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

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&#x27;t use machine learning to accomplish this.<p>I indicated that it was actually quite effective without ML, and that it was easier to explain to users this way. They kept prodding around on the ML stuff, and how we might be able to use ML to accomplish roughly the same thing.<p>A week later they said that they were no longer interested because, although they liked what our tech was able to accomplish, it didn&#x27;t fit with their investment thesis — which was all about ML.<p>My wife asked me why I didn&#x27;t just make some stuff up and say we could do v2 using ML. Perhaps she was right.<p>1: <a href="http:&#x2F;&#x2F;www.beelinereader.com&#x2F;individual" rel="nofollow">http:&#x2F;&#x2F;www.beelinereader.com&#x2F;individual</a><p>update: in response to feedback below, I edited the link to point to a page with relevant content instead of our generic landing page. Lesson learned!

What the article describes is called an &quot;expert system&quot; and is what AI in the enterprise used to look like.<p>Basically, you try to capture the instinct of a great salesmen by formalizing it into computer logic.<p>Often that&#x27;s done with rules like in the article.<p>It works good, but has its limits. The finer reasoning of human judgement are often not expressable, people don&#x27;t know why they made that decision. Making it hard to capture. And human also have their limits. Too many variables, too much noise, too much data and they won&#x27;t make the best prediction&#x2F;decision.<p>That&#x27;s when ML shines. Instead of trying to encode an expert&#x27;s intuition, instead you let the machine develop its own intuition, itself becoming an expert through training.<p>The downside is it now similarly becomes challenging to formalize the machine&#x27;s intuition. Why it made a given choice is no longer easily apparent.<p>I do think expert systems still have value. Especially when you lack the dataset to train a machine expert.

Companies have a large problem of having their data tucked away or inaccessible to the stakeholders. When people talk about AI &#x2F; 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 &#x2F; ML as consultants are really good already at cleaning data.<p>When companies actually hire data scientists what they typically do is clean data for a few months to a year . Then they interpret the data by probably being able to perform linear regression. At that point the data is in a state where it can be easily understood by those stakeholders and then they have created value. Whether or not the linear regression or whatever model has been learned may mean something. But, at the end of the day you need to tell stakeholders how they can create value and guess what SQL and Bash will do 90% of the job.

I thoroughly enjoyed this post, so maybe I&#x27;m biased.<p>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&#x27;t appreciate the simplistic elegance of sql+bash&#x2F;cron for simple tasks is the person who will bite a pitch for AI customer retention strategy. Customers are people. Business is people. You don&#x27;t need a rack of gpus to understand why sending someone an email who has a saved cart is a good idea. It&#x27;s common sense. It doesn&#x27;t matter if we can force machines through trillions of operations to vaguely capture a customer pattern of a guy at a console can write it by hand in five minutes.<p>(not always, I know, I work in finance so a lot of my business IS machines and not people, but you catch my drift)<p>I&#x27;m pro-AI research, and anti-AI hype train. They&#x27;re computers. They&#x27;re objects. They&#x27;re not us yet. Consider the magnitude of the AI research market, which is tens of billions, and compare that to what they are actually capable of doing relative to human performance.<p>&#x2F;rant<p>Maybe HN skews my perception on what the public tech enthusiast&#x27;s perception on AI is...

I almost took a &quot;big data&quot; data scientist job about a year ago with a local company.<p>After talking to a number of their engineers, it became quite clear to me that instead of a data scientist, they just badly needed a DBA &#x2F; someone with ownership and a complete vision of the data structure.<p>They had no foreign keys, poorly &#x27;designed&#x27; indexes, and tons of redundant tables with no rhyme or reason to them.<p>They&#x27;d organically grown their database with hardly any review. They did not have big data, they just had a big mess. And wanted someone else to clean it up.

This could&#x27;ve been a valid criticism of people that use ML where it&#x27;s not appropriate, but it ended up being a bit of an irrational rant, and a dishonest one too:<p>&gt; I mean, why send a letter with breast pumps to a man that just bought a pair of sneakers? It doesn&#x27;t even make sense. Typical open rate for most marketing emails is anywhere between 7 - 10%. But when we do our work well, we saw close to 25 - 30%.<p>How do you know what items are compatible to each other? Why only recommend sneakers to somebody with sneakers, instead of also recommending sport clothing?<p>Oh, I guess you could build some type of topology of all your shopping items. But what about recommending soccer balls to people that bought soccer shoes? You could also add that to your database, but now you also need a heuristic to score item similarity: `category_matches * 10 + subcategory_matches * 5 + color_matches * 2 + ...`<p>This is the whole point of ML. People have been building rule-based systems built on &quot;domain expertise&quot; for ages, only to find that they are limited and cannot compete with simple algorithms fed with enough data.

Well, we are in the peak of a wave of hype about AI&#x2F;ML, maybe even just past that peak. Many fundamental technological advancements in the field of AI&#x2F;ML have sort of coalesced together at the current time to form a strong feature set that can be more broadly applied by a wider audience, not just those hardcore computer scientists who invented the technology.<p>I&#x27;ve been in the thick of this previously, facing a complex rules-based engine that did most of its incredible feats in the fraud detection domain using a number of really complicated SQL queries. At the same time, I&#x27;ve used the results of such queries combined together with machine learning and predictive analytics, giving you the best of both worlds. Both have strengths and weaknesses.<p>These are tools in the toolbox, and I think the adage &quot;try to use the best tool for the job&quot; still applies. Sometimes, you use the tool you have and you know, and all the more power to you if you can get the job done using that tool. If you are a master of that tool (i.e. SQL in this case), you can often push its capabilities very, very far.<p>That said, I think the best thing to do right now is try to separate the signal from the noise regarding AI&#x2F;ML and find what really works and what does not. Then find how these new tools can either complement or replace previous approaches. I think they work together quite nicely - and we see that sometimes, for example, with AI&#x2F;ML tools integrated close to SQL engines.<p>AI&#x2F;ML has a place, and so does SQL. I will say, though, that I for one don&#x27;t want to be caught on the side of the discussion where I don&#x27;t learn enough about what is possible with AI&#x2F;ML, and then get left behind. I think many of my colleagues and professionals in the field and here on YC feel similarly.<p>Actually, I think even non-technical people feel the same way - the fear of being replaced by AI&#x2F;ML is higher than ever.<p>So, keep applying SQL and get that low-hanging fruit. But make sure to learn the new stuff too, and add it to your toolbox.

But if you say &quot;I&#x27;m going to use a bunch of shell scripts to parse logs&quot; you are boring. If you say &quot;I am going to use groundbreaking ML&#x2F;AI technologies to transform big data into customer retention solutions&quot;, you are a visionary.

The biggest threat of AI is not its ability of taking jobs or exterminating mankind, but the amount of distraction it creates.<p>When politicians say they improved the economy like 30%, nobody buys into that. It&#x27;s an overly exaggerated misleading political talk. But when some tech gurus talk about how AI improved their profit 30% or something, everyone seems to hop on. It&#x27;s an effective marketing, for sure, but this is a worrisome trend. The root cause of this is I think the lack of proper understanding of fundamentals (and intellectual sloppiness). AI will continue to plague us on this front, and I&#x27;m still not sure if the net gain is going to beat all the distractions it created.

As someone who sells both of these services, I can only add that it depends, and if you have a good dataset, it&#x27;s trivial to write either one.<p>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.

While I see the author’s point, I fail to understand what any if this has to do with SQL. The problem ML solves isn’t querying databases, it’s making decisions. If a human came up with the idea “let’s lookup people X and send them email Y” and it works, great. But a human made that decision, and SQL is just a tool for making it happen. If you want to take the human out of the loop, SQL won’t save you.

A huge part of the &quot;data revolutions&quot; that we&#x27;ve seen in the last few decades really has nothing to do with data and everything to do with process.<p>Data Warehouses changed the way people and companies do data. They expose all kinds of things that were never available before. It was magic!<p>No. It wasn&#x27;t. Not that Data Warehouses are bad or ineffective. But it&#x27;s a lot like the problem you face when you observing something changes it. The work you have to go through to build a real data warehouse is that you have to get disparate parts of an organization to codify process. Data warehouses don&#x27;t model data. They model processes.<p>The mere fact of forcing the company to pin the process is often more beneficial than the warehouse itself.<p>The same thing goes for ML and AI. The only way to extract features is for them to actually exist. And that means the data needs to exist in a certain form, and there&#x27;s a human process that leads to that. Absent that, it&#x27;s pretty useless.<p>I cut my teeth on SQL, and it&#x27;s a big part of my professional career. I think it&#x27;s great. It&#x27;s one of my favorite languages, and it does a lot that maybe a lot of people don&#x27;t know about.<p>But this title and the content are really pretty garbage. Anyone who thinks that good SQL can do what good AI&#x2F;ML can do is really misunderstanding both.

Hi, I&#x27;m the guy that wrote the tweets. Let me know if you have any other questions. I&#x27;m happy to answer any question.

eh. I built a lead gen system at a fortune 1000. The heuristic SQL version brought in 10M a year. The random forest version brings in 100M a year. It saw things we didn&#x27;t

&quot;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&quot;<p>Hah I wondered why I got so many notifications in the middle of the night. Now I know that it&#x27;s from people who think they&#x27;re helping - not realising that it actually sours my opinion on their company&#x2F;product.

The writer seems to describe very basic data mining in some cases, which in itself is a form of AI&#x2F;ML, but then other examples have no relevance to needing to use AI&#x2F;ML at all.<p>If their data is already clean enough for SQL queries to work reliably and they are familiar with the SQL syntax, why not look into things such as DMX in MSSQL to make predictions on what these customers are likely to want to buy. This solves the whole marketing breast pumps to a man who bought sneakers scenario, while it also providing more personalized recommendations.<p>If your current technique is to send an email about sneakers to recent sneaker purchasers, do you really thing they are in the market for another pair?<p>Sure, it might not make sense to implement a deep learning neural network just to send something like a semi-personal marketing email but their are so many varying levels of AI&#x2F;ML that seem to get ignored in favor of the flavor of the month Tensorflow&#x2F;IBM Watson&#x2F;Whatever else. Quite frankly, the whole thing just comes across as a very closed minded rant from someone who isn&#x27;t interested in exploring what new technologies are capable of.

Good points but really a false dichotomy. The purpose of AI and machine learning is to find patterns in data that aren&#x27;t simple heuristics like this.

The 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.

I don&#x27;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 &amp; seemingly basic SQL queries have to do with AI&#x2F;ML? What am I missing here? To me, this just sounds like a &quot;Get off my lawn&quot; type of rant. &quot;Why do we need the newfangled AI when we still have good ole&#x27; SQL &amp; bash!(waving fist in the air)&quot;<p>On the other hand comments are talking about hiring data scientists for months if not a year or more (yikes!) To clean data &amp; <i>wait for it</i> ... perform linear regression. To me this sounds like a great application of machine learning. Couldn&#x27;t someone train some models to clean the data, then do one of the things ML does best, linear regression, in a fraction of the time the human data scientists could do it in?

Who on earth are these people describing ?<p>I&#x27;ve never heard of anyone hiring expensive Data Scientists, spinning up Spark&#x2F;H2O clusters, building a data lake, doing a database offload to S3&#x2F;HDFS all for a &quot;select from orders table where basket size is the biggest&quot; query.<p>AI&#x2F;ML doesn&#x27;t even work like this. It&#x27;s simply not designed for giving 100% accurate answers to highly structured queries.

Although the author has fair results with his given case, the author is mistaken the use of AI&#x2F;ML in such scenario. In the example, they make the decision of &quot;We should send emails to people who did &#x27;case a&#x27;.&quot;. This is a pure &#x27;instinct&#x27; by the decision maker. But in AI&#x2F;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.

The article doesn&#x27;t convince me.<p>It can be summarized as &quot;don&#x27;t overengineer&quot; but quite frankly these days ML&#x2F;DL is so easy to apply from a technical point of view (taking care of the data or fully grasping the things you apply is another issue) that I don&#x27;t see why one wouldn&#x27;t at least try to use it. I don&#x27;t see why a ML-algorithm couldn&#x27;t grab the first name for example. I mean if your argument is &quot;just use SQL&quot; my counterargument is &quot;I agree but I can just try ML as SQL on steroids&quot;. If you already have well curated data that you run the SQL on you might as well play around with it in an ML setting. &quot;Customer with largest basket&quot; might work fine but why not try to prod the data to check for other interesting things. Same for the POD example. Why not at least try to see if a combination of variables might yield more interesting results than the simple stuff that might work. Occams razor should not cut out all curiosity :D<p>I like the overall idea of &quot;try the simple stuff first&quot; but quite frankly these days you can run very good ML with pretty much all it takes to do SQL queries (assuming you train your models on a separate machine).

This is the opposite of AI use cases in marketing. You are declaring a specific timeframe for your message delivery. That is not how a marketer should deploy AI. I haven’t been in any pitch meetings since AI assclownery took hold so I can’t comment on how the term is being abused. What I can say is that a model that used AI would take every parameter it could about each customer and determine the optimal time to sent an email to get a conversion. The only inputs the marketer should provide is raw historical data with clear parameters like order value, order items, estimated revenue, buyer classification, a stream of subsequent etc and date, and the model should solve for the correct timestamp to send the follow up message. I don’t think the AI is writing the message yet, and I don’t think you need a neural net to do a decent job at solving for the right datetime to send. I do think the approach I described would get superior conversion rates than a rule, cost more to make than that rule, and definitely demand a decently huge dataset to add much value.

I like the notion that AI is impossible to wrangle into a neat box, because it has always described the cutting edge of technology. Image manipulation, audio synthesis, and other techniques we&#x27;re once considered artificial intelligence. But now that they are far better defined and understood, they essentially have fallen out of the nebulous sphere of sci-fi tech.

In a layman term the difference between sql and ml is, ml predict things and sql just tell you things.<p>Things has changed and ml now a days can do far better things. If the competitor is using ml and making gain, then one should also catch up as soon as possible.<p>SQL analytics was past, predictive analytics is the future. ML can do more than predictive analytics for you :)

Most people forgets IT is the same as any other industry with marketing plots, promotions pretending to be articles, etc. Before AI&#x2F;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 - <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Network_Computer" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Network_Computer</a>, and nowadays Google tried the same thing with chromebook). I feel it&#x27;s the same thing as in every 5 years, healthy food is different. Do you remember those days when fat was deadly poison?

You can even make do with plain old client side JavaScript object arrays. After looking at your site, I can see your company has very good presentation skills. It very effectively appears to sell a simple algorithm that nearly anybody on earth with a little bit of experience, could do themselves. What the investor wants, is can you sell AI&#x2F;ML as successfully as some text coloured blue, white and red. If this HN post is anything to go by, it certainly generated a lot of interest and maybe I could hire your company to polish my A href link algorithm with some AI&#x2F;ML gloss

People written SQL scripts that check for scenarios, and even potentially action &#x2F; repair them is a form of intelligence. It&#x27;s not artificial, either.<p>Thinking back to successful ERP implementations, little was more useful during go-live or an ongoing basis than a script that ran every hour&#x2F;day&#x2F;week&#x2F;month to look for a condition and report it.<p>In one case, over a 3 year period where the organization grew from 0 to 60 million per year, every data issue was logged as a ticket, investigated, where needed, a Sql script written to monitor other occurrences, and ultimately, if there was a need to action, it would be forwarded to the right destination with a link to instructions on how to resolve or investigate if a decision could not be programmatically made.<p>The power of this was users received direct and immediate feedback anytime they wanted if their work was good and compliant with the system and process.<p>How did the list of scripts to build get made? Every time the system behaved correctly or incorrectly, and needed attention, whether due to data being incomplete, mis-entered, or correct and ready for the next step, the technology was busy working for the users.<p>Scripts reduced concerns that issues were being missed. Once something had happened and it was important enough, a custom insight could be built. It helped build a data driven culture instead of hoping the computer picked the right thing.<p>Sql scripts could one day feed into or fit with AI&#x2F;ML. I don&#x27;t see that day here in the short term.

Are SQL skills disappearing from companies? Could this be a reason people are reaching for more complicated solutions because they don&#x27;t know what a good SQL database can do?

The best is when you ask someone why they want an AI&#x2F;ML masterwork, they just say it&#x27;s the future and we don&#x27;t want to be left behind.<p>It&#x27;s interesting because this article shows the overlap of what a non-tech thinks is AI and what is common fodder for any decent programmer. So many things get lost in buzzword to English translation, it&#x27;s easy to forget that most people correlate the plastic box sitting in front of them with an intelligent Magic 8 Ball.

Maybe I&#x27;m completely missing the point here, but I thought the use case for AI&#x2F;ML was to find the cause, not the effect. For example:<p>&gt;&gt; If a person tries to checkout with 3 different cards at the same time and they all bounced, something funny is happening. Block their account temporary for a while.<p>That assumes you know that 3 different cards were used and they bounced. Sure, the SQL can answer the question, but you have to know the question first.<p>I&#x27;m happy to be corrected here.

Fully agreed that in simple use cases simple solutions make sense; I&#x27;ve been arguing similarly for the NoSQL movement for years (i.e. NoSQL being great for large scale systems; but for most companies day-to-day needs SQL wins out).<p>However, it would be good to have a bit more in the article to say what AI&#x2F;ML* is in this context, and a couple of scenarios where it beats SQL; i.e. otherwise it just sounds like the rantings of an old man &quot;in my day we only had turnips; you needed a snack: turnip; you needed a pillow: turnip&quot;. By showing a few good use cases allows you to better contrast the product &#x2F; get an understanding of where the boundaries are between the technologies.<p>*NB: When I first read this I assumed the author was talking about AIML (artificial intelligence markup language) rather than AI&#x2F;ML (artificial intelligence &#x2F; machine language)... as though the slash was included, there was no use of the full terms.

ML is best suited for situations where there is no practical solution using typical statistics techniques and where marginal improvements in accuracy lead to significant boosts in revenue or some other useful metric. It turns out there aren&#x27;t that many of those problems unless you&#x27;re operating on truly enormous scales.

Back in 1999 I worked at an early web consultancy that built apps for clients on top of Oracle. We used their DB + a programming language called PL&#x2F;SQL.<p>There was a feature of Oracle called SOUNDEX which was <i>magical</i>. Here&#x27;s an example from their docs page [1]:<p><pre><code> SELECT last_name, first_name FROM hr.employees WHERE SOUNDEX(last_name)= SOUNDEX(&#x27;SMYTHE&#x27;); </code></pre> This query will return all people with a last name that sounds like &#x27;Smythe&#x27;, including &#x27;Smith&#x27; and &#x27;Smithe&#x27;.<p>[1] <a href="https:&#x2F;&#x2F;docs.oracle.com&#x2F;cd&#x2F;B19306_01&#x2F;server.102&#x2F;b14200&#x2F;functions148.htm" rel="nofollow">https:&#x2F;&#x2F;docs.oracle.com&#x2F;cd&#x2F;B19306_01&#x2F;server.102&#x2F;b14200&#x2F;funct...</a>

I think the writer is overgeneralizing his particular use case. Surely, the situation he represents doesn&#x27;t need any AI&#x2F;ML, but it is the result of a simple use case, with little variables and with an easy workflow.<p>Does the same pattern apply also in more complex scenarios?

&quot;Machine learning&quot; is an excellent tool to separate extra money from &quot;customers&quot;. Founders are separating extra money from VCs. Engineers are separating extra money from the founders. Just reading this thread keeps illustrating this.<p>Want to get a job done? Use a tool that gets a job done. Want to talk about getting a job done and be &quot;listened&quot; to - use ML to beat around the bush.<p>This is no different from all these companies talking about Big Data[tm] a few years ago, hiding people to build large processing clusters when their entire dataset would fit into memory of $700 server obtained from Ebay.<p>Neither it is different from companies mumbling about availability challenges when the entire stack gets sub 100 hits per second.

For the examples mentioned in the article no, you don&#x27;t need statistical analysis but these are simple cases (which most cases are).<p>Late orders, biggest orders etc. etc. sure, those are all SQL queries.<p>However if you want to make statistical predictions or looks for the non obvious, these simple types of queries aren&#x27;t going to do it. So it&#x27;s an apples to oranges comparison.<p>There are a lot of cases where people don&#x27;t know what they are after. And also lots of cases were orgs don&#x27;t have a grasp on the simple things, but somehow think more complex things (especially buzzword things) are magically going to solve a lack of organization and insight.

AI, 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).<p>Iterating to the author&#x27;s given examples, we have probably been doing:<p>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.

You probably do not need SQL. You can make do with well written see awk scripts

Click bait of a title... I think the more important thing this article is trying to say is: use a good heuristic to solve your problem, if it can&#x27;t do so then ML may be something to look into.

&gt; select from orders table where basket size is the biggest. We will then email a nice thank you note to this customer and attach a small coupon&#x2F;voucher.... ...Guess what? 99% of these people became repeat customers<p>Sounds like you definitely need some ML there, in the form of statistics. Was there a difference in probability of repeat customers between sending and not sending the voucher? Was there a difference between basket sizes and probability of repeat customers? Is there an interaction between the two?

Gee. Thanks for the porn in the &quot;Recommended Threads&quot; at the bottom of the page :&#x2F;<p>If your work scans your web browsing for certain words etc., don&#x27;t click the link.

Well yes, if you own your shop and are one of 1-10 people working in this shop, then you don&#x27;t need these high tech things.<p>They are of course for companies that make so much money that they can afford to spend 6-digit pays for a Marketing Manager who doesn&#x27;t know sh*t about his job who in turn is spending millions on randomizing-diagram generators so it seems like he is working hard.

I find very handy the Postgres aggregate and stats functions: <a href="https:&#x2F;&#x2F;www.postgresql.org&#x2F;docs&#x2F;current&#x2F;static&#x2F;functions-aggregate.html" rel="nofollow">https:&#x2F;&#x2F;www.postgresql.org&#x2F;docs&#x2F;current&#x2F;static&#x2F;functions-agg...</a>.<p>I have also used Sparklines in Python for quick and dirty trends

I think SQL can also benefit from some of the progress around making things that &quot;feel like&quot; ML easier. For instance, dplyr is a refreshing change to the way you write operations that manipulate data in a table&#x2F;column structure, even though it uses mostly the same verbs and language constructs as SQL.

Why not both?<p><a href="http:&#x2F;&#x2F;sqldatamine.blogspot.com&#x2F;2013&#x2F;07&#x2F;single-multiple-regression-in-sql.html?m=1" rel="nofollow">http:&#x2F;&#x2F;sqldatamine.blogspot.com&#x2F;2013&#x2F;07&#x2F;single-multiple-regr...</a>

&gt; we will send a nice &quot;we miss you, come back and here&#x27;s X Naira voucher&quot; email. The conversation rate for this one was always greater than 50%.<p>Wow. I could never imagine so many people actually read marketing e-mails

Isn&#x27;t machine learning a concept, whereas sql or anything else is more about how you implement. I have in past seen a well season sql developer implementing collaborative filtering like algorithms.

You can do some types of ML with SQL all the main sql databases are Turing complete.<p>Not sure if its going to be efficient for clustering and entity extraction at scale tho

ML is not going from 0 -&gt; 25% it&#x27;s going from 25% to 28%, say, and that 3% being much more in profit than the cost of the ML work.

I guess if you&#x27;re investing for the long term, avoid anything with machine learning, as it&#x27;s overpriced...

You don&#x27;t need no AI&#x2F;ML, no Blockchain, SQL works just fine... I see where is it going today on HN...

This is one of those HN threads where I&#x27;m going to sit back with a bag of popcorn and hit refresh...

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

so many haters on this comment thread.<p>Just read and stop hating. <a href="https:&#x2F;&#x2F;www.thestreet.com&#x2F;investing&#x2F;nasdaq-all-in-on-blockchain-technology-14551134" rel="nofollow">https:&#x2F;&#x2F;www.thestreet.com&#x2F;investing&#x2F;nasdaq-all-in-on-blockch...</a>

Doesn’t scale.

Yeah but non technical people who don&#x27;t know what they are doing but for some reason have money to spend just <i>know</i> they want you to use machine learning for everything.<p>One time at work I wrote a simple web app with a search box (just doing an sql query, nothing fancy). One of the &quot;higher ups&quot; was impressed and decided to flex their knowledge, pointing to the search box saying &quot;and this uses nlp&quot;. It was a damn sql query on a full text field.

Am I misunderstanding something here?<p>Artificial Intelligence is about statistical analysis.<p>Such as: Is this picture of a man and his dog, actually a dog? Or is it a cat? Or is it a 4 legged creature? Or is it a turtle?<p>The AI is supposed to identify that the animal in the picture, is a dog with a 99.8% probability. And since it exceeded the 98% threshold, then it becomes accepted as a dog, until otherwise disproven.<p>Basically, it is a pattern matching mechanism, on a massive statistical scale.<p>And from this, then further actions can be taken.<p>Such as, the owner of the dog, can be mailed advertising and coupons that are related to dogs.<p>And then, the AI can go even further. What specific kind of dog is it? Is it a German Shepard? Is it a beagle? Is it a poodle?<p>The AI can determine the specific type of dog, and conclude that it is a German Shepard with a 99.7% probability. This exceeds the threshold, so then the computer system might mail out an advertising to the owner, about deals related to a German Shepard.<p>For something like this, then this is where social media can really shine. When you upload your pictures to Facebook, or Gmail, or Instagram, then Facebook or Google, can use an AI to analyze your picture. As well as reading your caption on it. And they can determine the context of your picture, such as whether you have a dog in it. Are you holding the dog? Are you walking the dog? Are you smiling in the picture? If the scenarios check out, then the AI can select you as a candidate, and send advertisements related to your dog.<p>In fact, I think our brain operates the same way, by using statistical analysis.<p>When we see a dog, in a picture or in real life, our brain is actually using a statistical analysis to determine that it is a dog. Our brain follows a neural network pathway to match that picture of a dog, to a similar variation of a dog that we have in our memory. It is thus statistically true, until otherwise disproven.<p>This of course, happens in the deep recesses of our brain, so it&#x27;s currently impossible to know what really is happening there, until we have a better scientific understanding of how our neurons work in our brain.<p>On the flip side, SQL scripts has no mechanism to view the picture, to determine if the animal in it, is a dog, or a cat, or even if it is a human.

Write me a SQL query that labels images, produces a transcript from sounds, recognizes handwritten text, does facial recognition or recommends items.<p>See the point? Welcome to 2018.