Gen AI in 2024 - Mid year check!

We are halfway through the year! Gen AI is evolving at breakneck speed, bringing forth new trends and innovations. There were some heady breakthroughs and innovations (I will list out my Top 20 soon)

I have broadly identified the following trends:?

?? Multimodal AI Models: These models integrate and process multiple types of data - text, audio, images, and video, to generate more appealing and versatile outputs.

These models enhance the intuitiveness and dynamism of AI, allowing for adoption in various fields, from creative industries to customer service and sciences as well.

OpenAI's GPT-4V and Meta's Llama 2 are leading examples showcasing richer AI interactions.

?? Small Language Models (SLMs): Although LLMs dominated much of the year, we see?a shift towards smaller, more efficient language models.

SLMs are trained on high-quality, curated datasets, making them more precise and less resource-intensive. They offer comparable performance to larger models but with greater efficiency. Meta’s Llama 2 70B and Mistral AI’s Mixtral-8x7B are notable SLMs that provide robust alternatives to proprietary models.

?? Cloud Native AI : The integration of generative AI with cloud-native technologies, particularly Kubernetes, is becoming the norm.

This allows for scalable, efficient deployment and management of AI models, supporting the entire lifecycle from pre-training to deployment. Tools like Hugging Face's Text Generation Inference and AnyScale's Ray Serve are leading the way in this domain, enabling seamless AI operations in cloud environments.

?? AI Democratization: Gen AI is becoming more accessible to a broader audience, including those without technical expertise.

This democratization is driven by user-friendly tools and platforms that allow individuals and businesses and increasing getting integrated into everyday?workflows enhancing productivity and creativity.

?? Sustainability and Efficiency: Overview: The energy consumption and environmental impact of AI are gaining attention, leading to efforts to make AI more sustainable.

Companies are focusing on developing more energy-efficient hardware and optimizing AI processes to reduce their carbon footprint. NVIDIA's efforts to enhance the efficiency of AI servers and the use of renewable energy sources by tech giants like Google highlight this trend.

These trends indicate enormous transformative power of Gen AI. We can expect to see significant advancements in model capabilities, accessibility, and sustainability.?

Please comment on any other broad trend or innovations I missed! Hoping rest of the year will see some more acceleration in the field!?