Top 10 Artificial Intelligence Trends for 2019

Artificial Intelligence (or AI) is changing everything. It is changing everything from how businesses run to how customers are served, how things connect, and how you can control everything remotely. AI is the future of business in almost every area. AI is rapidly evolving compared to other technologies due to its agility. Google, Facebook, and Microsoft are all investing heavily in AI research and development.
To make a niche in futuristic technology and keep your skills ahead, you need to understand current trends in science and how they are applied in different fields.
These are the top AI trends you should be following in 2019.
Deep Neural Networks: Deep learning deals or deep neural networks work by using the human brain mimicry that computers use to learn from data. Neural networks are able to extract information from images, text, audio, and video, without the need for human input. According to the most recent deep learning theory, deep neural networks tend not to retain the useful information provided by the data but to compress and forget the irrelevant information after the initial fitting phase. This process boosts learning by eliminating the unnecessary.Understanding the way deep learning works can provide deep insight of optimally design networks and architectures, along with assisting in making the right choices. This knowledge provides greater clarity for applications that require regulatory approval or have critical safety requirements.
Capsule Networks are a group of active neurons that study a specific entity to learn, such as an object. The probability that the entity will exist is determined by the length of the activity vector in the capsule network. The active capsules can make predictions. The capsule network can be trained using discriminative methods to show high performance on MNIST. It can also emulate the visual processing strengths and abilities of the brain. Capsule networks can identify tasks and reduce errors by up to 50%.
Deep Reinforcement learning: This is the interaction of computer systems and the environment to solve specific business problems. Deep reinforcement learning (or DRL) is used to develop strategies that can be used in games to beat human champion brains. It is used to train models through simulation, eliminating the need for stamped data completely. Business applications are now using a combination DRL and agent-based simulating in 2019.
Generative Adversarial Networks (GAN): Neural networks can be paired to stimulate learning and reduce processing load. GAN is a deep-learning system that is unsupervised, but is enforced by two neural networks, namely generator or discriminator. The generator creates data that is identical to real data, while the discriminator takes in real and synthetic data. Both networks improve over time, allowing the duo to understand the entire distribution of given data sets. The generator creates fake data and the discriminator absorbs the real data. GANs reduce the load on deep neural networks as they are shared by two networks. GANs are used in business applications such as cyber detection.
Augmented and Lean Learning with Data: This addresses the challenge in using labeled data in machine-language to train the system, particularly in deep learning. The lean and augmented data learning process is made easier by synthesizing new data and transfer of learning techniques. This allows a trained model to be transferred to another domain to perform a task. These techniques can be used to address a variety of situations that have less historical data for machine learning.
Probabilistic Programming: High-level programming languages that are used to ease the model development and augment their efficiency to solve all issues are known as probabilistic programminglanguages. These languages offer model libraries that can be reused while supporting the interactive modeling with formal verification.Probabilistic programming languages are capable of accommodating the incomplete and uncertain information commonly used in business. These languages are expected to be widely adopted in deep learning during the current year.
Hybrid Learning models: Deep learning models cannot be used to deal with uncertainty. Combining different approaches, such as probabilistic and Bayesian, can help model uncertainty. Hybrid learning models address diverse business problems by incorporating uncertainty into deep learning. This allows for improved performance and more efficient models.