Google – RoboRabbitLabs

The past few years has seen a blur of software giants releasing AI and Machine Learning themed APIs and services. I was, frankly, surprised at how many options there currently are for developers and companies. I think it’s a positive sign for the industry that there are multiple options from reputable brands when it comes to topics like visual and language recognition – these have almost become commodities. You also see strong consolidation into very typical categories like machine learning for building general predictive models, visual recognition, language and speech recognition, conversational bots and news analysis.

Google
- Google Cloud Platform
  - Google Prediction API
    - Hosted Models (Demo)
      - Language Identifier
        
        Spanish, English or French
      - Tag Categoriser
        
        Android, appengine, chrome youtube
      - Sentiment Predictor
        
        Positive or negative label for comments
    - Trained Models
      - Train your own model
  - Google Cloud Vision API
    - Label Detection
    - Explicit Content Detection
    - Logo Detection
    - Landmark Detection
    - Optical Character Recognition
    - Face Detection
    - Image Attributes
  - Cloud Speech API
    - Audio to text
    - >80 languages
    - Streaming Recognition
    - Inappropriate Content Filtering
    - Real-time or Buffered Audio Support
    - Noisy Audio Handling
  - Google Translate API
    - Text Translation
    - Language Detection
- Tensor Flow
  - Open Source graph-based numerical computation and model building

Facebook
- Bot for messenger
  - Ability to build a chat bot for your company that chats via facebook messenger

IBM
- Bluemix
  - Alchemy
    - Alchemy Language
      - Keyword Extraction
      - Entity Extraction
      - Sentiment Analysis
      - Emotion Analysis
      - Concept Tagging
      - Relation Extraction
      - Taxonomy Classification
      - Author Extraction
    - Alchemy Data News
      - News and blog analysis
      - Sentiment, keyword, taxonomy matching
  - Concept Insights
    - Linking concepts between content
  - Dialog
    - Chat interaction
  - Language Translation
  - Natural Language Classifier
    - Phrase classification
  - Personality Insights
    - Social media content analysis to predict personal traits
  - Relationship Extraction
    - Finds relationships between objects and subjects in sentences
  - Retrieve and Rank
    - Detects signals in data
  - Speech To Text, Text to Speech
  - Tone Analyzer
    - Emotion analysis
  - Tradeoff Analytics
    - Decision making support
  - Visual Recognition
  - Cognitive Commerce
    - Support for driving commerce, recommendations etc
  - Cognitive Graph
    - Creates a knowledge graph of data thats typically difficult for a machine to understand
  - Cognitive Insights
    - Personalised commercial insights for commerce

Amazon
- Amazon Machine Learning
  - Managed service to help you model your data with no prior experience
- Amazon Alexa Voice Service
  - Natural language processing

Microsoft
- Microsoft Cognitive Services
  - Vision
    - Categorise images
    - Emotion recognition
    - Facial detection
    - Anaylze video
  - Speech
    - Speech to text, text to speech
    - Speaker recognition
  - Language
    - Spell checking
    - Natural language processing
    - Complex linguistic analysis
    - Text analysis for sentiment, phrases, topics
    - Models trained on web data
  - Knowledge
    - Relationships between academic papers
    - Contextual linking
    - Interactive search
    - Recommendations
  - Search
    - Search
    - Search autosuggest
    - Image and metadata search
    - News search
    - Video search
  - Bot Framework
  - Content Moderator
  - Translator
  - Photo DNA Cloud Service

Did I miss something from this list? Comment and let me know!

This week, the Google test blog newsletter was about GTAC, the Google Test Automation Conference. I found this session on AI applied to testing really relevant to:

Free Tests Are Better Than Free Bananas: Using Data Mining and Machine Learning To Automate Real-Time Production Monitoring (Celal Ziftci of Google)

The session was about Google’s assertion framework which runs against their production logfiles. The framework runs on real time logs, checking for inconsistencies. Examples of meaningful assertions:

transaction.id > 0, transaction.date != null.

If any assertions fail, a notification is sent to a developer to take some kind of action. Usually a developer would have to design assertions, but now they use a tool to assist.

Daikon invariant detector identifies invariants (rules which are always true for a certain section of code). Although Daikon has been designed to analyse code, they have modified it to work on data like logfiles. Daikon starts with a set of hypotheses about what your input will look like, and as you push data through it, it eliminates those which prove to be false. The rules identified can be used as assertions, thereby automatically generating test cases. These test cases still need a developer to determine value and validity, however.

The other technique they use is Association Rule Learning, which finds relationships between different data items, e.g.

when country is uk, time zone is +0

These, too, are added into the assertion framework to identify issues occurring in production.

In this case, the work that developers used to do in defining test cases is now being done by machines. But human beings are still needed to made a decision on whether the identified rules make sense and add value or not.

The AI system, at times, identifies trivial rules, but is also capable of identifying complex relationships that would be less obvious to humans.

Tag: Google

Update on latest Artificial Intelligence API’s and services

What Google has to say on AI in Testing