Deep learning techniques are currently primarily used in cognitive computing, but they have immense potential for traditional analytics applications. For instance, SAS recently experimented with deep neural networks to solve speech-to-text transcription problems. In doing so, they eliminated 10 steps in feature engineering, data preprocessing, and modeling. The use of deep neural networks in this space represents a paradigm shift. But what will replace it? How will it replace traditional statistical and rule-based approaches?
Until recently, neural networks required humans to hand-code phonemes and face attributes. This made them difficult to understand and left machines incapable of processing data outside of their programmers’ parameters. But these days, the world is getting closer to this goal with modern voice assistants and automated phone menu systems. The new methods, which are loosely inspired by the human brain, make sense of data without any explicit algorithm. Moreover, they are more efficient, scalable, and economical.
For example, the techniques used in the last 25 years were invented during the 1950s. Then, they peaked in the 1960s, then briefly declined again in the ’80s, and then re-entered the market through deep learning. And with each decade, a new technique takes their place. The late ’50s saw the rise and fall of neural networks. Deep learning has replaced them. If Deep Learning isn’t the way to go, machine learning will replace it.
While deep learning may have many advantages, its biggest problem is getting high-quality data. A few companies have made huge investments in this area, such as Google. In 2015, the company demonstrated AlphaGo, a system that learned to play Go as well as a professional. In the same year, Google deployed Deep Learning on voice search and saw dramatic improvements in errors. Using deep learning in voice search, errors dropped by 25% overnight.
The rise of Deep Learning is meteoric. But, its future is still up in the air. As research advances, researchers will continue to apply the techniques to new tasks. Deep learning is on its way to becoming the dominant machine-learning technique. And it hasn’t even reached its potential. So what will replace Deep Learning?? A number of things. But first, let’s take a look at how deep learning will affect the future of machine learning.
The most notable benefit of deep learning is its reduced human intervention. It’s similar to the evolution of a personal computer. In the beginning, developers spent a lot of time cleaning up data and interpreting it. However, commercial deep learning solutions will be available within the next couple of years. They will be built using open source frameworks like TensorFlow. These open-source frameworks are capable of producing highly advanced systems.
The GPUs used in Deep Learning are the brains of the technology. The team at Stanford University developed them to meet the demands of video games. Before, a typical computer chip could only process one event at a time. But with GPUs, it was possible to process many events at the same time, doubling the speed and capability of the neural network. Today, machine learning can learn in a day, whereas it once took months.
This wave of automation will disrupt occupations across the wage scale. It will replace individual tasks within occupations and even re-design entire professions. For example, a radiologists job is composed of 26 different tasks, each of which is well-suited to machine learning. Similarly, machines are getting better at reading medical images than humans, but not yet at the same level of human image recognition. And as with any new invention, the human element will always be important, but humans will still need human interaction and interpersonal skills.
This system is based on the neural network model in the human neocortex, the area of the brain where higher-level cognition occurs. Neurons transmit electrical and chemical information from one cell to another. The process is called deep learning, and it uses multiple layers. While deep learning systems use many layers, they maintain theoretical universality under mild conditions. They also retain their ability to learn new features. The more layers in a network, the more complex the model becomes.
This technology has the potential to automate many jobs and predict the effects of service-system changes. However, it requires a significant upfront investment. Data for training AI may not always be available, so experts may have to label the data themselves. And experts may not understand the impact of changes in states. A new competitor may come along with better techniques and services. Businesses must prepare to meet this competition. For now, they must develop teams capable of working with AI.