Deep learning is a type of machine learning and artificial intelligence. It emulates the way humans gain knowledge from observing a task or data repeatedly. Deep learning enables computers to recognize, classify, and make predictions.
The evolution of artificial intelligence began with the idea that it was possible to make computers take on tasks that relied on humans. Machine learning aims to teach machines to learn from data organized by humans. With deep learning, neural networks that mimic the human brain take on the work of teaching machines.
How does Deep Learning Work?
The word ‘deep’ references the many layers in the design of deep learning. In traditional analytics, humans would develop a model to make predictions. They would come up with as many features as they could for that model and make a predetermination of the meaningfulness of each feature. The main problem is that when another feature comes up, the model would have to be redone.
With deep learning, humans do not have to formulate or come up with specifications for the model. Instead, deep learning relies on recognizing latent features of the data. The result is that deep learning leads to predictive systems that adapt and improve with new data.
A good example would be trying to build a model that enables a computer to identify a cat. With traditional machine learning, a programmer would have to be extremely specific in telling the computer what to mark as a cat. The accuracy of the model is entirely reliant on the specifications made by the programmer. With deep learning, however, the computer will build the feature by itself. The computer will be presented with training data, for instance a set of images marked as ‘not cat’ and ‘cat’. The computer will start to build a feature set of ‘cat’ by itself. With each iteration, the model becomes more accurate. It is the same way we learn what to identify things around us including objects, language, smells and so on.
Faster and Better Prediction Models
While a lot of deep-learning applications today are still at small-scale application levels, there are a few incredibly useful areas where deep learning has been scaled. For instance, in the healthcare sector, convolutional neural networks (CNN) are being used to scan through x-ray results and MRI results as well.
Interestingly, some of the models can identify crucial features at a higher accuracy level than humans. In one study, even when doctors had an advantage of knowing a patient’s age, gender, and medical history, the CNN model still identified diagnostic features at 7% higher accuracy. The point here is that deep learning can lead to improved accuracy in identification of diseases. They can help doctors scan images faster and make more accurate determination from scans.
In a New-York based medicine school, researchers built a neural network that could identify neurological complications 7500% faster than a normal radiologist could. Imagine analyzing an image and giving results within 1.2 seconds.
Getting Around Data Laws Breaches
Besides scanning images, deep learning neural networks can be used to generate new images from scratch for teaching purposes. After scanning through thousands of images, the model can generate realistic ones. This way, doctors can use real patient images to generate new ones without compromising privacy or needing consent.
Structuring Data from Unstructured Sources
Deep learning helps to make sense of data from overly complex, diverse, and unstructured sources. Human languages have dialects, syntax rules, slang, and so many other inconsistencies. It is almost impossible to build a model with proper specifications to help computers to consistently understand what people are saying. With deep learning, natural language processing is now possible. Computers can read text, understand speech, and understand sentiment.
Natural language processing can help companies scan through huge volumes of data including social media conversations and feedback. As highlighted, it becomes possible to determine what sentiments customers have on a brand. This shapes the kind of response the company decides to go with to shield their image.
Deep learning has benefited businesses that rely heavily on recommender-systems. Recommender systems are a form of information filtering systems that predict what a user would prefer to see or view. Netflix, YouTube, and Amazon are popular platforms that use recommender systems.
Replacing traditional methods with deep learning filtering improves the user experience. Each time a logged in user plays something on Netflix or YouTube, it improves the likelihood that the next recommended item will be something they will like. The deep learning systems for these platforms use implicit feedback. Implicit feedback would include how long you watch a show on Netflix. If you watch to the end, then it means you really liked it and that is used to recommend another title.
Challenges with Deep Learning Implementation
Despite the excitement around how machine learning, and artificial intelligence technologies can significantly replace humans in completing menial tasks, significant talent is still required for implementation. According to a McKinsey Global Survey, 43% of respondents complained about a shortage of talent. There is a feeling that universities have some work to do to keep up with trends in this field.
Another major challenge is the vast amount of data that deep learning and AI models require to fulfill the intentions for which developers intend. Collecting and ensuring the quality of such data is quite a challenge. In experiments where a little incorrect or irrelevant data added to deep learning models totally distort the accuracy of the output. In addition, in the real world, a neural network model uses a lot of different data formats and sources. The level of complexity is such that data security is a significant challenge. As such, all stakeholders who serve as sources of data must be briefed appropriately on ensuring data security.
Indeed, deep learning is transforming the field of analytics towards a more prescriptive place. Human built systems are mainly to predict what is likely to happen in the future. However, with deep learning, it becomes possible to decide on the best course of action once you are aware of the facts.
If your company needs help with learning systems, check out Transcendent Software’s services. We are a technology solutions company that helps businesses make better use of data available to them. Even if you are not sure whether there are any opportunities for using learning systems at your company, we can audit and identify such opportunities for your organization.