How can AI best communicate with humans?

Main discussion point can be here to differentiate AGI (Artificial General Intelligence) and ANI (Artificial Narrow Intelligence). And when it comes to AGI, it every time brings ethical considerations and concerns into debate. It is necessary to understand that generalization might also make the controllability harder. The harder controllability, the more security/ethical/safety concerns. Constant feedback loop based NLP models (LLMs), specific task oriented fine tuning can be useful steps towards to that direction.

For the baselines, AI does not currently come from Jupiter. We code it , we can value it and we can shape it. For conversational AI, the challenge is that the new normal is not fixed. Context, content, dialect, bias, accent , pun, sensitivity is changing. Security and privacy remain open points. Discussion, discovery and adoption is work in progress. We will settle down for a vertical approach where virtual assistants, chatbots and even humanoids will be subject experts. The efficiency and accessibility will depend on how we insert transparency in the black boxes and improve while on the way. The future of communication will still be based around STORY TELLING. The question will be : Who can tell the better Story ? We or AI ???

We are not at a point where humans will solely rely on AI for their decisions / results. This primarily stems from the communication gap. When two humans, trained on a similar dataset (of life) can still have miscommunication, we cannot expect a model to understand every connotation / intention in a conversation. Yet, I believe the best way to communicate with AI is still natural language processing, but talking to it with utmost simplicity, i.e. teaching the AI your goal as if it were a child. Most models today will learn from it. The more you teach it, the closer to artificial general intelligence we will be. Understanding the trade-offs until we reach the point where they can be overcome - that is the best way to communicate with AI.

As an AI Engineer, I've implemented natural language processing (NLP) in healthcare, particularly in hospital triaging and Electronic Medical Records (EMR) integrated with Clinical Decision Support Systems (CDSS). In triaging, NLP-powered AI systems analyze patient inquiries and medical histories, enhancing care prioritization and delivery. This results in more efficient and timely treatment. For EMR and CDSS, I've utilized NLP to extract and interpret complex clinical data, aiding healthcare professionals in making informed decisions. This includes assisting in diagnoses, suggesting treatments, and predicting patient outcomes. These NLP applications have significantly improved the quality of healthcare services, AI capabilities & More

AI doesn’t communicate with humans, it is an interface that humans use by interacting with it. To interact effectively, an AI application needs three things. First, it needs to begin and be about what the human wants on the other side of the interaction, not about the interaction itself. Second, AI needs to function well with minimal interaction required. Finally, AI needs to be continuously trained and be built to easily and diligently be refined. Ask three things about humans as you create any AI application: What is the human goal? How do those humans interact? How will it easily evolve?

In my view, the true potential of AI in communicating with humans is unlocked through multimodal communication. This blend of visual, auditory, and even gestural cues makes interactions with AI feel more natural and human-like. From my experience, when AI can express itself beyond mere words – using gestures or facial expressions, for instance – it significantly enhances user engagement. It’s like turning a basic conversation into a more dynamic and empathetic exchange. However, there’s a fine line to tread. It’s crucial to integrate these elements seamlessly, ensuring they complement rather than complicate the interaction. This approach doesn't just make AI more accessible; it transforms it into a more effective communicator.

Multimodal communications, especially through near-real-life holography, will transform AI-human interactions. By integrating audio, video, and touch factors to introduce expressive body language, these methods create deeply immersive experiences. This approach makes AI communication more intuitive, relatable, and human-like, greatly enhancing empathy and understanding, and seamlessly bridging the digital-physical divide.

In my view, humans are performing very different tasks at work today. There are very intellectually challenging tasks that require multi-field knowledge, creativity, and long hours. And there are tasks that not many LinkedIn active users would want to perform. The objective of AI is to replace those boring tasks that can be easily automated. This is how AI is going to improve humanity life style.

Multimodal communication in AI-human interactions indeed adds depth and richness to communication. It involves seamlessly integrating various communication modes like visuals, auditory cues, and gestures. This capability allows AI to convey not just information but also emotions and intentions effectively. It enables AI systems to adapt to different scenarios and user preferences. For instance, AI can enhance speech or text output with facial expressions, gestures, or sounds to convey emotions or emphasize key points. It can also respond to human cues, making interactions more engaging and personalized.

Offering multiple ways to interact, such as through text, voice, or gestures allows AI systems to accommodate diverse user preferences and accessibility needs. Adding all of these different inputs together results in a richer more effective way to communicate.

Explainable AI (XAI) fosters trust and understanding in AI-human interactions. By demystifying its decision-making processes, AI becomes more transparent and accountable. This clarity is crucial, especially when AI's choices significantly impact users. For example, AI explaining its reasoning for a specific recommendation or outcome aids in building user confidence and facilitates collaborative improvements.

Explainable AI (XAI) enhances ethical practices by providing transparency and accountability in decision-making. Through interpretable models, XAI helps identify and mitigate biases, fostering fairness and trust. It ensures privacy preservation through local interpretable models, revealing insights without compromising sensitive information. XAI contributes to relevance and user understanding with intuitive explanations, building trust and facilitating collaboration between humans and AI. While promoting ethical decision-making, XAI balances transparency with accuracy, emphasizing the importance of human involvement in critical judgments. This balance ensures that AI systems are respectful of privacy concerns in various contexts.

his capability enhances AI's transparency, trustworthiness, and accountability, crucial in building collaborative and effective AI-human partnerships. For instance, AI can elucidate why it made a particular choice, the reasoning behind a conclusion, or insights gained from past experiences. Yet, achieving explainability in AI involves navigating trade-offs, such as simplifying complex concepts without compromising accuracy, maintaining relevance while ensuring completeness, and offering clear explanations while respecting privacy concerns. Striking the right balance is key to making AI's decision-making processes more understandable and reliable.

If we emphasize dynamic learning, AI becomes a contextual maestro, shaping communication based on the ever-changing environment. Wouldn't it be amazing if an AI assistant not only comprehends your queries but also tunes into your context, whether it's your current location, recent interactions, or ongoing events? This dynamic adaptation ensures that the information provided is not just accurate but also tailored to the specific moment, making your interaction with AI feel seamless and effortlessly relevant. It's like having an assistant who not only understands what you say but also grasps the broader context, making your digital experience more intuitive and in tune with the rhythm of your life.

Explainable AI started with inherently white-box models like Decision Trees whose outputs were transparent and easy to interpret. Going into Deep Learning, explainability is often achieved through the application of probes which are essentially checkpoints that keep track of the models' inner state at various configurations. With the rise of LLMs, transparency has become easier. With specialized fine-tuning and RLHF-based techniques, LLMs are naturally poised to provide suitable explanations regarding their decisions.

In the context of AI-human communication, addressing ethical and social implications is crucial. AI must be designed to respect human values and norms, ensuring communications are free of bias and discrimination. This consideration helps in building trust and enhancing the effectiveness of AI in diverse settings.

AI systems must safeguard personal information, only using data for intended purposes and with consent. AI should provide clear explanations for its recommendations or decisions, making it understandable to users. Establish clear protocols for when AI systems cause unintended harm, including corrective measures and compensations. AI communication should be designed to be accessible to diverse populations, accounting for different languages and abilities. AI must be attuned to cultural nuances, ensuring that communications are appropriate and respectful across different societies.

AI systems must be fed with representative data, akin to how young humans learn to communicate effectively through exposure to a diverse range of information, and cultures. This data enables AI to make more accurate predictions and thus communicate more effectively. Additionally, considering AI's inability to interpret body language, it is crucial to develop alternative methods for AI to understand and convey nuances in communication. This could involve sophisticated language processing and contextual analysis to compensate for the lack of non-verbal cues, thereby bridging the communication gap and fostering more meaningful interactions with humans.

I think that the most important consideration in AI-human communication is whether the communication has a positive impact on collective wellbeing. This will depend significantly on how well we have succeeded with our attempts at alignment - trying to ensure that AI's values and goals align with the best versions of our own. This will be a big challenge! Part of the challenge is that it requires the AI to understand the diverse and complex nature of human values and goals, and how they change over time and across cultural and social contexts. For more on this and some ideas of how to resolve it, I recommend reading Human Compatible by Professor Stuart J. Russell.

In many contexts where AI systems are communicating with people directly it will be necessary to disclose the artificial nature of communication for legal or moral reasons. In particular, this may be necessary to avoid deception and to make sure people are aware of consequences, minding that, unlike humans, AI systems may memorise lots of data indefinitely as well as recall and combine it to predict our preferences and behaviour with high precision.

I read somewhere that Generative AI is the world’s best intern. I really like this analogy. AI will not replace people, but people augmented by AI will. In addition, the way we interact with computers is going to change drastically. I recommend all of you to get trained on “prompt engineering”. It will be very helpful for you to leverage Generative AI as much as possible.

At SensViz, we champion human-AI collaboration. Our philosophy? AI isn't a replacement for human skills but a partner that amplifies them. We design AI solutions to enhance human decision-making, not overtake it. This approach ensures our AI tools support, not overshadow, human intuition and creativity. It's about striking a balance where AI's analytical strengths complement human insights, making the decision-making process more efficient and human-centric.

Effective human-AI collaboration hinges on establishing mutual understanding and trust. AI can augment human capabilities, offering insights and support that enhance task efficiency and creativity. However, it's vital for AI to adapt to varying human roles and communication styles to foster a productive partnership.

Human-AI collaboration in healthcare enhances patient care by combining AI capabilities with human expertise. For instance, in surgery, AI assists surgeons with real-time data analysis and surgical recommendations, improving decision-making and patient outcomes. Successful collaboration requires AI to establish mutual understanding and trust with medical professionals, adapting to various roles and scenarios. This approach not only augments healthcare providers' skills but also contributes to a more effective and satisfying patient care experience.

Although we strive to fortify AI systems for sophisticated interactions, equal emphasis must be placed on human readiness. As AI becomes more attuned to human emotions, it necessitates a reciprocal effort from users. Understanding how to convey emotions and intentions to the AI effectively is a crucial aspect often overlooked. Educational initiatives should spotlight not just the capabilities of AI but also guide users in articulating emotions coherently. This dual awareness creates a symbiotic relationship where both AI and humans actively contribute to a more nuanced and harmonious AI-human interaction, fostering an environment where emotional intelligence truly thrives.

Certainly, effective AI-human communication hinges on smart design. #AI can adapt using - Clarity - Consistency - Coherence Tools like data analysis and user testing enable constant fine-tuning, ensuring seamless interactions, understanding, and engagement. In this AI era, communication design is the linchpin for meaningful tech-human connections.

Communication design plays a key role in fostering ethical and effective AI-human interactions, especially from a psychological perspective. AI systems use communication design to ensure information is easily understood and remembered. This involves strategies like simplifying complex details, repeating key points, and emphasizing actionable insights. AI can connect with users better by considering biases and emotions/ AI needs communication design that considers human cognition, emotion, and behavior to have a positive real-world impact. With thoughtful design grounded in psychological principles, AI-human communication can be tremendously enhanced.

Concluding the facets of AI-human communication is the crucial element of communication design. This encompasses the strategic planning, creation, and evaluation of communication strategies, methods, and outcomes. Communication design empowers AI to optimize its interaction with humans, measuring and enhancing effectiveness and impact. Imagine AI applying design principles—clarity, consistency, coherence, and relevance—to tailor communication to purpose, audience, and channel. Additionally, leveraging communication design tools like data analysis, user testing, and feedback loops enables AI to continually monitor and refine its communication for maximum effectiveness.

Communication design is like giving AI a helpful guide on how to talk better with us. You know, it's a bit like when you have a friend who knows just the right way to share something so everyone understands. For AI, it's like following a recipe for good communication—making sure things are clear, keeping the message the same, making sense, and being relevant. And just like how you might check if everyone got your friend's message, AI uses tools to see how well it's doing. It looks at data, asks people, and learns from feedback to chat even better next time. So, communication design is like AI's secret sauce for talking in a way that clicks with us.

This is where the user experience design of AI comes into play. We have to consider the different types of user interfaces & media-- audio, visual, and tactile. Depending on the use case, some AI applications are best suited on a screen, some are more applicable for voice, and some maybe tangible. (The latter aspect is still probably not available). There is already a good body of knowledge about how to best communicate with people, depending on the medium and the hardware.

Emotional Intelligence: For AI to best communicate with humans, it needs to understand and respond to human emotions. This involves incorporating emotional intelligence into its responses, recognizing sentiments, and adapting its tone accordingly. This fosters a more empathetic and human-like interaction.

To optimize AI communication with humans, focusing on context-aware responses is key. AI should be programmed to understand and adapt to the user's current situation and environment for more relevant interactions. Implementing user feedback mechanisms allows AI to learn from interactions and improve over time. Personalization is also crucial; AI systems that remember past interactions can offer more tailored and meaningful communication. Additionally, incorporating emotional intelligence, where AI recognizes and appropriately responds to human emotions, can greatly enhance the quality of interactions. Finally, ensuring clarity and simplicity in AI communications makes interactions more efficient and user-friendly.

Explainability and Interpretability, considering Large language models, LLMs are often complex and challenging to interpret. Strive to incorporate mechanisms for model explainability to enhance user trust and understanding. This involves making the decision-making process of the model more transparent and providing insights into how it arrives at specific outputs.

It can't because it's not sentient. It's not "intelligence", it's an algorithm that makes fine predictions. We're still talking about one-sided HCI, where the other side resembles genuine reactions.

One of the most important aspects of General AI (GenAI) is Emotional AI (EAI). What makes us humans is our ability to understand emotions and act upon them. Large Language Models (LLMs) can take you up to a certain level, but without incorporating the EAI component, they will remain within the same boundaries as AI-to-human interaction today. There are some technologies around voice interactions that are intended to complete the missing EAI component. The companies that will incorporate these technologies within their products will be the first ones to break through.