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Home Artificial Intelligence

Top 7 Artificial Intelligence (AI) Technology Trends For 2018

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December 12, 2018
in Artificial Intelligence
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In Brief
  • Plans concentrating on Artificial Intelligence (AI) are being used everywhere, with increasing discoveries in AI development.
  • Companies and countries are investing huge sums of money in developing new AI technologies.
  • Though it is not possible to list down all the popular AI trends for 2018, below are the top 7 ones who make the most difference...

Plans concentrating on Artificial Intelligence (AI) are being used everywhere, with increasing discoveries in AI development. Companies and countries are investing huge sums of money in developing new AI technologies. Though it is not possible to list down all the popular AI trends for 2018, below are the top 7 ones who make the most difference when it comes to AI tech.

  1. Deep Learning Theory:

Though not much is known about why and how deep neural networks have such great ability to learn and perform. But now, it appears like a new theory may change this fact. It suggests the information bottleneck principle is utilized in deep learning.

This happens when a big amount of data is consumed all at once. The little less important data is ignored and compressed initially to process more important data. By better understanding how deep learning functions, researchers will be able to use it in several applications in a better way.

  1. Hybrid Learning Models:

Such kind of model utilizes the strength of deep neural methods along with probabilistic or Bayesian approaches. For instance, Bayesian GANs is a kind of hybrid model. Hybrid Learning Models play a vital role as these make the fact of including deep learning along with an uncertainty level possible.

  1. Explainable AI:

AI development has led to various applications using several algorithms. Most of these algorithms are referred to as black boxes as these offer little insight as to how they provided the answer. Explainable AI is mainly about generating machine learning methods which are transparent and still attain similar results. This is important for enterprises to develop trust in AI and also allow other enterprises to deploy AI too.

  1. Probabilistic programming:

Such a kind of programming will help the programmers or developers in designing probability models by reutilizing model libraries to aid interactive modelling. This gives the experts the ability to work on incomplete and uncertain information.

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  1. Digital twin:

This is a virtual model which is utilized to evaluate and check psychological or physical systems. Digital twin is used to analyze customer behaviour. Besides, it also helps in boosting larger adoption of IoT and also helps maintain the present IoT systems in place. In the coming days, people may see much more of IoT systems in sectors like physical systems, etc.

  1. Deep Reinforcement Learning:

This is a neural network which understands and learns through actions, rewards and observations. Deep Reinforcement Learning is being used extensively to learn a few popular gaming strategies like AlphaGo program. It covers an array of applications and is a general kind of neural network among all other learning methods.

It does not require much information or methods to train the models. Besides, the more notable fact about this AI development is that one can train Deep Reinforcement Learning using simulation and avoid using labelled data completely. With such great advantages, it is expected that more enterprise applications will combine simulation and Deep Reinforcement Learning in the days to come.

  1. Capsule networks:

These are a new type of deep neural network. They process the visual data in the same way as a human brain does. This implies that it will be easy to preserve hierarchical relationships, resulting in lower mistake rate. This is extremely different and contrasting in nature when convolution neural network is compared. Capsule networks don’t need much information for training and thus remain one step forward.

Hence, with such major advancement in AI, automation and machine learning, one is and will also be seeing a change in the way consumers and businesses interact with one another. AI is changing the way companies think and operate when it comes to business intelligence. With all such growth, one can expect the AI landscape to change over time.

About The Author

Kavya gajjar is a Marketing Manager at AIS Technolabs which is Web design and Development Company, helping global businesses to grow by Augmented Reality Development  Services. I would love to share thoughts on Social Media Marketing Services and Game Design Development etc.

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