20 ms·

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

• threeseed 8y ago
  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.
  • jrs95 8y ago
    People who don’t know what AI actually is and buying it anyways. I’ve actually seen this first hand. The developers/data scientists involved simply did what they were asked even though it didn’t make much sense (we had tried and failed to explain why this was a waste too many times and got nowhere) Although to be fair, this outcome was still an improvement. At least with using machine learning unnecessarily the data actually meant something and wasn’t just arbitrary excel numerology.
    • threeseed 8y ago
      Nobody knows what AI is though. Even Data Scientists couldn't tell you if it just means neutral networks or if it include ML techniques. There are technologies like AutoML which automate feature engineering but is that ML or AI. Not sure. So I am not concerned whether people know AI/ML or not. What I have issue with is people thinking that you can 90% of AI/ML using SQL. Which makes no sense.
  • philliphaydon 8y ago
    > Who on earth are these people describing ? I was asked about my thoughts on AI/ML at work, I said it didn't really apply to us. I was told "but with ML we can figure out when deliveries are happening and scale the machines before the deliveries happen based on the peak traffic times". I tried to explain that we could so all that from SQL and looking at our data. We have all the data we just need to formulate it into something that makes sense to predict which times of day, days of week, for each region, where we have more traffic then use that data to pre-scale. I was shot down to "you clearly do not understand ML and should go read up on it".
    • edraferi 8y ago
      The catch it the “and looking at our data” part. ML is basically a collection of thorough ways to look at your data, understand the patterns and infer what that means for the future. In your example, you should absolutely start by cleaning your data up and run some basic SQL aggregations and plotting volume over time. So you look that that and notice (1) volume is increasing over time, (2) some holidays bump a few days ahead but drive very low volume day of (3) weekends are higher, but the effect isn’t pronounced the whole year and (4) summer is better for you than winter except for the Christmas season. Now: it’s two days before Halloween, what’s our anticipated sales volume? If you baked all those observations into an ARIMA model, it’s trivial to crank out a forecast with quantifiable accuracy. If you just have lines on a graph, it’s hard to pin down all the independent effects and recombine them for arbitrary scenarios.
      • philliphaydon 8y ago
        Haha, I wanted to have a bar graph which were by day of week: Bar 1: Last years deliveries. Bar 2: Predicted this years deliveries (based on % of increase from other markets if no previous years, or percentage of growth from last 2 years) Bar 3: Actual deliveries. Then another graph: Bar 1: Average processing time from previous year. Bar 2: Average processing time for current year. I'm terrible with graphs and such tho, I can get all the data, I really suck at displaying that data tho. (and also show the holidays in that region, India has a lot!)
        • edraferi 8y ago
          You're absolutely on the right track. There are well-established statistics tools that formalize your intuition and carry it forward to its logical conclusion. > "by day of week" This means you have a time series with daily resolution (one observation per day) and you expect Weekly Seasonality to matter. Model this as 7-period lag in your daily series. > based on % of increase from other markets if no previous years There are multiple markets, each with their own time series? Congratulations, you have Panel Data [0]. Do some regions have similar trends? Need to account for that correlation, maybe try a Mixed Model [1] > percentage of growth from last 2 years So there's a trend component (constant growth over time). Easy enough to fit the I term of an ARIMA model for this. You'll need to do some custom work to integrate this with your cross-regional correlations though. > Average processing time You'll want to model this at least as well as you're modelling the demand. That means seasonal effects, correlations between factories / warehouses, etc. PS any time you're dealing with a two-day weekend you'd better use at least 3 years of data in case major holidays happened to fall on a Saturday and Sunday, blindsiding you when it shows up on Monday this year. > show the holidays You'll definitely need to put together a calendar of major holidays for each region. These models will calculate the effect for each holiday. Specifically, the effect of the holiday AFTER accounting for the day of week, overall growth, time of year (season), and region. You might even get nifty charts like [2] =============== Anyway that's the basics. You can take graduate math courses in just this kind of modelling. Easier - you can contract a decent statistician for a couple weeks to build the model for you. They'll be delighted that you can produce SQL queries with the relevant data, and their models will help you get a lot more value out of those queries. =============== Resources 4 U Generic time series reference: [0] https://en.wikipedia.org/wiki/Panel_data https://en.wikipedia.org/wiki/Panel_data [1] https://en.wikipedia.org/wiki/Mixed_model https://en.wikipedia.org/wiki/Mixed_model [2] https://assets.digitalocean.com/articles/eng_python/prophet/fig-7.png https://assets.digitalocean.com/articles/eng_python/prophet/... [3] https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average https://en.wikipedia.org/wiki/Autoregressive_integrated_movi... R tutorials: [4] https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials https://www.datascience.com/blog/introduction-to-forecasting... [5] https://cran.r-project.org/web/views/TimeSeries.html https://cran.r-project.org/web/views/TimeSeries.html (Particularly the "Forecasting and Univariate Modeling" section for your problem) Python tutorials: [6] https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/ https://machinelearningmastery.com/arima-for-time-series-for... [7] http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/ http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA... [8] https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-prophet-in-python-3 https://www.digitalocean.com/community/tutorials/a-guide-to-...
  • hahla 8y ago
    These people are describing 99% of the Fortune 500 companies who have no idea what AI means other than hiring a team of data scientists that will hopefully solve all of their problems in the name of technology.
    • AndyNemmity 8y ago
      A far greater percentage know what it is, and use it than you are giving credit for.
    • threeseed 8y ago
      I've worked for half a dozen Fortune 500 sized companies within Data Science teams. Nobody, repeat nobody is spending tens of millions on Data Science programs for answers to problems that a Data Analyst could already do. If you know names please be specific but what you are saying is a bit ridiculous.
    • cortesoft 8y ago
      Man, I am really curious what position you hold that you know the AI strategy for 99% of Fortune 500 companies. Those same Fortune 500 companies would pay you a lot of money for this level of insight into their competitors.
  • AndyNemmity 8y ago
    Don't love your tone, but I agree. I have been working in what was called predictive analytics for 16 years. I've done tons of projects, for tons of companies, and this sort of refrain from people is pretty common when they don't have experience in the field. They think of it like some fad that doesn't make a lick of sense outside of a C-level discussion. But the reality is, predictive analytics is extremely powerful. One of the last projects I did was to save Trains from derailing. Another was to improve crop yield of a farming company by using satellite imagery to determine when a field was most needed to be harvested. Tons of other examples. To even explain the particular use cases would take quite awhile because they are domain specific issues. Cron isn't solving these problems. What the person is really saying is, I don't have experience in these topics, what can be so hard about them? The same style for people who arm chair sports, or politics, or programming, or any other topic. It all seems easy when you don't know the details.
    • didibus 8y ago
      Didn't you use expert systems 10 years ago for those? Rules running of SQL queries, which you crafted yourself or through the help of a domain expert? I think its fare to claim that companies who skipped that process might want to consider it first, as a cheaper way to start with predictive analytics. But, I'm not sure, I am actually intrigued, what was the techniques used before ML in that field otherwise?
      • AndyNemmity 8y ago
        custom c++, pulling data from a db, if that's the SQL rules. Data scientists, plus domain experts.
  • cosmie 8y ago
    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 invest in improvements like this, they reach towards the buzzwords they know instead of the solutions they're not aware of. What ends up happening is effectively the Data Science engagement becomes 90% data cleaning, a handful of SQL statements that should have existed beforehand but never did because the data infrastructure wasn't there, and possibly a veneer of ML/AI just to say it was used. Clients come out happy (sometimes), despite overpaying for what was a much more basic engagement than they think it was, and they go on preaching to their business exec friends the virtue of ML/AI and the cycle continues. [1] I built up a Business Intelligence/Analytics team at my last job, and currently work for a marketing agency managing digital analytics for Fortune 50 clients. Lots of exposure to analytics in lots of varying environments, and I've seen firsthand how ML engagements get pitched and results get presented. I also own a consultancy that's the anti-version of this phenomenon, offering digital analytics management and support services. 50% of my work involves being a knowledgeable resource for marketing and business execs to lean on to cut through the bullshit. With most of the rest being basic Google Analytics/Google Tag Manager management, CrazyEgg, and drip marketing campaigns. All of which seems like AI-level magic for clients when done correctly.
    • xamuel 8y ago
      Interesting comment, makes me wonder if people just generally underperform. You hire an "AI" team with multiple PhDs, you get work that a DBA could've done. You hire a DBA, you get work that a PHP intern could've done. You hire a PHP intern, you get work a kid could've done with Excel formulas :P
      • cosmie 8y ago
        It's less about over/under-performance and more with incentive alignment and political perception, and organizational maturity. It's really, really hard to get executive budgetary approval for a foundational data audit/cleaning project (comprehensive data cataloging, data cleaning, source auditing and validation, etc). Doing so implicitly admits that you weren't doing that before, and now you have to pay gobs of money to fix it. The larger the company, the more infeasible it is to push this through because of the breadth of technical/analytical debt that has accrued and the price tag associated with the project, combined with the perception of incompetency (i.e. it's an expensive project that's fixing a problem you as the executive shouldn't have let happen to begin with). Whereas an ML/AI project/initiative/push is a net new capability that you're spearheading, and it's easier to get the political traction to spend money on net new things, especially buzzwordy net new things that the firm can use to be viewed as cutting edge. The fact that you're rolling up the cost of a complete data management audit to be able to even do the ML/AI project is a minor bullet-point that doesn't matter. Executive expectation is that anything ML/AI/new-age-techy is going to be astronomically expensive anyway, so it doesn't get noticed that they're paying a premium on labor to do it as a combined project rather than as two separate projects. Effectively, the foundational work that's needed to support ML work is also the work that's needed to do basic SQL-based analytic work, but it's way easier to get that budgetary line approved in a flashy ML project, even if you're paying a premium by having the ML/AI firm do the foundational work instead of the specialist work. Plus, spearheading ML/AI initiatives make for better resume points than "data management initiatives". So there's little reason for anyone in this process to attempt to change anything, unless you happen to materially benefit from a firm's profit. For this reason, my main consulting clients are bootstrapped firms that actually care about being pragmatic over being trendsetters. Note: This is a huge generalization, and doesn't apply universally. But it's far more common than you would expect, especially as you veer away from the type of companies that pop up on HN towards more traditional industries.
  • cup-of-tea 8y ago
    Maybe they're describing a case where the systems have been well designed so they already have the data they need in a useful format and don't need to do any of that bullshit to make it usable.
  • vonseel 8y ago
    Wow, at least one other person shared my opinion. Clickbait article/post title. Also, the use-case for simple sales coupons mentioned in his article are not even close to the kind of things a company like Amazon does AI on in the recommendation process of WHAT product to advertise to each customer (as mentioned by another poster, I would not expect to see a discount for breast pumps when I just bought something for myself-male-such as men's deodorant).
• kthejoker2 8y ago
  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.
  • rokhayakebe 8y ago
    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?
    • smrtinsert 8y ago
      Give me all the time frames when there was statistically significant bump in X.
    • throwawayjava 8y ago
      controls and sensing.
    • firasd 8y ago
      Product Recommendations. Trending Items (Top items being sold this week as opposed to last week, while filtering out items that are generally popular.) Much easier with Elasticsearch than SQL https://www.elastic.co/blog/significant-terms-aggregation https://www.elastic.co/blog/significant-terms-aggregation
      • collyw 8y ago
        That's not really ML.
        • jon_richards 8y ago
          If I were ever to make ML bingo, "That's not really ML" would definitely be on it.
• zitterbewegung 8y ago
  Companies 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 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.
  • soared 8y ago
    Agreed. The advertising agency I previously worked at would take the first month and only work on how data is collected and stored. After that first month they would begin addressing goals, plans, etc. About a year later is when true value would be realized because data that previously never existed could be analyzed. Few things were more enjoyable for me than getting a new client, imagining what analysis I'd like to do, figuring out what data would be necessary, and then implementing the system to make it reality.
    • mygo 8y ago
      how did you convince them to stick on for a year when most clients want to see some results within 2 months or they give up? according to your schedule they’d ask what I’ve been doing and if I tell them I’ve just been “collecting data” that translates to them as “I haven’t done anything in 2 months”. If I say “keep paying me and you’ll see results in a year” that translates to “I haven’t done anything in 2 months but I want you to continue to pay me for another 10”. What do you do to qualify a client? How do you know your engagement with them won’t just waste everyone’s time when they quit halfway through and then damage your reputation?
      • 8y ago
• deleted 8y ago
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• reilly3000 8y ago
  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.
  • taeric 8y ago
    I think the big problem with your example is that most companies just don't have something I want to talk with them about. For large stretches of the year. No amount of ai is going to change that I am not in the market for a car, as an example. Even biking gear is off limited sellability to me most years. Hard to know when I'm going to buy new booking shoes, unless you know when my shoes are going to go bad.
  • zer00eyz 8y ago
    Though your generally right, the article sets a tone in the first paragraph. It is saying that if your looking for ML/AI solutions for marketing and you ARENT doing the basics already then your throwing good money after bad. You should START with some sql and targeted emails before you dive into a large and potentially expensive project.
  • nostrademons 8y ago
    I think the main point of the article is that 99% of the value in that is in a.) sending the follow-up email at all (which just takes a cronjob) and b.) identifying which customers to send that follow-up e-mail to (which just takes a SQL query). While it's probably nice to try and predict the ideal time to send it, the gains you get from that are marginal compared to steps a & b, which many companies aren't even doing today.
• i_feel_great 8y ago
  I find very handy the Postgres aggregate and stats functions: https://www.postgresql.org/docs/current/static/functions-aggregate.html https://www.postgresql.org/docs/current/static/functions-agg.... I have also used Sparklines in Python for quick and dirty trends
• internetman55 8y ago
  Why not both? http://sqldatamine.blogspot.com/2013/07/single-multiple-regression-in-sql.html?m=1 http://sqldatamine.blogspot.com/2013/07/single-multiple-regr...
• donatj 8y ago
  I almost took a "big data" data scientist job about a year ago with a local company. 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 / someone with ownership and a complete vision of the data structure. They had no foreign keys, poorly 'designed' indexes, and tons of redundant tables with no rhyme or reason to them. They'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.
  • flor1s 8y ago
    Maybe they should have a data warehouse (which is typically denormalized) instead of a database (which should be normalized)?
    • donatj 8y ago
      They had a data accessibility / source of truth issue. You need to normalize before you denormalize or else you're just spreading the mess into the warehouse.
  • adaml_623 8y ago
    I'll bet they found they got 10 times more applicants for a data scientist role as opposed to a dba/sql/data engineer.
    • vazamb 8y ago
      But what's to point? You will get a lot of applicants from non-cs backgrounds. Not only will they be unprepared for the job but also most likely very unhappy with their tasks.
      • gowld 8y ago
        The point is that you can get paid more by calling yourself a data scientist than a DBA, at the moment.
• dizzystar 8y ago
  The best is when you ask someone why they want an AI/ML masterwork, they just say it's the future and we don't want to be left behind. It'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's easy to forget that most people correlate the plastic box sitting in front of them with an intelligent Magic 8 Ball.
• sixdimensional 8y ago
  Well, we are in the peak of a wave of hype about AI/ML, maybe even just past that peak. Many fundamental technological advancements in the field of AI/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. I'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'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. These are tools in the toolbox, and I think the adage "try to use the best tool for the job" 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. That said, I think the best thing to do right now is try to separate the signal from the noise regarding AI/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/ML tools integrated close to SQL engines. AI/ML has a place, and so does SQL. I will say, though, that I for one don't want to be caught on the side of the discussion where I don't learn enough about what is possible with AI/ML, and then get left behind. I think many of my colleagues and professionals in the field and here on YC feel similarly. Actually, I think even non-technical people feel the same way - the fear of being replaced by AI/ML is higher than ever. 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.
  • itronitron 8y ago
    imho, AI and ML are being applied to problems for which they are not the best solution, primarily out of ignorance by the practitioners, or by their desire to hop on the AI/ML train. I expect machine learning to steal some attention from data science, but in name only as at the end of the day it is all based on statistics.
  • treydey 8y ago
    I disagree that this is the peak of AI/ML. Companies are desperately looking for PhDs in AI that can fulfill their business needs. I think we're about to see a lot more applications of AI/ML.
• exabrial 8y ago
  This is one of those HN threads where I'm going to sit back with a bag of popcorn and hit refresh...
• gnicholas 8y ago
  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 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. A week later they said that they were no longer interested because, although they liked what our tech was able to accomplish, it didn't fit with their investment thesis — which was all about ML. My wife asked me why I didn't just make some stuff up and say we could do v2 using ML. Perhaps she was right. 1: http://www.beelinereader.com/individual http://www.beelinereader.com/individual 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!
  • Someone 8y ago
    If your code has even a single magical constant (I guess it likely has, if only because your code has to figure out whether a period is sentence-ending), you can and, now that it is cheap and easy, probably should use machine learning (a moniker that includes such algorithms as gradient descent) to optimize that constant. Also, if you ever have considered two code variants where one is a bit better in situation A and the other in situation B, you can use machine learning to combine the two methods, (e.g. using a random forest (https://en.wikipedia.org/wiki/Random_forest https://en.wikipedia.org/wiki/Random_forest)
    • guy98238710 8y ago
      You don't need AI to find values for free parameters. Unless you consider all of statistics to be a subfield of AI.
      • disgruntledphd2 8y ago
        I personally consider all of modern AI (i.e. statistical pattern matching) to be a subfield of statistics. Mind you, I'm probably just a grumpy old man.
• stackzero 8y ago
  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't do so then ML may be something to look into.
• justonepost 8y ago
  Doesn’t scale.
• smsm42 8y ago
  But if you say "I'm going to use a bunch of shell scripts to parse logs" you are boring. If you say "I am going to use groundbreaking ML/AI technologies to transform big data into customer retention solutions", you are a visionary.
  • brootstrap 8y ago
    providing insights to your log data faster then ever before with machine learning big data
• ianamartin 8y ago
  A huge part of the "data revolutions" that we've seen in the last few decades really has nothing to do with data and everything to do with process. Data Warehouses changed the way people and companies do data. They expose all kinds of things that were never available before. It was magic! No. It wasn't. Not that Data Warehouses are bad or ineffective. But it'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't model data. They model processes. The mere fact of forcing the company to pin the process is often more beneficial than the warehouse itself. 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's a human process that leads to that. Absent that, it's pretty useless. I cut my teeth on SQL, and it's a big part of my professional career. I think it's great. It's one of my favorite languages, and it does a lot that maybe a lot of people don't know about. But this title and the content are really pretty garbage. Anyone who thinks that good SQL can do what good AI/ML can do is really misunderstanding both.
• flyingcircus3 8y ago
  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'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.
• et2o 8y ago
  Good points but really a false dichotomy. The purpose of AI and machine learning is to find patterns in data that aren't simple heuristics like this.
• master_yoda_1 8y ago
  In a layman term the difference between sql and ml is, ml predict things and sql just tell you things. 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. SQL analytics was past, predictive analytics is the future. ML can do more than predictive analytics for you :)
  • tejasmanohar 8y ago
    SQL analytics was past, predictive analytics is the future You're over-simplifying things. SQL is here to stay, regardless of how big ML, which I'm very bullish on, becomes. Start with the simplest approach and try alternatives when/if it doesn't work. Simply jumping to "predictive analytics" is silly.
    • master_yoda_1 8y ago
      I agree sql is here to stay. For ml you need data and sql is best place to store data. You can use various sql queries to get feature for ml system.
  • azernik 8y ago
    Predictive analytics is the future for people who are trying to predict things. Use the right tool for the task.
• euske 8y ago
  The biggest threat of AI is not its ability of taking jobs or exterminating mankind, but the amount of distraction it creates. When politicians say they improved the economy like 30%, nobody buys into that. It'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'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'm still not sure if the net gain is going to beat all the distractions it created.
• partycoder 8y ago
  Write me a SQL query that labels images, produces a transcript from sounds, recognizes handwritten text, does facial recognition or recommends items. See the point? Welcome to 2018.
  • cup-of-tea 8y ago
    Most companies don't have anything like that.
  • Piskvorrr 8y ago
    Except wan you gat whoa ird trance scriptions, people tagged as gorillas and recommendations "I see you bought $that, do you also want to buy $that?" There's No Silver Bullet, welcome to $any_year.
    • cup-of-tea 8y ago
      What language is this?
      • Piskvorrr 8y ago
        It's English...run through a transcription. ("Except when you get weird transcriptions")
      • templar_muse 8y ago
        Irony. Except wan you gat whoa ird trance scriptions = Except when you get weird transcriptions
• didibus 8y ago
  What the article describes is called an "expert system" and is what AI in the enterprise used to look like. Basically, you try to capture the instinct of a great salesmen by formalizing it into computer logic. Often that's done with rules like in the article. It works good, but has its limits. The finer reasoning of human judgement are often not expressable, people don'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't make the best prediction/decision. That's when ML shines. Instead of trying to encode an expert's intuition, instead you let the machine develop its own intuition, itself becoming an expert through training. The downside is it now similarly becomes challenging to formalize the machine's intuition. Why it made a given choice is no longer easily apparent. I do think expert systems still have value. Especially when you lack the dataset to train a machine expert.
  • dvfjsdhgfv 8y ago
    Yes, these are two main approaches to AI, with expert systems being the "old" AI and ML being the "modern" AI. In the old approach, you knew exactly what you wanted to achieve, and very often you had a relatively clear idea how to do it; the challenge was to construct the solution manually. In the new approach you are not so much concerned with how to do it as you train on some dataset anyway. The results are less predictable though, you might even not understand why certain things happen. The number of mistakes/false positives can be relatively high. Still, with these methods, I think we're very far from real artificial intelligence, where you can really learn something new on the basis of something old. I believe until we have a profound understanding of what it means "to understand", such an advancement in AI won't be possible.
    • mjburgess 8y ago
      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 asks, "how does a machine perceive the world?" the mystery arises only because it doesn't.
• cup-of-tea 8y ago
  Yeah but non technical people who don't know what they are doing but for some reason have money to spend just know they want you to use machine learning for everything. One time at work I wrote a simple web app with a search box (just doing an sql query, nothing fancy). One of the "higher ups" was impressed and decided to flex their knowledge, pointing to the search box saying "and this uses nlp". It was a damn sql query on a full text field.
• AzzieElbab 8y ago
  You probably do not need SQL. You can make do with well written see awk scripts
  • ben509 8y ago
    For parsing and prepping large amounts of data, awk is shockingly effective. And after a while, you even get used to your eyes bleeding...
• mythrwy 8y ago
  For the examples mentioned in the article no, you don't need statistical analysis but these are simple cases (which most cases are). Late orders, biggest orders etc. etc. sure, those are all SQL queries. However if you want to make statistical predictions or looks for the non obvious, these simple types of queries aren't going to do it. So it's an apples to oranges comparison. There are a lot of cases where people don't know what they are after. And also lots of cases were orgs don'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.
• elchief 8y ago
  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't
  • kgdinesh 8y ago
    can you elaborate?
    • elchief 8y ago
      Built a lead generation system. Looks at searches on our site and picks out the best people for our sales team to call. Set up a bunch of rules created by the sales team. Tweaked it over months. Made money Then used real sales data tied back to search history and built a machine learning model. It found new patterns that the sales team hadn't thought of, and performs much better
• salu222 8y ago
  salu2
• sbhn 8y ago
  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/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/ML gloss
• maltalex 8y ago
  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.
  • r3bl 8y ago
    I don't think you understand author's point. His point is highlighted in the first tweet, in which the author appears to be specifically annoyed by the potential founders and investors that can't understand that ML isn't a good solution for all of the problems. He then goes on and gives an example of such problem by explaining a shopping cart that doesn't actually need ML, but just some old-fashioned SQL. He doesn't claim that SQL is a solution to all ML problems, just this one.
    • dotmanish 8y ago
      (I'm not the parent commenter who you replied to, but I think I understood what his/her point was). Taking the shopping cart example: "In a former life, I used to write SQL to extract customer of the week. Basically, select from orders table where basket size is the biggest." The author decided that 'customer of the week' will be selected by 'biggest basket size'. Not by 'biggest $ amount spent', 'fastest time from add-to-cart to checkout' (and numerous other attributes or combination of them). This decision (the "best attribute") was taken by a human, leaving a field open where a combination of attributes could've resulted in overall better business outcome (how much did 99% of these retained customers shop for, in $ value over lifetime?, etc) This is possibly what the parent commenter is hinting at - this human decision leaves a lot of optimization scope, where ML could have helped.
• voltagex_ 8y ago
  Are SQL skills disappearing from companies? Could this be a reason people are reaching for more complicated solutions because they don't know what a good SQL database can do?
  • collyw 8y ago
    I blame the NoSQl nonsense from a few years ago. "Relational databases don't scale" apparently.
    • notyourday 8y ago
      https://www.youtube.com/watch?v=b2F-DItXtZs https://www.youtube.com/watch?v=b2F-DItXtZs
      • collyw 8y ago
        I had conversations that went like that.
• johnlbevan2 8y ago
  Fully agreed that in simple use cases simple solutions make sense; I'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). However, it would be good to have a bit more in the article to say what AI/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 "in my day we only had turnips; you needed a snack: turnip; you needed a pillow: turnip". By showing a few good use cases allows you to better contrast the product / get an understanding of where the boundaries are between the technologies. *NB: When I first read this I assumed the author was talking about AIML (artificial intelligence markup language) rather than AI/ML (artificial intelligence / machine language)... as though the slash was included, there was no use of the full terms.
• smcl 8y ago
  "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.
  • inopinatus 8y ago
    Or in the middle of the day for me, compounding the reputational damage by not thinking globally.
  • esrauch 8y ago
    What time would be better?
    • smcl 8y ago
      6am or later? Even if they played it safe with the usual business hours of 9-6 in the users locale (since companies generally know your location) it’d be fine. I'd be relatively forgiving for a US company who doesn't really know where I live sending me stuff at these times - they've no way to know what is "sensible". However I was getting pestered by Vodafone in my country at 2am nearly daily for a while
      • dzmien 8y ago
        This is why I set "do not disturb/alarms only" when I set my alarm before going to bed.
    • PeterisP 8y ago
      It depends? It's clear that the time chosen is important, it will influence the likelihood that the recipient will go back to your store or not. For example, if they get the notification while on their way to work, they're likely to set it aside because they won't open the shopping cart while driving; you want to "hit them" sometime when they're sitting with their device of choice and would have a spare minute to do the things you want them to do. That time wouldn't be the same for all people. And the choice to do this properly would depend on your volumes. If you're a small shop, you just pick one time - definitely not 2 AM, but, say, 11:30 AM (so office workers can do their thing in a lunchbreak) or 8 PM when they're likely to be home; it depends on your target audience. If you have a distributed client base, you'd want to take time zones into account. And if you're large enough so that small changes in this result make enough money to worry about it seriously, you might even do some ML to pick the optimal reminder time for each customer; e.g. training a predictor on what factors will influence the 'desired action' chance in a "multi-armed bandit" approach to explore the options initially and then start using the ones that work best. That's obviously overkill for most companies, but for large online retailers that would be a natural choice, it all depends on scale.
• blackrock 8y ago
  Am I misunderstanding something here? Artificial Intelligence is about statistical analysis. 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? 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. Basically, it is a pattern matching mechanism, on a massive statistical scale. And from this, then further actions can be taken. Such as, the owner of the dog, can be mailed advertising and coupons that are related to dogs. 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? 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. 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. In fact, I think our brain operates the same way, by using statistical analysis. 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. This of course, happens in the deep recesses of our brain, so it'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. 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.
• halflings 8y ago
  This could've been a valid criticism of people that use ML where it's not appropriate, but it ended up being a bit of an irrational rant, and a dishonest one too: > I mean, why send a letter with breast pumps to a man that just bought a pair of sneakers? It doesn'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%. 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? 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 + ...` This is the whole point of ML. People have been building rule-based systems built on "domain expertise" for ages, only to find that they are limited and cannot compete with simple algorithms fed with enough data.
  • reacharavindh 8y ago
    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?
    • visarga 8y ago
      Expert systems are brittle and don't generalise well outside the data on which were created. You know, counting items and dividing by total number is a kind of machine learned model, too. In technical terms it is "Computing the maximum likelihood estimate (MLE) for the PMF of a random variable taking finitely many values." But it's a poor man's model. That's why in order to solve complex problems we use stuff like neural nets and gradient boosting, and in unsupervised learning, matrix factorisation.
    • ju-st 8y ago
      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.
• jmpeax 8y ago
  > 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/voucher.... ...Guess what? 99% of these people became repeat customers 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?
  • collyw 8y ago
    Why couldn't you do that in SQL, sounds simple enough to me?
• erikb 8y ago
  Well yes, if you own your shop and are one of 1-10 people working in this shop, then you don't need these high tech things. 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'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.
• Gravityloss 8y ago
  I guess if you're investing for the long term, avoid anything with machine learning, as it's overpriced...
• debarshri 8y ago
  Isn'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.
• kriro 8y ago
  The article doesn't convince me. It can be summarized as "don't overengineer" but quite frankly these days ML/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't see why one wouldn't at least try to use it. I don't see why a ML-algorithm couldn't grab the first name for example. I mean if your argument is "just use SQL" my counterargument is "I agree but I can just try ML as SQL on steroids". 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. "Customer with largest basket" 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 I like the overall idea of "try the simple stuff first" 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).
  • soVeryTired 8y ago
    The right questions to ask are "what is the incremental benefit I can get from ML over a simple rules-based system? How much does that added complexity cost?". Costs include technical debt, increased maintenance, general opacity, and the risk that the complex model runs amok and does something stupid (which is more common than you might think). Sometimes those costs are justified, sometimes they aren't.
  • dzmien 8y ago
    I don't think the author was trying to suggest that people stop using/developing ML/AI. I think the point was that ML is not some kind of black magic panacea, and that a smart programmer is often more valuable than a 'smart' machine.
• miqueloc 8y ago
  A guy that rediscovered Direct Marketing. Congrats.
• miqueloc 8y ago
  A guy just rediscovered Direct Marketing. Congrats.
• RandyRanderson 8y ago
  ML is not going from 0 -> 25% it's going from 25% to 28%, say, and that 3% being much more in profit than the cost of the ML work.
• piyush_soni 8y ago
  Now write an SQL Query to find all photographs that have me and my wife sitting in a boat in them.
  • Piskvorrr 8y ago
    ...minus the ones which are obviously a toaster, while the AI insists they're you and your wife sitting in a boat ;) Now what?
    • piyush_soni 8y ago
      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.
      • cpburns2009 8y ago
        Well, Google receives free Mechanical Turk style image identification through their CAPTCHA (or whatever it's called nowadays).
        • deleted 8y ago
          [deleted]
        • piyush_soni 8y ago
          That's not the only source of their image identification, if you wanted to say that.
    • thrownaway954 8y ago
      select * from photos p inner join tags t where t.tag in('you','your wife','boat') and not in('toaster')
  • thrownaway954 8y ago
    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.
• 40four 8y ago
  I 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 comments are talking about hiring data scientists for months if not a year or more (yikes!) To clean data & wait for it ... perform linear regression. To me this sounds like a great application of machine learning. Couldn'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?
  • ashelmire 8y ago
    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.
    • 40four 8y ago
      I see what you're saying about cleaning the data. This is something I'm very unfamiliar with, so good to know! Yeah I think the article is garbage too, makes me wonder why it gained so much traction? The argument/ topic are not developed at all. I guess the point they were going for is there are people who want to use ML because it's 'trendy' or something, and simpler solutions would suffice. I could see that being true, but this article is BAD. I hate seeing low quality articles get rewarded.
• notyourday 8y ago
  "Machine learning" is an excellent tool to separate extra money from "customers". Founders are separating extra money from VCs. Engineers are separating extra money from the founders. Just reading this thread keeps illustrating this. Want to get a job done? Use a tool that gets a job done. Want to talk about getting a job done and be "listened" to - use ML to beat around the bush. 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. Neither it is different from companies mumbling about availability challenges when the entire stack gets sub 100 hits per second.
• cyberomin 8y ago
  Hi, I'm the guy that wrote the tweets. Let me know if you have any other questions. I'm happy to answer any question.
• sercant 8y ago
  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.
• viach 8y ago
  You don't need no AI/ML, no Blockchain, SQL works just fine... I see where is it going today on HN...
• free652 8y ago
  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.
  • QuantumAphid 8y ago
    Are you suggesting that AI/ML makes those new requirements go away? Or that managing those requirements becomes easier because AI/ML software figures it out?
  • danShumway 8y ago
    Not that I disagree with you, but does machine learning solve any of those problems? SQL is annoying to debug, ML is impossible to debug.
• epilogue 8y ago
  The writer seems to describe very basic data mining in some cases, which in itself is a form of AI/ML, but then other examples have no relevance to needing to use AI/ML at all. 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. 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? 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/ML that seem to get ignored in favor of the flavor of the month Tensorflow/IBM Watson/Whatever else. Quite frankly, the whole thing just comes across as a very closed minded rant from someone who isn't interested in exploring what new technologies are capable of.
• nicodds 8y ago
  I think the writer is overgeneralizing his particular use case. Surely, the situation he represents doesn't need any AI/ML, but it is the result of a simple use case, with little variables and with an easy workflow. Does the same pattern apply also in more complex scenarios?
  • ben509 8y ago
    Yes and no. AI/ML is real stuff that can do useful things; I worked at a government contracting firm and we made a lot of that stuff work. But as I recall, before anything landed in AI algos, we'd always have pages and pages of code handcrafted by SMEs to prep it. It's not hard to see that, for many cases, all the prep work gets you pretty close to the answer without any training. I think the issue the article hints at is there are way too many contractors willing to burn your cash on AI/ML. Contracting has a serious principal agent problem; there was a discussion I recall over how to implement a quick search feature in a system we maintained. I floated the idea of sampling the data to get approximate results, but that was instantly shot down in favor of buying a ton more hardware. There are serious arguments against sampling, it's very tricky to get right, but if we had been spending our own money I think it would have gotten a more careful hearing.
• jrq 8y ago
  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 people. Business is people. You don't need a rack of gpus to understand why sending someone an email who has a saved cart is a good idea. It's common sense. It doesn'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. (not always, I know, I work in finance so a lot of my business IS machines and not people, but you catch my drift) I'm pro-AI research, and anti-AI hype train. They're computers. They're objects. They'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. /rant Maybe HN skews my perception on what the public tech enthusiast's perception on AI is...
  • kevin_nisbet 8y ago
    I agree completely, at one of my previous employers, the CEO of the company sent out an email, with a list of links, and said everyone should learn AI/ML, and it would be important for the future products. And he gave a number of examples of potential features that AI could achieve. 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.
• crabasa 8y ago
  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/SQL. There was a feature of Oracle called SOUNDEX which was magical. Here's an example from their docs page [1]: SELECT last_name, first_name FROM hr.employees WHERE SOUNDEX(last_name)= SOUNDEX('SMYTHE'); This query will return all people with a last name that sounds like 'Smythe', including 'Smith' and 'Smithe'. [1] https://docs.oracle.com/cd/B19306_01/server.102/b14200/functions148.htm https://docs.oracle.com/cd/B19306_01/server.102/b14200/funct...
  • msumpter 8y ago
    I've used similar phonetic algos in Excel to deduplicate CRM data during corporate acquisitions, it always seemed like the source data was hopelessly duplicated, but running a few of theses algos against the data, and then providing the 'best guesses' to the sales team to then do the final massaging of which accounts are truly duplicate or should be left alone. Soundex is very simple but works well, calculating a strings Jaro–Winkler distance also helped.
  • PeterisP 8y ago
    Soundex, by the way, had it's 100th anniversary a few weeks ago - it was patented in 1918.
• martin-adams 8y ago
  Maybe I'm completely missing the point here, but I thought the use case for AI/ML was to find the cause, not the effect. For example: >> 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. 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. I'm happy to be corrected here.
• philipodonnell 8y ago
  I think SQL can also benefit from some of the progress around making things that "feel like" ML easier. For instance, dplyr is a refreshing change to the way you write operations that manipulate data in a table/column structure, even though it uses mostly the same verbs and language constructs as SQL.
• walshemj 8y ago
  You can do some types of ML with SQL all the main sql databases are Turing complete. Not sure if its going to be efficient for clustering and entity extraction at scale tho
• j45 8y ago
  People written SQL scripts that check for scenarios, and even potentially action / repair them is a form of intelligence. It's not artificial, either. Thinking back to successful ERP implementations, little was more useful during go-live or an ongoing basis than a script that ran every hour/day/week/month to look for a condition and report it. 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. 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. 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. 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. Sql scripts could one day feed into or fit with AI/ML. I don't see that day here in the short term.
• cirgue 8y ago
  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't that many of those problems unless you're operating on truly enormous scales.
• qwerty456127 8y ago
  > 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
• AngeloAnolin 8y ago
  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). 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.
• kexx 8y ago
  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 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?
• pugworthy 8y ago
  Gee. Thanks for the porn in the "Recommended Threads" at the bottom of the page :/ If your work scans your web browsing for certain words etc., don't click the link.
• newsum 8y ago
  so many haters on this comment thread. Just read and stop hating. https://www.thestreet.com/investing/nasdaq-all-in-on-blockchain-technology-14551134 https://www.thestreet.com/investing/nasdaq-all-in-on-blockch...