December 12, 2017

Notes on artificial intelligence, December 2017

Most of my comments about artificial intelligence in December, 2015 still hold true. But there are a few points I’d like to add, reiterate or amplify.

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

It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response.

2. Accordingly, it can be reasonable to equate machine learning and AI.

3. Similarly, it can be reasonable to equate AI and pattern recognition. Glitzy applications of AI include:

4. The importance of AI and of recent AI advances differs greatly according to application or data category. 

5. Right now it seems as if large companies are the runaway leaders in AI commercialization. There are several reasons to think that could last.

I’m sure there are many niches in which decision-making, decision implementation and feedback are so tightly integrated that they all need to be developed by the same organization. But every example that remotely comes to mind is indeed the kind of niche that smaller companies are commonly able to address.

6. China and Russia are both vowing to lead the world in artificial intelligence. From a privacy/surveillance standpoint, this is worrisome. China also has a reasonable path to doing so (Russia not so much), in line with the “Lots of data makes models strong” line of argument.

The fiasco of Japan’s 1980s “Fifth-Generation Computing” initiative is only partly reassuring.

7. It seems that “deep learning” and GPUs fit well for AI/machine learning uses. I see no natural barriers to that trend, assuming it holds up on its own merits.

Maybe CPU vendors will co-opt GPU functionality. Maybe not. I haven’t looked into that issue. But either way, it should be OK to adopt software that calls for GPU-style parallel computation.

8. Computer chess is in the news, so of course I have to comment. The core claim is something like:

My thoughts on that start:


4 Responses to “Notes on artificial intelligence, December 2017”

  1. Sebastian on December 21st, 2017 8:29 am

    I’d say pattern recognition and machine learning are only part of what constitutes AI. If siri would only be doing pattern recognition i.e. understanding natural language commands it would not be an impressive app. If self driving cars would only be doing pattern recognition they’d be going nowhere. What is the main differentiator about AI is that machines are actually taking action. Apps are responding to human commands, cars are driving themselfs and so on. This is mutch more than “just” pattern recognition or machine learning.

  2. Ranko Mosic on January 9th, 2018 8:50 am

    Pattern matching is the gist of the current state of ML. But ML people are trying and sometimes succeeding with more active algorithms ( which still have very limited uses i.e. do not generalize (yet) ) – Reinforcement Learning, GANs. It is very crude machinery ( more like a steam engine, as F. Cholett called it, and it is definitely not a rocket engine, as A. Ng calls it ).
    Here is an example of how RL is used to reduce trading impact:

  3. Ranko Mosic on January 9th, 2018 10:29 am
  4. Victor Smirnov on February 13th, 2018 11:26 pm

    Recently a new interesting trend in AI was actualizing. It’s merging/hybridization of DL and symbolic (GOFAI) methods based on search. Core investors stared observing diminishing returns from investments in “pure” DL that is just a complicated pattern matching. Scaling DL beyond pattern matching implies, for instance, logic inference. That is much more like traditional database than matrix crunching on GPUs.

    GOFAI failed in 80th because it couldn’t handle uncertainty and had zero-to-none learning abilities on the hardware of 80th. Both these problems have been mostly solved.

    I don’t expect penetration of AI into existing databases, especially into OLTP, thought they can start supporting some forms of AI and ML serving current needs of intelligent processing of data stored in DBMSes. Instead, I can predict emerging of new DBMS technologies enabling, for example, probabilistic data structures and supporting probabilistic queries over structured distributions of data instead of tables.

    Anyway, AI is data-hungry and it must be managed somehow. So databases will preserve their king role of IT.

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