Everyday Machine Intelligence For Your Everyday Applications
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This document provides an overview of machine intelligence and its everyday applications. It discusses artificial narrow and general intelligence, machine learning approaches including supervised and unsupervised learning, and deep learning and neural networks. It also demonstrates examples of computer vision, natural language processing, machine translation and other AI applications like cancer detection, image captioning and voice synthesis. The conclusion encourages embracing AI to improve applications.
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Everyday Machine Intelligence For Your Everyday Applications
- 1. Everyday Machine Intelligence For Your Everyday Applications
- 2. Artificial Narrow Intelligence - specific task Artificial General Intelligence - general tasks What is Artificial Intelligence?
- 3. Learning from data - instead of directly programming What is Machine Learning?
- 4. You need a lot of data Machine Learning
- 5. Supervised Learning - labeled data (prediction, classification) Unsupervised Learning - unlabeled data (cluster) Reinforcement Learning - feedback (win or lose in Go) Machine Learning Approaches
- 9. Neural Network with a lot of layers What is Deep Learning?
- 11. Problems Data Quality & Amount Bad Search No content control Unable to explore images Upload is hard Hard to rate
- 12. Green => Gras Blue => Water
- 14. Mop or Dog
- 15. Dog or chicken
- 16. First Demo
- 17. Cloud Vision API
- 21. Learning to swim
- 22. Similar images and tags You have only two choices: Train your own nets or steal the results of others.
- 23. GloVe Trained on matrix created by counting co-occurrences of words. No neural net ;)
- 24. Last Demo for today
- 25. Bots
- 26. Bots Notifications and alerts Provide information Basic Interactions
- 27. They will get smarter over time, we promise
- 30. Cancer Detection
- 31. Image Captioning man in blue wetsuit is surfing on wave woman is holding bunch of bananas a cat is sitting on a couch with a remote control
- 32. Image Captioning
- 33. Voice Synthesizing Parametric Concatenative WaveNet
- 35. Conclusion Do not fear the intelligence. Embrace it. Go out and level up your app.
- 42. Alpha Go Beats Human
Notas del editor
- “Old-school” machine learning via matrix factorization and loss function
- Show NLU and how we build Botfred
- AI levels up human capabilities
- Algorithm can differentiate between male / female Algorithm can detect the different parts although, for example, the sea and the suite are blue Algorithm can make mistakes, as it is no cat but quite similar
- From Google Pixel event this year Left caption from 2 years ago
- Deep Learning rocketed voice synthesizing - listen! (with this new problems might occur such as social engineering by emulating voices of other persons)
- Could be interesting to show what buzz this event created. In minute approx. 3 they shortly explain what Deep Learning is http://makeagif.com/youtube-to-gif
- From Google Pixel event this year Phrase-based translation is based on statistical approaches Neural machine translation looks like human translation
- From Google Pixel event this year Phrase-based translation is based on statistical approaches Neural machine translation looks like human translation
- Could be interesting to show what buzz this event created. In minute approx. 3 they shortly explain what Deep Learning is