Here are the corporate overlords are making humans into robots
Major organizations are in an arms race in offering Artificial intelligence and Machine Learning (ML) services in their clouds:
- Microsoft Cortana in Azure cloud
- IBM Watson
- Amazon Alexa
Each of the above are cloud vendors hoping to cash in by charging for processing of other people’s data.
Benedict Evans, resident futurist at venture capital firm Andreessen Horowitz, observes in a blog post that the future of AI remains opaque: “This field is moving so fast that it’s not easy to say where the strongest leads necessarily are, nor to work out which things will be commodities and which will be strong points of difference.”
Algorithmia.com provide API interfaces to algorithms offered by its partners.
awesome-machine-learning provides many links to resources, so they will not be repeated here.
Some utilities may involve conventional lookups of data:
https://algorithmia.com/algorithms/opencv/ChangeImageFormat (from jpg to png)
- Google Translate API has been working on websites for years.
Image Recognition / Computer Vision
https://algorithmia.com/algorithms/opencv/FaceDetection then https://algorithmia.com/algorithms/opencv/CensorFace
Some of these make use of OpenCV (CV = Computer Vision).
Google Cloud Speech API, which powers Google’s own voice search and voice-enabled apps.
NLP Sentiment Analysis
Analyze text for positive or negative sentiment (opinion), based on a training database of potential word meanings, which involved Natural Language Processing:
Andrew W. Trask, PhD student at University of Oxford Deep Learning for Natural Language Processing authored Grokking Deep Learning.
Use Bag of words and Word2vec transform words into vectors. Use TFLearn, a Python library for quickly building networks.
Document (article) Search
TF-IDF = Term Frequency - Inverse Document Frequency emphasizes important words (called a vector) which appear rarely in the corpus searched (rare globally). which appear frequently in document (common locally) Term frequency is measured by word count (how many occurances of each word).
The IDF to downweight words is the log of #docs divided by 1 + #docs using given word.
Cosine similarity normalizes vectors so small angle thetas identify similarity.
Normalizing makes the comparison invariant to the number of words. The common compromise is to cap maximum word count.
This is one of a series on AI, Machine Learning, Deep Learning, Robotics, and Analytics:
- Tableau Data Visualization
- AI Ecosystem
- Machine Learning
- Python installation
- Image Processing
- Code Generation