Groundbreaking. Revolutionary. The next big thing. These are some of the terms used to describe artificial intelligence. However, as government leaders and businesses grapple with the idea of harnessing the power of AI, they often forget one key element — innovation is only as good as its application.
You might have a fantastic concept, but without the proper execution, it remains just that — a concept instead of something you’re able to benefit from.
So, it’s about time we changed our focus from the theory behind artificial intelligence to AI-powered systems. After all, nobody doubts the potential of AI, but it would be worthwhile for business leaders and technologists to take a closer look at the following developments:
The neural network demystified
Deep neural networks, also known as deep learning, emulate human brain functioning, and “learn” from audio, images, and text. Unfortunately, even after more than a decade of research, deep learning still remains something of a mystery. All that may possibly change in 2018 as a new theory claims that deep neural networks eliminate and compress noisy data as soon as they pass the initial fitting stage, substituting it with information on what the data represents.
The more we unravel the inner workings of deep learning, the more development and use we get out of it. Exploring this theory on other deep neural network design and deep neural networks will yield more positive results.
Capturing the magic of our brain’s visual processing with capsule networks
Unlike convolutional neural networks, capsule networks are capable of establishing hierarchical relationships. This enables the network to consider spatial hierarchies between complex and simple objects, thereby preventing miscalculation and lowering the possibility of errors.
In fact, capsule networks can reduce errors by as much as 50 percent for identification tasks. No wonder so many deep neural network architectures and problem domains are switching to capsule networks,
Solving business problems with deep reinforcement learning
This one is of particular interest to organizations that seek to use AI for learning through environmental interactions. A combination of actions, rewards, and observations in deep reinforcement learning (DRL) allows companies like Go and Atari to learn gaming tactics. The most general purpose out of all the learning methods, DRL can be used for business applications as it uses fewer data than other training programs.
In fact, thanks to its ability to train via simulation, the need for labeled data is removed altogether. Considering how advantageous the whole situation is, it’s only a matter of time before more business applications combine agent-based simulation and DRL.
Promote learning with generative adversarial networks
The culmination of two conflicting neural networks, a GAN (general adversarial network) is an unsupervised deep learning system. The first network — the generator — develops fake data based on the dataset, while the other network, the discriminator, ingests synthetic and real data.
Given sufficient time, GAN learns the distribution sequence for a specific dataset and improves the network. The system even promotes the utilization of unsupervised jobs where labeled data is either too expensive to obtain or does not exist. Since the two networks now share all the responsibilities, the load for deep neural network decreases. Expect GAN to find its way to other business applications, such as cyber detection, soon.
Handle labeled data challenges with augmented data learning
One of the biggest hurdles facing machine learning, especially deep learning, is the availability of large labeled data volumes for training the system. But the solution presents itself as two broad methods:
- New data synthesization.
- Transfer of a model trained for a specific domain or task to another.
One-shot learning, transfer learning, and other techniques are classified as “lean data” learning techniques. Also, the synthesis of new data from interpolations or simulations leads to greater data production and augments the existing data for better learning.
In the grand scheme of things, these techniques address a vast array of issues, notably those with minimal historical data. It’s just a matter of time before more variations of augmented and lean data show up along with various kinds of learning for a wide range of business issues.
Simplify model development with probabilistic programming
Probabilistic programming is a high-level programming language that allows developers to easily design probability models and solve them automatically. Thanks to this programming language, model libraries can be reused, formal verification and interactive modeling can be supported, and the abstraction layer needed for efficient yet generic interference in universal model classes is offered.
The beauty of probabilistic programming language lies in its capacity to accommodate incomplete and uncertain information that is prevalent in the world of business. These languages are becoming widely adopted and they will soon be used for deep learning.
Hybrid learning models for model uncertainty
Neural networks, like DRL or GANs, have exhibited a lot of potential as far as performance and application with various kinds of data are concerned. But deep learning models have always had a bad track record with model uncertainty, just like probabilistic, or Bayesian, approaches do. Thankfully, by combining the dual approaches, hybrid learning models increase the strengths of each.
A few terrific examples would be Bayesian conditional GANs, Bayesian GANs, and Bayesian deep learning. Thanks to hybrid learning models, the range of business problems can now be extended to include deep learning with uncertainty.
As a result, better performance can be achieved, which further promotes adoption of the hybrid learning model. With time, further deep learning methods will acquire Bayesian equivalents while a mix of probabilistic programming languages will incorporate deep learning.
No-programming model creation with automated machine learning
Machine learning models have always required an expert-driven and time-consuming workflow, including feature selection, data preparation, tuning, training, and technique or model selection. Automated machine learning seeks to automate such a workflow through a variety of deep learning and statistical techniques.
Part of the democratization of AI tools, automated machine learning allows business users to create machine learning tools without the need for any deep background in the field of programming. Moreover, it speeds up the time required by data scientists to develop models. This is the year we will see more commercial automated machine learning packages along with the integration of the technology in larger platforms.
A novel year for AI
This year is going to be a big one for artificial intelligence, with lots of new technologies being developed for different businesses and industries. In fact, it might be hard to keep track of all the trends, which is precisely why we’ve gathered the ones worthy of your attention in the handy list above.
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