281–290 of 331 posts
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Well, yes, it's not perfect yet, but then I'm pretty sure SQL Queries won't fare any better here ;). Google photos does quite a decent job for me in many cases.
Well, Google receives free Mechanical Turk style image identification through their CAPTCHA (or whatever it's called nowadays).
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Well, yes, it's not perfect yet, but then I'm pretty sure SQL Queries won't fare any better here ;). Google photos does quite a decent job for me in many cases.
Well, Google receives free Mechanical Turk style image identification through their CAPTCHA (or whatever it's called nowadays).
"Set this as a CRON that fires at 2AM everyday, period with less activity and traffic. People wake up to emails reminding them about their abandoned carts" Hah I wondered why I got so many notifications in the middle of the night. Now I know that it's from people who think they're helping - not realising that it actually sours my opinion on their company/product.
Who on earth are these people describing ? I've never heard of anyone hiring expensive Data Scientists, spinning up Spark/H2O clusters, building a data lake, doing a database offload to S3/HDFS all for a "select from orders table where basket size is the biggest" query. AI/ML doesn't even work like this. It's simply not designed for giving 100% accurate answers to highly structured queries.
Anecdotally[1] speaking, you're correct but you're missing the message. You're correct that no one should hire expensive data scientists for this. But what happens is that there's no marketing against these sorts of pragmatic best practices, so it never comes on the radar of business executives making decisions. Instead they're inundated with ML/AI/Data Science pitches and mentions everywhere. And so when they go to…
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I went to your website, and I can't figure out what your product is or does. I had to click in to individuals before I figured out what it does. Why not put "BeeLine Reader makes reading on-screen easier, faster, and more enjoyable. We use a simple cognitive trick — an eye-guiding color gradient — to pull your eyes from one line to the next." on your front page? Maybe you guys have already done some kind of testing a…
Appreciate the feedback. We recently redesigned the website to offer silos, and one of the goals was to make the landing page not offer too many specifics that would make some audiences bounce. But perhaps your suggestion is just what we need to do. Thanks for taking the time to share! One lesson learned: I should have linked to the /individual page instead of the generic landing page in my comment. Updating link now…
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This is a quaint sentiment, but marketing, sales, PR is all about misleading and creating unnecessary want. This is a major tenant of running a business and living under capitalism. There is also a moral argument for doing what you need to do in order to survive the difficulties of the market in order to survive it and change it into something more akin to your honest attitude about it.
I think you'll find that a lot of stem people tend to lean Kantian in their ethics. This implies that misleading people for any reason -- even "for their own good" -- is frowned upon.
I thoroughly enjoyed this post, so maybe I'm biased. I think AI is extremely overhyped and under performant. In fact, I think a major strength of AI is founded in the technical ignorance of certain project managers or decision makers. The type of person who doesn't appreciate the simplistic elegance of sql+bash/cron for simple tasks is the person who will bite a pitch for AI customer retention strategy. Customers are…
When I looked at it, every feature shown, could be more reliably delivered and have a better customer experience through deterministic behaviour.
So I agree, I think AI has made certain technologies way better, but I see it as a tool, and like any tool, it sometimes applies to the situation and sometimes doesn't.
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Appreciate the feedback. We recently redesigned the website to offer silos, and one of the goals was to make the landing page not offer too many specifics that would make some audiences bounce. But perhaps your suggestion is just what we need to do. Thanks for taking the time to share! One lesson learned: I should have linked to the /individual page instead of the generic landing page in my comment. Updating link now…
I liked your reading challenge idea! Very interesting, even though apparently the results indicated that I didn't read any faster.
I should also note that reading ease is an important (but difficult to measure) piece of this as well. So if it felt better for you, then it might be worth trying out the browser plugin and then trying the reading challenge again. There could be a little bit of a learning curve, as your eyes adjust to how quickly you can jump from line to line.
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Funny, I would consider many ML usages to be toy problems. Coming up with funny nonsensical Shakespeare plays or generating random Pantone colors or Pokémon names isn’t something you hear from Bayesian systems. That’s the domain of ML. Recommending related products is again simple statistical analysis. And I can generate a prediction based on past performance real easy with some SQL and a couple of “if” statements. J…
I agree with all of this, except are you really implementing bayesian models in SQL? I'm not sure that is what the author is advocating in the first place. There are tools for bayesian everything, I much prefer using those over hacking together some illegible SQL script.
As someone who sells both of these services, I can only add that it depends, and if you have a good dataset, it's trivial to write either one. But once you start having to account for noise or seasonality or autoregression or dynamic weights or non linear kernel spaces, pure SQL really starts to fall down on the job.
Curious. OP gave a few examples for Ecommerce where SQL will do fine. Can you give a few where ML will do something otherwise impossible or harder with SQL?
You can't recreate the Netflix Prize in SQL, you can't really do NLP, you can't do pretty much any clustering or unsupervised learning. Again, "can't" meaning "can't do it easily or in a sustainable manner (i.e. shouldn't.)"
And if we add in AI, I don't need to explain that SQL has no abilities to generate new content - it can't write a new song or headline, understand context and provide feedback, or drive a car.
Now what OP is arguing is that you can get a lot of lift with SQL and basic exploratory data analysis and I totally agree. But ML can take you further out into the speculative realm and can work on a much less explicit and prescriptive model than SQL.
Basically in SQL if you don't write it (whatever it is, a pattern, an anomaly, a correlation, a context) in your query you will never learn a single thing about it.