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AI Playbook
- shadowmint 9y agoI found this really frustrating to read; on 'training your own models': > Deep learning is a class of machine learning that has gotten a lot of well-deserved attention in recent years because it's working to solve a wide variety of AI problems in vision, natural language processing, and many others. What others? Be specific. The problem is not that people don't know how to build neural networks. The tensorflow tutorial is extremely comprehensive and there are lots of resources for people who know what problem they're solving to try to build a solution. The problem is that people don't understand what they can do with this technology, or even, in a meaningful sense, what it is. Given a array of input numbers [i1, i2, i3...] it provides a black box that can map that input to a set of output numbers [o0, o2, ...] where the dimension of the input and output are different. That's. All. It. Does. It takes an input array and outputs an output array; a magic f(x) function. The thing people don't understand well is not how do I implement f(x); the problem is how do I convert my domain problem (I have 2000000 shelf products in my database and a tonnes of sales information) how to convert that into an input array of numbers? What is the output array of numbers I want out of the function? The reason this works well for images is because you can say, ok, this output number represents a tag (dog, cat, etc) for each discrete value, and the input numbers are just an array of the raw pixel data. That's why its good for images; because a traditional function implementation of f(x) that takes a I of dimension say, 65535, and produces an output of dimension 1 is a hard problem. ...but if your problem is, given an input of say [price, date, origin] and an output of [value to business], you can solve that problem quite trivially without needing any neural network. tldr; The problem people are having is figuring out input and output for domains other than image processing; not how to build a NN, or how to choose which NN to build. Guide misses the point entirely. (You might argue this is somewhat uncharitable to what is actually a well written guide to ML; but you can read the about section for yourself and see what you think; I feel like the guide needs to focus sharply on practical business outcomes if it wants to do what it says its doing)
- guscost 9y agoThanks for writing this. It sounds like what I've experienced in this kind of work isn't just accidental. I am working on leveling up math skills to be able to understand the process for TensorFlow etc, but I get the feeling that once it's finally tractable I'll still have the same problems of finding how to transform the inputs into whatever works best for the model, what to output, and how to use the output. This seems to be the part that can't be solved by off-the-shelf packages. And the part where many or most exciting discoveries will be made in the future.
- rs86 9y agoYou might find useful to study algebra. It helps finding mathematical representations. You are in luck if you can find a representation that can be mapped to a vector space.
- guscost 9y agoDefinitely, Linear Algebra is next up, and it is seriously needed.
- lilbobbytables 9y agoWould say Algebra (and Linear Algebra as mentioned below) are the most useful to learn for ML/AI?
- lechiffre10 9y agoI would suggest following Siraj on youtube: https://www.youtube.com/watch?v=N4gDikiec8E https://www.youtube.com/watch?v=N4gDikiec8E really insightful!
- zardo 9y agoI can't take it. He's blasting memes out at 1hz. That's 60 times the acceptable safe limit.
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- eanzenberg 9y agoIs this a subtle ad for clarifai? For the record, I've worked on building a classifier for nudity detection and not much get's better than starting with a pre-trained model and fine-tuning to your specific rules. I wouldn't be surprised if this is the future of deep learning: Start with the closest pre-trained model that has trained for months using custom clustered hardware and fine-tune further on commodity hardware.
- strzzz 9y agoThat's a very good business model as well to sell these pre-trained models. Like a market where you choose what you need.
- fancy_pantser 9y agoOr, better, you submit a pre-tagged sample set and it tells you which models match and how well.
- AndrewKemendo 9y agoThat's what Algorithmia is trying to do. [1]https://algorithmia.com/tags/machine-learning https://algorithmia.com/tags/machine-learning
- AndrewKemendo 9y agoThat's not the future, it's what is standard in ML application engineering. For example, a good portion of classification systems use models based on ImageNet, VGG, Ngram or AlexNet and then fine tune the last (or last few) layers. Transfer learning is an old and well known technique and is used ubiquitously. [1] [1]http://cs231n.github.io/transfer-learning/ http://cs231n.github.io/transfer-learning/
- spullara 9y agoHave you looked at the Yahoo Open NSFW model? I ported it to Algorithmia if you want to try it out to compare: https://algorithmia.com/algorithms/spullara/YahooOpenNSFW https://algorithmia.com/algorithms/spullara/YahooOpenNSFW
- sillysaurus3 9y agoIt was pretty incredible to open up https://vimeo.com/215926017 https://vimeo.com/215926017 and click at a random timestamp (29:30) and hear "we're trying to use AI to diagnose cancer by examining the DNA that's free-floating in your bloodstream." This is clearly the future, if it can work. That's probably one of the biggest startup ideas that you could possibly have. I hope that whoever's behind it is successful.
- sjg007 9y agoYou don't need AI tor that.
- jessaustin 9y agoYeah it seems like it's just lots of in vivo prototype tests plus statistics?
- dboreham 9y ago"AI" is what we're calling statistics these days..
- avip 9y agoYou need it raise money. Cancer + AI = $$$
- entee 9y agoFreenome is the company, they're doing interesting work but it's still early days. This isn't actually a very new idea, it's been floating around for years in academia (I've personally been hearing it about it for at least 5 years). The hard part isn't really the AI per se, it's getting enough DNA from the circulating copies (i.e. sample prep) and being lucky enough that the cancer you're trying to diagnose actually puts out enough DNA for you to measure at an early enough stage to be clinically relevant. In other words, if you can only get DNA when it's at stage 4 and metastatic, your DNA test isn't actually very useful. If you can get stage 1 cancers, now you're talking, but in those cases tumors tend to shed a lot less DNA (among other reasons because they're smaller). A very good friend of mine is their CSO, I think they've got a really cool idea and could indeed go far, but it's a fiendishly hard problem.
- dchichkov 9y agoDoes anyone have a list of VCs that are specializing in investing into AI startups? I'm looking for a seed and it'd be great to have a list like that (I'm experienced in the field, first started with Deep Learning in 2010, it's on my GitHub...)
- lpolovets 9y agoThere are tons: Data Collective, Array Ventures, Susa Ventures (my fund), Switch Ventures, Amplify Partners, Zetta Venture Partners, AME Cloud Ventures, Ten One Ten, Bloomberg Beta, etc. That's not an exhaustive list by any means, just some of the first ones that come to mind for me. To be honest, I think the majority of seed funds are interested in AI/ML startups these days. Which one is the best fit for you will depend on the specific flavor of company you're building, your location, your level of traction, et cetera. If you have any questions, you can email me at: leo at susaventures.com.
- robbomacrae 9y agoRob May states that he is an active investor in the A.I. space in his excellent AI roundup newsletter: Inside AI (formerly Technically Sentient).
- dchichkov 9y agoThank you!
- beders 9y agoSite is incomplete. Misses out on whole categories of software classified as AI (like Cognitive Computing with approaches like associative memory). Sad.
- mifeng 9y agoWhile this site has some great info for non-technical folk who are serious about learning AI, I think the site will fly over the heads of most "line-of-business leaders" at whom it's aimed. In my experience, those folks think of AI like... My company is collecting tons of data in [insert data warehouses here]. How can I bolt on some "AI" and find out what's "interesting"?
- riphay 9y agoThis is a great point, and I think I fall into that bucket you describe. I have just enough information to be dangerous. There does seem to be a lack of available tools even for someone with a well-defined problem with available labelled data to validate implementing machine learning to a problem. I think that there's a need out there for a software with a GUI that can accept data and let you try some ML approaches with it and take the coding out of it - and it doesn't seem like it would be insanely difficult to build.
- mifeng 9y agoCheck out outlier.ai. Also I think there was a company in the most recent YC batch that tackled this problem.
- _delirium 9y ago> My company is collecting tons of data in [insert data warehouses here]. How can I bolt on some "AI" and find out what's "interesting"? For quite a few business types, this can be further simplified to just: How can I bolt on some "AI"? It's a nice bonus if you have some data and can find interesting things from it. But the goal for a surprising number of managers is just to be able to say they have some advanced AI technology doing, well, who knows what, just so long as there is definitely some advanced AI technology doing it.
- fudged71 9y agoThis is spot-on. There's an assumption that if you have large/big data, then AI will be your next big step. That's absolutely not a given. I've been told that AI is just "Statistics at scale", but you wouldn't call simple decision making like A/B testing "AI".
- baxtr 9y agoOh man. I wish back the old days where AI would be short for Advanced Industries... ;-)
- robotresearcher 9y agoFor people who work with animals, it's Artificial Insemination. I worked with robots and animals and had an intense code-switching problem.
- fiatjaf 9y agoI can't find an use to AI in any of my projects. Am I stupid?
- JabavuAdams 9y agoOk, now it's a bubble.
- rs86 9y ago100% agree
- joshatidealspot 9y agoI find a lot of discussion of AI skips over all the hard parts of productizing. Recently wrote a quick article on that topic for anyone who is interested: https://medium.com/towards-data-science/hard-earned-advice-for-ai-products-c320d900862e https://medium.com/towards-data-science/hard-earned-advice-f...
- jonobelotti 9y ago"AI products are hard. They require solutions to problems that are impossible for the human mind to comprehend... " The start of your second sentence seems to me pretty inaccurate.
- derefr 9y agoHow about "They require solutions to problems that the average human mind shies away from attempting to comprehend..." (I assume the rest of the argument is "thus making it hard for anyone with the problem your product solves to realize they even have that problem.")
- eastWestMath 9y agoThat's still remarkably grandiose.
- Houshalter 9y agoHow so? No one really has any idea how, say, NNs trained on machine vision are working. Hand designed programs to do machine vision don't even get a tenth of the accuracy of NNs. That's just one domain. And one that has been very well studied by humans for decades with little progress. AI solves problems that are vastly too hard for humans to figure out.
- YeGoblynQueenne 9y ago>> Hand designed programs to do machine vision don't even get a tenth of the accuracy of NNs. "A tenth of the accuracy" of a 100% accurate model is 10%. So you're basically claiming that nobody had achieved anything with machine vision before NNs (you probably mean AlexNet in particular), which is patently absurd and completely unhistorical. Why is it that this subject attracts the attention of so many people who know so little of what they're talking about?
- deleted 9y ago[deleted]
- thr0waway1239 9y agoCollect underpants, er portfolio of AI companies ???? (release a self-anointed playbook) Profit!
- annnnd 9y agoJudging by contents, this should really be titled "ML playbook", but I guess this doesn't draw that many clicks... :)
- ahousley 9y agoFantastic resource - thanks, a16z! Do you accept pull requests? I'd like to make a suggestion for the software and services page: http://aiplaybook.a16z.com/docs/reference/software http://aiplaybook.a16z.com/docs/reference/software Also, I think there's an emerging category outside of MLaaS that focuses on ML/DL deployment (ops). Here's a quick intro: Seldon's mission is to help people predict and shape the future with machine learning. Our products increase efficiency and speed up the time to value for data scientists: Seldon Core - a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure). Seldon is framework agnostic, it supports models built with TensorFlow, Keras, Vowpal Wabbit, XGBoost, Gensim and any other model-building tool — it even supports models built with commercial tools and services where the model is exportable. Fork Seldon on Github: https://github.com/SeldonIO/seldon-server/ https://github.com/SeldonIO/seldon-server/ Seldon Deploy - in development - a machine learning deployment platform for data science teams. Features include workflows with approvals and audit trails, advanced experiments, continuous integration and model explanations. Seldon was part of the Barclays Techstars '16 and we’re working with customers in banking, technology and media sectors. Disclosure: I’m CEO at Seldon.
- tontonius 9y agoArtificial Intelligence is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it. Please don't write this off as a reddit-esque comment. I really think the analogy holds -- just take a look at Product Hunt front page, or HN for that matter.
- rjtavares 9y agoSome people are really doing it, but they're not really doing it properly. Some of them will get better at it, and some will realize they started doing it way too early. In a few years though, most people will be doing it, and it'll be great (they'll exagerate how much they do it though). Yes, the analogy holds.
- stared 9y agoTerm 'Artificial Intelligence' is vague. "Deep learning is a name for machine learning techniques using many-layered artificial neural networks. Occasionally people use the term artificial intelligence, but unless you want to sound sci-fi, it is reserved for problems that are currently considered “too hard for machines” - a frontier that keeps moving rapidly." from http://p.migdal.pl/2017/04/30/teaching-deep-learning.html http://p.migdal.pl/2017/04/30/teaching-deep-learning.html In particular, two decades ago, letter recognition was considered a holy grail of AI, see: "Indeed, I once even proposed that the toughest challenge facing AI workers is to answer the question: “What are the letters ‘A’ and ‘I’?" - Douglas R. Hofstadter (1995) Not it as simple assignment to anyone learning ConvNets (BTW: I really recommend notMNIST as a starting point).
- ska 9y agoI've seen that Hofstadter quote used in this context before, and always assumed it was being quoted out of context. Do you know where it comes from? I suspect the idea is related to his "Letter Spirit" project - if so approaches like notMNIST don't really speak to it. Hofstadter often wrote about the difficulty in semantic understanding, and how AI approaches often approached things in such a superficial way that they miss the deeper meaning by design. Many of his projects were aimed at taking on these much harder problems head on. I suspect this quote, while also being a play on words, is aimed at this deeper understanding.
- stared 9y agoI give some context in the blog post linked above. This quotation comes from https://web.stanford.edu/group/SHR/4-2/text/hofstadter.html https://web.stanford.edu/group/SHR/4-2/text/hofstadter.html. And yes, he expected machine to have some 'deep understanding' of what is a letter. My approach (partly in the lines of Dennett, partly my experience with deep learning) is that we overestimate human cognition, which may be more 'a bunch of heuristics' than something 'really deep'. Especially, as some problems can be solved by... pigeons: http://www.sciencemag.org/news/2015/11/pigeons-spot-cancer-well-human-experts http://www.sciencemag.org/news/2015/11/pigeons-spot-cancer-w....
- monkeydust 9y agoFor a business guy this is a very useful resource, nice and clear. Can understand some of the criticism but perhaps people are expecting too much. I specifically liked the Image Feature Analysis tool and the fact this is exposed for others to use on github. Thinking about linking it to my home security camera.
- monkeydust 9y agoFor a business guy this is a very useful resource, nice and clear. Can understand some of the criticism but perhaps people are expecting too much. I specifically liked the Image Feature Analysis tool and the fact this is exposed for others to use on github. Thinking about linking it to my home security camera.
- amelius 9y agoDeep Learning != AI Deep Learning is more about pattern matching, not about "understanding" patterns.
- smefi 9y agoI found this quite frustrating. It gives the impression that AI is "plug and play".. well it's not. :)