Top 10 Artificial Intelligence Trends in 2019

Humans efforts to make machines more intelligent lead to the invention of Artificial Intelligence technology.

AI is the intelligence demonstrated by the tools, the ability of a computer program to think, learn and respond, just like humans.

Artificial Intelligence is getting prevalence; its applications are visible in daily life from chatbots which effectively provide customer services to surveillance robots in shopping malls.

The continuous progress in the AI domain brings extraordinary inventions in the future. Some of the observed artificial intelligence trends of 2018–2019 are:

Deep Learning Theory:
Artificial Intelligence defines the functions of neural networks. Deep learning is a machine learning program which teaches computers what to do and how to do. The machines learn by experience through real-life examples to act like a human. Some of the popular applications of deep learning theory are: :

• Autonomous Car: The Deep learning theory, with well capable image recognization technology help driverless cars to recognize and differentiate between different images and objects like recognizing traffic signs, the difference between a lamppost and pedestrian, etc.
• Voice Command devices: AI deep learning is making a handheld device intelligent by controlling them over voice, i.e., speech recognition technology. AI have a significant influence on mobile technology.

Capsule Networks
Capsule Neural Network (CapsNet) is a machine learning system. It is used to model the hierarchical relationship by imitating a biological neural organization.

Deep Reinforcement Learning
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, optimize the points won in a game over many moves. That is, with time we expect them to be valuable to achieve goals in the real world.

A neural network learns by interacting with the environment with the help of observations, actions, and rewards. Deep reinforcement learning has been used to learn gaming strategies, such as Atari and Go. It includes the famous AlphaGo program that beat a human champion.

Reinforcement Learning, a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It enables machines and software agents to automatically determine the ideal behavior within a specific context, to maximize its performance.

Generative Adversarial Networks
It is a type of unsupervised deep learning system which implemented as two competing neural networks. The first network is known as “The Generator” it creates fake data that looks exactly like the real data set.

The second network is known as “The Discriminator,” it ingests real data and synthetic data. Each interface improves and enables the pair to learn the entire distribution of the given data set.

Lean and Augmented Data Learning
The biggest challenge in machine learning is the availability of large volumes of labeled data to train the system. Two broad techniques can help address this: Synthesizing new data and To Transfer, a model prepared for one task or domain to another.

Techniques, such as transfer learning or one-shot learning is used. It makes “lean data” learning techniques. To synthesize new data through simulations or interpolations helps to obtain more data, thereby augmenting existing data to improve learning.

Probabilistic Programming
It is a high-level programming language which allows a developer to design probability models. It solves these models automatically. Probabilistic programming languages allow reusing model libraries.

It supports interactive modeling and formal verification and provides the abstraction layer necessary to foster generic, efficient inference in universal model classes. Bringing probabilistic into AI development deliver useful products.

Hybrid Learning Models
It has different types of deep neural networks, such as GANs or DRL. It is promising regarding their performance. They have widespread applications with varying types of data.

Deep learning models do not model uncertainty. Hybrid learning models blend the two approaches to leverage the strengths of each. The few examples of hybrid models are Bayesian deep learning, Bayesian GANs, and Bayesian conditional GANs.

Automated Machine Learning
These models require a time-consuming and expert-driven workflow. It includes data preparation, feature selection, model or technique selection, training, and tuning.
AutoML tries to automate this workflow using some different statistical and deep learning techniques.

Digital Twin
A digital twin is a virtual model. It is used to promote detailed analysis and monitoring of physical or psychological systems.

The approach of the digital twin originated in the industrial world where it has been used widely to analyze and monitor things, for example, windmill farms or industrial systems.

It now, use agent-based modeling and system dynamics. The digital twins are being applied to nonphysical objects and processes, including predicting customer behavior.

Explainable Artificial Intelligence
There are applicable machine learning algorithms that sense, think act in a variety of different applications.

Many algorithms are considered “black boxes,” because they offer a little insight into how they reached their outcome.

Artificial Intelligence is a movement to develop machine learning techniques which produce more explainable models while maintaining prediction accuracy.

Conclusion:

The progress in Artificial Intelligence will bring extraordinary inventions in the future.

For a successful business, it is essential to keep it updated with the latest technology. Right now, artificial intelligence is bringing revolution in several industries by automating their manual task.

It not only eliminates the human workforce but enhances the output of the processes. For developing a successful business by taking the leverage of AI technology, it is essential to consider the services of startups providing AI-based software solutions.