Artificial Intelligence vs. Machine Learning (my summary notes)


  • Artificial intelligence (AI) includes:
    1. Natural language processing,
    2. Image recognition and classification
    3. Machine learning  (ML) –  so it’s a subset of AI and Deep Learning (artificial neural network –  more below) is a subset of ML
  • In 1950 Alan Turing published a groundbreaking paper called “Computing Machinery and Intelligence”.  Turning poses the question of whether machines can think?
  • He proposed the famous Turing test, which says, essentially, that a computer can be said to be intelligent if a human judge can’t tell whether he is interacting with a human or a machine.
  • Artificial intelligence was coined in 1956 by John McCarthy, who organized an academic conference at Dartmouth dedicated to the topic to explore aspect of learning “cognitive thinking” or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
  • The phrase “machine learning” also dates back to the middle of the last century.  In 1959, Arthur Samuel (one of the attendees of Dartmouth conference) defined machine learning as “the ability to learn without being explicitly programmed.”
  • Samuel to create a computer checkers application that was one of the first programs that could learn from its own mistakes and improve its performance over time.
  • Like AI research, machine learning fell out of vogue for a long time, but it became popular again when the concept of data mining began to take off around the 1990s.
  • Data mining uses algorithms to look for patterns in a given set of information.
  • Machine learning went one step further  – it changes its program’s behavior based on what it learns
  • Years go by with “AI Winters” due to lack of big data sets and computing power
  • Until to IBM’s Watson AI winning the game show Jeopardy and Google’s AI beating human champions at the game of Go have returned artificial intelligence to the forefront of public consciousness
  • Now Machine Learning is used for predicting and classification:
    • Natural language processing – IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data.
    • Image recognition – i.e. people face Facebook with Deep face –
    • Recommender system –  Amazon highlights products you might want to purchase and when Netflix suggests movies you might want to watch and Facebook with newsfeeds.  HIVERY also uses Recommender system to help our customers with having the right products in the right distribution channels at the right time and place.
    • Predictive analytics –  HIVERY around in fraud detection, price strategy and new product distribution placement strategy
  • Deep learning  – often called artificial neural network or neural net is a system that has been designed to process information in ways that are similar to the ways biological brains work.
  • Deep learning uses a certain set of machine learning algorithms that run in multiple layers. It is made possible, in part, by systems that use GPUs to process a whole lot of data at once.