back to article Congrats, Nvidia and Google: You're still the best (out of five) at training neural networks

Nvidia and Google continue to dominate in AI hardware, according to the latest benchmarking results from the ML Perf project published this week. Tech companies all want a little slice of the machine learning pie: Nvidia has rebranded GPUs for AI, Intel is busy trying to tout its CPUs and push its new ASIC known as the NNP-T …

  1. I.Geller Bronze badge

    Machine Learning is the addition of structured chunks of text, contextually and subtextually targeted to the execution of certain tasks, which this AI (for some reason, even having a set structured texts) cannot do. That is, this structured piece of text is a kind of program, which says what to do.

    For example, to perform a maneuver Tesla finds somewhere a paragraph:

    -- The car abruptly slows down and starts turning right. At the same time it has to turn on the turn signal to the right. --

    In this paragraph suppose Tesla removes lexical noise and identifies three synonymous clusters:

    I. the car abruptly slows down

    II. the car starts turning right

    III. the car turns the signal turn on to the right

    Now Tesla follows this instruction and checks the result. If the feedback gives the positive score Tesla remembers this paragraph and use it in situations when the sensors show the same.

    After that Tesla follows these instructions and checks the result. If the feedback gives positive result Tesla remembers the paragraph and use it in situations when the sensors show that it is necessary.

    You can see that Argo AI And Waymo agree with that my definition of Machine Learning:

    "At the end of a test day, all the data gets ingested into a data center from the vehicles and the good stuff is analyzed and labeled. Raw data by itself doesn’t have much value for training the machine learning systems that form the core of modern AV systems. The objects in the data that are of interest including pedestrians, cyclists, animals, traffic signals and more. Before any sensor data can be used to train or test an AI system, all of those targets need to be labeled and annotated by hand so that the system can understand what it is “seeing.”

    1. I.Geller Bronze badge

      A combination

      I don't think the use of AI solely, in isolation from traditional software solutions is reasonable. Most likely the optimal use is a combination of AI and software. So teaching AI you need to be flexible and think what you are doing.

    2. I.Geller Bronze badge

      Machine Learning is impossible without dictionary!

      Machine Learning helps computer to find answers to questions, which is the essence of AI. But questions and answers are always texts! And it is impossible to understand any texts without knowing dictionary definitions of its words.


      1. Helps computer to understand parts of speech for words, to create the right patterns (as combinations of words). For example, in the sentence " Alice and Bob swim." there are two patterns:

      - Alice swims

      - Bob swims.

      Without parts of speech computer may extract only one "Alice and Bob swim" pattern, which is an error.

      2. Helps to create tuples that give computer the ability to understand texts. (A tuple is a sequence (or ordered list) of patterns.) That is, dictionary definitions multiply texts' sizes and number of their patterns, and the computer can find the right patterns as an answer to questions asked.

      3. You can see how Google or Yandex translate without dictionary: they practically do not work or translate very poorely.

      Machine Learning is impossible without dictionary!

      1. I.Geller Bronze badge

        Re: Machine Learning is impossible without dictionary!

        To solve new problems - and the purpose of Machine Learning is automatic, without human intervention, finding and adding new solutions - a system and method of finding these new solutions is required.

        In order to find a new solution it is necessary that, at least, it is somehow described. For example by a text (or an image) because a human can comprehend only texts and images.

        Thus, sooner or later, everything comes down to the finding of texts and images. However computer recognition of images is far from perfect and textual annotations can help. Therefore, sooner or later, everything comes down to the finding of texts.

        I presented my patented system and method of structuring and finding texts, which I called AI because NIST TREC thinks that such a system (if it works) is AI.

  2. Starace Silver badge

    Narrow definition of AI

    You'd almost think that people keen on selling or renting out big piles of kit had an interest in promoting a vision of 'AI' that involved brute force via big piles of kit.

    Shame really, some of the most interesting stuff including things that people would considered closer to truly 'intelligent' systems doesn't rely on big piles of hardware. It's not that a crude version couldn't demand an impractical pile of kit but that's where clever design comes in and gets you stuff that runs happily on a Pi. Check out what a genetic fuzzy tree can manage for example.

  3. Gaius

    I read that as ML Perl for a second *shudder*

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