Top Ten Advanced Technology Trends in Artificial Intelligence in Year 2018
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February 2nd, 2019
Artficial Intelligence and Machine Learning have jointly contributed to being the game-changers of the coming decade. It has become the main focus with businesses, companies, and developers by the discoveries made academically. Everyone is trying to pursue Artificial Intelligence technology in an attempt to control the future, leading to innovations.
Considering that large companies and businesses are now relying on advancing Artificial Intelligence, we have observed some significant advancements in 2018 which are as follows:
1. Hybrid Learning Models
A number of different types of neural networks have proven to show great performance and efficiency with different types of data such as GANs or DRL. Hybrid Learning Models combine two types of approaches that is Deep Learning Models approach as well as the Bayesian approach to leverage strengths of each. Few examples of Hybrid Learning Models are Bayesian conditional GANS, Bayesian Deep Learning etc. The importance of these models is that they expand the variety of business problems by including deep learning as well as uncertainty, which ultimately helps in achieving better results and performance by the expansion of these models.
2. The Surge in Chatbot Numbers:
Recently Chat Bots have become another useful case of Artificial Intelligence which is put into use by many companies for their customer care services. It is not only helping individuals but also businesses in improving their customer care support by utilizing human intelligence for other productive tasks. The increase in the popularity of Chatbots made a number of companies like Facebook to announce their tools for building chatbots. With the observed popularity of chatbots recently, the whole customer service industry will be depending on it with time.
3. Use of Artificial Intelligence in Medical Research/Diagnosis/Treatment:
Healthcare is the number one industry that is progressing through Artificial Intelligence. A huge number of AI applications like Deep Learning, Big Data, Cognitive Learning is helping in a huge number of diagnosis, treatments and research processes. According to a recent research and analysis, it is seen that the healthcare market is being invested a lot by big companies like Google, Atomwise, IBM, for research in Artificial Intelligence so that the best healthcare solutions can be provided. This will be expanding in future as there is a lot of scope in this field through Artificial Intelligence.
4. Deep Reinforcement Learning:
Deep Reinforcement Learning (DRL) helps in interacting with the environment to solve many business problems. It is a type of neural network which learns by itself as it is made to interact with the environment through observations, actions. This has been used to learn various gaming strategies such as the AlphaGo which was a famous program that did beat the human champion. This feature matters as it is one of the most general-purpose among all learning techniques, as it requires fewer data to train its models, unlike other learning techniques. One of the most important features is that it can be trained via simulation which automatically eliminates the need for data. This has enabled to use this learning technique to solve business applications.
5. Deep Learning Theory: With the evolution of Artificial Intelligence, we know that Deep Learning networks have the ability to imitate the human brain and also to learn from images, audio as well as texts. There are a lot of things left to be discovered about deep learning. By understanding the way deep learning works, we can use it for greater development and put it into a lot of uses. This has enabled to also understand how neural networks operate. With the help of that, it enables to yield insights into things like architecture choice and also to provide regulatory applications. In time more, results will be developed from deep learning theory to apply to various types of deep neural networks.
6. Integration with Application with Artificial Intelligence:
Artificial Intelligence has driven much application to work through it and slowly we will witness the use of AI at some level on every web as well as the mobile application platform. This technology will be seen in almost every software industry in future with the growing demand. Machine Learning, Deep Learning, Computer Vision etc., are different forms of Artificial Intelligence that used by many developers to develop intelligent applications. This technology helps in providing suggestions, recommendations, smart search, healthcare, finance, fintech, etc. Currently, this technology is growing and in the near future, we will see the expansion of the same in developing productive applications which won't require developers to code explicitly anymore.
7. Capsule Networks:
Capsule Network is a new type of Deep Neural Network that can process visual information in almost the same way as the human brain does. This also means they can maintain hierarchical relationships, unlike Convolutional Neural Networks (CNN) that can't maintain hierarchical relationships which results in high error rate and misclassification. Capsule networks have proven to be effective as it reduces error percentage by almost fifty percent and has a better accuracy for identification tasks, as not much data is required for training models. This has enabled the use of capsule networks across many deep neural network architectures and problems.
8. Digital Twin:
To facilitate detailed analysis of physical as well as psychological systems, a Digital Twin is used which is a virtual model. This concept was first originated in the industrial world where it was widely used to analyze and keep a track on things like industrial systems and windmill farms. But now digital twins are applied to objects which are nonphysical and processes which also includes the behavior of customers. Digital Twin helps in the development and adopting the broader concept of the Internet of Things which provides a way to predict diagnosis to maintain the IoT systems. In future, we will see a greater use of Digital Twin for physical systems.
9. Automated Machine Learning (AutoML):
To develop Machine learning models, we require a time-consuming and expert workflow that includes preparing data, feature selection, selection of model or techniques, training, as well as tuning which can be done by Automated Machine Learning (AutoML) which helps in automating this workflow and uses a number of statistical techniques as well as deep learning techniques. This tool has enabled users to develop machine learning models in their businesses without having a deep programming background, which speeds up the process for creating models. Hence, we will be seeing more commercial AutoML packages with Machine Learning platforms.
10. Augmented Data Learning:
A large volume of data is required to train systems which is a challenging problem in Machine Learning. Augmented Data Learning is a way to help address this problem. Synthesizing new data and by transferring a model which is already trained for a particular task to domain another. Augmenting the data has helped in improving learning of the data by models. This has helped address a variety of problems and in future, we will see different learning methods to solve business problems.
To conclude, 2018 is very promising for technological advancements in the field of Artificial Intelligence and with the coming years, we will be discovering new applications with faster and accurate solutions. We will be witnessing some amazing results with the continuous evolvement and approach in this field.