> 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)
Unsupervised learning takes samples and generates more of the same kind of data. We can see unsupervised learning as being given samples (X) and training a function X = F(noise) that transforms a random vector into an output whose distribution resembles that of the original generating function.
Reinforcement learning sees a reward R(X) of policies X and learns to find X such that R(X) is high.
All of these have been known to operate on trees or other representations instead of arrays.
This matters because there are business problems that are not supervised learning. Reinforcement learning problems especially. Sometimes I can say how well an output is doing and I want an output that does better.
As a "Pull" programmer , I can't tell how I would use this technology to solve problems I have or my company has. This makes it difficult for me to get motivated to learn it, despite all the buzz and excitement.
I went through the tensorflow tutorial. While it is interesting, I didn't find it useful, the way learning a new programming language or framework or library would be.
That's one general statement over another. Consider https://arxiv.org/abs/1508.00021 as a counterexample.
...how you implement those functions, how you optimise for the best results and performance is an implementation detail.
If you are picking your solution before you know what your problem is, you are falling into premature optimisation.
Understanding how to reduce your problem domain into a series of functions that take numerical data and return numeric data is the key practical problem that most people face, and it's poorly described in the literature.
(That's why most people just pull out that classic line '...its great for image recognition and ...many other things...')
> 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
Nitpick maybe, but you do not represent each class by a number. This would create a bias to consider subsequent categories as similar (why would aardvark=0 be more similar to antilope=1 than cat=2?). Instead you have one dimension per possible-value (aka one-hot encoding) and the value in that dimension represents how likely the photo belongs to that class (with values summing to 1, softmax, if it's a one-class problem, or no such guarantee if it's a multiclass problem).
> That's. All. It. Does.
No. That's. WHAT. It. Does.
Your emotional overreaction to this site has induced bias leading to a false negative -- mistaking hype for signal. Fact is, using deep learning, newbie grad students have routinely and resoundingly outperformed nearly all the state-of-the-art techniques in signal processing in less than FIVE years of trying. Image analysis and speech recognition have been revolutionized; 40+ years of R&D expertise has been largely supplanted in the blink of an eye. Dismiss this tsunami at your own peril.
Does DL scale to harder problems? Why yes, it does. No computer implementation of the game Go had outperformed the best humans until deep learning was used, then it beat the best humans almost immediately (unlike chess which took decades to reach that level using traditional AI methods). I have little doubt that significant achievements await DL's application to more complex tasks like language analysis too. To believe otherwise would be severely short-sighted.
Does deep learning also solve trivial pattern rec problems like regression or curve fitting as efficiently as some linear ML models? No, but who cares? By that reasoning, we should dismiss Einstein because many 12 year old children can multiply numbers faster and for less money.
I feel https://www.tensorflow.org/tutorials/word2vec is really good example of what I'm actually talking about.
Given a corpus, how do you represent it in a form that is suitable for processing?
Actually, it turns out that's a really hard problem which a lot of work has gone into; but many tutorials and introductions to ML skip over it as though it were a trivial implementation detail on the back of which type of NN you decide to use, or how many layers you want.
I wrote the parent comment because on reading http://aiplaybook.a16z.com/docs/intro/survey-goals, I got excited that maybe someone had actually broached this topic in a meaningful way.
...but then as I read through yet another guide to installing tensorflow followed by assert!(example 101 works), I lost all enthusiasm for it. I don't know; I'm happy to admit I'm perhaps overly critical, but this wasn't a false negative for me, it was just a frustrating unfulfilled promise. Just read the tensorflow tutorials, they're better.
Here's someone else who cherry-picked a quote and ignored the central argument: https://news.ycombinator.com/item?id=14347068
Almost every one of your sentences are talking past the parent. If you want to be argumentative instead of helping the parent as he asked, then it might be best to find actual quotes to disagree with.
The traditional computer vision pipeline works like this: you preprocess the image, featurize it with features like SIFT, and then run an SVM classifier. However, this pipeline is totally beaten by end-to-end neural networks.
Same will hold true for other domains. As long as we've discovered the right architecture, aka. the family for f(x), learning still boils down to training your models.
This and some of your other comments in this thread lead me to believe you are unaware of how DL is being used in NLP. I would argue real industry use of DL for NLP dwarfs that of DL for images. Personal assistants, search, customer service, sales, chat are the big general cases, let alone company-specific tasks (.e.g. financial data, reviews) or other NLP domains that are not particularly industry-adopted (.e.g. translation, summarization). There's a lot of demand for NLP DL experts, and it's no surprise this year that Socher's NLP DL class had 700 Stanford students enrolled.
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.
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.
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.
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.
In other words,
The hard part isn't really the AI per se, it's getting enough DNA from the circulating copies (i.e. sample prep)
It sounds like there's a way to detect "This DNA is definitely cancerous," but the problem is that the test only works when you give it enough DNA. My question is, where does this "enough" criteria come from? Why doesn't it work with a single cell? (I know there are obvious reasons, but I don't know what they are.)
To detect these, you're going to use either something like microarrays, QPCR, or DNA sequencing. Today people talk almost exclusively about high-throughput sequencing (HTS) because it has gotten quite cheap, gives you large amounts of data, can be made massively parallel and isn't as hindered by needing to know very precisely what you're looking for (microarrays and QPCR require somewhat more targeting; HTS also needs you to know what you're looking for, but not in quite the same ways).
In all of those cases you need a sample of DNA (duh). These can all be made to work if you have a single cell in hand, it's tricky but sample prep does exist for single cells at a time. Trouble is, you need that cell in hand. There are about 5L of blood in the human body. A human cell will range from 1-10 pico-liters. You will take a blood sample of 2 milli-liters. Chances are pretty slim your one cell will be in that 2mL. Even if it is, there are billions of other cells in that 2mL adding additional noise. Usually though, we're looking at circulating DNA fragments with these technologies, and depending on the stage of the cancer and the type of cancer, there may be enough to pick up on in the blood. How to do that best is an area of active R&D.
Just dug up review if you want to learn more:
For some reason I assumed that this detector would be embedded into your circulatory system, so that it eventually scans all 5L of blood in your body rather than getting lucky with a 2mL draw. Now that I say it, it sounds pretty unrealistic, but I wonder if it's even remotely possible.
If you wanted to monitor for a very specific piece of DNA or a small number thereof, you could have an inline piece of hardware that samples the blood continuously, trying to enrich for those. You could then use a hybridization-based (kinda like a microarray) test to check for the presence of that DNA. Right now, the relevant technology just isn't there. You'd need new materials (of the top of my head they'd need to be highly resistant to fouling, among many other properties), probably a completely new filtering/enrichment system (again fouling and clogging and specificity and sensitivity are huge problems here), and a decent, cheap, high sensitivity and reliability hybridization based detector. All of those things are quite hard, but I don't think they're completely impossible. I do think it's highly unlikely we'll be seeing this in the next 5-10 years though. But I could be wrong! :)
Billions? I think you meant millions.
If only Theranos had actually worked...
Much like in CS/Software, it'll help if you've read a bit of their work and/or can show some way you could contribute directly. i.e. "I specialize in CS and did a side-project analyzing <gene_dataset_x> where I found <something_cool>. I read your <paper> and found <this_thing_you_did> was very cool for <insightful_reason>. I'd love to get more involved in the field and possibly find a volunteer position where I could contribute, would you have time to chat on the phone or over coffee sometime?"
One place to look for genomic data:
There are others, google around and if you could also look at https://www.plos.org journals to keep an eye out for new freely available articles in this and other areas of biology. Good luck! :)
The start of your second sentence seems to me pretty inaccurate.
AI solves problems that are vastly too hard for humans to figure out.
False. Plenty of work has been done to show what exactly is going on inside NNs including some very impressive visualizations.
> Hand designed programs to do machine vision don't even get a tenth of the accuracy of NNs.
And that's false too. Hand designed programs to do machine vision can get significant fractions of the accuracy of NNs and sometimes even perform better and/or faster depending on the domain.
It is a lot more work to write that code though and a NN might clue into features that you weren't aware even existed.
>And that's false too. Hand designed programs to do machine vision can get significant fractions of the accuracy of NNs and sometimes even perform better and/or faster depending on the domain.
Source? A few years ago people thought it was amazing an algorithm could recognize cats 12% of the time. And even the best non-neural approaches use some form of machine learning.
>a NN might clue into features that you weren't aware even existed.
That's the whole point...
It's just that NNs are rapidly moving ahead and maybe some time in the future both your statements will be true but today they are not.
Recognizing cats is not the sum total of machine vision problems and those 'pretty visualizations' go a lot further than what you give them credit for.
I've been doing nothing but reading up on machine learning and working through piles of example code and models for the last couple of months and I think based on what I've seen so far that we are definitely headed in the direction that you suggest. At the same time we aren't there yet and such hype does not help.
Keep in mind that the domain of machine vision is extremely broad and much wider than Kaggle competitions and that saying 'we have no idea' when clearly we do tries to sketch NNs as magic and they're not.
If anything the present generation is best characterized as an optimization of what you could have done for many years, the only reason they are the way they are is because we'd like to have our results in a reasonable time. That makes applying NNs to large datasets possible and that in turn is what helps to solve some of these problems.
I'd love a source for this. The last time I checked I didn't find much. Sure we know the first few layers do stuff like edge detection. But the higher layers are much harder. And even if you know images are activating them, writing code that can accomplish the same task is much harder.
>It's just that NNs are rapidly moving ahead and maybe some time in the future both your statements will be true but today they are not.
The best NNs have already surpassed human accuracy at imagenet. That's insane! No one expected that result a few years ago. Yes object recognition isn't the only task in machine vision, but it's pretty representative.
I will do some digging for you tomorrow, it's 3:30 am and I really should get some sleep.
> Sure we know the first few layers do stuff like edge detection.
Yes, basic primitives.
> But the higher layers are much harder.
Well, those higher layers are built up out of the primitives the lower layers contain. So you get things that are conceptually easier to understand rather than harder.
> And even if you know images are activating them, writing code that can accomplish the same task is much harder.
That I completely agree with, I've basically gone that route in the last couple of months.
> The best NNs have already surpassed human accuracy at imagenet.
Yep. It's extremely impressive.
It's a watershed moment in the history of computing and half of us are still asleep or in denial. Which is fine by me. But please don't hype. It isn't magic and it isn't perfect - yet.
edit: ok, found two of the things I've looked at:
And this is interesting code:
If anything I think that NNs as applied to vision problems are somewhat easier to make sense of than doing the same for NLP or audio.
Though it definitely feels like cheating compared to actually programming a solution.
An alternative name for 'deep learning' would be 'automatic multi-level feature engineering', but that wouldn't be nearly as sexy.
"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?
Additionally very few machine vision systems are completely hand designed. They all use some form of machine learning and optimization. Just not necessarily NNs.
Edit: Btw I just had a look to confirm and what you're saying about "1000 equally balanced classes" is inexact, to put it midly. ImageNet has 100,000 SynSets (as in WordNet) and aims to have about 1000 images illustrating each. Individual images may illustrate several synsets- you don't get one image per class, say.
Also: AlexNet was not a cat recogniser and having unbalanced classes is a problem for machine learning, but not a dealbreaker, per se.
Classification is extremely representative of the challenge of machine vision. Good classifiers can often be adapted to do localization and other tasks, e.g. by scanning the image to see what parts contain an object.
The Imagenet Large Scale Visual Recognition Challenge has 1000 classes which you can browse here: http://image-net.org/challenges/LSVRC/2015/browse-synsets I forgot that Imagenet itself has more classes than that. But it's best known for this specific competition of 1000 classes, and everyone uses that as a benchmark.
>freaking AlexNet was not a fraking cat recogniser.
I never said it was... It's actually a dog recognizer mostly : )
Just added my email to my profile - feel free to reach out if I'm missing something or you'd like to discuss more.
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.
Transfer learning is an old and well known technique and is used ubiquitously. 
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"?
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.
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.
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
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/
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.
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...)
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.
"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
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).
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.
This quotation comes from 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-w....
It is interesting to ask if he was asking the right question or not, but misleading to trivialize it.
In any case, what do you mean by "the right question"?
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.
???? (release a self-anointed playbook)
Deep Learning is more about pattern matching, not about "understanding" patterns.