Revolutionizing Real Estate Visuals with AI: The Euchre Project

Table of Contents

1. Introduction
2. The Challenge: Generating Engaging Imagery for Automated Content
3. Examining Generic Real Estate Imagery
4. The Potential of Automated Visuals
5. The Real Estate Beat: A Rich but Challenging Subject
6. The One-Stop Shop Prototype
7. User Research Insights
8. Expanding Beyond Automated Content
9. The Tech Stack Decision
10. The Creation of Euchre: A Tool for Image Generation
11. The Future of Euchre: Utilizing Machine Learning
12. Lessons Learned: AI in Journalism
13. Conclusion

Introduction

In today's digital age, visuals play a crucial role in content creation and engagement. The use of compelling imagery can enhance storytelling and capture readers' attention. However, generating engaging visuals can be a challenging and time-consuming task, especially in the realm of automated content. This article explores the journey of a team, consisting of members from Gannett and McClatchy, in developing a solution for automating visuals in their content creation process.

The Challenge: Generating Engaging Imagery for Automated Content

The team embarked on a project with a clear goal in mind: to generate captivating imagery for automated content. They noticed that the go-to images often used for real estate content were generic and unremarkable. Whether it was a for sale sign in front of a house or an exterior shot, these images lacked Originality and failed to engage readers. This realization sparked the idea of creating a tool that could Instantly generate social media visuals or data visualizations to enhance real estate stories.

Examining Generic Real Estate Imagery

The team delved further into the real estate beat, recognizing its high reader interest and data-rich nature. However, finding compelling visuals to accompany these stories proved to be a challenge. The use of repetitive and uninspiring imagery was prevalent across the industry, leading the team to explore the potential of harnessing machines to streamline the image selection process, particularly on a large Scale.

The Potential of Automated Visuals

The team acknowledged that not all stories receive prioritized treatment with original art and strong Photography. Yet, they realized the importance of captivating visuals in cutting through the noise and engaging readers. Thus, their goal was not to replace the work of photographers and graphic artists but to develop a tool that would enrich storytelling on their websites and fill the gap created by the increasing volume of real estate content across various platforms.

The One-Stop Shop Prototype

To tackle this challenge, the team created a simple low-resolution prototype that simulated an automated image generation workflow. They conducted user research interviews with producers, photographers, and journalists to Gather insights and feedback on the tool's usability. One critical aspect Mentioned during the research was the difficulty in creating social cards. As a result, the team recognized the need to develop a social card generator or automation to streamline this aspect of the workflow.

User Research Insights

The user research conducted by the team helped broaden the scope of their project. Participants highlighted the potential benefits of the tool beyond automatically generated content, including leveraging it for vendor or third-party contractor content lacking strong visuals. This insight pushed the team to evaluate different approaches and tech stacks to complete the project effectively. They weighed the benefits and drawbacks of writing bespoke code from scratch or utilizing an off-the-shelf tool or third-party vendor.

Expanding Beyond Automated Content

The team realized that the project's scope extended beyond automating content generation. They recognized the value of incorporating machine learning to extract and summarize text, enabling the tool to automatically populate social cards. While limited by time constraints during the fellowship, the team explored various options, with a promising candidate being the Python library called Spacey. The incorporation of machine learning opens up exciting possibilities for further automation and efficiency in the content creation process.

The Tech Stack Decision

In deciding the best approach for their project, the team considered the pros and cons of using a vendor solution versus developing their own bespoke code. Although relying on a third-party vendor posed challenges such as high costs, limited API access, and potential dependency issues, the team opted for a semi-bespoke option. They leveraged a similar project's codebase from their Gannett colleagues in the UK. This decision provided them with more control over the infrastructure and mitigated risks associated with relying heavily on an external source for maintaining the project.

The Creation of Euchre: A Tool for Image Generation

The team named their creation Euchre, drawing inspiration from a popular card Game in the Midwest. Built using Python, Flask, and Pillow, Euchre allows users to input text and combine it with a background photo to generate composite images for social media sharing. The team aimed to make templating and interactivity effortless, with opportunities for basic automation. Euchre is available on GitHub for download and local usage.

The Future of Euchre: Utilizing Machine Learning

While the current version of Euchre requires manual predefined text input, the team envisions harnessing machine learning to automate this process further. Machine learning algorithms could extract and summarize key information from a story, generating text that would populate the social card automatically. The team identified the Python library Spacey as a potential tool to achieve this. By incorporating machine learning, Euchre has the potential to revolutionize the content creation process, making it more efficient and scalable.

Lessons Learned: AI in Journalism

The team's journey with Euchre provided valuable insights into the intersection of AI and journalism. They recognized that human input and oversight, particularly from subject matter experts, are crucial at every phase of AI-assisted content creation. Ensuring accuracy, up-to-date information, and rights clearance for images requires a robust checks and balances process. It is important to collaborate with various stakeholders, including photographers, real estate reporters, editors, and graphic designers, to create a tool that enhances their work rather than replacing it.

Conclusion

In conclusion, the team's project to automate visuals in content creation showcased the immense potential of AI in journalism. By developing Euchre, they demonstrated how automated image generation can streamline the content creation process, particularly in industries like real estate. While their current prototype offers a glimpse into the future, their vision of incorporating machine learning promises even greater efficiency and innovation. Euchre serves as a testament to the power of collaboration, technology, and human oversight in creating engaging and impactful content.

Highlights

• Automating visuals in content creation
• Challenges of generic real estate imagery
• The potential of automated visuals
• The creation of Euchre: a tool for image generation
• Leveraging machine learning for automating text extraction
• The importance of human input and subject matter expertise
• Enhancing storytelling through AI in journalism

FAQ

Q: Can Euchre replace the work of photographers and graphic artists? A: No, Euchre is designed to enhance the work of photographers and graphic artists, not replace it. It aims to provide a tool that automates certain aspects of content creation, such as generating social media visuals, while still relying on human creativity and expertise for original art.

Q: How can Euchre benefit real estate reporters and editors? A: Euchre can assist real estate reporters and editors by automating the image selection process, making it easier to find compelling visuals for their stories. It can also create social cards, making it simpler to promote real estate stories on various platforms.

Q: Is Euchre a standalone app or a hosted service? A: Euchre can function both as a standalone app and a hosted service. Users have the flexibility to download and run it locally on their computers or explore the possibility of making it into a hosted service.

Q: What is the future of Euchre? A: The team plans to incorporate machine learning into Euchre, allowing it to automatically generate text for social cards. This will further streamline the content creation process and potentially revolutionize how stories are shared on social media.

Q: How can AI-generated content be Relevant and reliable? A: The team acknowledges that while AI-generated content can be powerful, relevance and accuracy are essential. Human input and oversight, along with subject matter expertise, are crucial in ensuring that visuals and text accurately reflect the story and meet ethical standards.

Q: Can Euchre be used for other industries beyond real estate? A: While the team primarily focused on real estate content, Euchre has the potential to be adapted for other industries that require automated image generation. Its underlying framework and flexibility make it a versatile tool for content creators.

Resources

• GitHub Repository of Euchre
• Spacey Python Library