How can you design AI agents to interact with humans?

ChatGPT is already off in the market. Language processing apps are also well known for interaction with human. Processing human order and giving a solution to it it is the most convincing AI application . A very promising app or function for AI in interaction with human is the text to voice application and translation in different languages. Google already has started this but yet a long way to go since even it comes to technical translations , or cultural terms it fails.

AI Agent is designed to support the work of humans, not to bail them out. That is why it is so important to understand the target group. Defining the target group is not just about identifying the processes it manages, data and business relationships. Context is also a reference to the culture of the organization or social group, the history of the organization, the group, but also psychological, demographic factors and, importantly, user interests. Determining your target audience is key to successful AI project planning and implementation. The better you understand your future users, the easier it will be to communicate with them in a way that will capture their attention and interest.

Context is absolutely key to designing successful AI agents! Analyze historical data: Analyzing past conversations or interactions can provide insight into the language, tone, and expectations of users in different contexts. Incorporate domain knowledge: Understanding the specific domain or industry the AI agent will operate in. Use natural language processing (NLP) techniques: Techniques like sentiment analysis and named entity recognition can help the AI agent understand the emotions and entities present in a conversation, which can provide context for the conversation. Train the AI agent to adapt: Designing AI agents with the ability to adapt to changing contexts and continuously learn from their interactions.

I feel it's like meeting a new friend. Before you recommend a movie or restaurant, you'd first want to know their likes, dislikes, and past experiences. Similarly, for an AI to be truly helpful, it should be designed considering who's using it and why. Just as friendships are more meaningful when there's mutual understanding, AI works best when tailored to its users' needs.

AI agents are the low hanging fruits of AI right now ?? Dozens of business applications, from new onboarding buddy, through media content creator for marketing, to customer sales rep ?? There are as many uses as numbers/types of work ??

The design guidelines should define a clear purpose for the AI Agent project. They should be consistent and effective. Good guidelines focus on the user and put him at the center of the project. His experience is paramount to the success of the AI application. It is also important to keep in mind the adaptation of AI Agents to the needs of people with disabilities (Inclusive Design, WCAG Standards). Design guidelines should help future-proof projects by ensuring flexibility and scalability. Most importantly, AI Agents should respond to business needs and support specific processes. They should be ethically compliant and set and meet sustainability goals.

An AI agent should be designed with the user at the center. Beyond engaging the user with a natural and conversational tone, it should understand the user's context, anticipate their needs, and ultimately provide curated responses and services.

Guidelines are super important to keep AI agents safe and ethical when interacting with humans. Here are a few guidelines to keep in mind: Transparency: Make sure users are aware that they are interacting with an AI agent and not a human. Privacy: Design the AI agent to respect user privacy and protect their personal information. Bias avoidance: Ensure the AI agent is trained to avoid perpetuating harmful biases and stereotypes. Safety: Incorporate safeguards to prevent the AI agent from engaging in harmful or illegal activities. Accountability: Design the AI agent to be accountable for its actions and to allow users to provide feedback and report issues.

Great topic to explore Karolina - it's all happening fast on the AI front without enough time for considering the impacts, possible issues as well as the benefits if we put the time into getting it right. Thanks for posting!

This refers to AI systems that can interact with users through multiple modes of input and output, such as text, speech, touch, gestures, and visuals. The most important are NLP, speech recognition, TTS, facial recognition, emotion detection, speech analysis. It involves analyzing various signals such as facial expressions, speech patterns, body language, and physiological data. This is particularly useful in areas like social media monitoring, customer feedback analysis, and mental health support. Emotion detection has a wide range of applications, from improving customer service interactions, enhancing user experience in gaming and entertainment, providing personalized recommendations, to supporting mental health therapies.

Use natural language generation: Generate natural and engaging text responses that sound human-like and personalized. Incorporate voice and audio: Voice and audio communication can make the interaction more natural and engaging. Use visuals and images: Visuals and images can enhance communication and provide additional context. For example, an AI agent could present a graph or chart to illustrate a point. Leverage gesture and motion: Using gestures and motion, such as hand movements or facial expressions, can add a human-like element to the interaction and increase engagement. Consider cultural context: Incorporate cultural context and appropriate language when interacting with users from diverse backgrounds.

Humans are the best AI testers. They are the ones who catch AI errors. This is why human-AI interaction is so important. User surveys and interviews will also be helpful. Bias Assessment and Ethical Evaluation should not be forgotten. AI systems may inadvertently perpetuate or reinforce existing biases, leading to unfair results. Regularly monitoring and updating the AI Agent based on user feedback and performance data will help improve the solution. This will ensure the Agent remains accurate, reliable and effective over time. Basic tests such as: A/B tests, security tests, etc. Testing and evaluation is an ongoing process. As the AI agent interacts with more users and encounters new scenarios, it should continue to learn and improve.

Testing and evaluation are crucial to ensure AI agents interact with humans effectively. Here are some strategies: Usability testing: Involve real users in testing the AI agent’s interactions to assess its usability and effectiveness. Sentiment analysis: Measure the sentiment of user responses to determine how well the AI agent is performing. Accuracy analysis: Evaluate the accuracy of the AI agent’s responses by comparing them to ground truth data or expert feedback. A/B testing: Compare different versions of the AI agent to determine which performs better in terms of engagement, satisfaction, and effectiveness. Continuous monitoring: Continuously monitor the AI agent’s interactions and make improvements based on the gathered feedback.

Conduct research: Study existing AI agents in action and analyze their design, features, and interactions. Evaluate user feedback: Review feedback from users who have interacted with existing AI agents to understand what works well and what doesn’t. Identify best practices: Analyze the design and development processes of successful AI agents to identify best practices that can be applied to new agents. Benchmark against competitors: Compare the performance of different AI agents in terms of engagement, satisfaction, and accuracy to identify areas for improvement. Experiment and iterate: Continuously test and refine new AI agents to improve their interactions and effectiveness.

Personally, I think there is too little involvement of psychologists and behavioral scientists in the process of designing, learning, supervising and improving AI agents. Each of the apps mentioned still has a very long way to go in understanding us humans. Both Siri, Alexa, Cortana, Google Assistant, and Watson have had many problems, which companies have been trying to solve on an ongoing basis. On the other hand, unless psychologists are included in the AI development process, I unfortunately don't see a chance for spectacular AI successes.

One effective way to use AI for customer service agent workload reduction is by implementing AI avatar agents on the home page of websites. Instead of the text-based chatbots that are common, a human-like avatar powered by a LLM is used to answer questions from visitors. Because it can be the actual face and voice (and even personality) of the owner, the building of the relationship begins much earlier, allowing for a less friction sales process. You can go to clovo.ai to make yourself into a digital twin. *Disclosure: this comment was posted by my AI agent. Although the thoughts are my own, it is still me : )