AI News, Can machine learning learn to stop learning?

Can machine learning learn to stop learning?

Machine learning is a set of tools, algorithms and technics rather than the name of some kind of systems.

The first set shows a lot of examples that are already classified for the system to get their main features and for it to know the way those features contribute to the given calssification.

In some systems, new examples are given from time to time to retrain it and if the performance is not better or, on the other hand, it’s worse, the system stops learning, at last for that iteration.

Training, test, and validation sets

Most approaches that search through training data for empirical relationships tend to overfit the data, meaning that they can identify apparent relationships in the training data that do not hold in general.

For example, if the most suitable classifier for the problem is sought, the training dataset is used to train the candidate algorithms, the validation dataset is used to compare their performances and decide which one to take and, finally, the test dataset is used to obtain[citation needed]

Since our goal is to find the network having the best performance on new data, the simplest approach to the comparison of different networks is to evaluate the error function using data which is independent of that used for training.

The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set is selected.

Since this procedure can itself lead to some overfitting to the validation set, the performance of the selected network should be confirmed by measuring its performance on a third independent set of data called a test set.

An application of this process is in early stopping, where the candidate models are successive iterations of the same network, and training stops when the error on the validation set grows, choosing the previous model (the one with minimum error).

These repeated partitions can be done in various ways, such as dividing into 2 equal datasets and using them as training/validation, and then validation/training, or repeatedly selecting a random subset as a validation dataset[citation needed].

Another example of parameter adjustment is hierarchical classification (sometimes referred to as instance space decomposition [11]), which splits a complete multi-class problem into a set of smaller classification problems.

on the validation set one can see which classes are most frequently mutually confused by the system and then the instance space decomposition is done as follows: firstly, the classification is done among well recognizable classes, and the difficult to separate classes are treated as a single joint class, and finally, as a second classification step the joint class is classified into the two initially mutually confused classes.

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This example shows how to train an R-CNN object detector for detecting stop signs.

R-CNN is an object detection framework, which uses a convolutional neural network (CNN) to classify image regions within an image [1].

To illustrate how to train an R-CNN stop sign detector, this example follows the transfer learning workflow that is commonly used in deep learning applications.

In transfer learning, a network trained on a large collection of images, such as ImageNet [2], is used as the starting point to solve a new classification or detection task.

The advantage of using this approach is that the pretrained network has already learned a rich set of image features that are applicable to a wide range of images.

A network is fine-tuned by making small adjustments to the weights such that the feature representations learned for the original task are slightly adjusted to support the new task.

In this example, the following layers are used to create a CNN: The network defined here is similar to the one described in [4] and starts with an imageInputLayer.

The ReLU layer adds non-linearity to the network, which allow the network to approximate non-linear functions that map image pixels to the semantic content of the image.

In a network with lots of layers, pooling layers should be used sparingly to avoid downsampling the data too early in the network.

During training, the initial learning rate is reduced every 8 epochs (1 epoch is defined as one complete pass through the entire training data set).

The goal of this example is not necessarily to achieve 100% accuracy on the test set, but to sufficiently train a network for use in training an object detector.

Now that the network is working well for the CIFAR-10 classification task, the transfer learning approach can be used to fine-tune the network for stop sign detection.

The training data is contained within a table that contains the image filename and ROI labels for stop signs, car fronts, and rears.

Training an R-CNN object detector from scratch using only 41 images is not practical and would not produce a reliable stop sign detector.

Because the stop sign detector is trained by fine-tuning a network that has been pre-trained on a larger dataset (CIFAR-10 has 50,000 training images), using a much smaller dataset is feasible.

The input to this function is the ground truth table which contains labeled stop sign images, the pre-trained CIFAR-10 network, and the training options.

The training function automatically modifies the original CIFAR-10 network, which classified images into 10 categories, into a network that can classify images into 2 classes: stop signs and a generic background class.

During training, the input network weights are fine-tuned using image patches extracted from the ground truth data.

Positive training samples are those that overlap with the ground truth boxes by 0.5 to 1.0, as measured by the bounding box intersection over union metric.

Try it out on a test image: The R-CNN object detect method returns the object bounding boxes, a detection score, and a class label for each detection.

The scores, which range between 0 and 1, indicate the confidence in the detection and can be used to ignore low scoring detections.

This is a useful debugging tool because it helps identify items in the image that are confusing the network, and may help provide insight into improving training.

Soon We Won't Program Computers. We'll Train Them Like Dogs

Before the invention of the computer, most experimental psychologists thought the brain was an unknowable black box.

The so-called cognitive revolution started small, but as computers became standard equipment in psychology labs across the country, it gained broader acceptance.

By the late 1970s, cognitive psychology had overthrown behaviorism, and with the new regime came a whole new language for talking about mental life.

Psychologists began describing thoughts as programs, ordinary people talked about storing facts away in their memory banks, and business gurus fretted about the limits of mental bandwidth and processing power in the modern workplace.

As software has eaten the world, to paraphrase venture capitalist Marc Andreessen, we have surrounded ourselves with machines that convert our actions, thoughts, and emotions into data—raw material for armies of code-wielding engineers to manipulate.

Facebook's Mark Zuckerberg has gone so far as to suggest there might be a “fundamental mathematical law underlying human relationships that governs the balance of who and what we all care about.” In 2013, Craig Venter announced that, a decade after the decoding of the human genome, he had begun to write code that would allow him to create synthetic organisms.

“It is becoming clear,” he said, “that all living cells that we know of on this planet are DNA-software-driven biological machines.” Even self-help literature insists that you can hack your own source code, reprogramming your love life, your sleep routine, and your spending habits.

(In Bloomberg Businessweek, Paul Ford was slightly more circumspect: “If coders don't run the world, they run the things that run the world.” Tomato, tomahto.) But whether you like this state of affairs or hate it—whether you're a member of the coding elite or someone who barely feels competent to futz with the settings on your phone—don't get used to it.

This approach is not new—it's been around for decades—but it has recently become immensely more powerful, thanks in part to the rise of deep neural networks, massively distributed computational systems that mimic the multilayered connections of neurons in the brain.

In February the company replaced its longtime head of search with machine-learning expert John Giannandrea, and it has initiated a major program to retrain its engineers in these new techniques.

“By building learning systems,” Giannandrea told reporters this fall, “we don't have to write these rules anymore.” But here's the thing: With machine learning, the engineer never knows precisely how the computer accomplishes its tasks.

And as these black boxes assume responsibility for more and more of our daily digital tasks, they are not only going to change our relationship to technology—they are going to change how we think about ourselves, our world, and our place within it.

Rubin is excited about the rise of machine learning—his new company, Playground Global, invests in machine-learning startups and is positioning itself to lead the spread of intelligent devices—but it saddens him a little too.

You can't cut your head off and see what you're thinking.” When engineers do peer into a deep neural network, what they see is an ocean of math: a massive, multilayer set of calculus problems that—by constantly deriving the relationship between billions of data points—generate guesses about the world.

They largely ignored, even vilified, early proponents of machine learning, who argued in favor of plying machines with data until they reached their own conclusions.

For the past two decades, learning to code has been one of the surest routes to reliable employment—a fact not lost on all those parents enrolling their kids in after-school code academies.

“I was pointing out how different programming jobs would be by the time all these STEM-educated kids grow up.” Traditional coding won't disappear completely—indeed, O'Reilly predicts that we'll still need coders for a long time yet—but there will likely be less of it, and it will become a meta skill, a way of creating what Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, calls the “scaffolding” within which machine learning can operate.

Just as Newtonian physics wasn't obviated by the discovery of quantum mechanics, code will remain a powerful, if incomplete, tool set to explore the world.

If the rise of human-written software led to the cult of the engineer, and to the notion that human experience can ultimately be reduced to a series of comprehensible instructions, machine learning kicks the pendulum in the opposite direction.

Over the past few years, as networks have grown more intertwined and their functions more complex, code has come to seem more like an alien force, the ghosts in the machine ever more elusive and ungovernable.

“One can imagine such technology outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand,” wrote Stephen Hawking—sentiments echoed by Elon Musk and Bill Gates, among others.

But discoveries in the field of epigenetics suggest that genetic material is not in fact an immutable set of instructions but rather a dynamic set of switches that adjusts depending on the environment and experiences of its host.

Venter may believe cells are DNA-software-driven machines, but epigeneticist Steve Cole suggests a different formulation: “A cell is a machine for turning experience into biology.” And now, 80 years after Alan Turing first sketched his designs for a problem-solving machine, computers are becoming devices for turning experience into technology.

Attacking Machine Learning with Adversarial Examples

Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake;

At OpenAI, we think adversarial examples are a good aspect of security to work on because they represent a concrete problem in AI safety that can be addressed in the short term, and because fixing them is difficult enough that it requires a serious research effort.

(Though we'll need to explore many aspects of machine learning security to achieve our goal of building safe, widely distributed AI.) To get an idea of what adversarial examples look like, consider this demonstration from Explaining and Harnessing Adversarial Examples: starting with an image of a panda, the attacker adds a small perturbation that has been calculated to make the image be recognized as a gibbon with high confidence.

For example, attackers could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a 'yield' or other sign, as discussed in Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.

When we think about the study of AI safety, we usually think about some of the most difficult problems in that field — how can we ensure that sophisticated reinforcement learning agents that are significantly more intelligent than human beings behave in ways that their designers intended?

This creates a model whose surface is smoothed in the directions an adversary will typically try to exploit, making it difficult for them to discover adversarial input tweaks that lead to incorrect categorization.

(Distillation was originally introduced in Distilling the Knowledge in a Neural Network as a technique for model compression, where a small model is trained to imitate a large one, in order to obtain computational savings.) Yet even these specialized algorithms can easily be broken by giving more computational firepower to the attacker.

In other words, they look at a picture of an airplane, they test which direction in picture space makes the probability of the “cat” class increase, and then they give a little push (in other words, they perturb the input) in that direction.

If the model’s output is “99.9% airplane, 0.1% cat”, then a little tiny change to the input gives a little tiny change to the output, and the gradient tells us which changes will increase the probability of the “cat” class.

Let’s run a thought experiment to see how well we could defend our model against adversarial examples by running it in “most likely class” mode instead of “probability mode.” The attacker no longer knows where to go to find inputs that will be classified as cats, so we might have some defense.

The defense strategies that perform gradient masking typically result in a model that is very smooth in specific directions and neighborhoods of training points, which makes it harder for the adversary to find gradients indicating good candidate directions to perturb the input in a damaging way for the model.

Neither algorithm was explicitly designed to perform gradient masking, but gradient masking is apparently a defense that machine learning algorithms can invent relatively easily when they are trained to defend themselves and not given specific instructions about how to do so.

Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron

Complex models such as deep neural networks can detect subtle patterns in the data, but if the training set is noisy, or if it is too small (which introduces sampling noise), then the model is likely to detect patterns in the noise itself.

In that case, a complex model may detect patterns like the fact that all countries in the training data with a w in their name have a life satisfaction greater than 7: New Zealand (7.3), Norway (7.4), Sweden (7.2), and Switzerland (7.5).

If we forced θ1 = 0, the algorithm would have only one degree of freedom and would have a much harder time fitting the data properly: all it could do is move the line up or down to get as close as possible to the training instances, so it would end up around the mean.

Figure 1-23 shows three models: the dotted line represents the original model that was trained with a few countries missing, the dashed line is our second model trained with all countries, and the solid line is a linear model trained with the same data as the first model but with a regularization constraint.

You can see that regularization forced the model to have a smaller slope, which fits a bit less the training data that the model was trained on, but actually allows it to generalize better to new examples.

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