Top 7 Machine Learning And AI Trends In 2020 - Solace Infotech Pvt Ltd

Top 7 Machine Learning And AI Trends In 2020 - Solace Infotech Pvt Ltd

Know the top 7 machine learning and AI trends in 2020 that you must follow for better security. 7 Important mahcine learning and AI trends in 2020.

Artificial Intelligence and machine learning has changed the way of working that we have continued for many years. A good example is the rise of chatbots that are taking over the businesses to manage the customer queries. Machine learning has helped in analysis of large data sets within minutes. There are a lot of innovative uses for Artificial intelligence and machine learning. AI powered virtual nurses like “Angel” and “Molly” are already saving lives and cost while robots are helping with everything, from less invasive procedures to Open heart surgery. Considering the rise in demand and interest in artificial intelligence and machine learning, lots of new trends are emerging. Let us see some of these.

1. Automation- Intelligent Process Automation(IPA), is a process of ensuring automation of manual tasks with the assistance of artificial intelligence. All organizations have bottlenecks in different business measures. IPA will assist them with distinguishing the trend and foresee future bottlenecks by allowing the management to enhance decision making effectively. Automation is a beneficial development for any business to drive its tasks. For example, automation can help to prevent cyber attacks by recognizing unusual user requests and the frequency of these requests.In such cases, the system can notify the administrator, allowing them to take the necessary actions. Next automation is automated testing tool for developers. Because of automation testing tool, programmers can focus on development by saving the time of testing smart systems and debugging.

2. Virtual Gaming- The AI games that are available today, don’t have a robust environment for users. The reason behind this is the lack of data storage required to create such environments. The recent upsurge in AI technology is the push that virtual gaming required. The upcoming virtual games will be very realistic and interactive. Through machine learning, games can evolve in the future based on character development taken by the user. Game developers are expected to adapt new skills in AI so as to stay aware of the demands of its users who no longer stay content with the visualization. Their desires are to enjoy games as close to real life as possible by using virtual reality and technology, such as, 3D augmentation. Also, mobile game developers have the opportunity to present their skills in such gaming apps.

3. Conversational AI- Conversational AI is becoming an integral part of businesses. Many companies are adopting the advantages of chatbots to bring customer service, sales and marketing. Although chatbots are an important asset for business, their performance is still a long way from human. Researchers from big institutions and tech leaders have explored the way to improve the performance of Dialog systems:

Dialog systems are improving at tracking long-term aspects of conversation. The main aim is to improve the system’s ability to understand complex relationships in conversation by using the conversation history and context. Most of the current chatbots generate boring and repetitive responses. The next aim to generate diverse and relevant responses is on the way. Emotion recognition is an important feature for open-domain chatbots. So experts are searching the best ways to include empathy into dialog systems.

4. Computer Vision- Computer vision systems have revolutionized industries and business functions with apps in security, healthcare, retail, transportation, agriculture and so on. Recently introduced architectures and approaches like EfficientNet and SinGAN improves the perspective and generative capacities of visual systems. The latest research topics in computer vision are-

Currently 3D is a leading research area in CV. The google research team introduced a novel approach for the generation of depth map of natural scenes. There is an increase in the popularity of unsupervised learning methods. For instance, research team of Stanford University introduced a promising Local Aggregation approach to object detection and recognition with unsupervised learning. Computer vision research is successfully combined with NLP. Recent research advances enable robust change captioning between two images in natural language, vision-language navigation in 3D environments and learning hierarchical vision-language representation for better image caption retrieval and visual grounding.

5. AI and Humans- Know more at-

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