The Evolution of AI in 2024: Multimodal Models, AI Agents, and More

Table of Contents

1. Introduction
2. The Emergence of Multimodal Models
   • Merging Different Modalities
   • Examples of Multimodal Models
   • The Benefits and Challenges of Multimodality
3. AI Agents: Virtual Intelligent Machines
   • AI Agents in Various Scenarios
   • Safety Concerns and Protections
   • Multimodality in AI Agent Interactions
4. Multimodal Models on Mobile Devices
   • Advancements in Mobile AI Models
   • Personalization and Privacy
   • Potential for AI-Assisted Virtual Assistants
5. Robotics and the Fusion of Multimodal Models
   • Integration of LLMs and Robotics
   • Applications of LLMs in Robotics
   • Progress and Limitations in Robotic Learning
6. AI Content and the Age of Misinformation
   • Concerns with AI-Generated Content
   • Challenges in Detecting AI-Generated Content
   • Efforts to Combat Fake News and Misinformation
7. AI News and the Debate on Data Governance
   • Legal Issues Surrounding AI-Generated Text
   • Artistic Protection and Image Poisoning
   • Policy and Regulation on AI-Generated Work
8. Alignment, Open Source, and Closed Source Models
   • Lack of Transparency in AI Models
   • Open-Source Movements in AI Research
   • Balancing Safety and Transparency in AI
9. AI Chips and the Future of Processing Power
   • Competition and Processing Power Acquisition
   • Quantum Computing and AI Development
   • Impact of Hardware on AI Advancements
10. Conclusion

🤖 The Emergence of Multimodal Models

Multimodal models have rapidly emerged as a revolutionary approach to AI in recent years. These models integrate different modalities, such as text, audio, image, and video, to enhance comprehension and generation abilities. The development of multimodal models has been driven by the need to break down the silos that initially separated these modalities. Researchers have made significant strides in merging different modalities, resulting in the emergence of powerful models like VideoBERT and VisualGPT. One notable example is Flamingo from DeepMind, which takes visual language models to a new level by enabling conversations about images across various tasks.

The concept of multimodality has evolved further with the introduction of "Any-to-Any" models. These models have the ability to process and generate different modalities, including text, audio, heat maps, and images, among others. Popular examples of Any-to-Any models include NextGPT and Meta's ImageBind. Although OpenAI's GPT4-V is a widely recognized multimodal model, it currently only accommodates text and images. The integration of multiple modalities in AI models offers a range of benefits, including assembling information in different formats and enabling positive transfer of knowledge. However, challenges arise when models mix up information due to the complex nature of multimodal input. Balancing all modalities is crucial for effective learning and comprehension.

🌐 AI Agents: Virtual Intelligent Machines

AI agents are virtual, intelligent machines that perform a series of tasks to achieve specific goals. These agents have the capability to interact with users and complete various actions, ranging from booking a room on Airbnb to purchasing items online. While the complexity of these tasks is a significant factor, there are also safety concerns associated with AI agents. For example, allowing AI agents to navigate the internet freely may lead to potential problems, such as unauthorized chemical purchases. As a result, safeguards must be put in place to monitor and regulate AI agent activities.

The power of multimodality plays a crucial role in enhancing AI agent capabilities. With the ability to analyze text online, navigate websites, and interact seamlessly, AI models equipped with multimodal understanding can provide more comprehensive and effective assistance. Early projects from companies like DeepMind and Adept AI have paved the way for the development of AI web agents. These agents have the potential to revolutionize not only website interactions but also gaming experiences.

Gaming provides a unique avenue for AI agents, as it offers an open-world platform for exploration and interaction. AI agents in gaming can provide a fantastic simulation of the real world, allowing for extensive testing and development before physical deployment, especially in robotics. Big Game companies like Rockstar have already started adopting multi-agent AI characters to bring more life and realism to their games. There have been exciting instances where in-game characters engage in conversations with gamers, avoiding repetitive behaviors and offering unique experiences. The potential for AI agents in gaming is vast and opens up numerous possibilities for enhancing user experiences.

📱 Multimodal Models on Mobile Devices

Traditionally, large AI models that provide high-quality output have required significant processing power and relied on cloud providers like Amazon and Microsoft. However, there have been noteworthy advancements in enabling multimodal models to run efficiently on mobile phones. Models like Llama have spearheaded this research field, ensuring that AI becomes more personalized for users without relying heavily on cloud providers. These developments not only enhance privacy but also improve the accessibility of AI systems.

The adoption of multimodal models on mobile devices poses immense potential, moving towards a future where AI-assisted virtual assistants like Siri or Google Assistant become more capable and personalized. Startups like Rabbit have already introduced AI-assisted devices that can perform a wide range of tasks based on user commands. For example, R1 is an AI agent that can be instructed to request an Uber ride or schedule appointments seamlessly. The evolution of AI-assisted devices offers improved response times and innovative features such as teach mode, further expanding the scope of AI agents.

Major tech companies like Apple and Google are undoubtedly working on similar features for their devices, aiming to provide a comprehensive AI experience to their users. The integration of multimodal models into mobile devices opens up diverse possibilities and elevates the capabilities of virtual assistants, revolutionizing the way we interact with technology.

🤖 Robotics and the Fusion of Multimodal Models

While multimodal models have made significant progress in various domains, their application in the physical world remains limited. This limitation paves the way for the fusion of multimodal models and robotics, enabling the planning capabilities of Language Learning Models (LLMs) to connect with robots for execution. The use of LLMs in robotics has already shown promise in areas such as autonomous driving and robotic motion.

One noteworthy example is Alter3 from the University of Tokyo, which enables robotic movements through simple GPT-4 commands. Despite progress in robotic learning, the insufficient data available for robots to learn from continues to pose a challenge. To overcome this, researchers have explored approaches like generating synthetic data for robots to learn quickly. Projects like Google's ROSIE illustrate how imagining various scenarios can help train robots effectively.

While significant advancements have been made, there is still significant progress to be made in realizing seamless integration between LLMs and robotics. The initial adoption of intelligent robots is expected to take place in factory settings, gradually extending to more cost-effective models for homes, akin to the Roomba vacuum cleaner. Companies such as Tesla, Boston Dynamics, and Unitree are actively implementing AI on different levels to develop more versatile and general-purpose machines. The fusion of multimodal models and robotics holds immense potential for transforming industries and our everyday lives.

🌐 AI Content and the Age of Misinformation

In the age of AI, the significance of honest and factual news cannot be overstated. As AI systems become more proficient in generating content across various media platforms, concerns about their dual uses and the spread of misinformation arise. Instances of AI-Generated Photos of public figures like former President Trump and the pope have already surfaced, raising questions about the authenticity of such content.

Recently, a principal in Maryland claimed that derogatory statements attributed to him were synthetically generated using his voice by students. The proliferation of AI-generated content adds to the confusion and unrest that can arise from misinformation and disinformation. The upcoming 2024 US elections stand as a pivotal moment to witness how these challenges unfold and the measures taken to address them.

Efforts are underway to detect and prevent the spread of AI-generated content, but detecting such content accurately remains a challenge. OpenAI, for instance, has outlined its plans and strict usage policies for the 2024 elections. Similarly, educational institutions are implementing mechanisms to identify assignments generated using AI. Misclassification of original work as AI-generated content adds to the complexity of this issue.

AI is also being utilized in media through the presentation of news by AI TV anchors with AI-assisted scripts and videos. This practice, while allowing for efficient news delivery, raises concerns about the authenticity and reliability of content. The vulnerability of individuals who are unaware of AI advancements presents a significant challenge, as fake advertisements and content mimicking popular online figures continue to increase.

As we brace ourselves for the storm of fake news, it becomes increasingly crucial to question the credibility of the information we Consume. While various labs work diligently to develop methods for detecting AI-generated content, achieving foolproof solutions remains an ongoing process. The effective regulation of AI-generated text, art, and voice requires a comprehensive understanding of the concept of Originality and robust policymaking. Governments and policymakers are grappling with this issue, trying to strike a balance between encouraging innovation and protecting against the misuse of AI-generated content.

🗞️ AI News and the Debate on Data Governance

AI-generated news is not limited to images or voices; it also encompasses text. However, the utilization of news articles by AI models for training purposes without the media company's consent has raised legal concerns. The New York Times, for example, is suing OpenAI and Microsoft for using their news articles to train GPT models without proper authorization. OpenAI argues that its use falls under fair use but intends to license news from media giants like CNN and Fox for a substantial fee.

To address concerns about unauthorized AI scraping of artists' images, institutions like the University of Chicago have developed tools like Nightshade. This free software aims to protect artists' rights by subtly altering images at the pixel level, fooling AI systems while remaining visually correct to human Perception. The debate surrounding data governance and AI-generated work poses a significant hurdle that policymakers must address. The European Union has taken an early lead in establishing initial rules to govern this sector.

Determining whether AI-generated text, art, or voice can be considered original work requires a reevaluation of the concept of originality on a philosophical level. Government regulations often lag behind technological advancements, and the rapid progress of artificial intelligence necessitates urgent policy and rule-making. Striking the right balance between encouraging innovation and protecting intellectual property rights in an AI-dominated world remains a complex and evolving challenge.

⚖️ Alignment, Open Source, and Closed Source Models

Understanding the inner workings of AI companies and their models has become increasingly difficult, as they provide less information about model architecture and training data. However, there is a push for more open-source approaches in AI research and a demand for increased transparency. Meta and HuggingFace are examples of companies advocating for open-source movements and promoting greater openness and transparency in AI.

Companies argue that closed-source models provide them with a competitive edge and enhance safety by allowing them to deploy AI in a controlled manner. However, determining what safety entails becomes a complex subject. Users continue to find ways to bypass safety measures in Large Language Models, exposing potential risks. ChatGPT, for instance, has been tricked into generating text that promotes drunk driving, highlighting the importance of responsible deployment.

OpenAI acknowledges the need to Align AI with human values and ensure models do not go rogue. They address this through research grants that target alignment issues. Additionally, they have published Papers proposing methods for supervising future super AI agents with human oversight. The debate surrounding alignment, open source, and closed source models presents a nuanced and multifaceted discussion, necessitating further exploration of the implications and potential solutions.

💻 AI Chips and the Future of Processing Power

Competition among AI companies has led to an influx of chip acquisition on a large Scale, making it increasingly challenging for smaller labs and educational institutions to keep up. Those lacking sufficient processing power are humorously referred to as "GPU Poor." Notably, Nvidia dominates the GPU market and plays a significant role in the ongoing semiconductor war between superpowers like the United States and China.

Companies like Meta have amassed an exceptional number of chips, with Meta alone amassing almost 600,000 GPUs of the H100s. This abundance of chips is essential to support the computational requirements of state-of-the-art AI models. The extent of processing power required for the best AI models is staggering, with billions of parameters necessitating millions of years' worth of compute and storage. These figures underscore the immense demand for processing power and its impact on advancing AI capabilities.

To reduce reliance on Nvidia for GPU chips, various initiatives are underway. For example, OpenAI's Sam Altman seeks funding to establish an AI chip supply chain that accelerates the company's research and development. Quantum computing has also been a topic of discussion in AI development, with the potential to significantly advance processing power and revolutionize AI. As the future of hardware evolves, it will continue to Shape the possibilities and potential of AI.

🎉 Conclusion

The landscape of AI in 2024 and beyond is incredibly exciting, filled with diverse advancements and challenges. Multimodal models have emerged as a powerful tool for integrating different modalities, offering enhanced comprehension and generation abilities. AI agents, enabled by multimodal understanding, are revolutionizing various scenarios, although safety concerns and regulations remain vital aspects to consider.

The availability of multimodal models on mobile devices opens up new possibilities for personalized and accessible AI systems. Furthermore, the fusion of multimodal models and robotics presents exciting prospects for leveraging planning capabilities in executing real-world tasks. Consequently, factories and homes may soon witness the deployment of intelligent robots that enhance productivity and everyday experiences.

However, the age of AI also brings significant challenges, such as the spread of misinformation through AI-generated content. Efforts are underway to detect and combat these issues to preserve the integrity of news and public information. The debate surrounding data governance, alignment, and the openness of AI models further shapes the exploration of AI's potential.

The demand for processing power, coupled with the ongoing chip war, drives innovation and competition among AI companies. Whether through massive GPU inventories or the potential integration of quantum computing, the Quest for more computational resources pushes the boundaries of AI research and development.

As we move forward, it is essential to stay informed and anticipate the impact of AI on various aspects of our lives. Embracing responsible and ethical practices will be instrumental in navigating the exciting and ever-evolving landscape of AI in the coming years.