December 1, 2015

Machine learning’s connection to (the rest of) AI

This is part of a four post series spanning two blogs.

1. I think the technical essence of AI is usually:

Of course, a lot of non-AI software can be described the same way.

To check my claim, please consider:

To see why it’s true from a bottom-up standpoint, please consider the next two points.

2. It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response. Examples of what I mean include: 

3. In most computational cases, pattern recognition and response boil down to scoring and/or classification (whether in a narrow machine learning sense of “classification” or otherwise). What I mean by this is:

4. If you want a good algorithm for classification, of course, it’s natural to pursue it via machine learning. And the same is true of scoring, at least if we recall that the domains of machine learning and statistics have essentially merged.

5. It took people remarkably long to figure out the previous point. Through at least the end of the previous century, it was generally assumed that the way to come up with clever algorithms for, for example, text analytics or machine vision was — well, to think them up.

6. As spelled out in my overview of present-day commercial AI, there’s a somewhat paradoxical industry structure, in that:

Of course, there are plenty of startups hoping to change that structure. I hope some of them succeed.

Comments

6 Responses to “Machine learning’s connection to (the rest of) AI”

  1. What is AI, and who has it? | DBMS 2 : DataBase Management System Services on December 1st, 2015 4:28 am

    […] One post explores the close connection between machine learning and (the rest of) AI. […]

  2. clive boulton on December 1st, 2015 10:09 am

    “The way to come up with clever algorithms for, for example, text analytics or machine vision was — well, to think them up.”

    MIT / BigDog labs etc seem to have accepted that AI can’t build it’s own model (no matter how much big data). The new era of AI requires feeding the system a model and apprentice learning is about as good as it gets.

    http://heli.stanford.edu/

  3. AI memories — expert systems | Software Memories on December 3rd, 2015 12:59 am

    […] One post explores the close connection between machine learning and (the rest of) AI. […]

  4. Historical notes on artificial intelligence | Software Memories on December 3rd, 2015 1:01 am

    […] One post explores the close connection between machine learning and (the rest of) AI. […]

  5. Ranko Mosic on January 5th, 2016 9:44 am

    It would be interesting to contemplate on how ML might affect classic enterprises. For example, how ML methods ( deep learning, neural networks ) might help with risk calculations, stock market predictions.
    Data mining is already well established discipline. As you said, ML brings huge volume of data in equation ( sample = ALL ). Technologies that Google, Facebook and others currenlty investigate will trickle down to enterprises the way Big Data technologies ( Hadoop etc. ) did.
    As Mike Olson said, Google is sending us messages from the future. And it looks like one of Google’s focal areas is Deep Learning.

  6. Notes on artificial intelligence, December 2017 – Cloud Data Architect on December 13th, 2017 1:21 am

    […] 1. As I wrote back then in a post about the connection between machine learning and the rest of AI, […]

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