2024 is the Year of the AI AGENT

1. AI agents in 2024 will autonomously perform various tasks.

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AI agents will be capable of using tools, surfing the web, playing games, planning, storing to memory, and self-reflection, offering a wide range of functionalities.

• Embodied tasks will be a significant focus, enabling AI agents to interact with the physical and digital world.
• AI agents will be able to generalize across different realities, including video games, physical world, and simulations.
• These agents will learn necessary skills in various forms as needed.

2. AI agents will work on behalf of users to execute tasks.

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AI agents will learn to perform tasks on mobile phones or desktops without relying on memorizing button locations, offering a user-friendly experience.

• AI agents will understand and execute tasks based on user instructions, without the need for explicit programming.
• These agents will be capable of generalizing and learning new tasks over time, enhancing their adaptability.

3. Creating universal AI solutions is a key focus for 2024.

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The emphasis is on developing AI solutions that can be universally applied across different platforms, such as Android, iOS, and Windows apps.

• Efforts are being made to build AI solutions that can understand and trigger actions across various applications.
• Real human interactions with different software are being used to train AI models for universal application.

4. Advancements in AI models enable effective navigation of the web.

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Current AI models, such as GPT-4, demonstrate improved capabilities in navigating and interacting with websites, although reliability remains a concern.

• GPT-4 and similar models show significant advancements in embodying AI games, web tools, and average performance across various tasks.
• Challenges persist in ensuring the reliability and completion of complex tasks by AI agents.

5. Comparative studies of AI models reveal significant performance gaps.

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Comparisons between different AI models, such as GPT-4 and others, highlight substantial performance discrepancies, indicating the dominance of certain models.

• GPT-4 demonstrates superior performance compared to other models, showcasing its advanced capabilities in various tasks.
• The need for comprehensive evaluations and competitions among AI models is evident to gauge their effectiveness.

6. Custom GPTs tailored to specific use cases require prompting, knowledge, and actions.

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Prompting, knowledge, and actions are the essential ingredients for creating powerful, specialized GPTs tailored to specific use cases.

• Prompting involves providing written instructions to the AI to control its behavior and tone.
• Adding knowledge allows the AI to draw from external files.
• Actions enable the AI to perform tasks beyond answering from a knowledge base.

7. Creating custom GPTs for businesses offers substantial value.

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The real value lies in creating custom GPTs for businesses using the Assistant API, enabling industrial-grade applications and programmatic operation.

• Assistant API for businesses can lead to substantial value creation compared to consumer-focused GPTs on the store.
• Specializing in AI agency solutions and adapting to new updates can ensure consistent revenue streams.

8. Longer context lengths do not necessarily provide additional benefits for AI agents.

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GPT 4 with a 16k context window did not perform better than GPT 3.5 Turbo, suggesting that longer context length does not necessarily provide additional benefits.

• Proprietary models outperform open weight ones, and GPT 4 outperforms other LMs by a substantial margin, particularly on the categories of games and embodied AI.