Throwback to + ten years ago when Node.js was created to address the issues that Apache HTTP Server showed in handling multiple network connections.
UX and Artificial Emotional Intelligence
AI is an advanced technology that has experienced rapid development in recent years in various industries such as technology, healthcare, finance and services.
It involves the ability of machines and computer systems to perform tasks that usually require human intelligence: image recognition, natural language processing, decision-making, and improving performance through machine learning.
As for its applicability, artificial intelligence can be used to enhance the efficiency and accuracy of processes, detect and prevent fraud, improve medical diagnostics, and provide personalized recommendations to users of a platform.
Artificial emotional intelligence is an emerging field of artificial intelligence research that focuses on developing technologies that can perceive, understand and respond to human emotions. The goal is to create artificial intelligence systems that can communicate and interact with humans in a more natural and intuitive way, thus providing a more human and personalized experience.
One of the most promising applications of artificial emotional intelligence is the improvement of human-machine interaction. Understanding human emotions can help artificial intelligence systems provide accurate and valuable responses tailored to individual user needs and preferences. This significantly improves the user experience, increases the level of trust and comfort in using AI-based technologies.
Artificial Emotional Intelligence can be applied in various fields, including medicine, psychology, education and marketing. In medicine, AEI can contribute to the identification and treatment of emotional disorders and stress, providing patients with a better understanding of their emotional state and offering personalized therapies. In education, technology can be used to adapt the learning process, but also to provide solutions to increase student motivation. In marketing, artificial emotional intelligence is beneficial in analyzing consumer preferences and behavior, helping companies create personalized and relevant experiences.
Another application of Emotional AI is in call centers where it significantly improves the customer experience. By analyzing the tone of voice, facial expressions, and words used, it can detect customer emotions and provide call center agents with a better understanding and real-time resolution of customer needs.
There are several tools available in call centers for transcribing and analyzing recorded conversations, including:
- Verint. It is software that uses artificial intelligence to automate, analyze, identify, and reproduce models and patterns, in various languages, from phone conversations, chats, emails, etc.
- NICE. It provides software solutions for call centers, including voice analysis and transcription capabilities.
- CallMiner. It is a speech analysis tool that includes automated call transcription, as well as analysis and reporting tools.
- Amazon Transcribe. It is a cloud-based speech recognition service that can be used to transcribe call recordings in real-time.
- Google Cloud Speech-to-Text. It is another cloud-based speech recognition tool that can be used to transcribe calls.
- Microsoft Azure Speech Services. It provides speech-to-text and text-to-speech conversion capabilities with Azure Speech resource. It can perform highly accurate transcriptions as well as translations and has the ability to identify the person who made the call (the agent) by certain unique voice characteristics.
In tense moments, artificial intelligence can suggest ways for agents to reduce stress. Moreover, it can provide both qualitative and quantitative information and metrics regarding the tone of the phone conversation, the discussed subject, and the trajectory of the call, in terms of customer satisfaction, from the beginning to the end of the call. These metrics include:
- Tone analysis. Through this metric, it is possible to detect whether the speaker is happy, angry, or frustrated. During a call, artificial intelligence can identify the emotional state of both the customer and the agent.
- Natural Language Processing (NLP). This is a set of techniques used to analyze the content of a conversation, including the words used, the expressed sentiment, and the overall meaning. NLP can be used to identify the key topics discussed during the call, the state of the customer, and the effectiveness of the agent's responses.
- Call abandonment rate. It shows the percentage of calls abandoned by customers before they are answered. In addition to predicting the likelihood of a customer abandoning a call, it can help call centers to optimize staffing levels to reduce the abandonment rate.
- Customer effort score. Shows the level of difficulty or ease with which a customer was able to solve their problem during the call. Certain techniques, based on artificial intelligence, can be used to analyze the conversation and identify areas where the agent could have provided more support to reduce the effort put in by the customer.
Generally, implementing artificial emotional intelligence in call centers can lead to increased customer loyalty and improved agent performance, resulting in higher job satisfaction. However, it is vital to ensure transparency and ethics in its use, as well as the protection of customer confidentiality.
Aligning AI Systems with UX Principles
If products using artificial intelligence systems are not aligned with basic UX design rules, the experience with that product will create frustration and difficulty in using it correctly. Artificial intelligence, by itself, does not have the ability to determine which problems need to be addressed and solved. This is where the UX designer comes in with their ability to understand user needs, recurring problems and think of appropriate and user-friendly solutions.
In order for a product using artificial intelligence to be successful, it requires a human-centered understanding and approach, that precisely identifies users' needs and how the AI system can help solve those needs.
To gain the user’s trust and loyalty in the AI system, it is essential to create a natural and user-friendly experience. In a word, an intuitive experience. In this regard, a UX Designer has a fundamental role in designing the user experience
Here are some of the ways user experience (UX) can be used to improve trust in artificial intelligence:
. In order to make the user feel confident about artificial intelligence systems, interfaces need to be personalized and tailored to their needs and preferences. This can be achieved by applying machine learning techniques that analyze user behavior and provide personalized recommendations.
Intuitive design - It helps the user to better understand how artificial intelligence systems work. Interfaces should be as intuitive and user-friendly as possible. In this way, the user can better understand how the system works and how it makes decisions.
Real-time feedback. To increase users’ trust in artificial intelligence, it is important to provide real-time feedback and explain the decisions made by the system. This can be achieved by using the clearest possible messages and notifications.
The respect of confidentiality and personal data protection . To gain users' trust in artificial intelligence, it is important to prioritize confidentiality and personal data protection. This can be achieved through the use of clear and transparent privacy policies, as well as compliance with data security regulations.
In conclusion, by employing intuitive design, providing real-time feedback, personalizing experiences, and ensuring privacy compliance, UX design has the potential to enhance user confidence in the efficacy of artificial intelligence while fostering user-friendly systems that enhance overall user well-being.
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