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Here are the corporate overlords are making humans into robots


Overview

Leading Companies

Major organizations are in an arms race in offering Artificial intelligence and Machine Learning (ML) services in their clouds:

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.”

Other companies

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.

Arxiv

Arxiv Paper Analysis Worksheet (Responses) on Google Sheet

Data Conversions

Some utilities may involve conventional lookups of data:

  • https://algorithmia.com/algorithms/alixaxel/CoordinatesToTimezone

  • https://algorithmia.com/algorithms/Geo/ZipData

  • https://algorithmia.com/algorithms/Geo/ZipToState

  • https://algorithmia.com/algorithms/Geo/LatLongDistance

  • https://algorithmia.com/algorithms/Geo/LatLongToUTM

  • https://algorithmia.com/algorithms/util/ip2hostname

  • https://algorithmia.com/algorithms/opencv/ChangeImageFormat (from jpg to png)

Translation

  • Google Translate API has been working on websites for years.

Image Recognition / Computer Vision

  • https://algorithmia.com/algorithms/z/ColorPalettefromImage

  • Google Cloud Vision API

  • https://algorithmia.com/algorithms/opencv/FaceDetection then https://algorithmia.com/algorithms/opencv/CensorFace

  • https://algorithmia.com/algorithms/ocr/RecognizeCharacters OCR

Some of these make use of OpenCV (CV = Computer Vision).

Voice Recognition

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:

  • https://algorithmia.com/algorithms/nlp/SentimentAnalysis

  • IBM’s algorithm

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.

Footnotes

https://www.wikiwand.com/en/Deep_learning

More

This is one of a series on AI, Machine Learning, Deep Learning, Robotics, and Analytics:

  1. Tableau Data Visualization
  2. Regression calculation and visualization using Excel

  3. AI Ecosystem
  4. Machine Learning
  5. Testing AI

  6. Python installation
  7. Juypter notebooks processing Python for humans

  8. Image Processing
  9. Amazon Lex text to speech

  10. Code Generation