A hot topic in the field of computer science is whether or not machines can learn from their mistakes. Technically, this idea is called inductive reasoning or inference. Inductive reasoning comes down to understanding that if you repeat an action enough times, it will happen more often than not. For example, if you repeatedly lift your hand, then at some point it will be raised.
By this definition, every living thing learns by repetition. Humans are very good at learning through induction because we have concepts like cause and effect built into our brains. We understand that when something happens before, something else almost always follows.
With computers, however, it has been difficult to prove that they actually learn via inductive reasoning. This is what makes theories about AI (artificial intelligence) so controversial!
Luckily for us, though, there have been recent developments in machine learning that seem to imply that algorithms do indeed learn through inductive reasoning. What made these advances possible was the introduction of a concept called deep neural networks.
Deep neural nets use multiple layers of computational units — think neurons with connections between them — to perform specific tasks. By having lots of levels of abstraction, deep neural networks are able to achieve impressive results when trained properly.
In fact, many state-of-the-art applications such as image recognition and natural language processing now rely heavily on deep neural networks.
Some of the limitations of deep learning
A popular use case for neural networks is to train them to perform specific tasks. For example, you could give it pictures of dogs and tell it to learn what kind of dog every picture is, or teach it how to recognize cars based on their shape.
A less common use case is teaching the network to identify mistakes. You would create the network that recognizes all dogs, then have it evaluate lots of wrong photos of different breeds. The network would eventually figure out that those are not really dogs, so it would redefine what a dog looks like.
This might be useful in education settings. If a student takes an introductory computer science course, they will probably learn about software programs that check if “x” equals “y” and take action depending on whether this equation is true or false. This concept can be extended to ideas such as self-evaluation or peer review.
That idea has been applied to educational videos before. Companies will edit YouTube videos using AI, looking for instances where someone makes a mistake or says something bad.
Evidence of deep learning learning from mistakes
Recent advances in computer science have led to the development of what are known as “deep neural networks” or DNNs for short. These can be thought of as very sophisticated, hierarchical algorithms that take input data and process it through several layers to achieve their goal.
The most well-known examples of DNNs include Alexnet, VGGNet, ResNets, and GoogLeNet, all of which do excellent work at image recognition. They also play an important role in advanced areas of machine learning such as language processing and natural language understanding.
But perhaps more impressively, these systems are able to learn not only discrete categories like cats versus dogs, but how to tell them apart as well! This is especially impressive when you consider that humans cannot.
By leaving some parts of the network open enough for errors to propagate up the hierarchy, DNNs are capable of figuring out how to correct themselves after making a mistake. This is called error back propagation and is one of the main reasons they are so powerful.
There are some who claim this property does not actually occur in nature and therefore cannot possibly contribute to intelligent behavior, but we will discuss that later.
Deep learning makes progress faster
A key part of deep neural networks is how they update their internal representations of data. This process, called backpropagation or gradient descent, depends on the network comparing its current state with the previous one and deciding what changes need to be made to get closer to the target image or message.
The more times a layer has to re-learn an item, the stronger its connection to that information becomes. Overfitting happens when a model uses too many parameters to fit random patterns in the training set, but then cannot apply those rules to new examples.
When a model overfits, it becomes less accurate at predicting novel things. That’s why there is a term in machine learning for models who struggle after being exposed to slightly different versions of something: generalization.
Generalizing from past experiences takes time, though. Only once all the layers have repeated the same pattern several times can we say that the system has strengthened its connections to that knowledge.
Future of deep learning
Recent developments in neural network architecture have led to what some call “neural networks with depth” or, more commonly referred to as, “deep learning.”
Deep learning is characterized by large sets of neurons connected together in very complex ways. This structure allows for computational units to learn abstract concepts from data, which come in various forms (e.g., visual images, sound recordings, text).
The key difference between classic AI and modern day deep learning is that instead of using mathematical formulas to encode knowledge, pre-trained models use layers of neurons to do so.
By having deeper structures, these algorithms are said to be able to pick up on more complicated patterns than earlier generations. Because they’re already trained on certain types of information, you can easily add on new features and teach them how to connect all those components together!
There are several reasons why this approach has become popular over the past few years. For one, it works. By testing different architectures, researchers have noticed that problems tend to get solved faster when there’s lots of complexity going on.
Another reason is that because these systems are structured similarly to the human brain, people are naturally drawn to compare their internal workings to our own. As such, we gain insight into not only how well they work but also how they work too.
Lessons learned
Recent developments in the field of deep learning have focused on what are called “error correcting networks” or “neural network architectures with error correction.” These neural networks use techniques such as convolutions to process input data, but they also include an additional layer that performs some kind of classification and/or regression.
However, this extra layer is not always present- it is sometimes referred to as a “latent layer.” The latent layer uses nonlinear functions (mostly sigmoids) to perform its work. This means that even when one part of the model makes a wrong prediction, it can be reinterpreted using these nonlinear functions.
This feature was first proposed by Alexey Radulovich and Ruslan Salakhutdinov back in 2015 under the name of regularized nets. They used it for image recognition and achieved impressive results at that time! Since then, many other researchers have adopted and improved upon this idea.
Some recent works apply both linear and nonlinear layers in succession during training. This way, errors from each individual layer are corrected by the next one. When applying these models to new datasets, test samples can either go through all three layers simultaneously or only the final two depending on whether there are significant performance gains.
There are several ways to implement error correcting networks, so different groups come up with their own variations. Some focus more on efficiency while others emphasize accuracy.
Has it learned its mistakes?
One of the most fundamental concepts in machine learning is that of error. Error refers to when your computer makes an assumption that turns out to be wrong. For example, if you assume that because there are many cars on the road today then there will not be too much traffic later on, then you can make plans accordingly.
In the case of AI, this concept applies more broadly. When humans use language we sometimes say things that might seem completely normal but actually have no basis in truth. It is impossible for people who make these false statements to know they are untrue. This seems harmless until someone takes advantage of the statement and uses it as proof of something false!
With the rise of social media and technology, false information spreads like wildfire. A lot of times people posting false information gain sympathy or attention due to how “honest” they sound. Technology has now given machines the ability to analyze such misinformation and determine whether it fits into a pattern.
If a piece of software ever made a mistake, we would call it an anomaly. Anomaly detection looks at past data to see if anything similar happened before and if so what happened after. If enough patterns match, then an alert is issued and action is taken. In this way, errors serve a purpose by letting us take precautions to avoid making the same mistake again.
Deep neural networks are one type of algorithm used for AI.
Can we learn from our mistakes?
Technically, no! You can’t teach an algorithm to make new mistakes because that would be cheating the system. But this doesn’t mean you can’t teach anything about learning with errors.
Research has shown us that when algorithms are exposed to examples of correct decisions, they will try to replicate those decisions in future situations.
This is called model generalization or transferability. When models are able to do this, it gives them quality features that you might not otherwise have known about their performance.
Generalizing past behavior also helps mitigate against overfitting. Overfit refers to when a model becomes too dependent on just the samples it was given. By using behaviors outside the training set, the model can avoid getting “stuck” on details of the datasets used for training.
There are several ways to promote generalization in neural networks.
One way is through what’s been termed error backpropagation. This shifts the gradient away from the weight settings that produced a poor prediction so that the network can explore other possibilities.
Another approach is via regularizers like dropout which reduce the importance of individual neurons by dropping out random portions of their weights. Both of these work to prevent overspecialized solutions that may be good enough for your purposes but lack broader applicability.
Deep learning is prone to many problems
There are several reasons why deep neural networks (DNNs) can be difficult to train. Some of these issues arise because DNNs require lots of data, so you need large amounts of examples for every possible situation that the network will encounter in the real world.
Another reason is that some of the layers within the network learn simple rules or functions, but then they are connected to more complex logic. This means that when the layer before it makes a mistake, its output may influence how much the next layer learns.
A third problem arises due to what we mentioned earlier: if each layer merely copies what comes after it, then small changes to any part of the network can have big effects on the rest of the system. If one tiny neuron gets stuck in a pattern, it can prevent the whole network from working properly.
The final issue has to do with overfitting. Once again, there isn’t quite enough training material to cover all the cases, which results in the model fitting too closely to the examples given and forgetting other patterns.
All of these situations put pressure on the individual neurons in the network to make decisions that aren’t necessarily logical. An example of this was discussed at the beginning of this article, where an input image containing a boat got fused into a sea of similar images.
