10 Applications that Require Deep Learning
This article is too short. It can’t even begin to describe the ways in which deep learning will affect you in the future. Consider this article to be offering a tantalizing tidbit — an appetizer that can whet your appetite for exploring the world of deep learning further.
These deep learning applications are already common in some cases. You probably used at least one of them today, and quite likely more than just one. Although the technology has begun to see widespread usage, it’s really just the beginning. We’re at the start of something, and AI is actually quite immature at this point.
This article doesn’t discuss killer robots, dystopian futures, AI run amok, or any of the sensational scenarios that you might see in the movies. The information you find here is about real life, existing AI applications that you can interact with today.
Deep learning can be used to restore color to black-and-white videos and pictures
You probably have some black-and-white videos or pictures of family members or special events that you’d love to see in color. Color consists of three elements: hue (the actual color), value (the darkness or lightness of the color), and saturation (the intensity of the color). Oddly enough, many artists are color-blind and make strong use of color value in their creations. So having hue missing (the element that black-and-white art lacks) isn’t the end of the world. Quite the contrary, some artists view it as an advantage.
When viewing something in black and white, you see value and saturation but not hue. Colorization is the process of adding the hue back in. Artists generally perform this process using a painstaking selection of individual colors. However, AI has automated this process using Convolutional Neural Networks (CNNs).
The easiest way to use CNN for colorization is to find a library to help you. The Algorithmia site offers such a library and shows some example code. You can also try the application by pasting a URL into the supplied field. This Petapixel.com article describes just how well this application works. It’s absolutely amazing!
Deep learning can approximate person poses in real time
Person poses don’t tell you who is in a video stream, but rather what elements of a person are in the video stream. For example, using a person pose could tell you whether the person’s elbow appears in the video and where it appears. This article tells you more about how this whole visualization technique works. In fact, you can see how the system works through a short animation of one person in the first case and three people in the second case.
Person poses can have all sorts of useful purposes. For example, you could use a person pose to help people improve their form for various kinds of sports — everything from golf to bowling. A person pose could also make new sorts of video games possible. Imagine being able to track a person’s position for a game without the usual assortment of cumbersome gear. Theoretically, you could use person poses to perform crime-scene analysis or to determine the possibility of a person committing a crime.
Another interesting application of pose detection is for medical and rehabilitation purposes. Software powered by deep learning could tell you whether you’re doing your exercises correctly and track your improvements. An application of this sort could support the work of a professional rehabilitator by taking care of you when you aren’t in a medical facility (an activity called telerehabilitation).
Fortunately, you can at least start working with person poses today using the tfjs-models (PoseNet) library. You can see it in action with a webcam, complete with source code. The example takes a while to load, so you need to be patient.
Deep learning can perform real-time behavior analysis
Behavior analysis goes a step beyond what the person poses analysis does. When you perform behavior analysis, the question still isn’t a matter of whom, but how. This particular AI application affects how vendors design products and websites. Articles such as this one from Amplitude go to great lengths to fully define and characterize the use of behavior analysis. In most cases, behavior analysis helps you see how the process the product designer expected you to follow doesn’t match the process you actually use.
Behavior analysis has a role to play in other areas of life as well. For example, behavior analysis can help people in the medical profession identify potential issues with people who have specific medical conditions, such as autism, and help the patient overcome those issues. Behavior analysis may also help teachers of physical arts show students how to hone their skills. You might also see it used in the legal profession to help ascertain motive. (The guilt is obvious, but why a person does something is essential to fair remediation of an unwanted behavior.)
Fortunately, you can already start performing behavior analysis with Python.
Deep learning can be used to translate languages
The Internet has created an environment that can keep you from knowing whom you’re really talking to, where that person is, or sometimes even when the person is talking to you. One thing hasn’t changed, however: the need to translate one language to another when the two parties don’t speak a common language. In a few cases, mistranslation can be humorous, assuming that both parties have a sense of humor.
However, mistranslation has also led to all sorts of serious consequences, including war. Consequently, even though translation software is extremely accessible on the Internet, careful selection of which product to use is important. One of the most popular of these applications is Google Translate, but many other applications are available, such as, DeepL. According to Forbes, machine translation is one area in which AI excels.
Translation applications generally rely on Bidirectional Recurrent Neural Networks (BRNNs). You don’t have to create your own BRNN because you have many existing APIs to choose from. For example, you can get Python access to the Google Translate API using the library. The point is that translation is possibly one of the more popular deep learning applications and one that many people use without even thinking about it.
Deep learning can be used to estimate solar savings potential
Trying to determine whether solar energy will actually work in your location is difficult unless a lot of other people are also using it. In addition, it’s even harder to know what level of savings you might enjoy. Of course, you don’t want to install solar energy if it won’t satisfy your goals for using it, which may not actually include long-term cost savings (although generally it does). Some deep reinforcement learning projects now help you take the guesswork out of solar energy, including Project Sunroof. Fortunately, you can also get support for this kind of prediction in your Python application.
AI can beat people at computer games
The AI-versus-people competition continues to attract interest. From winning at chess to winning at Go, AI seems to have become unbeatable — at least, unbeatable at one game. Unlike humans, AI specializes, and an AI that can win at Go is unlikely to do well at chess. Even so, 2017 is often hailed as the beginning of the end for humans over AI in games. Of course, the competition has been going on for some time, And you can likely find competitions that the AI won far earlier than 2017. Indeed, some sources place the date for a Go win as early as October 2015. The article at Interesting Engineering describes 11 other times that the AI won.
The problem is to custom create an AI that can win a particular game and realize that in specializing at that game, the AI may not do well at other games. The process of building an AI for just one game can look difficult. This article describes how to build a simple chess AI, which actually won’t defeat a chess master but could do well with an intermediate player.
However, it’s actually a bit soon to say that people are out of the game. In the future, people may compete against the AI with more than one game. Examples of this sort of competition already abound, such as people who perform in a triathlon of games, which consists of three sporting events, rather than one. The competition would then become one of flexibility: the AI couldn’t simply hunker down and learn only one game, so the human would have a flexibility edge. This sort of AI use demonstrates that humans and AI may have to cooperate in the future, with the AI specializing in specific tasks and the human providing the flexibility needed to perform all required tasks.
Deep learning can be used to generate voices
Your car may already speak to you; many cars speak regularly to people now. Oddly, the voice generation is often so good that it’s hard to tell the generated voice from a real one. Some articles talk about how the experience of finding computer voices that sound quite real are becoming more common. The issue attracts enough attention now that many call centers tell you that you’re speaking to a computer rather than a person.
Although call output relies on scripted responses, making it possible to generate responses with an extremely high level of confidence, voice recognition is a little harder to perform (but it has greatly improved). To work with voice recognition successfully, you often need to limit your input to specific key terms. By using keywords that the voice recognition is designed to understand, you avoid the need for a user to repeat a request. This need for specific terms gives it away that you’re talking to a computer — simply ask for something unexpected and the computer won’t know what to do with it.
The easy way to implement your own voice system is to rely on an existing API, such as Cloud Speech to Text. Of course, you might need something that you can customize. In this case, using an API will prove helpful. This article tells how to build your own voice-based application using Python.
Deep learning can be used to predict demographics
Demographics, those vital or social statistics that group people by certain characteristics, have always been part art and part science. You can find any number of articles about getting your computer to generate demographics for clients (or potential clients). The use of demographics is wide ranging, but you see them used for things like predicting which product a particular group will buy (versus that of the competition). Demographics are an important means of categorizing people and then predicting some action on their part based on their group associations. Here are the methods that you often see cited for AIs when gathering demographics:
- Historical: Based on previous actions, an AI generalizes which actions you might perform in the future.
- Current activity: Based on the action you perform now and perhaps other characteristics, such as gender, a computer predicts your next action.
- Characteristics: Based on the properties that define you, such as gender, age, and area where you live, a computer predicts the choices you are likely to make.
You can find articles about AI’s predictive capabilities that seem almost too good to be true. For example, this Medium article says that AI can now predict your demographics based solely on your name. The company in that article, Demografy, claims to provide gender, age, and cultural affinity based solely on name. Even though the site claims that it’s 100 percent accurate, this statistic is highly unlikely because some names are gender ambiguous, such as Renee, and others are assigned to one gender in some countries and another gender in others. Yes, demographic prediction can work, but exercise care before believing everything that these sites tell you.
If you want to experiment with demographic prediction, you can find a number of APIs online. For example, the DeepAI API promises to help you predict age, gender, and cultural background based on a person’s appearance in a video. Each of the online APIs do specialize, so you need to choose the API with an eye toward the kind of input data you can provide.
AI can create art from real-world pictures
Deep learning can use the content of a real-world picture and an existing master for style to create a combination of the two. In fact, some pieces of art generated using this approach are commanding high prices on the auction block. You can find all sorts of articles on this particular kind of art generation, such as this Wired article.
However, even though pictures are nice for hanging on the wall, you might want to produce other kinds of art. For example, you can create a 3-D version of your picture using products like Smoothie 3-D. It’s not the same as creating a sculpture; rather, you use a 3-D printer to build a 3-D version of your picture. Check out an experiment that you can perform to see how the process works.
The output of an AI doesn’t need to consist of something visual, either. For example, deep learning enables you to create music based on the content of a picture. This form of art makes the method used by AI clearer. The AI transforms content that it doesn’t understand from one form to another. As humans, we see and understand the transformation, but all the computer sees are numbers to process using clever algorithms created by other humans.
Deep learning can be used to forecast natural catastrophes
People have been trying to predict natural disasters for as long as there have been people and natural disasters. No one wants to be part of an earthquake, tornado, volcanic eruption, or any other natural disaster. Being able to get away quickly is the prime consideration in this case given that humans can’t control their environment well enough yet to prevent any natural disaster.
Deep learning provides the means to look for extremely subtle patterns that boggle the minds of humans. These patterns can help predict a natural catastrophe, according to the article on Google’s solution. The fact that the software can predict any disaster at all is simply amazing. However, this article warns that relying on such software exclusively would be a mistake.
Overreliance on technology is a constant theme, so don’t be surprised that deep learning is less than perfect in predicting natural catastrophes as well.