?? What Comes After Large Language Models (LLMs)? The Future of AI ??

The Evolution of AI ??

From Classification to Generative AI ??

We've come a long way from simple classification tasks to the current phase of generative AI, where machines can create new data based on what they've learned. This shift has driven significant advancements and investments in AI hardware and cloud-based services.

The Next Phase: Interactive AI ??

The future of AI will focus on interaction. We're moving from text-based interactions with AI to using voice and even wearable devices like the Humane AI pin. The ultimate goal is for AI to make decisions and take actions in the real world, using external tools and real-time information from sensors.

Physical AI and Real-World Applications ??

From Software to Physical AI ??

Physical AI involves using multimodal large language models to make decisions based on real-time data from various sensors. A major breakthrough in this area is Nvidia's Eureka algorithm, which accelerates robot learning by running parallel sessions in virtual worlds, making training thousands of times faster.

AI in Robotics ??

This iterative deployment approach, where AI learns from real-world feedback, is paving the way for more advanced and practical applications in robotics and beyond.

Key AI Companies and Investments ??

The Big Players and Their Strategies ??

Major companies like Amazon, Google, Salesforce, Nvidia, and Microsoft are heavily investing in AI startups and technologies. For instance, Microsoft's strategic partnership with OpenAI provides unlimited computing resources, benefiting both companies.

Nvidia's Role in the AI Ecosystem ??

Nvidia is not just a hardware giant but also a major player in AI software and frameworks. Their latest GPU, the H200, promises double the performance of its predecessor, reinforcing Nvidia's position in the AI market.

Custom Silicon and AI Hardware Trends ???

Microsoft's Custom AI Chips ??

Microsoft's introduction of the Azure Cobalt CPU series and the Azure Maya AI accelerator marks their entry into the custom silicon space. These chips are designed for efficient large language model training and inference, showcasing the trend of companies developing their own AI hardware.

The Importance of Custom Silicon ??

Custom silicon allows companies to optimize the entire stack from hardware to software, enhancing efficiency and performance. This trend is evident in companies like Apple, Tesla, and now Microsoft.

Future Outlook and AI Trends ??

Transition to Physical AI ??

The trend is moving towards physical AI, where AI systems will interact more seamlessly with the physical world. This includes advancements in robotics, autonomous systems, and real-world applications.

The Goal of Artificial General Intelligence (AGI) ??

The ultimate target is AGI, a single AI system capable of performing any task a human can do. While we're still on the journey towards AGI, current advancements in AI are setting the stage for this future.