Key insights

• ⚙️ Generative AI is advancing in multi-modal capabilities, combining natural language processing and computer vision.
• 🔍 Smaller models are being developed to address the energy consumption issue of massive models.
• 🔢 AI models are becoming more efficient with smaller parameter sizes, allowing for reduced energy consumption and lower GPU and cloud costs.
• ☁️ Cloud costs are increasing due to infrastructure updates and GPU demands, but model optimization techniques like quantization and low rank adaptation can reduce compute needs.
• 🌐 Opportunities for developing powerful custom AI models by training on proprietary data, local AI training and inference, virtual agents for task automation, increasing regulations, and the continuation of shadow.
• ⚠️ Employees using AI at work without corporate policies may lead to security, privacy, and compliance issues. Generative AI poses risks such as exposing trade secrets and copyright violations. It's important to acknowledge the responsibility that comes with AI capabilities.

Q&A

• What risks are associated with generative AI?

  Employees using AI at work without corporate policies may lead to security, privacy, and compliance issues. Generative AI also poses risks such as exposing trade secrets and copyright violations. It's important to acknowledge the responsibility that comes with AI capabilities.

• What opportunities are there in AI development?

  There are opportunities for developing powerful custom AI models by training on proprietary data, local AI training and inference, virtual agents for task automation, and navigating increasing regulations and continuation of trends like 'Shadow.'

• What can reduce cloud costs related to AI?

  Cloud costs can be reduced using model optimization techniques like quantization and low rank adaptation that can decrease compute needs. More model optimization techniques are expected to emerge.

• How are AI models becoming more efficient?

  AI models are becoming more efficient with smaller parameter sizes, allowing for reduced energy consumption and lower GPU and cloud costs. There is a trend towards smaller models driven by the high energy consumption and costs associated with larger models.

• What is generative AI capable of?

  Generative AI is advancing in multi-modal capabilities, combining natural language processing and computer vision. It is also being developed into smaller models to address the energy consumption issue of massive models.

• What are the AI trends for 2024?

  The AI trends for 2024 include a move towards more realistic expectations and the integration of generative AI into existing tools.

• 00:00 AI trends for 2024 include a move towards more realistic expectations and integration of generative AI into existing tools.
• 01:22 Generative AI is advancing in multi-modal capabilities, combining natural language processing and computer vision. Smaller models are being developed to address the energy consumption issue of massive models.
• 02:58 AI models are becoming more efficient with smaller parameter sizes, allowing for reduced energy consumption and lower GPU and cloud costs.
• 04:38 Cloud costs are increasing due to infrastructure updates and GPU demands, but model optimization techniques like quantization and low rank adaptation can reduce compute needs. Expect more model optimization techniques to emerge.
• 06:12 Opportunities for developing powerful custom AI models by training on proprietary data, local AI training and inference, virtual agents for task automation, increasing regulations, and the continuation of shadow.
• 07:53 Employees using AI at work without corporate policies may lead to security, privacy, and compliance issues. Generative AI poses risks such as exposing trade secrets and copyright violations. It's important to acknowledge the responsibility that comes with AI capabilities.