Although the author has fair results with his given case, the author is mistaken the use of AI/ML in such scenario. In the example, they make the decision of "We should send emails to people who did 'case a'.". This is a pure 'instinct' by the decision maker. But in AI/ML case, this would be learnt from the feedback of the click rates etc. Naturally, decision maker becomes the AI, which actually can find interesting scenarios and exploit these behaviours to increase the desired outcomes.
You probably don't need AI/ML. You can make do with well written SQL scripts
191–200 of 331 posts
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#192Earlier quoted context omitted.
You don't need AI to find values for free parameters. Unless you consider all of statistics to be a subfield of AI.
Why use the term "AI" when the GP specifically used the less ambiguous Machine Learning? Machine Learning = statistics + linear algebra + computer science, mostly. Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems. Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.
> Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems.
I think they just feel "toy" because they've been used with great accuracy for so long.
Complex problems are rarely solved by a single approach. The harder the problem the more likely a suite will be used, and often naive bayes will be part of that in some capacity.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#193Companies have a large problem of having their data tucked away or inaccessible to the stakeholders. When people talk about AI / ML what they actually need is their data cleaned to the point where they can communicate to their stakeholders. Also, all of the companies who sell AI / ML as consultants are really good already at cleaning data. When companies actually hire data scientists what they typically do is clean d…
Where you can learn to become a data scientist? Linear regression and similar things? At what field you need to study? B.S. on Computer Science?
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#194Earlier quoted context omitted.
You don't need AI to find values for free parameters. Unless you consider all of statistics to be a subfield of AI.
Why use the term "AI" when the GP specifically used the less ambiguous Machine Learning? Machine Learning = statistics + linear algebra + computer science, mostly. Naive Bayes and Graphical Models are pure statistics, but they are mostly used for toy problems. Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.
> Machine Learning scales these approaches to high dimensionality problems, and tasks where data is abundant.
Machine learning is the use of computers to make decisions (or classifications) based on data without human intervention.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#195Earlier quoted context omitted.
I think we do have a good-enough understanding of "understanding" to characterize what is required. However this is to be found in the textbooks of neuroscience, not in the machine manufacture manuals of a silicon lithography plant. There isn't as much mystery as there seems. When a neuroscientist asks "how does a animal perceive the world?" the answer is reasonably methodologically obvious. When a computer scientist…
There is a huge difference between perception and understanding. http://news.itu.int/reality-check-not-nearly-close-strong-ai... > Marcus also observes “a bias in the field which is to assume that everything is learnt.” Marcus makes the argument that human beings do not learn everything by ‘trial-and-error’ – that part of our knowledge is innate, learnt over evolution – leading him to suggest that “we need more innat…
Well, not as much as you think.
Perception is structured by concepts which are learnt. Perception is the prototypical mechanism of understanding.
Insofar as we talking about "abstractions" we are just using language to model the conceptual structure of perception; and then higher order concepts to model the structure of these "perceptual" concepts.
We tend to think of understanding as being equivalent to the linguistic act of accounting for a higher-order concept, when this is really only something humans can do -- and then only with tremendous difficulty needing quite significant methodological assistance from others, etc.
This is the mistake Socrates makes when he asks for "definitions", as-if the dog doesn't understand the geography of where he lives only because he has no linguistic model of the concepts he's using. Or as-if the general has no knowledge of justice because he has no precise linguistic model of it.
When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct. The concept "pornography" is part of seeing pornography, it is knowledge which structures your perception. The lack of perfect linguistic model for that is neither here nor there, and a bit of a blind alley for AI (and socrates).
Animals, including humans, understand a great deal more than any one person can model linguistically. It is this which structures our perception and allows even trivial engagements with our environment.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#196You don't need no AI/ML, no Blockchain, SQL works just fine... I see where is it going today on HN...
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#197The problem with SQL is that eventually you will end up with thousands of SQL scripts. Have you ever tried to debug a 100k SQL? It’s a nightmare. Some of the scripts used to be simple, but got too complicated due to new requirements like this article doesn’t mention how he would deal with multiple time zones, currencies, different type of customers, multiple promotions for repeat customers and etc.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#198Earlier quoted context omitted.
But, that might be in the realm of SQL too. Find out what items were frequently bought with the item that this customer bought, and send them as recommendations.. Rule-based does not always mean that a user is sitting down writing that tennis balls and tennis shoes are related items. Don't you think?
Such a simple system would recommend many items that are frequently bought by everyone (like bread, toilet paper, batteries). You would have to weight the items in some fancy way to get useful recommendations... And I have just described the introducing slides of a applied machine learning university lecture.
This article and thread gives the impression that it’s dominated by people who don’t even have a rudimentary understanding of ML. If they think SQL is a replacement for ML, I’m really not sure what they’re doing in this field. ML is for making sense of data in a large number of ways beyond “hey let’s query a database for some trivial information”.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#199Earlier quoted context omitted.
There is a huge difference between perception and understanding. http://news.itu.int/reality-check-not-nearly-close-strong-ai... > Marcus also observes “a bias in the field which is to assume that everything is learnt.” Marcus makes the argument that human beings do not learn everything by ‘trial-and-error’ – that part of our knowledge is innate, learnt over evolution – leading him to suggest that “we need more innat…
Well, not as much as you think. Perception is structured by concepts which are learnt. Perception is the prototypical mechanism of understanding. Insofar as we talking about "abstractions" we are just using language to model the conceptual structure of perception; and then higher order concepts to model the structure of these "perceptual" concepts. We tend to think of understanding as being equivalent to the linguist…
> When Potter Steward said in the pornography case, " I know it when I see it " he was literally correct.
Here you refer to categorization, not understanding. From the article I refer to:
> “Deep learning is good at certain aspects of perception, particularly categorization, but perception is more than categorization and cognition or intelligence is more than just perception. There are many things that go into intelligence … And what we have made real progress on is perception, but the rest of it, we still haven’t made that much progress collectively in the field.”
Understanding, real understanding, the "aha moment", is the very basis of new discoveries and real evolution. What we see now are systems quite good at categorizing, pattern recognition in a given set once prepared correctly and so on. There is no relation whatsoever with real intelligence, as in being able to make a new discovery based on analytical and synthetic skills.
There are quite a few voices in the community saying the opposite and maintaining that mimicking intelligent action is practically the same as the intelligent action itself, but there are quite obvious limits to what can be achieved in this way.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#200I don't get this article at all. The author does not really back up their argument with any examples of ML. What in the world does common marketing practice & seemingly basic SQL queries have to do with AI/ML? What am I missing here? To me, this just sounds like a "Get off my lawn" type of rant. "Why do we need the newfangled AI when we still have good ole' SQL & bash!(waving fist in the air)" On the other hand comme…
Data cleaning is reeeally messy. It requires a lot of training data to get a system that does even a bad job of it automatically. So you still end up needing a lot of clean data, and getting the system to that point probably isn’t worth it if you’re not one of the biggest tech companies (you can spend time creating a system to clean the data or just clean the data). But your other points are spot on. This article is garbage.