So there's no a priori reason to apply that initialization again. This is a rather ad hoc procedure, but works well enough in practice. Okay, we've looked at all the layer classes. What about the Network class? Most of this is self-explanatory, or nearly so. The line self.
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As anticipated above, the Network. SGD method will use self. The lines self. These will be used to represent the input and desired output from the network. And if you get stuck, you may find it helpful to look at one of the other tutorials available online. For instance, this tutorial covers many basics. But the rough idea is that these represent mathematical variables, not explicit values. We can do all the usual things one would do with such variables: add, subtract, and multiply them, apply functions, and so on.
Indeed, Theano provides many ways of manipulating such symbolic variables, doing things like convolutions, max-pooling, and so on. But the big win is the ability to do fast symbolic differentiation, using a very general form of the backpropagation algorithm. This is extremely useful for applying stochastic gradient descent to a wide variety of network architectures. In particular, the next few lines of code define symbolic outputs from the network.
We start by setting the input to the initial layer, with the line. Note that the inputs are set one mini-batch at a time, which is why the mini-batch size is there. Note also that we pass the input self. The for loop then propagates the symbolic variable self. Now that we've understood how a Network is initialized, let's look at how it is trained, using the SGD method.
The code looks lengthy, but its structure is actually rather simple. Explanatory comments after the code. The next few lines are more interesting, and show some of what makes Theano fun to work with. Let's explicitly excerpt the lines here:. In these lines we symbolically set up the regularized log-likelihood cost function, compute the corresponding derivatives in the gradient function, as well as the corresponding parameter updates. Theano lets us achieve all of this in just these few lines. The only thing hidden is that computing the cost involves a call to the cost method for the output layer; that code is elsewhere in network3.
But that code is short and simple, anyway. By averaging over these functions, we will be able to compute accuracies on the entire validation and test data sets. The remainder of the SGD method is self-explanatory - we simply iterate over the epochs, repeatedly training the network on mini-batches of training data, and computing the validation and test accuracies. Okay, we've now understood the most important pieces of code in network3. Let's take a brief look at the entire program. You don't need to read through this in detail, but you may enjoy glancing over it, and perhaps diving down into any pieces that strike your fancy.
The best way to really understand it is, of course, by modifying it, adding extra features, or refactoring anything you think could be done more elegantly. After the code, there are some problems which contain a few starter suggestions for things to do. In particular, it's easy to make the mistake of pulling data off the GPU, which can slow things down a lot. I've tried to avoid this. With that said, this code can certainly be sped up quite a bit further with careful optimization of Theano's configuration.
See the Theano documentation for more details. Supports several layer types fully connected, convolutional, max pooling, softmax , and activation functions sigmoid, tanh, and rectified linear units, with more easily added. When run on a CPU, this program is much faster than network. However, unlike network. Because the code is based on Theano, the code is different in many ways from network. However, where possible I have tried to maintain consistency with the earlier programs. In particular, the API is similar to network2. Note that I have focused on making the code simple, easily readable, and easily modifiable.
It is not optimized, and omits many desirable features. Written for Theano 0. This allows Theano to copy the data to the GPU, if one is available. A more sophisticated implementation would separate the two, but for our purposes we'll always use them together, and it simplifies the code, so it makes sense to combine them. RandomStreams np. RandomState 0. At present, the SGD method requires the user to manually choose the number of epochs to train for. Earlier in the book we discussed an automated way of selecting the number of epochs to train for, known as early stopping.
Modify network3. Hint: After working on this problem for a while, you may find it useful to see the discussion at this link. Earlier in the chapter I described a technique for expanding the training data by applying small rotations, skewing, and translation. Note: Unless you have a tremendous amount of memory, it is not practical to explicitly generate the entire expanded data set. So you should consider alternate approaches. A shortcoming of the current code is that it provides few diagnostic tools. Can you think of any diagnostics to add that would make it easier to understand to what extent a network is overfitting?
Add them. We've used the same initialization procedure for rectified linear units as for sigmoid and tanh neurons. Our argument for that initialization was specific to the sigmoid function. Consider a network made entirely of rectified linear units including outputs. How does this change if the final layer is a softmax?
What do you think of using the sigmoid initialization procedure for the rectified linear units? Can you think of a better initialization procedure? Note: This is a very open-ended problem, not something with a simple self-contained answer. Still, considering the problem will help you better understand networks containing rectified linear units. Our analysis of the unstable gradient problem was for sigmoid neurons. How does the analysis change for networks made up of rectified linear units?
Can you think of a good way of modifying such a network so it doesn't suffer from the unstable gradient problem? Note: The word good in the second part of this makes the problem a research problem. It's actually easy to think of ways of making such modifications. But I haven't investigated in enough depth to know of a really good technique. Recent progress in image recognition. In , the year MNIST was introduced, it took weeks to train a state-of-the-art workstation to achieve accuracies substantially worse than those we can achieve using a GPU and less than an hour of training.
Thus, MNIST is no longer a problem that pushes the limits of available technique; rather, the speed of training means that it is a problem good for teaching and learning purposes. Meanwhile, the focus of research has moved on, and modern work involves much more challenging image recognition problems. In this section, I briefly describe some recent work on image recognition using neural networks. The section is different to most of the book.
Through the book I've focused on ideas likely to be of lasting interest - ideas such as backpropagation, regularization, and convolutional networks. I've tried to avoid results which are fashionable as I write, but whose long-term value is unknown. In science, such results are more often than not ephemera which fade and have little lasting impact. Given this, a skeptic might say: "well, surely the recent progress in image recognition is an example of such ephemera? In another two or three years, things will have moved on. So surely these results are only of interest to a few specialists who want to compete at the absolute frontier?
Why bother discussing it? Such a skeptic is right that some of the finer details of recent papers will gradually diminish in perceived importance. With that said, the past few years have seen extraordinary improvements using deep nets to attack extremely difficult image recognition tasks. Imagine a historian of science writing about computer vision in the year They will identify the years to and probably a few years beyond as a time of huge breakthroughs, driven by deep convolutional nets. That doesn't mean deep convolutional nets will still be used in , much less detailed ideas such as dropout, rectified linear units, and so on.
But it does mean that an important transition is taking place, right now, in the history of ideas. It's a bit like watching the discovery of the atom, or the invention of antibiotics: invention and discovery on a historic scale. And so while we won't dig down deep into details, it's worth getting some idea of the exciting discoveries currently being made.
Note that the detailed architecture of the network used in the paper differed in many details from the deep convolutional networks we've been studying. Broadly speaking, however, LRMD is based on many similar ideas. I'll refer to this paper as LRMD, after the last names of the first four authors. LRMD used a neural network to classify images from ImageNet , a very challenging image recognition problem. The ImageNet data that they used included 16 million full color images, in 20 thousand categories. The images were crawled from the open net, and classified by workers from Amazon's Mechanical Turk service.
Qualitatively, however, the dataset is extremely similar.
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These are, respectively, in the categories for beading plane, brown root rot fungus, scalded milk, and the common roundworm. If you're looking for a challenge, I encourage you to visit ImageNet's list of hand tools , which distinguishes between beading planes, block planes, chamfer planes, and about a dozen other types of plane, amongst other categories.
I don't know about you, but I cannot confidently distinguish between all these tool types. That jump suggested that neural networks might offer a powerful approach to very challenging image recognition tasks, such as ImageNet. Hinton KSH trained and tested a deep convolutional neural network using a restricted subset of the ImageNet data.
Using a competition dataset gave them a good way of comparing their approach to other leading techniques. The validation and test sets contained 50, and , images, respectively, drawn from the same 1, categories. Suppose an image shows a labrador retriever chasing a soccer ball. The so-called "correct" ImageNet classification of the image might be as a labrador retriever.
Should an algorithm be penalized if it labels the image as a soccer ball? It's worth briefly describing KSH's network, since it has inspired much subsequent work. It's also, as we shall see, closely related to the networks we trained earlier in this chapter, albeit more elaborate. So they split the network into two parts, partitioned across the two GPUs.
The details are explained below. Recall that, as mentioned earlier, ImageNet contains images of varying resolution. This poses a problem, since a neural network's input layer is usually of a fixed size. They did this random cropping as a way of expanding the training data, and thus reducing overfitting. This is particularly helpful in a large network such as KSH's. In most cases the cropped image still contains the main object from the uncropped image. Moving on to the hidden layers in KSH's network, the first hidden layer is a convolutional layer, with a max-pooling step.
The second hidden layer is also a convolutional layer, with a max-pooling step. This is because any single feature map only uses inputs from the same GPU. In this sense the network departs from the convolutional architecture we described earlier in the chapter, though obviously the basic idea is still the same. The third, fourth and fifth hidden layers are convolutional layers, but unlike the previous layers, they do not involve max-pooling. The KSH network takes advantage of many techniques. Instead of using the sigmoid or tanh activation functions, KSH use rectified linear units, which sped up training significantly.
KSH's network had roughly 60 million learned parameters, and was thus, even with the large training set, susceptible to overfitting. To overcome this, they expanded the training set using the random cropping strategy we discussed above. They also further addressed overfitting by using a variant of l2 regularization , and dropout. The network itself was trained using momentum-based mini-batch stochastic gradient descent. That's an overview of many of the core ideas in the KSH paper.
I've omitted some details, for which you should look at the paper. You can also look at Alex Krizhevsky's cuda-convnet and successors , which contains code implementing many of the ideas. The code is recognizably along similar lines to that developed in this chapter, although the use of multiple GPUs complicates things somewhat. Berg, and Li Fei-Fei As one of the authors, Andrej Karpathy, explains in an informative blog post , it was a lot of trouble to get the humans up to GoogLeNet's performance:.
First we thought we would put it up on [Amazon Mechanical Turk]. Then we thought we could recruit paid undergrads. Then I organized a labeling party of intense labeling effort only among the expert labelers in our lab. Then I developed a modified interface that used GoogLeNet predictions to prune the number of categories from to only about In the end I realized that to get anywhere competitively close to GoogLeNet, it was most efficient if I sat down and went through the painfully long training process and the subsequent careful annotation process myself The labeling happened at a rate of about 1 per minute, but this decreased over time Some images are easily recognized, while some images such as those of fine-grained breeds of dogs, birds, or monkeys can require multiple minutes of concentrated effort.
I became very good at identifying breeds of dogs Based on the sample of images I worked on, the GoogLeNet classification error turned out to be 6. My own error in the end turned out to be 5. In other words, an expert human, working painstakingly, was with great effort able to narrowly beat the deep neural network. About half the errors were due to the expert "failing to spot and consider the ground truth label as an option".
These are astonishing results. Indeed, since this work, several teams have reported systems whose top-5 error rate is actually better than 5. This has sometimes been reported in the media as the systems having better-than-human vision. While the results are genuinely exciting, there are many caveats that make it misleading to think of the systems as having better-than-human vision.
The ILSVRC challenge is in many ways a rather limited problem - a crawl of the open web is not necessarily representative of images found in applications! We are still a long way from solving the problem of image recognition or, more broadly, computer vision. Still, it's extremely encouraging to see so much progress made on such a challenging problem, over just a few years. Other activity: I've focused on ImageNet, but there's a considerable amount of other activity using neural nets to do image recognition. Let me briefly describe a few interesting recent results, just to give the flavour of some current work.
In their paper, they report detecting and automatically transcribing nearly million street numbers at an accuracy similar to that of a human operator. The system is fast: their system transcribed all of Street View's images of street numbers in France in less than an hour! They say: "Having this new dataset significantly increased the geocoding quality of Google Maps in several countries especially the ones that did not already have other sources of good geocoding.
I've perhaps given the impression that it's all a parade of encouraging results. Of course, some of the most interesting work reports on fundamental things we don't yet understand. Consider the lines of images below. On the left is an ImageNet image classified correctly by their network. On the right is a slightly perturbed image the perturbation is in the middle which is classified incorrectly by the network.
The authors found that there are such "adversarial" images for every sample image, not just a few special ones. This is a disturbing result. The paper used a network based on the same code as KSH's network - that is, just the type of network that is being increasingly widely used. While such neural networks compute functions which are, in principle, continuous, results like this suggest that in practice they're likely to compute functions which are very nearly discontinuous.
Worse, they'll be discontinuous in ways that violate our intuition about what is reasonable behavior. That's concerning. Furthermore, it's not yet well understood what's causing the discontinuity: is it something about the loss function? The activation functions used?
The architecture of the network? Something else? We don't yet know. Now, these results are not quite as bad as they sound. Although such adversarial images are common, they're also unlikely in practice. As the paper notes:.
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Indeed, if the network can generalize well, how can it be confused by these adversarial negatives, which are indistinguishable from the regular examples? The explanation is that the set of adversarial negatives is of extremely low probability, and thus is never or rarely observed in the test set, yet it is dense much like the rational numbers , and so it is found near virtually every test case.
Nonetheless, it is distressing that we understand neural nets so poorly that this kind of result should be a recent discovery. Of course, a major benefit of the results is that they have stimulated much followup work. This is another demonstration that we have a long way to go in understanding neural networks and their use in image recognition.
Despite results like this, the overall picture is encouraging. We're seeing rapid progress on extremely difficult benchmarks, like ImageNet. We're also seeing rapid progress in the solution of real-world problems, like recognizing street numbers in StreetView. But while this is encouraging it's not enough just to see improvements on benchmarks, or even real-world applications. There are fundamental phenomena which we still understand poorly, such as the existence of adversarial images. When such fundamental problems are still being discovered never mind solved , it is premature to say that we're near solving the problem of image recognition.
At the same time such problems are an exciting stimulus to further work. Other approaches to deep neural nets. It's a juicy problem which forced us to understand many powerful ideas: stochastic gradient descent, backpropagation, convolutional nets, regularization, and more. But it's also a narrow problem. Neural networks is a vast field. However, many important ideas are variations on ideas we've already discussed, and can be understood with a little effort.
In this section I provide a glimpse of these as yet unseen vistas. The discussion isn't detailed, nor comprehensive - that would greatly expand the book. Rather, it's impressionistic, an attempt to evoke the conceptual richness of the field, and to relate some of those riches to what we've already seen. Through the section, I'll provide a few links to other sources, as entrees to learn more.
Of course, many of these links will soon be superseded, and you may wish to search out more recent literature. That point notwithstanding, I expect many of the underlying ideas to be of lasting interest. Recurrent neural networks RNNs : In the feedforward nets we've been using there is a single input which completely determines the activations of all the neurons through the remaining layers. It's a very static picture: everything in the network is fixed, with a frozen, crystalline quality to it.
But suppose we allow the elements in the network to keep changing in a dynamic way. For instance, the behaviour of hidden neurons might not just be determined by the activations in previous hidden layers, but also by the activations at earlier times. Indeed, a neuron's activation might be determined in part by its own activation at an earlier time. That's certainly not what happens in a feedforward network. Or perhaps the activations of hidden and output neurons won't be determined just by the current input to the network, but also by earlier inputs.
Neural networks with this kind of time-varying behaviour are known as recurrent neural networks or RNNs. There are many different ways of mathematically formalizing the informal description of recurrent nets given in the last paragraph. You can get the flavour of some of these mathematical models by glancing at the Wikipedia article on RNNs.
As I write, that page lists no fewer than 13 different models. But mathematical details aside, the broad idea is that RNNs are neural networks in which there is some notion of dynamic change over time. And, not surprisingly, they're particularly useful in analysing data or processes that change over time.
Such data and processes arise naturally in problems such as speech or natural language, for example. One way RNNs are currently being used is to connect neural networks more closely to traditional ways of thinking about algorithms, ways of thinking based on concepts such as Turing machines and conventional programming languages. A paper developed an RNN which could take as input a character-by-character description of a very, very simple!
Python program, and use that description to predict the output. Informally, the network is learning to "understand" certain Python programs. This is a universal computer whose entire structure can be trained using gradient descent. They trained their NTM to infer algorithms for several simple problems, such as sorting and copying. As it stands, these are extremely simple toy models. It's not clear how much further it will be possible to push the ideas. Still, the results are intriguing. Historically, neural networks have done well at pattern recognition problems where conventional algorithmic approaches have trouble.
Vice versa, conventional algorithmic approaches are good at solving problems that neural nets aren't so good at. No-one today implements a web server or a database program using a neural network! It'd be great to develop unified models that integrate the strengths of both neural networks and more traditional approaches to algorithms.
RNNs have also been used in recent years to attack many other problems. They've been particularly useful in speech recognition. Approaches based on RNNs have, for example, set records for the accuracy of phoneme recognition. They've also been used to develop improved models of the language people use while speaking. Better language models help disambiguate utterances that otherwise sound alike. A good language model will, for example, tell us that "to infinity and beyond" is much more likely than "two infinity and beyond", despite the fact that the phrases sound identical.
RNNs have been used to set new records for certain language benchmarks. This work is, incidentally, part of a broader use of deep neural nets of all types, not just RNNs, in speech recognition. For example, an approach based on deep nets has achieved outstanding results on large vocabulary continuous speech recognition.
And another system based on deep nets has been deployed in Google's Android operating system for related technical work, see Vincent Vanhoucke's papers. I've said a little about what RNNs can do, but not so much about how they work. It perhaps won't surprise you to learn that many of the ideas used in feedforward networks can also be used in RNNs. In particular, we can train RNNs using straightforward modifications to gradient descent and backpropagation. Many other ideas used in feedforward nets, ranging from regularization techniques to convolutions to the activation and cost functions used, are also useful in recurrent nets.
And so many of the techniques we've developed in the book can be adapted for use with RNNs. Long short-term memory units LSTMs : One challenge affecting RNNs is that early models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem discussed in Chapter 5.
Recall that the usual manifestation of this problem is that the gradient gets smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time that can make the gradient extremely unstable and hard to learn from. The units were introduced by Hochreiter and Schmidhuber in with the explicit purpose of helping address the unstable gradient problem. DBNs were influential for several years, but have since lessened in popularity, while models such as feedforward networks and recurrent neural nets have become fashionable.
Despite this, DBNs have several properties that make them interesting. One reason DBNs are interesting is that they're an example of what's called a generative model. In a feedforward network, we specify the input activations, and they determine the activations of the feature neurons later in the network. A generative model like a DBN can be used in a similar way, but it's also possible to specify the values of some of the feature neurons and then "run the network backward", generating values for the input activations.
More concretely, a DBN trained on images of handwritten digits can potentially, and with some care also be used to generate images that look like handwritten digits. In other words, the DBN would in some sense be learning to write. In this, a generative model is much like the human brain: not only can it read digits, it can also write them. In Geoffrey Hinton's memorable phrase, to recognize shapes, first learn to generate images.
A second reason DBNs are interesting is that they can do unsupervised and semi-supervised learning. For instance, when trained with image data, DBNs can learn useful features for understanding other images, even if the training images are unlabelled. And the ability to do unsupervised learning is extremely interesting both for fundamental scientific reasons, and - if it can be made to work well enough - for practical applications.
Given these attractive features, why have DBNs lessened in popularity as models for deep learning? Part of the reason is that models such as feedforward and recurrent nets have achieved many spectacular results, such as their breakthroughs on image and speech recognition benchmarks. It's not surprising and quite right that there's now lots of attention being paid to these models. There's an unfortunate corollary, however. The marketplace of ideas often functions in a winner-take-all fashion, with nearly all attention going to the current fashion-of-the-moment in any given area.
It can become extremely difficult for people to work on momentarily unfashionable ideas, even when those ideas are obviously of real long-term interest. My personal opinion is that DBNs and other generative models likely deserve more attention than they are currently receiving. And I won't be surprised if DBNs or a related model one day surpass the currently fashionable models. For an introduction to DBNs, see this overview. I've also found this article helpful. It isn't primarily about deep belief nets, per se , but does contain much useful information about restricted Boltzmann machines, which are a key component of DBNs.
Other ideas: What else is going on in neural networks and deep learning? Well, there's a huge amount of other fascinating work. Active areas of research include using neural networks to do natural language processing see also this informative review paper , machine translation , as well as perhaps more surprising applications such as music informatics.
There are, of course, many other areas too. In many cases, having read this book you should be able to begin following recent work, although of course you'll need to fill in gaps in presumed background knowledge. Let me finish this section by mentioning a particularly fun paper. It combines deep convolutional networks with a technique known as reinforcement learning in order to learn to play video games well see also this followup.
The idea is to use the convolutional network to simplify the pixel data from the game screen, turning it into a simpler set of features, which can be used to decide which action to take: "go left", "go down", "fire", and so on.
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What is particularly interesting is that a single network learned to play seven different classic video games pretty well, outperforming human experts on three of the games. Now, this all sounds like a stunt, and there's no doubt the paper was well marketed, with the title "Playing Atari with reinforcement learning". But looking past the surface gloss, consider that this system is taking raw pixel data - it doesn't even know the game rules! That's pretty neat. On the future of neural networks. Intention-driven user interfaces: There's an old joke in which an impatient professor tells a confused student: "don't listen to what I say; listen to what I mean ".
Historically, computers have often been, like the confused student, in the dark about what their users mean. But this is changing. I still remember my surprise the first time I misspelled a Google search query, only to have Google say "Did you mean [corrected query]? Google CEO Larry Page once described the perfect search engine as understanding exactly what [your queries] mean and giving you back exactly what you want. This is a vision of an intention-driven user interface.
In this vision, instead of responding to users' literal queries, search will use machine learning to take vague user input, discern precisely what was meant, and take action on the basis of those insights. The idea of intention-driven interfaces can be applied far more broadly than search. Over the next few decades, thousands of companies will build products which use machine learning to make user interfaces that can tolerate imprecision, while discerning and acting on the user's true intent.
We're already seeing early examples of such intention-driven interfaces: Apple's Siri; Wolfram Alpha; IBM's Watson; systems which can annotate photos and videos ; and much more. Most of these products will fail. Inspired user interface design is hard, and I expect many companies will take powerful machine learning technology and use it to build insipid user interfaces. The best machine learning in the world won't help if your user interface concept stinks. But there will be a residue of products which succeed. Over time that will cause a profound change in how we relate to computers.
Not so long ago - let's say, - users took it for granted that they needed precision in most interactions with computers. Indeed, computer literacy to a great extent meant internalizing the idea that computers are extremely literal; a single misplaced semi-colon may completely change the nature of an interaction with a computer. But over the next few decades I expect we'll develop many successful intention-driven user interfaces, and that will dramatically change what we expect when interacting with computers.
Machine learning, data science, and the virtuous circle of innovation: Of course, machine learning isn't just being used to build intention-driven interfaces. Another notable application is in data science, where machine learning is used to find the "known unknowns" hidden in data. This is already a fashionable area, and much has been written about it, so I won't say much.
But I do want to mention one consequence of this fashion that is not so often remarked: over the long run it's possible the biggest breakthrough in machine learning won't be any single conceptual breakthrough. Rather, the biggest breakthrough will be that machine learning research becomes profitable, through applications to data science and other areas. If a company can invest 1 dollar in machine learning research and get 1 dollar and 10 cents back reasonably rapidly, then a lot of money will end up in machine learning research.
Put another way, machine learning is an engine driving the creation of several major new markets and areas of growth in technology. The result will be large teams of people with deep subject expertise, and with access to extraordinary resources. That will propel machine learning further forward, creating more markets and opportunities, a virtuous circle of innovation.
The role of neural networks and deep learning: I've been talking broadly about machine learning as a creator of new opportunities for technology. What will be the specific role of neural networks and deep learning in all this? To answer the question, it helps to look at history. Back in the s there was a great deal of excitement and optimism about neural networks, especially after backpropagation became widely known. That excitement faded, and in the s the machine learning baton passed to other techniques, such as support vector machines.
Today, neural networks are again riding high, setting all sorts of records, defeating all comers on many problems. But who is to say that tomorrow some new approach won't be developed that sweeps neural networks away again? Or perhaps progress with neural networks will stagnate, and nothing will immediately arise to take their place? For this reason, it's much easier to think broadly about the future of machine learning than about neural networks specifically.
Part of the problem is that we understand neural networks so poorly. Why is it that neural networks can generalize so well? How is it that they avoid overfitting as well as they do, given the very large number of parameters they learn?
Why is it that stochastic gradient descent works as well as it does? How well will neural networks perform as data sets are scaled? These are all simple, fundamental questions. And, at present, we understand the answers to these questions very poorly. While that's the case, it's difficult to say what role neural networks will play in the future of machine learning.
I will make one prediction: I believe deep learning is here to stay. The ability to learn hierarchies of concepts, building up multiple layers of abstraction, seems to be fundamental to making sense of the world. This doesn't mean tomorrow's deep learners won't be radically different than today's. We could see major changes in the constituent units used, in the architectures, or in the learning algorithms. Those changes may be dramatic enough that we no longer think of the resulting systems as neural networks.
But they'd still be doing deep learning. Will neural networks and deep learning soon lead to artificial intelligence? In this book we've focused on using neural nets to do specific tasks, such as classifying images. Let's broaden our ambitions, and ask: what about general-purpose thinking computers? Can neural networks and deep learning help us solve the problem of general artificial intelligence AI? And, if so, given the rapid recent progress of deep learning, can we expect general AI any time soon? Addressing these questions comprehensively would take a separate book.
Instead, let me offer one observation. It's based on an idea known as Conway's law : Any organization that designs a system So, for example, Conway's law suggests that the design of a Boeing aircraft will mirror the extended organizational structure of Boeing and its contractors at the time the was designed. Or for a simple, specific example, consider a company building a complex software application. If the application's dashboard is supposed to be integrated with some machine learning algorithm, the person building the dashboard better be talking to the company's machine learning expert.
Conway's law is merely that observation, writ large. Upon first hearing Conway's law, many people respond either "Well, isn't that banal and obvious? As an instance of this objection, consider the question: where does Boeing's accounting department show up in the design of the ? What about their janitorial department? Their internal catering? And the answer is that these parts of the organization probably don't show up explicitly anywhere in the So we should understand Conway's law as referring only to those parts of an organization concerned explicitly with design and engineering.
What about the other objection, that Conway's law is banal and obvious? This may perhaps be true, but I don't think so, for organizations too often act with disregard for Conway's law. Teams building new products are often bloated with legacy hires or, contrariwise, lack a person with some crucial expertise. Think of all the products which have useless complicating features. Or think of all the products which have obvious major deficiencies - e. Problems in both classes are often caused by a mismatch between the team that was needed to produce a good product, and the team that was actually assembled.
Conway's law may be obvious, but that doesn't mean people don't routinely ignore it. Conway's law applies to the design and engineering of systems where we start out with a pretty good understanding of the likely constituent parts, and how to build them. It can't be applied directly to the development of artificial intelligence, because AI isn't yet such a problem: we don't know what the constituent parts are. Indeed, we're not even sure what basic questions to be asking. In others words, at this point AI is more a problem of science than of engineering.
Imagine beginning the design of the without knowing about jet engines or the principles of aerodynamics. You wouldn't know what kinds of experts to hire into your organization. Is there a version of Conway's law that applies to problems which are more science than engineering? To gain insight into this question, consider the history of medicine. In the early days, medicine was the domain of practitioners like Galen and Hippocrates, who studied the entire body.
But as our knowledge grew, people were forced to specialize. I won't define "deep ideas" precisely, but loosely I mean the kind of idea which is the basis for a rich field of enquiry. The backpropagation algorithm and the germ theory of disease are both good examples. Such deep insights formed the basis for subfields such as epidemiology, immunology, and the cluster of inter-linked fields around the cardiovascular system. And so the structure of our knowledge has shaped the social structure of medicine. This is particularly striking in the case of immunology: realizing the immune system exists and is a system worthy of study is an extremely non-trivial insight.
So we have an entire field of medicine - with specialists, conferences, even prizes, and so on - organized around something which is not just invisible, it's arguably not a distinct thing at all. This is a common pattern that has been repeated in many well-established sciences: not just medicine, but physics, mathematics, chemistry, and others. The fields start out monolithic, with just a few deep ideas.
Early experts can master all those ideas. But as time passes that monolithic character changes. We discover many deep new ideas, too many for any one person to really master. As a result, the social structure of the field re-organizes and divides around those ideas. Instead of a monolith, we have fields within fields within fields, a complex, recursive, self-referential social structure, whose organization mirrors the connections between our deepest insights.
And so the structure of our knowledge shapes the social organization of science. But that social shape in turn constrains and helps determine what we can discover. This is the scientific analogue of Conway's law. Well, since the early days of AI there have been arguments about it that go, on one side, "Hey, it's not going to be so hard, we've got [super-special weapon] on our side", countered by "[super-special weapon] won't be enough". See, for example, this thoughtful post by Yann LeCun. This is a difference from many earlier incarnations of the argument.
The problem with such arguments is that they don't give you any good way of saying just how powerful any given candidate super-special weapon is. Of course, we've just spent a chapter reviewing evidence that deep learning can solve extremely challenging problems. It certainly looks very exciting and promising. But that was also true of systems like Prolog or Eurisko or expert systems in their day.
And so the mere fact that a set of ideas looks very promising doesn't mean much. How can we tell if deep learning is truly different from these earlier ideas? Is there some way of measuring how powerful and promising a set of ideas is? Conway's law suggests that as a rough and heuristic proxy metric we can evaluate the complexity of the social structure associated to those ideas. So, there are two questions to ask. First, how powerful a set of ideas are associated to deep learning, according to this metric of social complexity?
Second, how powerful a theory will we need, in order to be able to build a general artificial intelligence? As to the first question: when we look at deep learning today, it's an exciting and fast-paced but also relatively monolithic field. There are a few deep ideas, and a few main conferences, with substantial overlap between several of the conferences. And there is paper after paper leveraging the same basic set of ideas: using stochastic gradient descent or a close variation to optimize a cost function.
Use a script typeface or photograph real handwriting. Then get ready for many interesting hours of mask animations. You will need dozens of masks. The trick is to realize that handwriting is nonlinear and requires what is known as lifts. The pen comes off the page and the hand moves before the pen contacts the page again. You lift between words, some glyphs, and to dot or cross some characters. Some characters take much more time to draw that others.
If all of your masks move at the same pace the effect is obviously fake. Communities Contact Support. Sign in Sign in Sign in corporate. Browse Search. Ask a question. User profile for user: pcalvin pcalvin. App Store Speciality level out of ten: 0. Question: Q: Question: Q: Animated Handwritten Text Is there a plugin, or another way, to make text appear to be handwritten on the screen. More Less. All replies Drop Down menu. Loading page content. User profile for user: Ian R.