Original author: Rocky
Reprinted: Daisy, Mars Finance
If you want to understand #AI Agent, this book (paper) is a must-read for everyone. Fei-Fei Li (AGENT AI), this is the most refreshing and forward-looking book I've read this year, and it’s not difficult to understand. There are no profound professional terms or algorithmic logic; it’s worth reading for every ordinary person. The end of the article has a link to the full text.
I can responsibly tell everyone: AI Agent is one of the most worthwhile investment areas in artificial intelligence (whether in the US stock market or Web3), and it's the closest direction that ordinary people can perceive. It is the most directly accessible and scalable area for the public.
As described in its introductory paper: Overview of the AI Agent system, which can perceive and act in different fields and applications. The AI Agent is a promising pathway to general artificial intelligence (AGI). Training of AI agents has proven the ability to understand multimodal information in the physical world. It provides a framework for training unrelated to reality by leveraging generative artificial intelligence combined with multiple independent data sources. We present an overview of an agent-based artificial intelligence system capable of perceiving and acting in many different fields and applications as a paradigm towards AGI.
The article emphasizes the current technical status, application prospects, and future development directions of AI Agent in multimodal human-computer interaction (HCI). Some core technologies and innovative directions revealed are worth our deep consideration and exploration. We shouldn't let AI Agent remain only in the realm of voice interaction and visual interaction; its scope is much broader:
1. Core concepts and significance of multimodal HCI
Multimodal HCI achieves natural, flexible, and efficient interaction between humans and computers by integrating various information modes such as voice, text, images, and haptics. The core goal of this technology is:
• Enhance the naturalness and immersion of interactions.
• Expand the applicability of human-computer interaction scenarios.
• Enhance the ability of computers to understand diverse human input patterns.
2. Future development directions
The article systematically organizes five research areas:
1. Big data visualization interaction
Concept: Transform complex data into easily understandable graphical representations, enhancing user experience through multiple sensory channels (visual, haptic, auditory, etc.).
Progress:
• Data visualization exploration based on virtual reality (VR) and augmented reality (AR);
• In the medical and research fields, use haptic feedback (such as force and vibration feedback) to help users better understand data distribution.
Applications:
• Smart city monitoring: Dynamically display city traffic data in real-time through heat maps.
• Medical data analysis: Explore multidimensional data in conjunction with haptic feedback.
2. Interaction based on sound field perception
Concept: Using microphone arrays and machine learning algorithms to analyze sound field changes in the environment, helping to achieve non-visual human-computer interaction.
Progress:
• Improvement in the accuracy of sound source localization technology;
• Robust voice interaction technology in noisy environments.
Applications:
• Smart home: Voice control devices to complete tasks without contact.
• Assistive technology: Provide sound-based interaction methods for visually impaired users.
3. Mixed reality physical interaction
Concept: Integrate virtual information with the physical world through mixed reality technology (MR), allowing users to manipulate the virtual environment using physical objects.
Progress:
• Optimization of virtual object interaction based on physical haptics;
• High-precision physical-virtual object mapping technology.
Applications:
• Education and training: Provide immersive teaching through simulated real environments.
• Industrial design: Use virtual prototypes for product validation.
4. Wearable interaction
Concept:
Interact through wearable devices such as smartwatches and health monitoring devices using gestures, touch, or skin electronic technology.
Progress:
• Improvement in the sensitivity and durability of skin sensors;
• Multichannel fusion algorithms enhance interaction accuracy.
Applications:
• Health monitoring: Real-time tracking of heart rate, sleep, and exercise status;
• Gaming and entertainment: Control virtual characters through wearable devices.
5. Human-computer dialogue interaction
Concept:
Research voice recognition, emotion recognition, speech synthesis, and other technologies to help computers better understand and respond to users' language input.
Progress:
• The popularity of large language models (such as GPT) greatly enhances the naturalness of dialogue systems;
• Improvement in the accuracy of voice emotion recognition technology.
Applications:
• Customer service robots: Support multilingual voice interaction.
• Intelligent assistants: Personalized voice command response.
So we see many AI Agent projects, especially in the Web3 field, still mostly remain at the level of intelligent assistants for human-computer dialogue interaction, such as 24-hour tweeting, personalized AI voice chat, couple chatting, etc. However, we have also observed some projects that combine intelligent wearables with #Depin and #AI to provide innovations in the field of health data, such as rings (I won't name specific ones; you can look it up, it's also within the #SOL chain ecosystem), smartwatches, pendants, etc. The opportunities here are more valuable and interesting than those that only do standalone #AI public chains or applications, and investors will prefer it. After all, we have invested in two companies: hardware + software + AI; this will be a potential direction!
3. Fields where technology companies are currently investing heavily
1. Expand interaction methods: Explore new interaction means, such as olfactory and temperature perception, to further enhance the dimensions of multimodal fusion.
2. Optimize multimodal combinations: Design efficient and flexible multimodal combination methods to enable more natural collaboration between different modes.
3. Device miniaturization: Develop lighter and more energy-efficient devices for daily use.
4. Cross-device distributed interaction: Enhance interoperability between devices to achieve seamless multi-device interaction.
5. Enhancing algorithm robustness: Particularly in open environments, improve the stability and real-time performance of multimodal perception and fusion algorithms.
4. Investment-worthy application scenarios
• Medical rehabilitation: Assist patients with rehabilitation training and psychological counseling through voice, image, and haptic feedback.
• Office education: Provide intelligent office assistants and personalized education platforms to improve efficiency and experience.
• Military simulation: Combine mixed reality technology for combat simulation and tactical training.
• Entertainment and gaming: Create immersive gaming and entertainment experiences to enhance user interaction with virtual environments.
Summary: Dr. Li's article systematically organizes the core technologies of multimodal HCI utilizing the application scenarios of AI Agent in the future and combines practical applications and future research directions, providing investors in #AIAgent with clear directions and investment logic. This article can be regarded as a must-read AI book for 2024, allowing me to gain a clearer understanding of the key role of multimodal human-computer interaction technology in promoting future intelligent life and revealing its enormous potential in open environments and complex scenarios. Investing in the future is the key to seizing wealth! The same old saying: Layout #AI, learn #AI, invest #AI. Time is of the essence!
