Artificial Intelligence in practice
summary
Overview of modern artificial intelligence research and the practical steps to run an AI project, including phases, tools, and concrete case studies. Covers common problems encountered during AI projects and proposes practical solutions and approaches to address them.
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chapters (30)
- 0:00Introduction and speaker background
- 1:08Why AI matters today
- 3:40Turing test and AI history
- 5:04AI in medical and software
- 6:09AI applications in biology
- 7:12Neural networks basics
- 9:42AI architectures and alternatives
- 10:27Video and motion capture with AI
- 11:51Generative adversarial networks
- 13:38AI in robotics and simulations
- 15:41AI in customer service
- 16:45AI in natural language processing
- 18:36AI for small businesses
- 20:02AI in media and content
- 21:32AI in logistics and inventory
- 22:36AI in logo and envelope detection
- 24:10AI in image generation
- 25:42AI in autonomous driving
- 27:07AI and data compression
- 28:59Word embeddings and vector spaces
- 30:21Embedding spaces and clustering
- 31:44AI in content selection
- 32:50Hardware requirements for AI
- 34:13GPUs and AI development
- 35:39Cloud and hardware trends
- 36:44AI frameworks and tools
- 37:48AI in production and research
- 38:51AI in quality control
- 39:52AI in movie production
- 40:45Q&A and closing remarks
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