Earlier quoted context omitted.
I blame the NoSQl nonsense from a few years ago. "Relational databases don't scale" apparently.
https://www.youtube.com/watch?v=b2F-DItXtZs
I had conversations that went like that.
You probably don't need AI/ML. You can make do with well written SQL scripts
261–270 of 331 posts
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#262Earlier quoted context omitted.
That's not really ML.
As a classic example, how about music or movie recommendations? You have a giant set of attributes about songs and a binary classification (thumbs up/thumbs down) from millions of listeners. You can use this this to train a random forest or neural net (or other algorithms) to make song recommendations to someone based on that person's pattern of thumbs up/down. I'd guess most recommendation systems do something along…
I didn't say that it was SQL, I said that its not ML. Just aggregation / scoring as far as I can tell.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#263Earlier quoted context omitted.
That's not really ML.
If I were ever to make ML bingo, "That's not really ML" would definitely be on it.
I see, so just how everything became "big data" a few years ago, now everything is ML.
"Managing big data with MySQL" - the syllabus mentions nothing of clustering or sharing. Ten years ago that was just "using a database". I am getting to old for the faddish nature of this industry.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#264> we will send a nice "we miss you, come back and here's X Naira voucher" email. The conversation rate for this one was always greater than 50%.
Wow. I could never imagine so many people actually read marketing e-mails
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#265AI, ML, Data Science, Algorithms - these are just the fancy buzzwords we have tended to associate with how we analyze data. We have been doing a lot of these stuff (especially if you are in the software engineering world for business and consumer products).
Iterating to the author's given examples, we have probably been doing:
What would be the net effect in terms of sales and profit if we reduce our price by 5 cents, but increased our sales 25x? Those are already models that encompasses predictive modeling, where we provide inputs and determine from a given set of output based on general assumptions backed by data.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#266My startup was approached by a corporate VC that wanted to make a strategic investment. Based on the attendee list from our meeting, which included very high up folks from the company, I felt good going in. They expressed interest in our technology that makes reading on screen easier [1], but they were surprised to learn that we didn't use machine learning to accomplish this. I indicated that it was actually quite ef…
General comment on colour highlighting - I read the updated /individual page and found it very... difficult. The colours led me to speed up and slow down at an uncomfortable rate, to the extent I had to re-read it three times. I typically receive 200-1000 emails per day which all need to be read (yay!) plus at least 30k words per day out of email (business stuff but excluding newspapers, books, etc). I speed-read abo…
Wow! Would you mind sharing some practical advice on how to actually train to read this fast?
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#267Earlier quoted context omitted.
As a classic example, how about music or movie recommendations? You have a giant set of attributes about songs and a binary classification (thumbs up/thumbs down) from millions of listeners. You can use this this to train a random forest or neural net (or other algorithms) to make song recommendations to someone based on that person's pattern of thumbs up/down. I'd guess most recommendation systems do something along…
I didn't say that it was SQL, I said that its not ML. Just aggregation / scoring as far as I can tell.
It was clear that you were only saying that it wasn't ML.
I looked up a bit on elasticsearch's text classification, and it's interesting. There's a overview of "traditional" ML (similar to the song review classification I mentioned) and how elastic search differs.
https://www.elastic.co/blog/text-classification-made-easy-wi...
"The MLT query is a very important query for text mining. How does it work? It can process arbitrary text, extract the top n keywords relative to the actual "model" and run a boolean match query with those keywords. This query is often used to gather similar documents."
Yeah, looks like aggregation and scoring. It'd be interesting to see if/where this outperformed various ML algorithms (accuracy as well as performance).
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#268Earlier quoted context omitted.
I bounced as soon as I couldn’t find pricing information.
Good point — this is something we need to address. Our most popular products (browser plugin and iOS app) both have free trials so that people can see how much they like to read with BeeLine. The iOS app actually has very significant free functionality, so if you just want to use it as a reading list (like Pocket/Instapaper) you can do so for free via the Share Extension, which populates an offline reading list. Thin…
Thanks for the follow up. What if I have a website and I want all my pages rendered in Beeline? I immediately noticed being able to read faster and more comfortably. I view Beeline as a conversion optimization tool.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#269Earlier quoted context omitted.
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.
AI and ML have different goals, which sometimes overlap. The goal of ML is to discover relationships in data that can only be (easily) observed by a machine. That usually serves as decision support data for a human decision maker or another more conventional algorithm. This is usually based on an entire data set at once. Accuracy and precision are valued over quickness and robustness. AI is about substituting human d…
ML is one of subdomains in the AI field, so instead of "sometimes overlap" it's technically correct to state that ML is a strict subset of AI.
What you use as description for AI tempts me to use the "use it as a buzzword" angle back at you here - it is a stereotypical description of some use cases of AI approaches; but there are others - [machine] learning, knowledge representation, planning and scheduling, reasoning (both formal logic reasoning and also reasoning under uncertainty e.g. Bayesian approaches), intelligent agent representation, etc, are all parts of the AI field.
Re: You probably don't need AI/ML. You can make do with well written SQL scripts
#270Earlier quoted context omitted.
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.
AI and ML have different goals, which sometimes overlap. The goal of ML is to discover relationships in data that can only be (easily) observed by a machine. That usually serves as decision support data for a human decision maker or another more conventional algorithm. This is usually based on an entire data set at once. Accuracy and precision are valued over quickness and robustness. AI is about substituting human d…
This last line seems unnecessarily rude. GP seems to understand well the two ML methods mentioned.