11 ms·

Ways to think about machine learning

309 points by anastalaz 8y ago

almostarockstar 8y ago
A couple weeks ago I was asked to meet with two actuaries. Their company (a big one, I'm working for them on contract) is trying to promote the use of Machine Learning and when they heard I had just completed post-grad in ML, they were eager to pick my brains. In a 60 minute meeting, we spent 5 minutes discussing how ML worked and 55 minutes going in circles. They had lots of data, but no problems to solve. And there are only so many ways to explain that a large dataset is not equivalent to a business problem. I'll be sending this on to them.
- ggg9990 8y ago
  If actuaries don’t have business problems that could use ML what the heck do they do all day? Insurance is a great target for ML.
  - HenryTheHorse 8y ago
    That's a really good point. Discovering patterns and correlations is an actuary's only competency.
    - sulam 8y ago
      Which may mean that all the statistical techniques ML would use are fairly rudimentary for them.
  - lordnacho 8y ago
    As with many lines of work, the cool part may be a very small part of their day. With insurance, there's probably an already entrenched way to think about the data that's been there for decades, as well as regulations that help to entrench it. It might also be the case that the business problem isn't risk analysis at all, as one might expect. It may be finding investments for the float that provide a sensible return within the regulatory remit is harder than figuring out how much needs to be charged for dinging someone's car.
    - nerdponx 8y ago
      Correct on all points. There is also the issue that the pricing models themselves need to be able to stand up to some form of regulatory scrutiny (depending on the state and the nature of the insurance product).
- jcims 8y ago
  I’ve been noodling a little with ML for infosec and if I spent an hour with a pro I might wind up doing the same thing. The problem is likely to be one of a cacophony of choice. It’s not that they don’t have any problems, it’s that they have a million of them and have no solid footing from which to frame their answer in the form of a question. I bet if you picked one field in one dataset and started drilling down into how they could possibly use it, the use cases will start to melt out of their frozen brains. Get ready.
  - Kalium 8y ago
    I can think of some really useful infosec applications. "Is this binary malicious?" springs quickly to mind.
    - jcims 8y ago
      There are a bunch. Just about any will-instrumented platform presents many opportunities to do basically side-channel detection of compromise (e.g. cpu utilization, network utilziation, network flows, syslog data, process tree, etc)
  - robertk 8y ago
    Maybe they could use an ML model to narrow down which one of their million use cases would benefit most from an ML model. ;)
- ssivark 8y ago
  While I get what you're saying, isn't that a very "supervised ML" way of thinking? In a world where unsupervised learning was easier to apply, it might be possible to start with data, compress it to a low-dimensional manifold, and use that understanding as inspiration for better directions/questions to pose. So I guess the real problem is that humans expect anything branded as "intelligence" to have the ability to learn in an unsupervised manner, while breakthroughs are closer to the regime where the goals are supervised while the solutions are discovered. Of course, the amount of supervision probably lies on a continuum, between everything being completely specified (classical programming) and everything unsupervised ("true" "human" intelligence).
  - taeric 8y ago
    Lots of data does nothing to help when the person you are talking to is not given a) knobs to turn, and b) a goal to achieve. To that end, just having loads of data doesn't necessarily help. You also have to have loads of control over some part of the system. And even then, you can't expect ML to help, all on its own. So, the worst is folks that think just because you have full trace logs of your system, it should be trivial to apply anomaly detection. At face value, yes. Yes you can. But also at face value, your trace logs aren't free and it isn't like folks are keen on letting their detection system cut off anomalous transactions without some sort of kill switch.
    - ssivark 8y ago
      I think my point may not have gotten across clearly, so let me clarify. I agree with most of what you said, which IMHO comes under the spirit of supervised machine learning paradigm, which is the most advanced tool we have pragmatically available to use today. Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. Many people (including some experts, rightly or wrongly) believe that neural networks might be that model class, and (clever tweaks of) gradient descent might be an acceptable learner. All I'm saying is that the current hype about AI fails to separate the potential of the latter class from the currently available successful tools of the former class.
brootstrap 8y ago
Wow good write up, i just made it a few paragraphs in but had to comment. especially after just reading thru the IBM watson 'AI' bullshizz, this is refreshing and reminds me that there are folks who are trying to think about this stuff in new ways rather then just hyping/selling the out of 'AI' so your new BigCo can burn thru 10/100s of millions on some shy project =)
- AndrewKemendo 8y ago
  In fact the majority of people who actually do ML/DL or Applied AI in other forms think this way by default. As usual, Ben Evans backs into the standard understanding from the experts in the field as though it's revelation.
  - benedictevans 8y ago
    That's the point - to explain how people in the field are thinking about this stuff. Most people outside SV don't know what the experts in the field are saying. Indeed, many people in SV don't know this ;) If you already know all of this, you're not the target audience.
    - AndrewKemendo 8y ago
      That's fair enough. It's a running joke with applied ML people that half of your job, if you have any sense of professionalism, is convincing possible clients that no, they probably don't need to use a 41 layer DNN to send a follow up email.
      - benedictevans 8y ago
        A database consultant once told me that half his gigs were telling people with a database to use Excel
        boxy310 8y ago
        Funny - half of my experiences talking with nonprofits were telling people with an Access database to migrate to a proper SQL database.
CGamesPlay 8y ago
This article is pretty great and I agree with the framework it sets up around automation and future jobs. One thing I felt was dismissed too easily was the "Google has all the data" line. Sure, nobody said "Oracle has all the database", but people ARE saying the former. Why should we ignore the piles of collected data when thinking about how the landscape will look in the future?
- zerostar07 8y ago
  There are probably plenty of problems that do not require that much data to solve (e.g. in reinforcment learning). And google doesn't have all the kinds of data - they didn't own medical imaging data, they had to ask for it. There are some problems where they have advantage (speech / translation) and they 've probably already solved those problems, but their data advantage is not as important as they make it to be. Whats more worrying IMO is that they 're hoarding all the researchers.
graycat 8y ago
It's tough to know just what machine learning covers, includes, consists of. From what I've been able to see, currently in practice, apparently 90+% of machine learning is curve fitting and nearly all of that is some form of classic linear regression. Linear regression and curve fitting more generally have been around, available, and used going way back in electronic digital computing and well before. E.g., for software we've long had the IBM Scientific Subroutine Package (SSP), SPSS (Statistical Package for the Social Sciences), SAS (Statistical Analysis System), ..., R. There are stacks of polished textbooks in statistics and specialized to some fields, e.g., econometrics, time series analysis, etc. There has been some usage, but headlines have been rare for decades. But, there's a lot more to applied math than anything much like that curve fitting. E.g., maybe take the learning as the ability to do statistical estimates -- well, there's a lot to statistical estimation than curve fitting. There's also the field of optimization -- linear programming, integer linear programming, network linear programming, dynamic programming (discrete time, continuous time, deterministic, under uncertainty), quadratic programming, other cases of non-linear programming, and more. And lot more can be done in applied probability and stochastic processes. IMHO it would be better progress and description of the current state to discuss both the applications and the solution techniques in more detail. E.g., the crucial core of my startup is some applied math I derived (with theorems and proofs) based mostly on some advanced pure math prerequisites. I wrote the corresponding software. Then from 50,000 feet up or from the point of view of a user of the work, what I did can look like a machine that learns a lot very quickly, is very smart, and puts out really intelligent stuff. Still, my math is not covered by any of the machine learning I've heard of. Point: There's a LOT of applied math that can be done and, really, has been done far from current descriptions of machine learning.
- cschmidt 8y ago
  Is your startup Datathings?
  - graycat 8y ago
    Naw. The project is for a new Web site. I have the hard technical work done, e.g., the crucial core applied math and the corresponding code apparently ready for production, but I have to do some routine work, e.g., add more data, pick a company name and get trademark protection, get a static IP address, pick and register a domain name, get a tax ID, find out what the ad networks want so that I can run and get paid for ads, tell my county that I'm "doing business as ...", maybe set up an LLC, get a business checking account, get an e-mail address just for the business and another one for Web site feedback, do some more testing internally, tweak some of the software, tweak my code for my Web site session state server to get a better Web site log server, kick back and give a critical appraisal of the effort and make some tweaks, announce an alpha test here on HN and elsewhere, then a beta test, then get some publicity by some of the usual ways, then maybe have a business. The startup is supposed to be the first good solution for a problem pressing for nearly every user on the Internet around the world, smartphone to ... workstation. The potential of the business as currently envisioned would be, on average, about three sessions per week, 30 minutes of eyeball time per session, for over 50% of the users of the Internet in the world. The site will be able to do some relatively good ad targeting while having some of the best protection of user privacy, e.g., no use of cookies, logins, Web browser user agent strings, or third party tracking. So, make some assumptions about ad rates, multiply, and get an estimate of a good business. The problem: Given an interest of a person, typically a narrow interest, maybe a short term, recent, or new interest, that interest treated as unique in all the world, find the Internet content with the meaning that person wants for their interest. So, part of project is addressing meaning of Internet content. The content might be in any of the common data types of text, still images, videos, music, Web cams, pod cast audio, etc. The content might be in Web pages, PDF files, YouTube videos, Instagram images, art gallery images, etc. The interests might be narrow topics in crafts, politics, skills, academic subjects, art, social, interior decorating, travel, intersections of those, etc. So, really an interest can be essentially anything, and the content can be essentially anything on the Internet. And again the main criterion is meaning. To the users, the Web site is just how to find content with the meaning they want for each of their interests. So, the site is a new form of engine for search, discovery, recommendation, custom curation, etc. To heck with these categories: The site is for the users to find the content with the meaning they want. Well, characterizing meaning with just keywords and phrases is usually from difficult to impossible in practice. So, my work makes no use of keywords or phrases. So, really, my site is not direct competition for Google, Bing, etc. If a user (A) knows what content they want, (B) knows that the content exists, and (C) has keywords and/or phrases that accurately characterize that content, then there is a good chance that Google, Bing, etc. will do well for them, and my work will rarely do better. But for nearly all people, interests, meaning, and Internet content, (A)-(C) is asking way too much. E.g., (A)-(C) often works poorly for meaning, even when the content is based on just simple text. For meaning of audio, video, still images, etc., (A)-(C) and keywords/phrases are still less effective. E.g., tough to use keywords/phrases to characterize accurately the meaning of most art. So, that's my startup. Again, the code appears to be ready for production, say, to a few dozen new users per second. On average, each user will see, in about 30 minutes, a few dozen ads. Then multiply out and get an estimate of a significant business. The way the internals work, a lot of scaling will be possible just from simple sharding. The key to the work, the enabling crucial core, is some original applied math I derived (theorems and proofs) based on some advanced math prerequisites I got before, during, and after my applied math Ph.D. From all I can see, my applied math has nothing in common with current computer science, data science, machine learning, or artificial intelligence -- I certainly have intended no such connections. But the users will have no sense of anything mathematical. Initially, I'm borrowing some from the Paul Graham http://www.paulgraham.com/13sentences.html http://www.paulgraham.com/13sentences.html in particular on his "love" "5. Better to make a few users love you than a lot ambivalent." So, initially my site will be focused on some users and on some of their more likely interests and will not really be equally good for all interests of all users, i.e., will not be comprehensive. If then the site is successful, it will grow. In an important sense, the site will grow automatically, organically, to please the users. Feedback welcome!
kenjackson 8y ago
> They address a class of questions that were previously ‘hard for computers and easy for people’, or, perhaps more usefully, ‘hard for people to describe to computers’. Those aren't the only problems. ML can also solve problems that were previously 'hard for people and with no good algorithm for computers'. These are problems where there is a good labeled dataset, but no good algorithm to map from data to label. For example, the work determining sexual orientation from images (https://osf.io/fk3xr/ https://osf.io/fk3xr/). The problem with this approach is you get predictive ability, but no insight. But still can be of great value, and potentially great danger too.
- fnbr 8y ago
  To be clear, it's not obvious that the paper you linked is actually accurate. A lot of researchers consider that paper to be deeply flawed, and to show something other than what it claims to.
  - lhnz 8y ago
    What are others saying it shows?
    - claytonjy 8y ago
      I enjoyed this take on it: http://callingbullshit.org/case_studies/case_study_ml_sexual_orientation.html http://callingbullshit.org/case_studies/case_study_ml_sexual...
- bagrow 8y ago
  If there is good labeled data, is it still 'hard for people'?
  - anjc 8y ago
    Presumably the labels aren't coming from human's visual classification of the images.
- _raoulcousins 8y ago
JoeSmithson 8y ago
There's an XKCD [0] that hasn't aged well at all about how non-programmers fail to understand that some things that seem easy are actually really hard for a computer. Modern ML research moved a big chunk of these tasks into the "easy for a computer" category, which is very exciting for programmers. However, as the comic points out, most of these things are stuff that normal people sort of felt a computer could do already. I think this is the reason for some of the backlash/disappointment. [0] https://xkcd.com/1425/ https://xkcd.com/1425/
- glial 8y ago
  Given that this was created 4-5 years ago, I'd say it aged perfectly.
sebleon 8y ago
This article is more realistic than most ML posts, but it’s clear the author is not a practitioner. > More handwriting data will make a hand-writing recognizer better, and more gas turbine data will also make a system that predicts failures in gas turbines better, but the one doesn't help with the other. Data isn’t fungible. Transfer learning is one of the most interesting aress of machine learning. The focus is on taking learnings for one task, and applying them directly for another. More directly, Jeff Dean from Google had a fascinating talk about using these techniques to create a single super-model that combines learnings from thousands of tasks to accomplish new things quickly. [1] [1] https://youtu.be/HcStlHGpjN8 https://youtu.be/HcStlHGpjN8
- outworlder 8y ago
  > This article is more realistic than most ML posts, but it’s clear the author is not a practitioner. No so fast. Genuine question: how close is the research on 'transfer learning' to something that can be readily used to solve business problems today? If you can't fire up tensorflow and the like and use it to solve a real problem, or if only the likes of Google are able to successfully apply it, then the author would be correct.
  - claytonjy 8y ago
    You _can_ fire up TF to solve real problems without being Google. Transfer learning is _the_ way to do image classification for most kinds of images in 2018, and is covered heavily in most classes. In the fast.ai class, you use transfer learning in the very first lesson to build a dog/cat classifier. Takes less than an hour to get to 97+% accuracy with no prior knowledge of deep learning.
    - mercutio2 8y ago
      It sounds like you’re saying that transfer learning is helpful for image classification, which seems like an uncontentious position. Are you really arguing that you think transfer learning would be useful from handwriting models to turbine failure models? Using techniques that are successful with image classification as an example and generalizing to other domains that don’t look much like imaging seems like a stretch to me. But perhaps I’ve missed some more convincing examples of the state of the art in transfer learning.
ggregoire 8y ago
> 1) Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google's open-source tools Keras and Tensorflow). > 2) Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches. > 3) Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible. Are there any online resources to learn and implement step-by-step some ML to solve one of these use cases (or other ones)? Like a GitHub repo with data samples, a programming environment and then a step-by-step guide to solve trivial but real business problems?
- vishvananda 8y ago
  You might find http://www.fast.ai/ http://www.fast.ai/ useful. Depending on your learning style, their courses can either be amazing or somewhat annoying. Their library includes jupyter notebooks so that you can work through the examples.
- anant90 8y ago
  Check out Commonlounge: https://www.commonlounge.com/discussion/8d5612f5517944059a61f92aa8b86770 https://www.commonlounge.com/discussion/8d5612f5517944059a61... The courses have hands-on assignments that make you work through real-world problems.
- chasil 8y ago
  I am working through Google's machine learning crash course. https://developers.google.com/machine-learning/crash-course/ml-intro https://developers.google.com/machine-learning/crash-course/... Tensorflow sprays a lot of calculus. The idea is that all your known quantities ("features") become terms in an n-dimensional polynomial. The act of "training the model" is finding minima by traversing the negative gradient (the terms of the partial derivatives of any point when projected as a vector at that point). I'm glad I had calc 3, even if that was only 3-dimensional.
bunkydoo 8y ago
Just take the machine, bring it outside, and light it on fire - should fix your problems with machine learning you dumbass fuckwads
bunkydoo 8y ago
sdfasdfasdfasdfasd
bunkydoo 8y ago
sdfasdfasdfasdfasdfasdfasd
bunkydoo 8y ago
hjmbv8568456735667fghsxdasfg 453645345645 gytfghhdhyfg
qznc 8y ago
My mental model: Neural networks are for gut decisions. This fits their application domain quite well imho. Gut decisions are quick. You look at a picture and decide its a dog quicker than you can say it. Executing a neural net is feasible even on mobile devices on battery. Good gut decisions require experience/training. You must train extensively to be good and your training data must be good. An example from "Thinking, Fast and Slow": Experienced stock traders often claim to have a gut feeling, but it is bogus because their training data is bogus (good decisions lead to bad outcomes and vice versa). In contrast, experienced firefighters have a gut feeling if it safe to enter a burning house. This works in practice. The observe stuff about the environment reliably without being able to point them out consciously. Gut decisions are not about planning or knowledge. This requires different AI techniques than neural nets and intuitively shows their shortcomings.
mcrad 8y ago
Sophisticated machine vision has been around a heck of a lot longer than the author suggests. Only because that technology has gotten so accessible do we argue about who has the role to apply it (and ML in general) in a responsible or sensible way.

309 points by anastalaz 8y ago

A couple weeks ago I was asked to meet with two actuaries. Their company (a big one, I'm working for them on contract) is trying to promote the use of Machine Learning and when they heard I had just completed post-grad in ML, they were eager to pick my brains. In a 60 minute meeting, we spent 5 minutes discussing how ML worked and 55 minutes going in circles. They had lots of data, but no problems to solve. And there are only so many ways to explain that a large dataset is not equivalent to a business problem. I'll be sending this on to them.

ggg9990 8y ago
If actuaries don’t have business problems that could use ML what the heck do they do all day? Insurance is a great target for ML.
- HenryTheHorse 8y ago
  That's a really good point. Discovering patterns and correlations is an actuary's only competency.
  - sulam 8y ago
    Which may mean that all the statistical techniques ML would use are fairly rudimentary for them.
- lordnacho 8y ago
  As with many lines of work, the cool part may be a very small part of their day. With insurance, there's probably an already entrenched way to think about the data that's been there for decades, as well as regulations that help to entrench it. It might also be the case that the business problem isn't risk analysis at all, as one might expect. It may be finding investments for the float that provide a sensible return within the regulatory remit is harder than figuring out how much needs to be charged for dinging someone's car.
  - nerdponx 8y ago
    Correct on all points. There is also the issue that the pricing models themselves need to be able to stand up to some form of regulatory scrutiny (depending on the state and the nature of the insurance product).
jcims 8y ago
I’ve been noodling a little with ML for infosec and if I spent an hour with a pro I might wind up doing the same thing. The problem is likely to be one of a cacophony of choice. It’s not that they don’t have any problems, it’s that they have a million of them and have no solid footing from which to frame their answer in the form of a question. I bet if you picked one field in one dataset and started drilling down into how they could possibly use it, the use cases will start to melt out of their frozen brains. Get ready.
- Kalium 8y ago
  I can think of some really useful infosec applications. "Is this binary malicious?" springs quickly to mind.
  - jcims 8y ago
    There are a bunch. Just about any will-instrumented platform presents many opportunities to do basically side-channel detection of compromise (e.g. cpu utilization, network utilziation, network flows, syslog data, process tree, etc)
- robertk 8y ago
  Maybe they could use an ML model to narrow down which one of their million use cases would benefit most from an ML model. ;)
ssivark 8y ago
While I get what you're saying, isn't that a very "supervised ML" way of thinking? In a world where unsupervised learning was easier to apply, it might be possible to start with data, compress it to a low-dimensional manifold, and use that understanding as inspiration for better directions/questions to pose. So I guess the real problem is that humans expect anything branded as "intelligence" to have the ability to learn in an unsupervised manner, while breakthroughs are closer to the regime where the goals are supervised while the solutions are discovered. Of course, the amount of supervision probably lies on a continuum, between everything being completely specified (classical programming) and everything unsupervised ("true" "human" intelligence).
- taeric 8y ago
  Lots of data does nothing to help when the person you are talking to is not given a) knobs to turn, and b) a goal to achieve. To that end, just having loads of data doesn't necessarily help. You also have to have loads of control over some part of the system. And even then, you can't expect ML to help, all on its own. So, the worst is folks that think just because you have full trace logs of your system, it should be trivial to apply anomaly detection. At face value, yes. Yes you can. But also at face value, your trace logs aren't free and it isn't like folks are keen on letting their detection system cut off anomalous transactions without some sort of kill switch.
  - ssivark 8y ago
    I think my point may not have gotten across clearly, so let me clarify. I agree with most of what you said, which IMHO comes under the spirit of supervised machine learning paradigm, which is the most advanced tool we have pragmatically available to use today. Looking beyond, if we find a model+learner which can discover a low dimensional latent space (through certain indirectly specified biases) then that low dimensional formulation of the domain can guide us towards interesting questions worth asking. To have a factorized low-dimensional formulation is roughly what it means to "understand" a subject, so such a toolkit would be enormously useful. Many people (including some experts, rightly or wrongly) believe that neural networks might be that model class, and (clever tweaks of) gradient descent might be an acceptable learner. All I'm saying is that the current hype about AI fails to separate the potential of the latter class from the currently available successful tools of the former class.

brootstrap 8y ago

Wow good write up, i just made it a few paragraphs in but had to comment. especially after just reading thru the IBM watson 'AI' bullshizz, this is refreshing and reminds me that there are folks who are trying to think about this stuff in new ways rather then just hyping/selling the out of 'AI' so your new BigCo can burn thru 10/100s of millions on some shy project =)

AndrewKemendo 8y ago
In fact the majority of people who actually do ML/DL or Applied AI in other forms think this way by default. As usual, Ben Evans backs into the standard understanding from the experts in the field as though it's revelation.
- benedictevans 8y ago
  That's the point - to explain how people in the field are thinking about this stuff. Most people outside SV don't know what the experts in the field are saying. Indeed, many people in SV don't know this ;) If you already know all of this, you're not the target audience.
  - AndrewKemendo 8y ago
    That's fair enough. It's a running joke with applied ML people that half of your job, if you have any sense of professionalism, is convincing possible clients that no, they probably don't need to use a 41 layer DNN to send a follow up email.
    - benedictevans 8y ago
      A database consultant once told me that half his gigs were telling people with a database to use Excel
      - boxy310 8y ago
        Funny - half of my experiences talking with nonprofits were telling people with an Access database to migrate to a proper SQL database.

CGamesPlay 8y ago

This article is pretty great and I agree with the framework it sets up around automation and future jobs. One thing I felt was dismissed too easily was the "Google has all the data" line. Sure, nobody said "Oracle has all the database", but people ARE saying the former. Why should we ignore the piles of collected data when thinking about how the landscape will look in the future?

zerostar07 8y ago
There are probably plenty of problems that do not require that much data to solve (e.g. in reinforcment learning). And google doesn't have all the kinds of data - they didn't own medical imaging data, they had to ask for it. There are some problems where they have advantage (speech / translation) and they 've probably already solved those problems, but their data advantage is not as important as they make it to be. Whats more worrying IMO is that they 're hoarding all the researchers.

graycat 8y ago

It's tough to know just what machine learning covers, includes, consists of. From what I've been able to see, currently in practice, apparently 90+% of machine learning is curve fitting and nearly all of that is some form of classic linear regression. Linear regression and curve fitting more generally have been around, available, and used going way back in electronic digital computing and well before. E.g., for software we've long had the IBM Scientific Subroutine Package (SSP), SPSS (Statistical Package for the Social Sciences), SAS (Statistical Analysis System), ..., R. There are stacks of polished textbooks in statistics and specialized to some fields, e.g., econometrics, time series analysis, etc. There has been some usage, but headlines have been rare for decades. But, there's a lot more to applied math than anything much like that curve fitting. E.g., maybe take the learning as the ability to do statistical estimates -- well, there's a lot to statistical estimation than curve fitting. There's also the field of optimization -- linear programming, integer linear programming, network linear programming, dynamic programming (discrete time, continuous time, deterministic, under uncertainty), quadratic programming, other cases of non-linear programming, and more. And lot more can be done in applied probability and stochastic processes. IMHO it would be better progress and description of the current state to discuss both the applications and the solution techniques in more detail. E.g., the crucial core of my startup is some applied math I derived (with theorems and proofs) based mostly on some advanced pure math prerequisites. I wrote the corresponding software. Then from 50,000 feet up or from the point of view of a user of the work, what I did can look like a machine that learns a lot very quickly, is very smart, and puts out really intelligent stuff. Still, my math is not covered by any of the machine learning I've heard of. Point: There's a LOT of applied math that can be done and, really, has been done far from current descriptions of machine learning.

cschmidt 8y ago
Is your startup Datathings?
- graycat 8y ago
  Naw. The project is for a new Web site. I have the hard technical work done, e.g., the crucial core applied math and the corresponding code apparently ready for production, but I have to do some routine work, e.g., add more data, pick a company name and get trademark protection, get a static IP address, pick and register a domain name, get a tax ID, find out what the ad networks want so that I can run and get paid for ads, tell my county that I'm "doing business as ...", maybe set up an LLC, get a business checking account, get an e-mail address just for the business and another one for Web site feedback, do some more testing internally, tweak some of the software, tweak my code for my Web site session state server to get a better Web site log server, kick back and give a critical appraisal of the effort and make some tweaks, announce an alpha test here on HN and elsewhere, then a beta test, then get some publicity by some of the usual ways, then maybe have a business. The startup is supposed to be the first good solution for a problem pressing for nearly every user on the Internet around the world, smartphone to ... workstation. The potential of the business as currently envisioned would be, on average, about three sessions per week, 30 minutes of eyeball time per session, for over 50% of the users of the Internet in the world. The site will be able to do some relatively good ad targeting while having some of the best protection of user privacy, e.g., no use of cookies, logins, Web browser user agent strings, or third party tracking. So, make some assumptions about ad rates, multiply, and get an estimate of a good business. The problem: Given an interest of a person, typically a narrow interest, maybe a short term, recent, or new interest, that interest treated as unique in all the world, find the Internet content with the meaning that person wants for their interest. So, part of project is addressing meaning of Internet content. The content might be in any of the common data types of text, still images, videos, music, Web cams, pod cast audio, etc. The content might be in Web pages, PDF files, YouTube videos, Instagram images, art gallery images, etc. The interests might be narrow topics in crafts, politics, skills, academic subjects, art, social, interior decorating, travel, intersections of those, etc. So, really an interest can be essentially anything, and the content can be essentially anything on the Internet. And again the main criterion is meaning. To the users, the Web site is just how to find content with the meaning they want for each of their interests. So, the site is a new form of engine for search, discovery, recommendation, custom curation, etc. To heck with these categories: The site is for the users to find the content with the meaning they want. Well, characterizing meaning with just keywords and phrases is usually from difficult to impossible in practice. So, my work makes no use of keywords or phrases. So, really, my site is not direct competition for Google, Bing, etc. If a user (A) knows what content they want, (B) knows that the content exists, and (C) has keywords and/or phrases that accurately characterize that content, then there is a good chance that Google, Bing, etc. will do well for them, and my work will rarely do better. But for nearly all people, interests, meaning, and Internet content, (A)-(C) is asking way too much. E.g., (A)-(C) often works poorly for meaning, even when the content is based on just simple text. For meaning of audio, video, still images, etc., (A)-(C) and keywords/phrases are still less effective. E.g., tough to use keywords/phrases to characterize accurately the meaning of most art. So, that's my startup. Again, the code appears to be ready for production, say, to a few dozen new users per second. On average, each user will see, in about 30 minutes, a few dozen ads. Then multiply out and get an estimate of a significant business. The way the internals work, a lot of scaling will be possible just from simple sharding. The key to the work, the enabling crucial core, is some original applied math I derived (theorems and proofs) based on some advanced math prerequisites I got before, during, and after my applied math Ph.D. From all I can see, my applied math has nothing in common with current computer science, data science, machine learning, or artificial intelligence -- I certainly have intended no such connections. But the users will have no sense of anything mathematical. Initially, I'm borrowing some from the Paul Graham http://www.paulgraham.com/13sentences.html http://www.paulgraham.com/13sentences.html in particular on his "love" "5. Better to make a few users love you than a lot ambivalent." So, initially my site will be focused on some users and on some of their more likely interests and will not really be equally good for all interests of all users, i.e., will not be comprehensive. If then the site is successful, it will grow. In an important sense, the site will grow automatically, organically, to please the users. Feedback welcome!

kenjackson 8y ago

> They address a class of questions that were previously ‘hard for computers and easy for people’, or, perhaps more usefully, ‘hard for people to describe to computers’. Those aren't the only problems. ML can also solve problems that were previously 'hard for people and with no good algorithm for computers'. These are problems where there is a good labeled dataset, but no good algorithm to map from data to label. For example, the work determining sexual orientation from images (https://osf.io/fk3xr/ https://osf.io/fk3xr/). The problem with this approach is you get predictive ability, but no insight. But still can be of great value, and potentially great danger too.

fnbr 8y ago
To be clear, it's not obvious that the paper you linked is actually accurate. A lot of researchers consider that paper to be deeply flawed, and to show something other than what it claims to.
- lhnz 8y ago
  What are others saying it shows?
  - claytonjy 8y ago
    I enjoyed this take on it: http://callingbullshit.org/case_studies/case_study_ml_sexual_orientation.html http://callingbullshit.org/case_studies/case_study_ml_sexual...
bagrow 8y ago
If there is good labeled data, is it still 'hard for people'?
- anjc 8y ago
  Presumably the labels aren't coming from human's visual classification of the images.
_raoulcousins 8y ago

JoeSmithson 8y ago

There's an XKCD [0] that hasn't aged well at all about how non-programmers fail to understand that some things that seem easy are actually really hard for a computer. Modern ML research moved a big chunk of these tasks into the "easy for a computer" category, which is very exciting for programmers. However, as the comic points out, most of these things are stuff that normal people sort of felt a computer could do already. I think this is the reason for some of the backlash/disappointment. [0] https://xkcd.com/1425/ https://xkcd.com/1425/

glial 8y ago
Given that this was created 4-5 years ago, I'd say it aged perfectly.

sebleon 8y ago

This article is more realistic than most ML posts, but it’s clear the author is not a practitioner. > More handwriting data will make a hand-writing recognizer better, and more gas turbine data will also make a system that predicts failures in gas turbines better, but the one doesn't help with the other. Data isn’t fungible. Transfer learning is one of the most interesting aress of machine learning. The focus is on taking learnings for one task, and applying them directly for another. More directly, Jeff Dean from Google had a fascinating talk about using these techniques to create a single super-model that combines learnings from thousands of tasks to accomplish new things quickly. [1] [1] https://youtu.be/HcStlHGpjN8 https://youtu.be/HcStlHGpjN8

outworlder 8y ago
> This article is more realistic than most ML posts, but it’s clear the author is not a practitioner. No so fast. Genuine question: how close is the research on 'transfer learning' to something that can be readily used to solve business problems today? If you can't fire up tensorflow and the like and use it to solve a real problem, or if only the likes of Google are able to successfully apply it, then the author would be correct.
- claytonjy 8y ago
  You _can_ fire up TF to solve real problems without being Google. Transfer learning is _the_ way to do image classification for most kinds of images in 2018, and is covered heavily in most classes. In the fast.ai class, you use transfer learning in the very first lesson to build a dog/cat classifier. Takes less than an hour to get to 97+% accuracy with no prior knowledge of deep learning.
  - mercutio2 8y ago
    It sounds like you’re saying that transfer learning is helpful for image classification, which seems like an uncontentious position. Are you really arguing that you think transfer learning would be useful from handwriting models to turbine failure models? Using techniques that are successful with image classification as an example and generalizing to other domains that don’t look much like imaging seems like a stretch to me. But perhaps I’ve missed some more convincing examples of the state of the art in transfer learning.

ggregoire 8y ago

> 1) Machine learning may well deliver better results for questions you're already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google's open-source tools Keras and Tensorflow). > 2) Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches. > 3) Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible. Are there any online resources to learn and implement step-by-step some ML to solve one of these use cases (or other ones)? Like a GitHub repo with data samples, a programming environment and then a step-by-step guide to solve trivial but real business problems?

vishvananda 8y ago
You might find http://www.fast.ai/ http://www.fast.ai/ useful. Depending on your learning style, their courses can either be amazing or somewhat annoying. Their library includes jupyter notebooks so that you can work through the examples.
anant90 8y ago
Check out Commonlounge: https://www.commonlounge.com/discussion/8d5612f5517944059a61f92aa8b86770 https://www.commonlounge.com/discussion/8d5612f5517944059a61... The courses have hands-on assignments that make you work through real-world problems.
chasil 8y ago
I am working through Google's machine learning crash course. https://developers.google.com/machine-learning/crash-course/ml-intro https://developers.google.com/machine-learning/crash-course/... Tensorflow sprays a lot of calculus. The idea is that all your known quantities ("features") become terms in an n-dimensional polynomial. The act of "training the model" is finding minima by traversing the negative gradient (the terms of the partial derivatives of any point when projected as a vector at that point). I'm glad I had calc 3, even if that was only 3-dimensional.

bunkydoo 8y ago

Just take the machine, bring it outside, and light it on fire - should fix your problems with machine learning you dumbass fuckwads

bunkydoo 8y ago

sdfasdfasdfasdfasd

bunkydoo 8y ago

sdfasdfasdfasdfasdfasdfasd

bunkydoo 8y ago

hjmbv8568456735667fghsxdasfg 453645345645 gytfghhdhyfg

qznc 8y ago

My mental model: Neural networks are for gut decisions. This fits their application domain quite well imho. Gut decisions are quick. You look at a picture and decide its a dog quicker than you can say it. Executing a neural net is feasible even on mobile devices on battery. Good gut decisions require experience/training. You must train extensively to be good and your training data must be good. An example from "Thinking, Fast and Slow": Experienced stock traders often claim to have a gut feeling, but it is bogus because their training data is bogus (good decisions lead to bad outcomes and vice versa). In contrast, experienced firefighters have a gut feeling if it safe to enter a burning house. This works in practice. The observe stuff about the environment reliably without being able to point them out consciously. Gut decisions are not about planning or knowledge. This requires different AI techniques than neural nets and intuitively shows their shortcomings.

mcrad 8y ago

Sophisticated machine vision has been around a heck of a lot longer than the author suggests. Only because that technology has gotten so accessible do we argue about who has the role to apply it (and ML in general) in a responsible or sensible way.