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You probably don't need AI/ML. You can make do with well written SQL scripts

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Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#271

Post by CodeWriter23 »

CodeWriter23 wrote: post #268
gnicholas wrote: post #249

Earlier quoted context omitted.

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.

PS While your wife is pretty awesome thinking on her feet, I don’t see the case for ML providing meaningful optimization for Beeline. Maybe you dodged a bullet getting a pass from a VC with unrealistic expectations.

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#272

Post by PeterisP »

nkozyra wrote: post #192
halflings wrote: post #116

Earlier 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.

> 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.

Can you give one example where Naive Bayes has been used with great accuracy for so long?

Naive Bayes is a common baseline solution because it's very simple to implement and very fast to run; however, pretty much any other ML method gives better accuracy than Naive Bayes. Sometimes the accuracy improvement is small and doesn't justify the highly increased computing cost, though, so Naive Bayes becomes the appropriate method to use, but it's not because of its accuracy.

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#273

Post by matchagaucho »

gnicholas wrote: post #40

My 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…

Often times the output of ML/AI will result in writing code for an expert system.

The system doesn't actually use ML or AI in realtime. ML just validates the correct conditionals and decisions are used in the software.

Most ML models can be distilled down using the Pareto principle anyway, and the 80/20 rule written into the code.

A politically correct and high-integrity story to tell investors might be "We use machine learning regression analysis to validate our expert systems are operating on optimal statistical models."

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#274

Post by PeterisP »

halflings wrote: post #116
guy98238710 wrote: post #104

Earlier 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 is machine learning. A very simple machine learning method, but machine learning.

Also, the argument that it's pure statistics doesn't really fly - support vector machines, random forests or deep learning multilayer perceptrons are just as much statistics as probabilistic graphical models.

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#275

Post by ravenstine »

halflings wrote: post #116
guy98238710 wrote: post #104

Earlier 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.

Aren't Bayesian models essentially statistics + linear algebra? It sounds like you're characterizing ML as something beyond Bayes, which isn't really the case. I don't know what you mean by "toy" problems... that might be kind of true for Naive Bayes, but a Bayesian statistical model can be built in a non-naive fashion and provide useful multi-dimensional probabilities. It's really a kind of underrated technique, though I suppose it's because you can't simply `import Bayes from 'bayes'` to build a non-naive model.

Bayesian models are also advantageous in that they are much easier to describe to stakeholders. Neural networks, on the other hand, are highly difficult(if not impossible in many cases) to describe how they function.

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#276

Post by gnicholas »

zhte415 wrote: post #253
gnicholas wrote: post #40

My 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…

I'm curious to know if you tried it in the more subtle color schemes. OTOH, if you're a speed reader who uses other techniques, this might not fit your reading patterns.

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#277

Post by gnicholas »

CodeWriter23 wrote: post #268
gnicholas wrote: post #249

Earlier quoted context omitted.

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.

We have a JS widget that can be dropped into websites. I'd be interested to see if we can increase your conversion rate or other key metrics.

CNET did a study [1] that showed BeeLine readers were much more likely to finish an article, but it would be great to take things to the next level and see how that translates into purchases or other conversions.

Shoot me an email! contact@[domain]

1: the CNET study is discussed at the bottom of this article http://www.theatlantic.com/technology/archive/2016/05/a-bett...

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#278

Post by kexx »

Most people forgets IT is the same as any other industry with marketing plots, promotions pretending to be articles, etc. Before AI/ML and big data, we had cloud (which is basically a server), web2.0 (it does not even make any kind of sense technologically), ajax (how was that a new thing in any way?) or really long time ago NETWORK COMPUTER (this one kinda hilarious, oracle tried to sell dumb terminals as future - https://en.wikipedia.org/wiki/Network_Computer, and nowadays Google tried the same thing with chromebook). I feel it's the same thing as in every 5 years, healthy food is different. Do you remember those days when fat was deadly poison?

Re: You probably don't need AI/ML. You can make do with well written SQL scripts

#280

Post by piyush_soni »

thrownaway954 wrote: post #225
piyush_soni wrote: post #175

Now write an SQL Query to find all photographs that have me and my wife sitting in a boat in them.

no problem: select * from photos p inner join tags t where t.tag in('you','your wife','boat') point being is "crap in, crap out". if you properly tag/label your data, you can accomplish anything with sql or machine learning.

Your SQL solution is that each user (or someone sitting in Google) manually tags each and every photo they take for all the tags in the world they can think of (and can't, but will think of in future). Seriously, the general case of this problem just cannot be solved using SQL in a feasible and automatic manner. What Google/Apple (or Microsoft in parts) have achieved here is not an easy thing to achieve by any SQL Query, because that works on nicely populated tables, and without ML those tables are probably not there.
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