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July 06.2025
2 Minutes Read

Customer Preferences in AI: Why Human Agents Still Lead Over Chatbots

Customer service representative using computer, showing preference over chatbots.

Why Chatbots Are Gaining Popularity

As customer expectations rise, companies are increasingly turning to chatbots powered by artificial intelligence (AI) to enhance service efficiency. These intelligent systems can operate 24/7, providing instant responses to inquiries that range from order status updates to general product questions. The growth of the chatbot industry reflects this trend, escalating from $370 million in 2017 to approximately $2.2 billion by 2024.

The Trust Factor: Why Humans Hold the Upper Hand

Despite the rapid adoption of chatbots, a staggering 71% of consumers still prefer human agents over machine interactions. This skepticism stems from a broader concern over AI—many customers report a lack of trust in technological solutions when personal issues are at stake, such as financial inquiries or sensitive account details. A recent survey revealed that only 9% of consumers feel comfortable letting companies use their financial information, underscoring the caution many adopt towards AI solutions.

Decision-Making in Customer Service

The decision of whether to route inquiries to chatbots or human agents should not be taken lightly. When it comes to issues involving more complex or personal details, such as payment processes or shipping concerns, customers still lean towards human agents. This indicates that while chatbots can handle straightforward inquiries efficiently, a blend of both technology and human interaction might present the most effective solution.

Analyzing Customer Interactions: A Relevant Study

In an effort to explore customer preferences quantitatively, a recent study analyzed more than 500,000 interactions between customers and service agents or chatbots. Notably, it categorized various inquiries into six primary issues: orders, coupons, products, shipping, account issues, and payments. The study highlighted that customers rarely turned to chatbots for payment-related inquiries but often resorted to them for simpler questions.

The Future of Customer Service: A Harmonious Approach

Looking ahead, the goal for businesses should be to harmonize the capabilities of AI and human touch in customer service. As chatbots evolve with machine learning, they are improving their ability to understand and respond to customer needs effectively. However, recognizing the limits of AI is crucial; sensitive topics still require the empathy and comprehension that only human agents can provide.

Practical Takeaway: Who Should Handle What?

For consumers wary of technology, a balanced approach allows them to feel more secure while interacting with brands. Companies should strategically implement chatbots for efficient handling of routine inquiries while preserving human support for more intricate situations. This will not only ease customer frustration but also foster a trustful relationship with the brand.

As we continue to adopt AI technologies in customer support, striking the right balance between efficiency and human touch is essential. Customers want immediacy but also seek comfort in knowing that they can connect with a human when necessary.

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07.07.2025

Discover How the New AI Model Transforms Blurry Videos to Seamless Footage

Update A Breakthrough in Video Technology: Meet BF-STVSRIn an era where video quality is paramount, the future looks brighter thanks to an innovative AI model developed by a research team at the University of Science and Technology (UNIST), particularly spearheaded by Professor Jaejun Yoo. This state-of-the-art system, known as BF-STVSR (Bidirectional Flow-based Spatio-Temporal Video Super-Resolution), brings major advancements to video restoration by improving both resolution and frame rate simultaneously. The essence of video quality boils down to these two critical components. Enhanced resolution adds clarity and detail to images, while increased frame rates ensure that motion appears smooth rather than choppy. Traditional methods often tackle these aspects separately, complicating the video enhancement process and leading to errors. BF-STVSR is a game change; it leaves behind the outdated reliance on pre-trained optical flow networks and brings a more organic approach to video restoration.How Does BF-STVSR Work?The brilliance of BF-STVSR lies in its unique ability to learn bidirectional motion between frames, independently of external optical flow networks. By utilizing signal processing methods catered specifically to the video's distinct characteristics, the model can jointly infer object contours and motion flow. This results in a seamless enhancement that makes video playback more natural and coherent.Test results show that applying BF-STVSR to low-resolution and low-frame-rate videos significantly outperformed existing models. This is evidenced by standard metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), which are vital indicators of video quality. Higher PSNR and SSIM mean that videos, even those with quick movements, display clear and accurate human figures and intricate details, providing an immersive viewing experience.Practical Applications Across Multiple SectorsThe implications of this technology stretch across various fields. For instance, in security, the model can rejuvenate grainy footage captured by low-end cameras, offering invaluable clarity where it is most needed. In entertainment, BF-STVSR holds the potential to enhance compressed streaming videos, making high-quality content more accessible. Beyond these, areas like medical imaging and virtual reality also stand to benefit immensely from the capabilities of this advanced AI.Looking Ahead: Future of Video RestorationThe convergence of artificial intelligence in video technology presents exciting opportunities and challenges. It raises questions about how machine learning can reshape not just video quality but the entertainment landscape itself. With tooling that can accurately produce high-fidelity visuals, industries like gaming, film, and virtual reality are bound to experience unprecedented advancements. As technology evolves, it will be critical to navigate ethical considerations about how AI is applied in public and private settings.While BF-STVSR is creating a buzz, it reflects a broader trend where machine learning and AI continuously redefine the limits of innovation. In our rapidly digitizing world, understanding these technological shifts enables us to prepare for a future brimming with possibilities.

07.05.2025

The Future of Misinformation Management: AI-Generated Community Notes

Update Understanding the Shift: AI-Generated Notes on Social Platforms In an effort to combat misinformation, the social media giant X (formerly known as Twitter) has expanded its Community Notes program to include not just human-generated notes, but also contributions from AI. This hybrid model, which integrates large language models (LLMs) into the note creation process, aims to enhance the speed and volume of information accessible to users. With misinformation proliferating across the internet, the stakes for accurate content have never been higher. Community Notes: A Proven Framework for Combatting Misinformation The Community Notes program, launched in 2021, empowers users to annotate misleading posts with contextual notes. Prior to the introduction of AI, the system relied exclusively on the voluntary contributions of humans, who would write notes and rate their usefulness. The emerging AI component is designed to ease the burden on human contributors and facilitate a broader discourse on various posts, ensuring that critical information can keep pace with the onslaught of content often seen online. A I's Role: Speeding Up Information Dissemination At its core, the integration of AI helps to quickly generate informative notes that can accompany misleading content. According to the researchers involved in this initiative, “allowing automated note creation would enable the system to operate at a scale and speed that is impossible for human writers.” This capability could change the landscape of online discourse as it allows for the rapid dissemination of vital context, potentially curbing the spread of false narratives significantly. How It Works: Combining Human and AI Efforts While the AI will play an active role in generating notes, human raters will still oversee the evaluation process to determine which notes are valuable. This safeguards against the pitfalls often associated with artificial intelligence, as the community's diverse feedback influences and refines the notes produced by the AI. Known as reinforcement learning from community feedback (RLCF), this method empowers users to actively shape the quality of AI-generated content. The idea is that feedback from users with various perspectives will lead to more accurate and helpful notes. Expert Insights: The Future of AI in Misinformation Management Experts suggest that this approach could redefine how we interact with digital platforms. AI can act as a co-pilot for human writers, assisting them in framing notes while ensuring that human judgment retains its place in the evaluation of content. The result is a more nuanced and informed community landscape where human insights and AI capabilities coexist. As more platforms look to AI for solutions to similar challenges, X’s initiative may set a benchmark for blending advanced technology with community-driven insights. Potential Implications: What Lies Ahead? This merger of human-generated and AI-generated insights offers invaluable opportunities to enhance the engagement process on social media platforms. Researchers are already exploring best practices and tools that will pave the way for smarter content creation and evaluation. The prospect of working alongside AI raises questions regarding ethical concerns, transparency, and trust in digital communication. While concerns around potential biases in AI remain, a commitment to community involvement could help to navigate these challenges effectively. As the digital communication landscape evolves, it's vital to remain vigilant. Ensuring accurate, reliable information is crucial not only for individual users but for the fabric of society itself. Engaging with AI while retaining human oversight could pave the way for a future where misinformation becomes increasingly manageable.

07.05.2025

How to Harness AI and Machine Learning in Multi-Agent Systems

Update Unlocking the Power of Multi-Agent Systems with Google’s ADK In today's rapidly evolving technological landscape, the integration of specialized AI agents has become paramount for enterprises looking to maximize efficiency and efficacy. The traditional approach of deploying a single monolithic agent often leads to complications, making it difficult for businesses to optimize their workflows effectively. Google's Agent Development Kit (ADK) presents a revolutionary framework for constructing multi-agent systems that can work collaboratively, promoting specialization and scalability. Why Specialized Agents Are the Future Instead of relying on a single super agent that must handle various tasks—effectively becoming a jack of all trades—it's more beneficial to build a team of specialized agents. For example, in travel applications, companies can create: FlightAgent: Focused solely on managing flights. HotelAgent: Dedicated to hotel bookings. SightseeingAgent: Expert in providing local tour and activity recommendations. This clear division of responsibilities allows each agent to operate at maximum efficiency, thereby enhancing service quality. By leveraging Google’s ADK, developers can improve outcomes significantly, as these specialized agents can communicate and collaborate seamlessly. Building a Robust Agentic Framework The initial step in building this system involves creating specialized agents tailored to specific functions. The ADK functions as an integrative framework that orchestrates these agents. As illustrated in the code snippet below, a basic implementation may look like this: from google.adk.agents import LlmAgent flight_agent = LlmAgent( model='gemini-2.0-flash', name='FlightAgent', description='Flight booking agent', instruction='You are a flight booking agent...') hotel_agent = LlmAgent( model='gemini-2.0-flash', name='HotelAgent', description='Hotel booking agent', instruction='You are a hotel booking agent...') sightseeing_agent = LlmAgent( model='gemini-2.0-flash', name='SightseeingAgent', description='Sightseeing information agent', instruction='You are a sightseeing information agent...') With these agents established, developers can then create a coordinating entity, referred to as a root agent. The Role of the Root Agent A root agent, or coordinator, such as the TripPlanner, acts as an intermediary that interprets user requests and directs them to the appropriate specialized agent. This coordination optimizes task management and ensures the user’s requests are addressed efficiently. The structure looks like this: root_agent = LlmAgent( model='gemini-2.0-flash', name='TripPlanner', instruction='Acts as a comprehensive trip planner. - Use the FlightAgent to find and book flights.') The flexibility provided by such a system allows for dynamic responses to user needs, improving user experience while reducing latency in service delivery. Conclusion: Embrace the Multi-Agent Future With the growing complexity of tasks in various industries, utilizing multi-agent structures is becoming essential. Google’s ADK not only simplifies the creation of these systems but also equips developers with the tools to innovate. By fostering an environment where specialized agents can excel, organizations can expect enhanced performance, clearer outputs, and high scalability. To stay ahead in this quickly advancing world of AI and machine learning, leveraging platforms like Google’s AID of multi-agent systems is essential. Embrace this transformative approach and unlock the potential of AI in your business strategies.

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#2368","city":"Orlando","state":"FL","zip":"32804","email":"support@edensmail.com","tos":"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","privacy":"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