Finding Swimming Pools with IBM Watson

A hobbyist's introduction to the IBM Watson APIs.

Transcript

1. Finding Swimming Pools with IBM Watson Tom Robertshaw @bobbyshaw
2. Disclaimer At most, I’m a hobbyist. I’m a little bit in love with IBM Watson
3. SaaS APIs IBM Watson Google Cloud Machine Learning Amazon Machine Learning Microsoft Azure Machine Learning
4. IBM Watson
5. IBM Watson According to IBM, Watson uses "more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses.
6. IBM Watson DeepQA software commercially available. Minimum Server Specs: $1 million
7. So what can it do?
9. Concept Insights Use case - Recommendation of related documents Input - Text - Collection of documents Output - Related documents (based on your collection) - Concepts mentioned in the input document
12. Personality Insights Input - JSON, or Text or HTML written by one individual Output - A tree of cognitive and social characteristics in JSON or CSV format
14. Eliza
16. Natural Language Classifier Input - Mapping of text to classifications Output - Classification
17. Sorting Hat
19. Tradeoff Analytics Use case - When shopping for a phone, a user wants to consider the importance of price, screen size, battery life and weight. Tradeoff analysis is a pareto optimisation of user’s preferences, e.g. fuzzy filters in ecommerce. Input - A decision problem with objectives and options Output - JSON objects that represent the optimal options and highlight the trade- offs between them.
20. Others - Retrieve and Rank - Conversation - Tone Analyser - Relationship Extraction - Alchemy News - Document Conversion - Speech to Text - Text to Speech - Language Translation
22. Visual Recognition Content Categorisation - Took screenshots of websites - Training set of ecommerce categories - More accurate than Bayesian text classification
24. Kyero - 100,000s of properties submitted be estate agents or come in via an API. - Structured data gives more opportunity to optimise. - Manually - Show swimming pool - Automatically - Optimisation for category of image that increases conversion rate.
26. Kyero - Swimming Pool - Sea - Outside - Inside - Building - Blue Sky - Bathroom - Bedroom - Living Room - Dining Room - Kitchen - Photo - Drawing - Logo - Watermarked
27. Training & Testing What proportion do you use for training? (What proportion do you use for validation?) What proportion do you use for testing?
28. Training & Testing - Many different models. - Balance of simplicity and effectiveness is K-fold mechanism, e.g 2-fold - Train on 50%, test on 50% - Swap data around, test again - Take an average.
29. Create Classifier curl -u "{username}":"{password}" \ -X POST \ -F "positive_examples=@swimming-pool-positive-training.zip" \ -F "negative_examples=@swimming-pool-negative-training.zip" \ -F "name=swimming-pool" \ "https://gateway.watsonplatform.net/visual-recognition- beta/api/v2/classifiers?version=2015-12-28"
30. Test Positive curl -u "{username}":"{password}" \ -X POST \ -F "images_file=@swimming-pool-positive-validation.zip" \ -F 'classifier_ids={"classifiers":[{"classifier_id":" swimming_pool_2056231781"}]}' \ "https://gateway.watsonplatform.net/visual-recognition- beta/api/v2/classify?version=2015-12-28"
31. Test Negative curl -u "{username}":"{password}" \ -X POST \ -F "images_file=@swimming-pool-negative-validation.zip" \ -F 'classifier_ids={"classifiers":[{"classifier_id":" swimming_pool_2056231781"}]}' \ "https://gateway.watsonplatform.net/visual-recognition- beta/api/v2/classify?version=2015-12-28"
32. Kyero Category Accuracy (0.7 threshold) Swimming Pool 0.80 Outside 0.94 Building 0.71 Blue Sky 0.80 Watermarked 0.33 Inside 0.89 Living Room 1.00 Sea 0.8
34. Potential Comes from combining services
35. Combine Services
36. Combine Services
37. Getting Started Pricing is very attractive for prototyping - Free to get started - First ~1000 calls are normally free too - Pricing ranges from $0.0009 to $0.25 per call.
38. AI XPrize Competition - Use IBM Watson to tackle the world’s grand challenges. - $5 million in prizes - $3 million grand prize - http://ai.xprize.org/
39. Cognify - ML is now much easier to get good enough results. - Experiment with some of Watson’s “approachable APIs”. - Build smarter digital products.
40. Thanks! Questions?