🧠 GenAI 2.0: Integrated and Interactive

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In today’s Sunday Special:

• 📱Smartphones Are Like GenAI

• ⚔️Chatbots vs. Design Tools

• ⚙️What Does Integrated GenAI Look Like?

• 🔑Key Takeaway

Read Time: 6 minutes

🎓Key Terms

• Generative AI (GenAI): Uses AI models trained on text, image, audio, video, or code to generate new content.

• Software as a Service (SaaS): When users access software applications hosted by cloud providers like Salesforce over the internet.

• Direct Manipulation Interfaces: Allows users to interact directly with digital objects on screens, similar to manipulating physical objects in real life.

• Retrieval-Augmented Generation (RAG): Improves the accuracy of AI models by retrieving relevant, up-to-date data directly related to a user’s query.

🩺 PULSE CHECK

📱SMARTPHONES ARE LIKE GENAI

Accessing Information?

GenAI represents a technological breakthrough similar to the smartphone revolution. Smartphones have changed how we access information. Now, GenAI is changing whose information we access.

Smartphones have transformed our daily lives by redefining how we create, consume, and communicate. Most of us start our days and end our days staring at one. For instance, the average American spends over 4 hours on their smartphone daily, which amounts to roughly 12 years of their life.

Now, GenAI is redefining how we assign ownership of ideas. It’s no longer clear who created what content and if original creators will be compensated for their original content used to train GenAI models. For example, OpenAI believes training GenAI models using publicly available internet materials falls under Transformative Use: Adding new expression, meaning, or messaging to the original work. This belief is rooted in the idea that GenAI models “learn” how to generate human-like text from publicly available internet materials rather than directly copying them.

User Interface?

When Apple’s iPhone 2G launched in 2007, mobile website traffic surged, prompting companies to adapt by creating “m.google.com” or “m.facebook.com.” These mobile websites crammed desktop experiences into smaller screens. These early solutions were functional but failed to harness the full potential of mobile devices. It took nearly a decade of innovation to develop native mobile experiences such as “pinch-to-zoom,” “pull-to-refresh,” and “swipe-to-advance.” These innovations enabled apps like Instagram, Snapchat, and DoorDash to thrive.

Today, we’re at a similar inflection point with GenAI interfaces. We’re replicating the “m.google.com” approach by squeezing GenAI into chatbot windows. Today, most GenAI tools integrate chatbot windows into existing SaaS platforms. While chatbots can be helpful, GenAI should go beyond the confines of chatbot windows.

So, what does integrated GenAI look like? How can we effectively harness the full capabilities of GenAI?

⚔️CHATBOTS VS. DESIGN TOOLS

One of the most significant drawbacks of conversational chatbots is the mental effort required to use them. While LLMs offer enormous value, their reliance on conversational interactions creates unnecessary friction and inefficiency.

For instance, imagine using a conversational chatbot to debug code without an integrated code editor like Github Copilot. In order to do this, you must:

1. Copy and paste code snippets into the conversational chatbot.

2. Describe the issue within the code snippets.

3. Wait for the conversational chatbot to generate a response.

4. Review the response to adjust any mistakes.

5. Reintegrate the fixed code snippets into your code editor.

This tedious process can become frustrating, especially if you need to refine the conversational chatbot’s responses. Simple code debugging tasks become labor-intensive due to the rigid constraints of chatbot interfaces. These rigid constraints underscore the need for better alternatives. Luckily, the history of user interfaces can help us get there.

In 1983, American computer scientist Ben Shneiderman introduced a concept called Direct Manipulation Interfaces, where users could see digital objects and interact with them on screens through dragging, clicking, or pinching to make everything more intuitive and accessible.

Conversational chatbots have undoubtedly revolutionized how humans interact with computers. However, when it comes to debugging code, their limitations become apparent. Their reliance on copying, pasting, and reviewing can become time-consuming. Direct Manipulation Interfaces highlight the need to make GenAI more functionally accessible, intuitive, and direct. So, how do we achieve this?

⚙️WHAT DOES INTEGRATED GENAI LOOK LIKE?

The future of making GenAI interfaces more user-friendly relies on combining a mix of different ways to interact with them based on the specific task at hand. This approach ensures GenAI is equipped to handle the scale of every task, from minor tweaks to in-depth problem-solving.

An AI code editor called Cursor demonstrates how to achieve this:

• Minimal Interactions: Cursor offers automatic character-by-character code completion without interrupting the developer’s workflow.

• Moderate Interactions: Cursor can generate inline code suggestions through a pop-up window that enables developers to review and accept them as they code.

• Maximum Interactions: Cursor provides a dedicated sidebar chatbot with an in-depth understanding of the codebase, allowing developers to interact with it to explain complex code segments or identify potential coding bugs.

The future of GenAI interfaces lies in purpose-built, domain-specific tools that make GenAI’s capabilities accessible, intuitive, and direct. Designers and engineers must move beyond generic chatbot interfaces that force users to fiddle with conversational chatbots like OpenAI’s ChatGPT in a separate browser tab to achieve their tasks.

🔑KEY TAKEAWAY

GenAI is at a transformative crossroads similar to the early days of smartphones. Just as mobile interfaces evolved from clunky adaptations of desktop websites to dedicated apps, GenAI interfaces must progress from chatbot windows to purpose-built, domain-specific tools that integrate directly into workflows to offer intuitive, task-specific interactions.

📒FINAL NOTE

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