

- August 2023 core update
- March 2023 core update
- September 2022 broad core update
- May 2022 broad core update
- November 2021 core update
- July 2021 core update
- June 2021 core update

## Upcoming Updates

Simultaneously with the October 2023 core update, Google has also begun implementing the October 2023 spam update. This rollout is estimated to take three weeks, meaning there will be a period of overlap between the spam update and the core update. In the wake of these updates, keeping an eye on your analytics becomes even more vital as it will help you isolate which update or updates are causing shifts in your website's performance.

## Concluding Notes

Google's regular updates to its search ranking algorithms greatly influence a website's performance in search results. By effectively tracking these updates and diligently monitoring your analytics, you can gain valuable insights into what is causing changes in your ranking, be it adjustments on your website or algorithmic changes from Google. For more detailed information on past updates, consider reviewing our comprehensive archive on Google Updates.

Google's October 2023 core update is now rolling out and may take up to two weeks to complete. This is the third broad core algorithm update.

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Google releases October 2023 broad core update

# Massive Adoption of Generative AI Accelerates Regulation Plans

Generative AI has seen an unprecedented growth in the consumer market driven by the launch of AI tools including OpenAI's ChatGPT and Stability AI's Stable Diffusion in 2022. Concurrently, malevolent usage of these tools has amplified, leading to an urgent calling for governance by regulatory bodies.

## Regulatory Roadmaps

Sam Altman, OpenAI's CEO, called for the creation of a regulatory body for AI companies on May 16, 2023, before a US Senate committee. This follows the incident in March where Italy's data protection agency (GPDP) temporarily banned ChatGPT, citing concerns over the absence of a legal basis that legitimises "the mass collection and storage of personal data for the purpose of 'training' the algorithms underlying [ChatGPT’s] operation." 

Simultaneously, other data protection agencies, including Canada's Privacy Commissioner and the European Data Protection Board, began investigations into ChatGPT, further easing fears about privacy and the potential misuse of generative AI tools. For instance, these tools could spread disinformation by indiscriminately scraping the internet to build the AI models. 

## AI Legislation Pipeline

Legislative steps for AI regulation have been in various stages in countries such as the EU, where the AI Act was introduced in April 2021, and Canada, with the AI and Data Act introduced in June 2022. The EU regulation's latest draft proposed comprehensive measures to control "foundational models" and proposes that "general purpose AI models" should fall into one of four categories based on their risk level: low and minimal risk, limited risk, high risk, and unacceptable risk. 

## Approaches Across the Globe

The Canada’s AI & Data Act, part of the federal Bill C-27 for the Digital Charter Implementation, renders a different approach with no ban on AI automated decision-making tools, including in critical areas. Instead, it mandates creating a risk mitigation plan to increase transparency when using high-risk systems.

In contrast, the US has only issued non-binding guidelines concerning AI applications, with New York City introducing one of the country's first AI laws aimed at preventing bias in the employment process. With the rise of generative AI and its potential misuse, advocacy organizations are urging the US government to take firmer action.

The UK government has emphasized a "pro-innovation approach to AI regulation." It plans investment of $125m to launch a Foundation Model Taskforce, which is expected to help spur the development of AI systems within the nation. 

While acknowledging potential dangers his product could present to jobs and democracy, OpenAI’s Altman proposed that AI companies should be independently audited and licensed to operate by a dedicated government agency.  

In conclusion, the rapid adoption of generative AI is accelerating plans for regulation. It remains to be seen how this technology will be managed and how regulation will balance privacy concerns with the potential for innovation and growth.

The use of AI in the consumer market has exploded like never before, driven by the launch of general-purpose AI tools like OpenAI's ChatGPT.

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Massive Adoption of Generative AI Accelerates Regulation ...

# Should You Build or Buy Generative AI?

When it comes to adopting generative AI, you can either take, shape, or make the models that meet your needs. However, it's important to consider the amount of resources needed, particularly if you're planning to build your own AI. Even off-the-shelf systems will require some form of modification to best fit your organization. 

# Building vs Buying Your Own Generative AI - Pros and Cons

Artificial Intelligence (AI) has transformed contemporary business practices, and generative AI, a subset of AI that can understand and recreate human-like content, is leading the way in revolutionizing fields such as music, literature, and the visual arts. The prominent question for organisations is whether to build or buy their own generative AI. This decision is not straightforward and depends on multiple factors like budget, skills, resources, and the unique needs of the business. 

## Building Your Own Generative AI

### Pros

1. **Customized Solution:** Building your own generative AI means that you can customize the solution to meet the specific needs of your business. You have control over every bit of functionality, and it can be designed to interact seamlessly with other technology and systems you already use.

2. **Intellectual Property:** By building your own AI, you own the intellectual property rights, which can be a significant asset for your business in the long-term.

3. **Competitive Advantage:** A unique, customized AI solution that cannot be replicated by competitors can offer significant competitive advantage and serve as a differentiating factor for your business.

### Cons

1. **Cost:** Building a generative AI model from scratch can be expensive. Not just in terms of capital cost, but also the resources and time spent in development and troubleshooting.

2. **Time-consuming:** It can take several months, or even years, to develop and perfect an AI model.

3. **Requires Deep Expertise:** Generative AI development necessitates a rare blend of skills. Hiring a team with such expertise can be challenging and expensive.

## Buying a Generative AI

### Pros

1. **Speed:** Buying an existing generative AI means you can implement the technology quickly.

2. **Cost-effective:** The upfront cost of buying an AI solution is typically less than building one, making it a more economical choice for businesses with tight budgets.

3. **Predictable Updates & Support:** Vendors typically offer ongoing support and regular updates, ensuring your business always benefits from the latest advancements.

### Cons

1. **Lack of Customization:** Existing solutions may not meet all your specific needs and might not be compatible with other existing systems.

2. **Dependence on Vendor:** You are dependent on the vendor for updates, bug fixes, and any customization. This can lead to an increased cost over time.

3. **Limited Differentiation:** Since the same solution is available to others, it does not offer a unique competitive advantage.

In conclusion, the decision to build or buy a generative AI should be taken based on factors such as the unique needs of the business, budget, time constraints, and the technical skills of the staff. Remember, AI is an investment, and the best choice is the one that brings the greatest benefits to your business in the long run.

## Adopting Generative AI

With generative AI becoming a crucial tool not just for text, images, or video applications but for an array of models and services, it's necessary for organizations to take advantage of it in the best way possible - through building or buying. With public tools increasingly utilized for generative AI models, organizations have to set out clear policies on how to source and use these models, particularly for CIOs who want to tap into the technology's potential with their specific terms and data.

## The Take, Shape, Make Model of Generative AI

Eric Lamarre, the senior partner leading McKinsey Digital in North America, suggests a more comprehensive approach to adopting generative AI. He proposes seeing it in terms of taking, shaping, and—in very few cases—making generative AI models. 

### Taking Generative AI Models

Taking generative AI involves consuming it either through an API, like ChatGPT, or via another application, such as GitHub Copilot, to facilitate software acceleration when coding. Although these readily available generative AI applications may not provide a significant competitive edge, their adoption is essential to avoid competitive disadvantages. 

### Shaping Generative AI Models

Shaping involves leveraging existing foundational models off the shelf but retraining them with your data. This process reduces the 'hallucination' problem and leads to more accurate and relevant results. For instance, in a contact center scenario where applications are tailored to the company's products, services, and the types of problems that frequently arise, a general large language model (LLM) may not be calibrated for this, but you can recalibrate it—a process known as fine-tuning—to your own data. This level of shaping or 'shaping' doesn't commit you to one approach.

### Making Generative AI Models

Making or building would be the most challenging option and likely a rarity, requiring more resources and sophisticated capabilities. However, with the right skills, shaping generative AI systems can deliver applications that offer competitive differentiation. 

## Considering Your Organizational Size and Resources

Smaller organizations might find building their own generative AI a daunting prospect, and it may be more practical to focus on automating existing manual processes and exploring new possibilities. Larger organizations with significant technology expertise may choose to fine-tune LLMs like ChatGPT rather than build their own—allowing multiple departments to experiment with different approaches and learn how to use the technology effectively.

## Ensuring Data Protection and Compliance

Regardless of whether you choose to build or buy the LLM, organizations will need to consider the importance of data privacy and governance, as well as protection. Legal and compliance teams will also need to consider the impacts on workflows and outcomes and clarify their role in the use of ML and AI.

## Conclusion

Whether an organization should build or buy generative AI depends on various factors, such as its size, resources, and particular needs. As the technology evolves, organizations should revisit their decisions about building or buying and benchmark how they contribute and compare to commercially available models. It's crucial not to overlook the importance of user training and acceptance testing, ensuring that the adopted tools can be effectively used.

You can adopt generative AI by taking, shaping or making the models you need.  Find out if you should buy vs build?

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Should you build or buy generative AI?

# Crash Course on WordPress Website Maintenance and Security 

Secure and well-maintained websites not only provide better user experience but also rank higher in search engine results. Fruition, with two decades of experience in digital marketing, website development, and hosting services, offers the knowledge and skills for optimal WordPress website maintenance and security.

## What is WordPress Website Maintenance?

WordPress website maintenance is a comprehensive set of tasks necessary to ensure the smooth functioning of your website. These tasks include:

1. Regularly updating WordPress themes and plugins
2. Checking and fixing broken links
3. Monitoring website performance and loading speed
4. Ensuring website compatibility with different browsers and devices
5. Checking and resolving any issues with the website's layout 
6. Regularly backing up website data 

While performing these tasks might seem daunting, Fruition's expertise can make the process streamlined and efficient, ensuring that your website operates at its prime at all times.

## WordPress Website Security

Securing a WordPress site entails all actions taken to prevent, detect, and remove malicious software and cyberthreats. With rising cyber threats, website security is no longer an option, but a necessity. Here are the critical elements you need to focus on for your WordPress website security:

1. Regularly updating WordPress core, themes, and plugins
2. Implement strong access controls 
3. Use two-factor authentication 
4. Regularly backup your site
5. Employing a reliable security plugin 
6. Implementing an SSL Certificate 
7. Regularly scan for malware 

You may consider security as a highly technical field, but with Fruition's backing, securing your WordPress website can be achieved effortlessly.

## An Alternative Viewpoint: Managed WordPress Hosting

While it's entirely possible to maintain and secure your WordPress website on your own, it's a task that requires time, technical knowledge, and consistent effort. An alternate way would be to opt for Managed WordPress Hosting like what's offered by BlueHost. Such services handle website maintenance and security tasks while you focus on your core business.

However, selections like this should align with the specific needs of your business. With Fruition's deep and broad experience in website development and hosting services, you'll have insights about the most suitable decision for your business. 

In sum, WordPress website maintenance and security is vital to ensure that your site stays up-to-date, secure, and functional. Leverage Fruition's experience and expertise to make the most out of your WordPress Website.

Looking to create a website with WordPress having full security? Fruition has the right hosting solution for you from simple to high security solutions for governments. 

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Crash Course on WordPress Website Maintenance and Security

# The Impact of Generative AI: A Comprehensive Analysis

Generative AI, a subset of artificial intelligence that generates new content based on existing data, is rapidly redefining multiple industries. From facilitating content creation at speed and scale to enabling innovative business models and applications, this groundbreaking technology promises significant benefits. However, its rise isn't without potential challenges, including job displacement, copyright issues, data security risks, and significant implementation cost. 

## Understanding Generative AI
 
Generative AI is a type of machine learning algorithm that produces new data resembling its training set. These algorithms produce a range of outputs, including text, images, videos, and audio. 

Notable examples include:
-**FruGPT**: Private AI models for influencers, celebrities, businesses, and consumers with the ability for influencers to earn revenue on their AI. 
- **ChatGPT**: A text-based generative AI developed by OpenAI that has advanced conversational capabilities.
- **DALL-E 2**: A generative AI that produces digital images from natural language descriptions.

## Benefits of Generative AI

Generative AI technology effectively reduces the financial and temporal resources allocated to content creation, enabling businesses to increase their output and profitability. Besides playing a pivotal role in business productivity, generative AI can foster innovation across various sectors by paving the way for new business models and applications.

The potential of generative AI extends to creating AI systems specifically designed for unique verticals and datasets, whether it's market intelligence or medical research. This technology is also likely to spur demand for AI computing hardware, particularly memory chips.

## Challenges Associated With Generative AI

Despite the unmistakable advantages, the rise of generative AI does not come without risks.

**Job Displacement**: As generative AI takes over human tasks, it could cause job displacement in fields like programming and copywriting. Nevertheless, as generative AI often produces inaccurate results, it currently complements human efforts rather than replace them entirely.

**Copyright Infringement**: Since generative AI uses internet data, it might inadvertently generate plagiarized content or infringe copyright, potentially resulting in legal issues.

**Data Security Risks**: Generative AI systems could pose significant data security risks since inputted information is added to shared data repositories. This issue could raise concerns about client confidentiality.

**Deployment Cost**: Generative AI systems entail considerable costs due to the extensive computing infrastructure they require. Therefore, its commercialization might be challenging unless it offers value that justifies its high cost.

In light of these potential hurdles, it's crucial for businesses to employ generative AI technology responsibly and ensure sound governance measures are in place to mitigate potential disruption.

The boom of generative AI symbolizes a monumental shift in the technological landscape - reimagining the impossible and steadily growing smarter. It's crucial, however, that this game-changing advancement is employed responsibly and diligently governed to amplify its benefits while limiting potential disruptions.

## Summary

Generative AI has the potential to significantly alter how businesses operate across industries. The technology offers the promise of faster and cheaper content creation, the dawn of innovative business models and applications, and increased demand for AI computing hardware. Yet, generative AI also presents risks, including potential job displacement, copyright issues, data security concerns, and cost-intensive deployment. It is, therefore, invaluable that we navigate this new vista responsibly, ensuring we maximise its potential benefits while managing its challenges.

However, the technology could displace jobs and lead to copyright issues and data security risks. What are the advantages of generative AI?

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The Rise of Generative AI

# How Should Your Organization Address Generative AI?
Generative AI models have rapidly transformed contemporary industries, paving the way for a revolution in diverse areas such as music, literature, and visual arts. For businesses looking to leverage this powerful subset of artificial intelligence, a critical question arises: should you build or buy your own generative AI? Addressing this question involves considering an array of factors including budget, existing skills and resources, time constraints, and the specific needs of the business.

## Why Consider Generative AI?
Generative AI can generate human-like content, thereby enhancing creativity, improving efficiency, and driving innovation in businesses. It can be tailored to perform a variety of tasks, from creating music and literature to designing visual art, expanding the potential for new and unique products or services.

Given the potential liabilities and benefits of this technology, organizations should consider establishing AI use policies. Key issues such Operating and Privacy terms, Intellectual Property protection, the accuracy of AI-generated content and possible applications should be addressed.

## Privacy Policies and Terms of Use Governance

Generative AI applications often include terms-of-use agreements and privacy policies that give parent companies vast rights to use information they receive. These policies should prohibit the sharing of confidential company and customer information.

## Intellectual Property and Copyright Protection

Works created by AI are not currently protected under U.S. Copyright law. Changes to existing materials by human beings can make them eligible for protection. To avoid the possible legal complications, employees should be given clear guidelines on when and how they can use content produced by Generative AI.

## Accuracy of AI-Generated Content

Generative AI users should be aware that the applications are not always accurate. Professionals should not rely solely on these services for their expertise and should always cross-check any information provided by generative AI sources.

## Generative AI as Research Tool

Despite these liabilities, Generative AI can be a useful tool to provide a first impression of a research topic, as long as there's a follow-up process to verify the information. 

## Tool for Brainstorming and Ideation

Generative AI has its strengths in producing content that could provide fresh perspective or inspiration for human-created projects. This can prove to be a valuable tool for enhancing creativity and productivity.

By implementing well-drafted policies, businesses can harness the strengths of Generative AI while mitigating potential risks.

Generative AI, an artificial intelligence technology that identifies patterns in large quantities in training data and then generates original content,

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How Should Your Organization Address Generative AI?

# How Generative AI Could Disrupt Creative Work

Artificial Intelligence (AI) continues to evolve, delivering a significant impact across numerous industries, including creativity and content generation. A key player in this digital transformation is Generative AI, a branch of artificial intelligence that leverages machine learning models to produce new, original content.

## Artificial Creativity: What is Generative AI?

Generative AI uses models like Generative Adversarial Networks (GANs) and Transformer models to mimic the human ability to create. They are trained using vast datasets, which allow them to learn patterns and generate new content, such as text, images, music, and even voices. Notably, some AI, like OpenAI's GPT-3, can create coherent, contextually relevant texts that are difficult to distinguish from human-written content.

## Disruption to Creative Industries

With its ability to emulate human creativity, Generative AI poses significant implications for the creative industry — including both opportunities and challenges.

### Speed and Volume

Generative AI has the ability to produce a large volume of content quickly. For example, tools like Jukin Media can generate videos at a speed which a human creator simply cannot match.

### Cost Efficiency

Generative AI tools often maintain a competitive edge by slashing production costs. This could potentially lead to job displacement in industries traditionally populated by human creators, such as journalism, music, design, and entertainment.

### Quality and Authenticity Concerns

The question of authenticity arises with the utilization of AI. AI-generated content, while rapid and cost-effective, might lack the depth, emotion, and authenticity that come with human creations.

## Mitigating the Potential Downsides

To navigate this transformative wave while minimizing its potential downsides, certain proactive strategies can be adopted.

### Ensuring Human-in-the-loop

Including a human in the creative loop will not only enable oversight and quality control, but also bring the required emotional intelligence to the content. The idea is to foster a symbiosis where AI assists human creators in producing novel and out-of-the-box ideas, rather than replacing them.

### New Career Opportunities

The rise of Generative AI doesn't necessarily mean the end of human creativity; instead, it could give birth to new job roles. For instance, AI literacy will become a valuable skill enabling creators to better leverage AI tools and steer their outputs.

### Legislation and IP Rights

As Generative AI blurs the line between human-made and AI-made creations, clear legislation needs to be put in place to protect intellectual property rights and determine who the creator of an AI-made content is.





For example, recent lawsuits against prominent generative AI platforms allege copyright infringement on a massive scale.

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How Generative AI Could Disrupt Creative Work

## Grappling with Copyright Issues & AI Innovations
The framework of copyright law as set by the 17 US Code 701 acknowledges human creativity and protects investments in original ideas. A significant example of this framework is the case of [Thaler vs Perlmutter](https://www.authorsalliance.org/2023/08/24/copyright-protection-in-ai-generated-works-update-decision-in-thaler-v-perlmutter), which delves into human authorship and interaction levels necessary for copyright protection.

## Copyright Protection: Human or AI?
As technology streaks ahead, it raises pertinent questions about the criteria needed to secure copyright protection. Current laws cover only human-authored works, leaving AI creations unprotected. This absence of coverage poses a challenge for visual artists as AI-generated images are not under copyright protection.

## Of AI, Human Creativity & Copyright Laws
The Copyright Office's webinar on AI stressed the relationship between AI and human creativity. Debates ensued revolving around instances such as the 'Zarya of the dawn' case, '[Theate d opera spatial](https://www.copyright.gov/rulings-filings/review-board/docs/Theatre-Dopera-Spatial.pdf)', and the 'Gigapixel' case. These debates underscore the necessity to broaden our understanding of copyrighted work and to consider its implications on creator stakeholders.

## Copyrights: Safeguarding Creators in the Digitalized Age
In a competitive market, copyrights shield creators from infringements, preserving market efficiencies. Yet, it remains a subject of discussion whether this traditional protection mechanism is fit for the accelerating digitalization and AI implementation.

## The FruGPT Solution to the Copyright Conundrum
To offset the shortcomings of copyright laws, we present [FruGPT.com](https://FruGPT.com). FruGPT allows creators to monetize their works, offering a tangible solution in the agile creative landscape.

## Crafting the Fuure: Market Solutions & Copyright Law
As we adjust to the digital age and the creative expressions it brings, our focus remains on the protection and celebration of human innovation. Market solutions, like FruGPT, must continue to align with our steadfast legal norms, bridging the gap between the evolution of creativity and human-centric principles.

Exploring the intersection of AI, copyright law, & FruGPT. Understand copyright issues and discover how FruGPT aids in monetizing creator's works

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AI, Copyright Law, & FruGPT: Navigating the Creative Landscap

# 7 Questions You Should Ask When Hiring A Digital Marketing Agency

Finding a digital marketing agency that has just the right balance of expertise, personality, and transparency can be tough. To help you find your perfect match, we’ve shared the 7 questions you should ask a digital marketing agency before you hire them, as well as 3 red flags that should send you running in the opposite direction.

## 1. How Do You Share Reporting and Results?
Transparency is key to a good partnership, so you should expect access to reporting platforms as well as regular meetings to go over results. To get the most out of your partnership, expect your agency to review the data, its significance, and how it impacts your future strategy.

## 2. How Will You Interact With Our Team?
A digital marketing agency should be an extension of your team. Ask how the agency will work with your core team of employees and how much collaboration and communication your employees should expect. Be wary of any agencies that want to limit their interaction with you or team members. To be most successful, you’ll want an agency that establishes clear lines of communication and regular updates on your business’ progress.

## 3. What’s Your Process For Getting To Know Our Business?
To give you the best marketing strategy, a company should have an in-depth onboarding process to help them fully understand your business and your industry. Ask what kind of research, competitive analysis, and assessments the agency will do to prepare for your partnership.  

## 4. How Will You Build Our Marketing Strategy?
There’s a big difference between custom/semi-custom strategies and plug & play marketing models. Understanding how the agency plans on building your marketing strategy will tell you if they’ll re-use past formulas or create an individualized plan for your business.

## 5.  Do You Have Case Studies or Examples Showing Your Previous Results?
A reputable agency should have case studies or examples of their work  Not only will you get an idea of the quality of their work, but you’ll find out if the agency’s approach matches your business’ style and values.

## 6. Is Your Team In-House or Contract-Based?
Since internal communication is easier when employees are in-house, these teams may focus more on their clients and be able to get up to speed faster than contract-based agencies.  If you highly value close collaboration with your agency, you’ll probably prefer an in-house agency. If those aspects are less important to you, a contract-based agency may be right for you. Either way, ensure you know who is doing the work and how you can expect to work with them.

## 7.  How are your fees structured?
An agency’s cost and payment structure has a big impact on determining if you’ll hire them. Some agencies charge monthly or yearly retainers, while others may charge hourly rates or project-based fees. Understanding the agency's fee structure can help you tell if it’s a good fit for your needs.

## Red Flags
Throughout your conversation with your potential digital marketing agency, be on the lookout for these red flags:

- **They guarantee specific results:** If an agency says something along the lines of “we guarantee we can increase your traffic by 20% in less than 6 months,” be wary. Though agencies can lean on past results from other clients, each business is different and requires testing to determine what will be successful. If an agency gives you specific numbers, they may be using black hat strategies (which could end up penalizing your company), or they may just be making it up.
- **They can’t demonstrate who the team is:** The agency should be able to tell you about the team members, including their experience and strengths. They should also tell you who your designated point of contact is. If they can’t tell you who will be on your team, be cautious. You want to know who you’re hiring, right? 
- **They don’t have references:** Just like showing off previous work, an agency should be able to provide references. A good digital marketing agency fosters strong relationships with their clients, and a lack of references could indicate an inability to create strong partnerships.  
- **They set up Adwords and other paid advertising on their own account:** If an agency sets up your business’ campaigns in their own account, you won’t be able to see the results yourself. You’ll also lose access to your data if the partnership ends, which could make it more difficult to start again with a new agency. Make sure your agency is open to sharing data, results, and answers. We unfortunately see this tactic often. Further, make sure you have admin/owner access to your accounts and you know where the logs are of who can and when they access your accounts.  

Hiring a digital marketing agency is a decision you shouldn’t make lightly - after all, they can greatly impact the visibility and reputation of your business. To see the best results, make sure to take the time to find an agency you feel confident partnering with.

Find the perfect digital marketing agency by asking these top 7 questions that reveal expertise, reporting transparency, and more. Watch out for 3 red flags to avoid bad partnerships.

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7 Key Questions to Ask Your Digital Marketing Agency | Hiring Guide

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