How to layout the wealth future through AI Agent? This article gives you the answer

Original author: Rocky

Reprint: Daisy, Mars Finance

You need to understand #AI Agent, this book (paper) is a must-read for everyone. Li Feifei's 'AGENT AI' is the most refreshing and forward-looking book I have read this year, and the whole text is not difficult to understand, without profound professional terms and algorithmic logic. It is worth reading for every ordinary person, and there is a full-text link in the comments at the end of the article.

I can responsibly tell everyone: AI Agent is the most worthy investment field in the middle and later stages of artificial intelligence (whether it is in the U.S. stock market or the Web3 field), and it is also the most direct and widely used field that is closest to what To C can perceive for the general public.

As described in its opening paper, the AI Agent system is able to perceive and act in different fields and applications. AI Agent is a promising approach to achieving general artificial intelligence (AGI). AI Agent training has demonstrated the ability to understand multiple modalities in the physical world. It provides a framework for training that is independent of reality by combining generative artificial intelligence with multiple independent data sources. We present an overall overview of an agent artificial intelligence system that can perceive and act in many different fields and applications as a proxy paradigm for AGI.

The article focuses on the current status, application prospects, and future development direction of AI Agent in multimodal human-computer interaction (HCI), showing some core technologies and innovative directions that are worth further thinking and exploring. Don't let AI Agent only stay in the level of voice interaction and visual interaction, its scope is broader:

1. Core Concepts and Significance of Multimodal HCI

Multimodal HCI integrates various information modes such as voice, text, image, and touch to achieve natural, flexible, and efficient interaction between humans and computers. The core goal of this technology is:

• Improve the naturalness and immersion of interaction.

• Expand the applicability of human-computer interaction scenarios.

• Promote the ability of computers to understand diverse input modes from humans.

2. Future Development Direction

The article systematically combs through five research fields:

1. Big Data Visualization Interaction

Concept: Transforming complex data into easily understandable graphical representations to enhance user experience through multi-sensory channels (visual, tactile, auditory, etc.).

Progress:

• Data visualization exploration based on virtual reality (VR) and augmented reality (AR);

• In the medical and scientific research fields, providing tactile feedback (such as force and vibration feedback) helps users better understand data distribution.

Apply:

• Smart City Monitoring: Real-time display of city traffic data through dynamic heat maps.

• Medical Data Analysis: Exploring multidimensional data combined with tactile feedback.

2. Interaction based on sound field perception

Concept: Using microphone arrays and machine learning algorithms to analyze the changes in the sound field in the environment, and help achieve non-visual human-computer interaction.

Progress:

• Improved accuracy of sound source localization;

• Robust speech interaction technology in noisy environments.

Application:

• Smart Home: Voice control devices to complete tasks without touching them.

• Assistive technology: providing sound-based interaction for visually impaired users.

3. Mixed Reality Physical Interaction

Concept: By using Mixed Reality (MR) technology to blend virtual information with the physical world, users can manipulate virtual environments using real-world objects.

Progress:

• Optimizing virtual object interaction based on haptic feedback;

• High-precision physical-virtual object mapping technology.

Application:

• Education and training: immersive teaching through simulated real-life environments.

• Industrial design: using virtual prototypes for product validation.

4. Wearable interaction

Concept:

Interaction is achieved through gestures, touch or skin electronic technology by wearable devices such as smart watches and health monitoring devices.

Progress:

• Improved sensitivity and durability of skin sensors;

• Multi-channel fusion algorithm enhances interaction accuracy.

Application:

• 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 technologies such as speech recognition, emotion recognition, speech synthesis, etc., to enable computers to better understand and respond to user language input.

Progress:

• The popularization of large language models (such as GPT, etc.) greatly improves the naturalness of dialogue systems;

• Improved accuracy of voice emotion recognition technology.

Application:

• Customer service robot: supports multi-language voice interaction.

• Intelligent Assistant: Personalized voice command response.

So we see a lot of AI Agent projects, especially in the Web3 field, are still mostly at the level of intelligent assistant for human-computer conversation interaction, such as 24-hour tweeting, personalized AI voice chat, and couple chat. But recently we have also observed some innovations in the field of body health data by combining #Depin 项目+ #AI with intelligent wearables, such as rings (I won't specify which company, you can search for it yourself), which also come from #SOL 链生态的)，比如手表，比如吊坠等。这里面的机会比传统只做单一的 #AI public chains or applications, and are more valuable and interesting. Investors will also prefer them. After all, we have invested in two companies, hardware + software + AI, which will be a potential direction!

3. The areas that technology companies are currently heavily investing in

4. Expand interactive methods: Explore new interactive methods, such as smell and temperature perception, to further enhance the dimension of multimodal fusion.

5. Optimize multimodal combination: design efficient and flexible multimodal combination methods to enable natural collaboration between different modes.

6. Miniaturization of devices: Develop lighter and more energy-efficient devices to adapt to daily use.

7. Cross-device distributed interaction: Improve interoperability between devices to achieve seamless multi-device interaction.

8. Algorithm Robustness Improvement: Especially in open environments, improve the stability and real-time performance of multimodal perception and fusion algorithms.

9. Worth investing in application scenarios

• Medical Rehabilitation: Help patients with rehabilitation training and psychological counseling through voice, image, and tactile feedback.

• Office and Education: Provide intelligent office assistants and personalized education platforms to improve efficiency and user experience.

• Military Simulation: Combined with augmented reality technology for combat simulation and tactical training.

• Entertainment and Games: Create immersive gaming and entertainment experiences, enhance the interaction between users and virtual environments.

Summary: Dr. Li systematically sorts out the core technologies of multimodal HCI and combines practical applications and future research directions to promote the landing scenarios of AI agents in the future. It's urgent for learning AI.