Recent developments in artificial intelligence (AI) have ushered in an era of intelligent machines that can perform complex, reasoning-based tasks. These new AI systems are referred to as “deep learning” or sometimes just “neural networks.”
Deep neural nets work by using multiple layers of software instructions called neurons to achieve their goal. For example, let’s say your computer is trying to recognize all instances of “cat” within a large amount of data. It would use a neuron that recognizes patterns of letters and numbers to process the next layer of information, which then looks for instances of cats with a tail. Finally, it would look at the shape and structure of the body to identify what was defined as a cat.
The more examples it has seen, the better it gets at identifying things that match this definition of what a cat is. By having lots of experiences with different types of animals, it acquires knowledge about how they’re built. This is why people who know a lot about wildlife often describe certain creatures as being like other ones they’ve encountered before.
With deep learning, instead of teaching individual neurons about specific concepts, it teaches entire layers of neurons together. This allows each newly added layer to learn something slightly more abstract than its predecessors, until one day it performs a task on its own.
Definitions of sensitivity and specificity
Sensitivity is defined as the proportion of positive tests that are correctly identified, or in other words, how well your test does at detecting disease. Specificity is the opposite: it measures whether your test can tell you with certainty that someone who has no disease will also come up negative on the test.
A diagnostic test with both high sensitivity and high specificity is considered to be very accurate. A sensitive test may identify many people with the disease as “positive” while a specific one may only identify few without the disease as “negative.”
By this definition, a diagnostic test that cannot determine if a patient has the disease is not really meaningful-it doesn’t help rule out anything! A test that makes lots of false positives (diagnosing healthy individuals as having the disease) is less useful than one that doesn’t do much for sick patients either because it just confirms their suspicion that they have the disease. On the other hand, a diagnostic test that produces too many false negatives (missing the presence of disease when it should have detected it) isn’t great either because it won’t confirm that a person does not have the disease.
Sensitivity and specificity aren’t the only ways to evaluate accuracy of a diagnostic tool, but they play an important role in determining its usefulness.
Sensitivity and specificity in the context of deep learning
When talking about sensitivity and specificity, there is some confusion as to what they mean. This article will clear up that confusion!
Sensitivity refers to how well your model can identify or classify instances of the condition it was trained on. For example, if your model was designed to recognize dogs then its sensitivity would be determined by how well it identifies actual dogs.
Specificity refers to how well your model can tell cats apart from other animals. If your model has high specificity then it will have a hard time telling between different species because it does not differentiate very much.
A lot of times people get confused about these terms because they are used interchangeably throughout various sources and blogs. It is important to know the difference so you can evaluate whether or not a paper or topic is worth reading.
Examples of black box in deep learning
Recent developments in artificial intelligence (AI) have made many believe that AI has reached “self-consciousness” or, at least, consciousness beyond humans. Some refer to this as strong AI or superintelligence.
If you listen to early talk show hosts like Jay Leno or David Letterman, you will find yourself laughing hard at their jokes due to how smart they are. Amazon Alexa is now even able to give you tips for making dinner recipes!
But all these tools require advanced computer software called deep neural networks or DNNs.
These systems are designed to learn patterns from data using algorithms that mimic how our brains work. But researchers cannot easily access what these machines are thinking because it is heavily encrypted information.
There are some theories about why companies develop such technology but no one really knows for sure. It is also difficult to tell whether these technologies can truly achieve superhuman performance since most tests use pre-trained models that were tuned specifically for the test environment.
Overall though, there is an increasing sense among experts that we are entering an era where intelligent agents controlled by computers will soon outstrip human capabilities in almost every field. This includes things like voice recognition, image processing, and logical reasoning.
Examples of white box
Recent developments in artificial intelligence (AI) have been defined as deep learning or, more specifically, convolutional neural networks (CNNs). CNNs are computer algorithms that use very specialised functions to identify objects in images or videos.
The reason why they’re so popular is because they work well with lots of data; if there’s one thing we’ve got plenty of, it’s pictures and video!
By using these advanced AI techniques, computers can now find patterns in large amounts of data quickly and effectively. This is what makes them powerful tools for making predictions based on past experiences.
For example, let’s say your job requires you to check every cart at a grocery store for expired food stamps. You would need an extremely efficient system to do this quickly and correctly. A smart machine could create such a system by memorising examples of food stamp cards and matching unknown ones using pattern recognition.
That way, it would know which features indicate a valid card and which don’t, and could make its prediction efficiently. The technology used to implement this kind of algorithm is called black box optimization, because it doesn’t give much insight into how it works.
With big businesses investing heavily in AI, there has been growing concern over whether or not those applications contain enough safeguards to protect individuals’ personal information.
Challenges with black box
There is an ongoing debate about whether or not AI systems are intelligent or if they can be understood by humans. Some refer to this as the “AI-humanity” argument, where some believe that even though these algorithms have learned complex patterns, there is no way for us to know how they come up with those patterns.
This challenge — why AI technology is often described as having “no soul” — comes down to one of two things: computational power or bias. Computational power refers to the ability to carry out large amounts of mathematical calculations quickly, which makes it possible to apply machine learning to ever more complicated problems. Bias refers to the system being trained on past examples, only applying what it has been taught before without taking into account any new situations.
Both of these challenges arise because most people who use AI technologies are not educated in computer science. Rather, they may have taken a few courses related to computers, or maybe they took business classes where concepts like linear regression were explained. Because of this, they might not fully understand how algorithmic thinking works and therefore cannot critique whether or not something has biases.
There is also controversy over just how general purpose AI systems are. Many argue that advanced applications require very specific context and conditions to work properly, making them less practical than imagined.
Challenges with white box
Technically, anyone can use deep learning algorithms to perform tasks, just like how you could learn computer programming using an easy to follow process.
However, this doesn’t mean that everyone will be able to utilize the algorithm for their own purposes.
In other words, even if someone knows what all of the layers in a neural network do, they may not know how to apply that knowledge to solve a new problem.
This is called applying the black box analogy. It sounds very abstract and complicated, but it really refers to something that happens every day — people use advanced software applications without ever looking at the source code!
A classic example of this would be using Google Maps. You don’t have to understand binary data to use GPS technology, so why should AI be any different?
There are many reasons why having access to the inner workings of a machine learning model is important. Chief among them is transparency. By adding this layer of understanding, we as users can help ensure that the models we consume are correct, work well, and provide quality results.
But there is another reason why knowing the inner-workings of ML models is essential – achieving better accuracy. That said, there are some ways to get insights into how individual components influence output, which we will talk about later in this article.
So now that we’ve defined the importance of white box research, let’s take a closer look at some misconceptions around the topic.
Recent developments in deep learning have made it difficult to evaluate how much of the performance is due to the underlying algorithm and technology, and how much is due to the specific implementation and layer-by-layer tweaking used by the engineers who designed the network.
Deep neural networks are complex mathematical functions that rely on lots of data to learn how to perform their tasks. Because they work with very large amounts of data, there are often prebuilt layers or features that you can add onto your net to get started more quickly.
By adding these into the already learned function, the system uses this existing knowledge to facilitate faster processing. This is what’s referred to as “transferring knowledge” from one set of concepts to another.
Because these additions are usually done manually, however, engineers may be inclined to use whatever feature seems most aesthetically pleasing or intuitively makes sense to them. This may not necessarily contribute to better overall performance, however — especially if those choices negatively affect the way the net was originally designed!
Furthermore, because most people are not trained in artificial intelligence or computer science, it’s hard for others to determine whether certain tweaks make sense or not. As a result, many AIs do not perform as well as they could potentially – even when some key components were tweaked after the initial design.
Deep Learning Is Like Gambling
Recent developments in deep learning are like gambling. You may feel lucky when you enter the casino, but that doesn’t mean you will win money quickly or easily.
Deep strategies are similar to investing in the stock market. They can be difficult to understand for the average person because they require significant mathematics concepts and logic to work.
That is why there are so many people who “gamble” with AI systems – it looks cool. A lot of companies have products that use advanced neural networks, and people seem to give them credit for being clever.
But most of these applications are not accessible to anyone else due to their deep nature. If you don’t have the math background or software to train your own system, then you are left out of the game.
AI has become very trendy lately, which makes it even more appealing to gamble with. But before you do, make sure you understand what kind of risk you are taking on.