AI Trends for 2025

Summary

In this video, the speaker outlines eight key AI trends for 2025, focusing on the evolution of AI technology. Key trends include the rise of AI agents capable of reasoning and taking action, improved inference time computation for smarter AI responses, the development of both massive and compact AI models, and the advancement of AI applications in customer service, IT, and cybersecurity. Other trends involve near-infinite memory for bots, human-AI collaboration in professional settings, and an open call to the audience to contribute their own predictions for future AI developments. The speaker emphasizes a shift towards more practical, specialized AI tools and systems.

Takeaways

• 😀 AI agents are becoming more capable, evolving to reason, plan, and take action in more complex scenarios.
• 🤖 Inference time compute will allow AI models to reason before answering, improving problem-solving without needing retraining.
• 📈 AI models are expected to grow significantly in size, with the next generation possibly reaching up to 50 trillion parameters.
• 📱 Smaller AI models with a few billion parameters will run on everyday devices, reducing the need for large data centers.
• 💼 Advanced enterprise AI use cases will emerge, including complex customer service bots and proactive IT network optimization.
• 🧠 AI models will have near-infinite memory, allowing bots to remember past interactions and provide more personalized experiences.
• 👩‍⚕️ Human-in-the-loop AI will combine human expertise with AI to improve overall decision-making, making professionals smarter with AI assistance.
• 📊 Effective AI systems will allow professionals to integrate AI tools into their workflow without needing to be experts in AI.
• 🧑‍🤝‍🧑 The audience plays a key role in shaping AI trends, with the open-ended call for viewers to share their predictions for 2025.
• 🔮 The future of AI includes improved logical reasoning and greater reliability, especially in handling multi-step problems and decision-making.

Q & A

What is agentic AI, and why is it important for 2025?

Agentic AI refers to AI systems that can reason, plan, and take action autonomously. These agents can break down complex problems and create multi-step plans to achieve goals. In 2025, agentic AI is expected to become more advanced, with improved logical reasoning and decision-making abilities, addressing current limitations in handling complex scenarios.

How does inference time compute contribute to AI advancements?

Inference time compute refers to the ability of AI models to spend time reasoning before giving an answer. In 2025, this process will improve the performance of AI models by allowing them to enhance reasoning during real-time data processing, without needing to retrain the entire model. This advancement will lead to smarter, more efficient AI agents.

What is the significance of very large AI models, and how will they evolve by 2025?

Very large AI models are expected to expand significantly in 2025, potentially reaching 50 trillion parameters, a major leap from the current frontier of 1-2 trillion parameters. These larger models will allow AI systems to process more complex data and handle more sophisticated tasks with better performance and accuracy.

How do very small AI models differ from larger models, and what are their advantages?

Very small AI models, with only a few billion parameters, are designed to run efficiently on personal devices like laptops or smartphones. Despite their smaller size, they are capable of performing specific tasks with minimal computational power, making them more accessible and cost-effective for everyday use.

What are some advanced enterprise use cases for AI expected in 2025?

By 2025, AI will be used for more advanced enterprise applications, such as customer service bots that solve complex problems, AI systems that proactively optimize entire IT networks, and security tools that adapt to real-time threats. These applications will provide greater efficiency and more tailored solutions in the workplace.

What does 'near infinite memory' mean in the context of AI, and how will it impact customer service?

'Near infinite memory' refers to AI models that can retain and recall all interactions with users at any time. In 2025, this will enable customer service bots to remember every conversation they've had with a user, allowing for more personalized and continuous service, improving overall customer experience.

How will human-in-the-loop augmentation improve AI's effectiveness in professional settings?

Human-in-the-loop augmentation refers to integrating AI into professional workflows, enabling experts to work alongside AI tools for enhanced decision-making. In 2025, this will allow professionals to leverage AI's capabilities without needing to be AI experts themselves, improving efficiency and outcomes in various fields.

What was the result of the study comparing chatbots and physicians in clinical reasoning?

In the study, chatbots outperformed physicians in clinical reasoning when diagnosing medical conditions from case reports. However, when doctors used a chatbot to assist them, their performance was lower than when the chatbot worked alone. This highlights the need for better AI and human collaboration to ensure the best outcomes.

Why does the speaker mention 'very large models' and 'very small models' together?

The speaker contrasts very large AI models (with trillions of parameters) and very small models (with a few billion parameters) to emphasize the diversity in AI development. While large models offer high computational power for complex tasks, small models will be more accessible, efficient, and able to run on personal devices, providing balance in AI evolution.

How does the video encourage audience engagement with AI trends for 2025?

The speaker invites viewers to share their thoughts on which AI trends they think will be significant in 2025, allowing for audience interaction and crowdsourced insights. This engagement fosters a sense of community and encourages diverse perspectives on the future of AI.