Deep Learning is the term sometimes used synonymously with machine learning, but it is not the same thing. Machine learning is a type of AI where a computer learns to do something without being programmed to do it. Whereas deep learning is the process where machines learn to do something using an artificial neural network that is composed of a number of levels arranged in a hierarchy.
Traditionally machines Learn through algorithms that need a lot of domain expertise and human intervention in order to reduce the complexity of the data and to make the patterns more visible to learning algorithms to work. And they are only capable of what they are designed for; nothing more, nothing less. That’s where deep learning holds a bit more promise.
Deep learning, a subset of machine learning, is an advanced level of machine learning that utilizes a multi-layered hierarchical level of artificial neural networks to carry out the process of machine learning and deliver high accuracy in tasks such as speech recognition, object detection, language translation and other recent breakthroughs that you hear every day.
The hierarchical function of deep learning systems enables machines to process data with a nonlinear approach. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web.
The artificial neural networks learn something simple in the first level of the hierarchy and then sends it to the next level. In the next level, this simple information is combined into something that is a bit more complex, and passes it on to the next level and so on. Each level in the hierarchy builds something more complex from the input received from the previous level and can automatically learn to extract and translate the features from data sets such as images, video or text, without introducing traditional code or rules.
Significance of Deep Learning
Deep learning is on rage and is gaining much popularity due to its supremacy in terms of accuracy nowadays. Major tech companies are investing heavily in deep learning as it has become necessary in every sector as a way of making machines intelligent. Google AlphaGo is just an example of deep learning that made the headlines when it crushed Lee Sedol, one of the highest-ranking Go players in the world.
Google’s search engine, voice recognition system and self-driving cars all rely heavily on deep learning. Google announced Smart Reply, a deep learning network that writes short email responses for you. Netflix knows which show you will want to watch next. Facebook recognizes your friend’s face in a digital photo. YouTube’s deep learning networks pick out an attractive still from a video to use as a thumbnail.
Deep learning is clearly powerful but is also somewhat mysterious. Let’s examine some of the facts about how it can be useful to you.
A good way to get a handle on how deep learning can be useful is by taking a look at some of the companies that are doing interesting things with deep learning systems.
Deep learning is very useful for image/video processing or computer vision applications. It is used primarily to classify images, cluster them by similarities, and perform object recognition within scenes. For example, ViSENZE has developed commercial applications that can empower image recognition and tagging using their deep learning networks. This allows customers to use pictures rather than keywords to search a company’s products or matching similar items.
A more complex variation of this task is called object detection which involves specifically identifying one or more objects within the scene of the photograph and drawing a box around them. This is achieved through algorithms that can identify faces, individuals, street signs, flowers and many other aspects of visual data.
These algorithms have already dominated our daily life. For example, Facebook automatic tagging, Google photo search, Pinterest home feed personalization, etc. Algorithm sees the image differently as compared to the human brain. Each image is a 3-dimensional array of numbers, known as pixels where you have width, height, and depth. Width and height depend on the image resolution. The depth is of the red, green and blue values for the color code. Technically, deep learning receives these images to pass through a series of convolution layers with filters to be used for real-life scenarios. Obviously, it is way more complex than this but this is the super high level and simplified logic for how most of the companies use deep learning network to gather knowledge-based prediction for producing actionable results.
A traditional approach to detecting fraud or money laundering rely on a model with parameters built around the number of transaction that trails, while a deep learning technique would include time, geographic location, IP address, type of retailer and any other feature that is likely to point to fraudulent activity.
A deep learning technique learns categories incrementally through its hidden layer architecture. Each layer of its neural network builds on its previous layer with added data. The first layer of the neural network processes a raw data input like the amount of the transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user’s IP address, geographic location and makes the machine’s pattern even better. This continues across all levels of the neuron network.
Skymind with its open-source deep learning platform provides set-up, support and training services such as customer relations management, detecting fraud or evaluating the risk for frauds or insurance underwriting, and more.
If you want to detect the occurrence or potential for fraud in the system, the algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set and point out any anomaly detected by the pattern. If the machine learning system created a model with parameters built around the number of dollars a user sends or receives, the deep-learning method can start building on the results offered by machine learning.
Deep learning algorithms are trained to not just create patterns from all transactions, but also know when a pattern is signaling the need for a fraudulent investigation. The final layer relays a signal to an analyst who may freeze the user’s account until all pending investigations are finalized.
Significance in Drug Discovery
With artificial intelligence being used in drug discovery, the market’s value is growing rapidly. New research shows that the global drug discovery informatics market size was estimated at nearly $800 million in 2018 and it is anticipated to progress at an annual growth rate of 12.6% by 2025. The numbers show that the drug industry will heavily leverage the possibilities provided by machine learning.
This paradigm shift to machine learning in the pharmaceutical industry enables researchers to use novel computational algorithms to support the drug discovery process for improved diagnostics. Use of algorithms in designing new drugs has become more possible than it has ever been as machine learning can enhance many stages of the drug discovery process such as designing a drug’s chemical structure, investigating the effect of a drug as well as finding new patterns in those data can be facilitated by machine learning.
For example, Atomwise applies its deep learning networks to solve the problems of drug industries. They use deep learning networks to test the drugs for use against new diseases as well as explore the possibility of known drugs for other uses.
Furthermore, biomedical data from research experiments could be analyzed and interpreted using deep learning network to predict a drug’s effects and side effects. It is made possible to design better preclinical experiments to come up with the most effective therapies with the fewest side effects.
These examples are just a small number of the many companies using deep learning to do innovative and exciting things. There are many other significant uses of deep learning that we will discuss in this article.
AI companies are getting involved in various activities in the healthcare industry such as better treatment processes, diagnosis, therapy, and drug development.
By applying convolution neural networks, deep learning significantly improved the diagnostic process by speeding up and automating diabetic retinopathy screenings. And the studies are ongoing in building the next step of machine learning that can be trained to run by controlling the muscles attached to the virtual skeleton. With that doctors can predict if a patient is able to walk, jump or run properly after the treatment. Furthermore, the work done during the research might be later used to design new, AI-powered leg prostheses.
Colorization of Black and White Images
Traditionally, adding color to black and white photographs was done by hand with human effort because it is such a difficult task. Deep learning can use the objects and their context within the photograph to color the image, much like a human operator might approach the problem. This is achieved by using high quality and very large convolution neural networks and supervised layers that recreate the image with the addition of color.
Translation of Text and Images
Automatic machine translation has been around for a long time, but deep learning is achieving top results in two specific areas:
Automatic Translation of Text
Automatic Translation of Images
There are applications available that allow you to just take a picture of a text written in some foreign language and translate it for you to be read in a language that you can understand. That’s a deep learning network. It can automatically translate given words, phrase or sentences in one language into another language as you would expect. It uses convolution neural networks to identify images that have letters. Once identified, they can be turned into text, translated and recreates the image with the translated text. This is often called instant visual translation.
Similarly, text translation can be performed without any preprocessing of the sequence, allowing the algorithm to learn the dependencies between words and their mapping to a new language.
Automatic Text Generation
This is an interesting task, where deep learning networks form a relationship between the pen or the movement of a finger and the new text is generated, word-by-word or character-by-character.
The model is capable of learning how to spell, punctuate, form sentences and even capture the style of the text in the corpus. This is achieved by using large recurrent neural networks to learn the relationship between the items in the sequences of input strings and then generate text.
What is more fascinating is that it can learn and mimic the given corpus of handwriting examples and generate new handwriting in a different style for a given word or phrase. The handwriting is provided as a sequence of coordinates used by a pen or finger when the handwriting samples were created. From this corpus, the relationship between the movement and the letters is learned and new examples can be generated ad hoc.
Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation. Deep learning has many significant applications in our daily life activities that make it easier for us to perform a job, especially where the big data is involved. It can mimic the workings of the human brain in processing data for use in decision making. Furthermore, its ability to learn from the data that is both unstructured and unlabeled can be used to help detect fraud or money laundering.